Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
Purpose Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods A systematic search was performed in PubM...
Saved in:
| Published in | Intensive care medicine Vol. 46; no. 3; pp. 383 - 400 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.03.2020
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Purpose
Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.
Methods
A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.
Results
After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU,
n
= 15; 54%), followed by hospital wards (
n
= 7; 25%), the emergency department (ED,
n
= 4; 14%) and all of these settings (
n
= 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.
Conclusion
This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside. |
|---|---|
| AbstractList | Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.PURPOSEEarly clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.METHODSA systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.RESULTSAfter screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.CONCLUSIONThis systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside. Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside. Purpose Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside. PurposeEarly clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.MethodsA systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.ResultsAfter screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.ConclusionThis systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside. Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68-0.99 in the ICU, to 0.96-0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside. Purpose Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards ( n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings ( n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside. |
| Audience | Academic |
| Author | Guo, Tingjie Ercole, Ari Fleuren, Lucas M. Zwager, Charlotte L. Roggeveen, Luca F. Klausch, Thomas L. T. Schoonmade, Linda J. Swart, Eleonora L. Hoogendoorn, Mark Thoral, Patrick Girbes, Armand R. J. Elbers, Paul W. G. |
| Author_xml | – sequence: 1 givenname: Lucas M. orcidid: 0000-0002-4056-1692 surname: Fleuren fullname: Fleuren, Lucas M. email: l.fleuren@amsterdamumc.nl organization: Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam, Computational Intelligence Group, Department of Computer Science, VU Amsterdam – sequence: 2 givenname: Thomas L. T. surname: Klausch fullname: Klausch, Thomas L. T. organization: Department of Epidemiology and Biostatistics, Amsterdam UMC, location VUmc, VU Amsterdam – sequence: 3 givenname: Charlotte L. surname: Zwager fullname: Zwager, Charlotte L. organization: Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam – sequence: 4 givenname: Linda J. surname: Schoonmade fullname: Schoonmade, Linda J. organization: Medical Library, Amsterdam UMC, location VUmc, VU Amsterdam – sequence: 5 givenname: Tingjie surname: Guo fullname: Guo, Tingjie organization: Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam – sequence: 6 givenname: Luca F. surname: Roggeveen fullname: Roggeveen, Luca F. organization: Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam, Computational Intelligence Group, Department of Computer Science, VU Amsterdam – sequence: 7 givenname: Eleonora L. surname: Swart fullname: Swart, Eleonora L. organization: Department of Pharmacy, Amsterdam UMC, location VUmc, VU Amsterdam – sequence: 8 givenname: Armand R. J. surname: Girbes fullname: Girbes, Armand R. J. organization: Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam – sequence: 9 givenname: Patrick surname: Thoral fullname: Thoral, Patrick organization: Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam – sequence: 10 givenname: Ari surname: Ercole fullname: Ercole, Ari organization: Division of Anaesthesia, University of Cambridge, Data Science Section, European Society of Intensive Care Medicine – sequence: 11 givenname: Mark surname: Hoogendoorn fullname: Hoogendoorn, Mark organization: Computational Intelligence Group, Department of Computer Science, VU Amsterdam – sequence: 12 givenname: Paul W. G. surname: Elbers fullname: Elbers, Paul W. G. organization: Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, location VUmc, VU Amsterdam, Data Science Section, European Society of Intensive Care Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31965266$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv1DAUhSNURKeFP8ACWWLDJsWv2AkLpKriJRWxgbV1x7nJuErswc4U5d_jdFpoq1HlhSX7O8f28TkpjnzwWBSvGT1jlOr3iVImZElZU9Kq1rycnxUrJgUvGRf1UbGiQvJSKsmPi5OUrjKuVcVeFMeCNariSq2K7XewG-eRDAjRO9-TLkQybZBsI7bOTi54EjqScJtc-kCApDlNOMLkLIl47fAPAd-SEScowcMwZ2wRtA56H9KCTZgmAtbuItj5ZfG8gyHhq9v5tPj1-dPPi6_l5Y8v3y7OL0urZDWVTChbM9FC23QKOcg1tkw2nGsF6xaV6ipBtaVNLS3tamBraIUQoJVao4VanBYf977b3XrE1qKfIgxmG90IcTYBnHm4493G9OHaaKq0liwbvLs1iOH3Lj_BjC5ZHAbwGHbJcCFFRUVzg759hF6FXcxhLFSOnCql71E9DGic70I-1y6m5lxz0XCtWZOp8gDVo8d8yfz_ncvLD_izA3weLY7OHhS8uR_Mv0TuOpGBeg_YGFKK2BnrJliKkJ3dYBg1S_3Mvn4m18_c1M_MWcofSe_cnxSJvShl2PcY_6f3hOovB9Dtnw |
| CitedBy_id | crossref_primary_10_1016_j_ccc_2023_02_003 crossref_primary_10_1038_s41598_023_28421_6 crossref_primary_10_1007_s00129_024_05253_7 crossref_primary_10_3389_fmed_2022_1043589 crossref_primary_10_3390_jcm12103493 crossref_primary_10_3390_app13169049 crossref_primary_10_12998_wjcc_v12_i25_5681 crossref_primary_10_2196_59649 crossref_primary_10_1016_j_annemergmed_2020_11_007 crossref_primary_10_3389_frhem_2024_1331109 crossref_primary_10_3389_fmed_2021_617486 crossref_primary_10_1016_j_tranon_2025_102306 crossref_primary_10_3389_fsurg_2022_857218 crossref_primary_10_1007_s11908_023_00818_4 crossref_primary_10_3390_jcm12175658 crossref_primary_10_1097_SHK_0000000000002181 crossref_primary_10_1186_s12911_021_01653_0 crossref_primary_10_1007_s00431_024_05441_6 crossref_primary_10_3389_fcvm_2022_911987 crossref_primary_10_1136_bmjopen_2022_071430 crossref_primary_10_1007_s00134_020_06000_x crossref_primary_10_1093_jamiaopen_ooac022 crossref_primary_10_1186_s12911_022_01803_y crossref_primary_10_3390_antibiotics11030304 crossref_primary_10_1186_s12879_024_09203_5 crossref_primary_10_1371_journal_pone_0294557 crossref_primary_10_2196_63216 crossref_primary_10_1080_07853890_2023_2179104 crossref_primary_10_31083_j_rcm2411315 crossref_primary_10_3389_fmed_2022_935366 crossref_primary_10_1038_s41591_020_1037_7 crossref_primary_10_17116_labs20221102118 crossref_primary_10_1093_clinchem_hvab239 crossref_primary_10_7717_peerj_12997 crossref_primary_10_2217_bmm_2022_0433 crossref_primary_10_1111_ijlh_13549 crossref_primary_10_1016_S2589_7500_20_30218_1 crossref_primary_10_3390_jcm11020312 crossref_primary_10_1007_s11908_021_00765_y crossref_primary_10_3934_mbe_2023595 crossref_primary_10_3390_medicina58070844 crossref_primary_10_3390_diagnostics11101844 crossref_primary_10_1136_bmjopen_2022_065429 crossref_primary_10_1097_ACO_0000000000001193 crossref_primary_10_1002_ams2_70037 crossref_primary_10_1016_j_ijmedinf_2022_104706 crossref_primary_10_2196_28000 crossref_primary_10_1016_j_cll_2023_10_009 crossref_primary_10_2196_27275 crossref_primary_10_1007_s42979_022_01507_0 crossref_primary_10_1097_CCE_0000000000000302 crossref_primary_10_1109_JBHI_2021_3092835 crossref_primary_10_1017_ash_2023_494 crossref_primary_10_3389_fmed_2021_661309 crossref_primary_10_1007_s00134_021_06446_7 crossref_primary_10_1007_s11547_024_01825_8 crossref_primary_10_2196_67488 crossref_primary_10_1515_mr_2023_0039 crossref_primary_10_1038_s41746_021_00529_x crossref_primary_10_1109_JSAIT_2023_3342569 crossref_primary_10_1007_s00464_020_08168_1 crossref_primary_10_1186_s12911_024_02647_4 crossref_primary_10_3389_fpubh_2024_1381328 crossref_primary_10_12677_MOS_2023_123212 crossref_primary_10_1002_biof_2128 crossref_primary_10_2139_ssrn_4149256 crossref_primary_10_2147_IJN_S441133 crossref_primary_10_2196_55492 crossref_primary_10_1016_j_phrs_2023_107056 crossref_primary_10_5492_wjccm_v12_i2_89 crossref_primary_10_1001_jamanetworkopen_2023_35377 crossref_primary_10_1016_j_acra_2021_05_014 crossref_primary_10_1038_s41598_021_00220_x crossref_primary_10_1186_s13054_022_03916_2 crossref_primary_10_1007_s00134_023_07102_y crossref_primary_10_1016_S2589_7500_20_30219_3 crossref_primary_10_1007_s40272_024_00638_6 crossref_primary_10_1016_j_jgar_2022_03_019 crossref_primary_10_1186_s40001_024_01849_w crossref_primary_10_3389_fmed_2023_1284081 crossref_primary_10_1016_j_gie_2020_04_039 crossref_primary_10_3748_wjg_v27_i26_4088 crossref_primary_10_1097_CCM_0000000000005758 crossref_primary_10_3390_biomedicines12061220 crossref_primary_10_3389_fonc_2023_1289050 crossref_primary_10_1186_s12879_024_09421_x crossref_primary_10_1097_MCC_0000000000001150 crossref_primary_10_3389_fmed_2021_663739 crossref_primary_10_1016_j_eclinm_2024_102820 crossref_primary_10_1016_j_jinf_2021_02_019 crossref_primary_10_1109_JBHI_2022_3171673 crossref_primary_10_1002_mds_29376 crossref_primary_10_1007_s00521_021_06631_1 crossref_primary_10_3389_fmed_2021_777142 crossref_primary_10_1186_s12893_023_02151_y crossref_primary_10_1016_j_jss_2020_07_047 crossref_primary_10_1016_j_jcrc_2023_154400 crossref_primary_10_1109_ACCESS_2024_3519219 crossref_primary_10_1111_jog_15266 crossref_primary_10_1186_s12873_022_00764_9 crossref_primary_10_1016_j_ijmedinf_2022_104689 crossref_primary_10_1016_j_rtbm_2023_101031 crossref_primary_10_1007_s41782_021_00149_1 crossref_primary_10_1088_2632_2153_acc1c0 crossref_primary_10_1097_FM9_0000000000000257 crossref_primary_10_2147_IJGM_S348797 crossref_primary_10_3390_electronics13224405 crossref_primary_10_1001_jamainternmed_2023_7965 crossref_primary_10_1002_jcla_24053 crossref_primary_10_1016_j_scitotenv_2025_179018 crossref_primary_10_1016_j_ccc_2023_03_007 crossref_primary_10_1007_s11948_024_00522_z crossref_primary_10_1016_j_sopen_2023_09_013 crossref_primary_10_3390_biology11040490 crossref_primary_10_1080_10837450_2024_2448777 crossref_primary_10_1093_clinchem_hvab201 crossref_primary_10_1093_jamia_ocab236 crossref_primary_10_1016_j_clinbiochem_2022_02_011 crossref_primary_10_1177_14777509221138750 crossref_primary_10_3390_diagnostics14040445 crossref_primary_10_3389_fmed_2023_1109411 crossref_primary_10_1186_s12911_020_01284_x crossref_primary_10_3390_electronics11091507 crossref_primary_10_1016_j_cmpb_2024_108335 crossref_primary_10_2196_42452 crossref_primary_10_1111_dom_15457 crossref_primary_10_1016_j_ejrad_2022_110457 crossref_primary_10_1097_CCE_0000000000000453 crossref_primary_10_1155_2021_5592472 crossref_primary_10_1136_bmj_m3164 crossref_primary_10_3389_fmed_2024_1491358 crossref_primary_10_1186_s12911_024_02623_y crossref_primary_10_2196_56155 crossref_primary_10_2196_57486 crossref_primary_10_1186_s12967_022_03469_6 crossref_primary_10_1016_j_neunet_2023_02_020 crossref_primary_10_1109_ACCESS_2022_3230324 crossref_primary_10_3390_computation13010008 crossref_primary_10_1016_j_jik_2024_100517 crossref_primary_10_1016_j_ijmedinf_2025_105874 crossref_primary_10_1007_s42979_025_03818_4 crossref_primary_10_1109_JBHI_2023_3253208 crossref_primary_10_3390_biomedicines8110494 crossref_primary_10_37882_2223_2966_2021_11_39 crossref_primary_10_1371_journal_pone_0296355 crossref_primary_10_1016_j_ijmedinf_2023_105151 crossref_primary_10_3389_fpubh_2022_1006955 crossref_primary_10_13004_kjnt_2023_19_e63 crossref_primary_10_1371_journal_pdig_0000459 crossref_primary_10_1542_peds_2023_062967 crossref_primary_10_1007_s10238_022_00895_0 crossref_primary_10_1186_s40249_022_00946_4 crossref_primary_10_1186_s12871_022_01888_y crossref_primary_10_1016_j_medine_2022_06_024 crossref_primary_10_1186_s12911_023_02383_1 crossref_primary_10_3389_fmed_2025_1521660 crossref_primary_10_1093_jamia_ocad075 crossref_primary_10_1016_j_bbe_2021_06_009 crossref_primary_10_1016_j_procs_2023_12_198 crossref_primary_10_1186_s12911_022_01751_7 crossref_primary_10_1186_s13054_024_04948_6 crossref_primary_10_1038_s41598_021_99105_2 crossref_primary_10_1371_journal_pone_0251800 crossref_primary_10_1080_0886022X_2022_2151468 crossref_primary_10_1093_clinchem_hvae001 crossref_primary_10_1007_s40746_020_00212_5 crossref_primary_10_1186_s40560_025_00776_0 crossref_primary_10_26633_RPSP_2024_12 crossref_primary_10_3390_jcm10020301 crossref_primary_10_26633_RPSP_2024_13 crossref_primary_10_3390_e26080625 crossref_primary_10_1177_0272989X221121279 crossref_primary_10_3390_diagnostics15030302 crossref_primary_10_1007_s40688_023_00455_7 crossref_primary_10_1016_j_heliyon_2023_e22458 crossref_primary_10_1093_bmb_ldac001 crossref_primary_10_1016_j_jointm_2023_10_001 crossref_primary_10_1016_j_bspc_2024_106854 crossref_primary_10_1017_ice_2022_218 crossref_primary_10_2139_ssrn_4176362 crossref_primary_10_1016_j_eclinm_2023_102124 crossref_primary_10_3390_diagnostics15030307 crossref_primary_10_1016_j_isci_2021_102106 crossref_primary_10_1016_j_rcsop_2023_100317 crossref_primary_10_2139_ssrn_4130480 crossref_primary_10_1155_2022_5682599 crossref_primary_10_1111_1742_6723_14345 crossref_primary_10_1186_s13054_024_04860_z crossref_primary_10_3389_fmed_2021_607952 crossref_primary_10_4103_jpgm_jpgm_357_24 crossref_primary_10_3389_fmmed_2023_1250508 crossref_primary_10_1093_jamiaopen_ooaf015 crossref_primary_10_1080_10255842_2025_2453922 crossref_primary_10_1016_j_cca_2024_119724 crossref_primary_10_1038_s41598_023_46021_2 crossref_primary_10_18231_j_ijmr_2024_029 crossref_primary_10_7759_cureus_80095 crossref_primary_10_3390_make7010006 crossref_primary_10_1186_s40635_021_00400_z crossref_primary_10_1038_s41591_020_1034_x crossref_primary_10_1093_postmj_qgad024 crossref_primary_10_3389_fphys_2025_1549138 crossref_primary_10_1016_j_medin_2022_06_004 crossref_primary_10_3390_jcm9030875 crossref_primary_10_1177_20552076241289732 crossref_primary_10_11124_JBIES_24_00042 crossref_primary_10_3390_jcm13175002 crossref_primary_10_1097_PCC_0000000000003039 crossref_primary_10_32604_cmes_2024_055065 crossref_primary_10_1097_SHK_0000000000002335 crossref_primary_10_7717_peerj_18213 crossref_primary_10_2196_26426 crossref_primary_10_1016_j_eclinm_2025_103112 crossref_primary_10_1007_s11739_024_03535_5 crossref_primary_10_1016_j_cmpb_2024_108017 crossref_primary_10_1002_sim_9311 crossref_primary_10_1016_j_heliyon_2024_e28143 crossref_primary_10_1016_j_fertnstert_2023_05_148 crossref_primary_10_1016_j_ibmed_2023_100115 crossref_primary_10_3390_diagnostics13101781 crossref_primary_10_1016_j_compbiomed_2024_109251 crossref_primary_10_1001_jamainternmed_2021_3333 crossref_primary_10_1186_s12874_023_02078_1 crossref_primary_10_3918_jsicm_28_415 crossref_primary_10_1111_acem_14962 crossref_primary_10_2147_JIR_S505813 crossref_primary_10_3389_fimmu_2022_905601 crossref_primary_10_3390_app13148289 crossref_primary_10_1038_s41746_023_00961_1 crossref_primary_10_1371_journal_pone_0304133 crossref_primary_10_1002_phar_2683 crossref_primary_10_1093_gigascience_giad041 crossref_primary_10_1093_jamiaopen_ooac080 crossref_primary_10_3389_fonc_2022_946038 crossref_primary_10_1016_j_numecd_2024_05_020 crossref_primary_10_2196_48320 crossref_primary_10_1016_j_isci_2021_103651 crossref_primary_10_1007_s00011_023_01787_z crossref_primary_10_1016_j_eswa_2023_122982 crossref_primary_10_1016_j_ebiom_2022_104394 crossref_primary_10_1177_08850666221085181 crossref_primary_10_2147_IDR_S427790 crossref_primary_10_2196_37689 crossref_primary_10_1016_j_cll_2022_09_002 crossref_primary_10_1038_s41390_022_02380_6 crossref_primary_10_1007_s12020_023_03637_8 crossref_primary_10_1016_j_jhqr_2022_09_006 crossref_primary_10_1109_TCBB_2021_3122405 crossref_primary_10_1111_acem_14190 crossref_primary_10_1109_JBHI_2022_3216055 crossref_primary_10_1016_j_semradonc_2023_06_004 crossref_primary_10_1016_j_ijmedinf_2022_104776 crossref_primary_10_1007_s00134_024_07629_8 crossref_primary_10_1016_j_jbo_2024_100622 crossref_primary_10_1097_MLR_0000000000001421 crossref_primary_10_3389_fmed_2021_634197 crossref_primary_10_1186_s12245_022_00472_y crossref_primary_10_1016_j_imu_2023_101236 crossref_primary_10_2147_JIR_S499512 crossref_primary_10_1093_ageing_afad182 crossref_primary_10_2196_48330 crossref_primary_10_1371_journal_pdig_0000365 crossref_primary_10_1016_j_jointm_2024_04_006 crossref_primary_10_1093_milmed_usab220 crossref_primary_10_1186_s12911_022_01817_6 crossref_primary_10_1111_jebm_12565 crossref_primary_10_1016_j_icte_2023_07_007 crossref_primary_10_1016_j_artmed_2024_103008 crossref_primary_10_1038_s41746_021_00504_6 crossref_primary_10_3390_machines12120867 crossref_primary_10_1001_jamanetworkopen_2021_4514 crossref_primary_10_1016_j_heliyon_2024_e29603 crossref_primary_10_1097_MCC_0000000000000945 crossref_primary_10_1155_2024_7347876 crossref_primary_10_1177_20552076241297729 crossref_primary_10_4240_wjgs_v16_i2_345 crossref_primary_10_3390_nu15122705 crossref_primary_10_2196_43251 crossref_primary_10_1080_13102818_2024_2349587 crossref_primary_10_1287_ijoc_2022_1176 crossref_primary_10_1097_CCM_0000000000005793 crossref_primary_10_1007_s11356_022_23392_z crossref_primary_10_1016_j_heliyon_2023_e19526 crossref_primary_10_1056_AIoa2400867 crossref_primary_10_1186_s12874_021_01284_z crossref_primary_10_2196_29986 crossref_primary_10_1159_000511429 crossref_primary_10_1016_j_compbiomed_2024_109284 crossref_primary_10_1159_000538639 crossref_primary_10_1038_s41746_024_01066_z crossref_primary_10_1371_journal_pone_0319460 crossref_primary_10_1080_22221751_2025_2479634 crossref_primary_10_1186_s13054_021_03787_z crossref_primary_10_1038_s41598_022_27134_6 crossref_primary_10_1007_s00101_024_01480_8 crossref_primary_10_1055_s_0042_1742369 crossref_primary_10_1097_AOG_0000000000005513 crossref_primary_10_3390_jcm10235710 crossref_primary_10_3390_jcm14020350 crossref_primary_10_1093_infdis_jiae348 crossref_primary_10_1007_s11908_021_00747_0 crossref_primary_10_1038_s41391_023_00704_z crossref_primary_10_3389_fdgth_2022_848599 crossref_primary_10_2196_22421 crossref_primary_10_1016_j_ijantimicag_2022_106684 crossref_primary_10_1016_j_csbj_2024_04_020 crossref_primary_10_3389_fgene_2023_1100352 crossref_primary_10_1038_s41598_023_38858_4 crossref_primary_10_4266_acc_2024_00031 crossref_primary_10_1038_s41598_022_12497_7 crossref_primary_10_1093_ehjdh_ztae098 crossref_primary_10_1016_j_jns_2022_120529 crossref_primary_10_1109_ACCESS_2025_3534803 crossref_primary_10_1093_brain_awab453 crossref_primary_10_3389_fimmu_2025_1526174 crossref_primary_10_1016_j_resuscitation_2022_07_026 crossref_primary_10_1097_MAO_0000000000004205 crossref_primary_10_2147_CLEP_S404580 crossref_primary_10_2196_54490 crossref_primary_10_3389_fnins_2023_1130831 crossref_primary_10_1007_s00234_022_03098_2 crossref_primary_10_1089_sur_2022_363 crossref_primary_10_1080_0886022X_2024_2337289 crossref_primary_10_3390_jpm11111055 crossref_primary_10_1098_rsta_2020_0252 crossref_primary_10_1080_14787210_2023_2237192 crossref_primary_10_1159_000516891 crossref_primary_10_1016_j_bspc_2023_105146 crossref_primary_10_1016_j_cmi_2022_03_029 crossref_primary_10_1186_s40001_023_01255_8 crossref_primary_10_1200_CCI_21_00023 crossref_primary_10_1016_j_eclinm_2024_103048 crossref_primary_10_3389_fmed_2024_1510792 crossref_primary_10_5664_jcsm_8388 crossref_primary_10_1038_s41746_024_01361_9 crossref_primary_10_1038_s41746_024_01369_1 crossref_primary_10_4258_hir_2021_27_3_241 crossref_primary_10_1016_j_chaos_2023_113718 crossref_primary_10_1155_2023_8819052 crossref_primary_10_1183_23120541_00166_2024 crossref_primary_10_3390_diagnostics13193091 crossref_primary_10_1109_ACCESS_2023_3349242 crossref_primary_10_23736_S0375_9393_21_16241_8 crossref_primary_10_3389_fmed_2023_1174429 crossref_primary_10_1136_bmj_m3210 crossref_primary_10_1007_s13755_023_00210_5 crossref_primary_10_1097_PTS_0000000000001069 crossref_primary_10_1038_s41598_024_57712_9 crossref_primary_10_1016_j_jad_2023_04_026 crossref_primary_10_1016_j_pdpdt_2025_104528 crossref_primary_10_1016_j_procs_2021_10_040 crossref_primary_10_1016_j_ijmedinf_2022_104855 crossref_primary_10_1142_S0218488522400013 crossref_primary_10_1016_j_smhl_2022_100335 crossref_primary_10_3389_fmed_2021_793815 crossref_primary_10_1016_j_compbiomed_2023_107156 crossref_primary_10_1016_j_artmed_2021_102036 crossref_primary_10_1016_j_ijnurstu_2023_104529 crossref_primary_10_3389_fmed_2021_775047 crossref_primary_10_1097_INF_0000000000004409 crossref_primary_10_1097_CCE_0000000000001165 crossref_primary_10_3389_fmed_2021_759013 |
| Cites_doi | 10.1007/s00134-019-05680-4 10.1002/jhm.2347 10.2196/medinform.5909 10.1097/CCM.0000000000001738 10.1016/j.amsu.2016.04.023 10.1001/jama.2017.19163 10.1145/2975167.2985841 10.1016/j.ajic.2017.04.005 10.1371/journal.pone.0155705 10.1016/j.jelectrocard.2017.08.013 10.1088/1361-6579/aa9772 10.1136/bmjopen-2017-017833 10.3389/fimmu.2018.01502 10.1007/s00134-018-5389-0 10.1371/journal.pmed.1002022 10.1007/s00134-018-5228-3 10.1186/s12873-016-0095-0 10.1097/CCM.0000000000002936 10.1016/J.CHEST.2017.12.015 10.1126/scitranslmed.aab3719 10.7717/peerj.343 10.1186/s12911-014-0105-7 10.1016/j.annemergmed.2010.12.008 10.1371/journal.pone.0198847 10.1016/j.compbiomed.2019.04.027 10.1056/NEJMoa1703058 10.1007/s42242-018-0030-1 10.1016/j.compbiomed.2019.103395 10.1016/s2589-7500(19)30123-2 10.1002/jhm.530 10.1371/journal.pone.0174708 10.1109/TKDE.2008.239 10.1164/rccm.201504-0781OC 10.7326/0003-4819-155-8-201110180-00009 10.1109/JBHI.2019.2894570 10.1001/jama.2017.13836 10.7861/clinmedicine.12-6-501 10.1164/rccm.201609-1848OC 10.1016/j.compbiomed.2017.08.015 10.1186/s13054-016-1497-9 10.1097/ccm.0000000000003891 10.1097/SLA.0000000000002706 10.1136/bmjresp-2017-000234 10.1097/CCM.0b013e318250a887 10.1097/CCM.0000000000000330 10.1001/jama.2016.0287 10.1097/CCM.0000000000003891 10.1186/s12963-018-0160-x 10.1038/s41551-018-0305-z 10.1136/bmjoq-2017-000158 10.1097/01.CCM.0000217961.75225.E9 10.1001/jama.2016.0288 10.1016/j.annemergmed.2018.11.036 10.1007/s00134-018-5134-8 10.1016/j.ijmedinf.2018.12.002 10.1007/s00134-017-4683-6 10.1001/jama.2017.18391 10.1016/j.compbiomed.2016.05.003 10.3414/ME18-01-0014 10.1109/ICHI.2018.00033 10.1109/SIEDS.2015.7116970 10.1109/ICHI.2018.00068 10.1109/ICHI.2018.00032 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2020 COPYRIGHT 2020 Springer Intensive Care Medicine is a copyright of Springer, (2020). All Rights Reserved. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2020 – notice: COPYRIGHT 2020 Springer – notice: Intensive Care Medicine is a copyright of Springer, (2020). All Rights Reserved. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7RV 7X7 7XB 88E 8AO 8FD 8FI 8FJ 8FK ABUWG AFKRA BENPR CCPQU FR3 FYUFA GHDGH K9. M0S M1P M7Z NAPCQ P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM |
| DOI | 10.1007/s00134-019-05872-y |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central ProQuest One Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database Biochemistry Abstracts 1 Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Biochemistry Abstracts 1 Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE Technology Research Database |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1432-1238 |
| EndPage | 400 |
| ExternalDocumentID | PMC7067741 A723927719 31965266 10_1007_s00134_019_05872_y |
| Genre | Meta-Analysis Systematic Review Journal Article |
| GeographicLocations | Netherlands |
| GeographicLocations_xml | – name: Netherlands |
| GroupedDBID | --- -53 -5E -5G -BR -EM -Y2 -~C .55 .86 .GJ .VR 06C 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29J 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 36B 3V. 4.4 406 408 409 40D 40E 53G 5GY 5QI 5RE 5VS 67Z 6NX 78A 7RV 7X7 88E 8AO 8FI 8FJ 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AAQQT AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAWTL AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHFT ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABOCM ABPLI ABQBU ABQSL ABSXP ABTEG ABTKH ABTMW ABULA ABUWG ABUWZ ABWNU ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHVE ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACUDM ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFFNX AFJLC AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AKMHD ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARMRJ AXYYD AZFZN B-. BA0 BBWZM BDATZ BENPR BGNMA BKEYQ BPHCQ BSONS BVXVI C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EBD EBLON EBS EIOEI EJD EMB EMOBN EN4 ESBYG EX3 F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GRRUI GXS H13 HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I09 IAO IEA IHE IHR IJ- IKXTQ IMOTQ INH IOF ITC ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z J5H JBSCW JCJTX JZLTJ KDC KOV KOW KPH LAS LLZTM LMP M1P M4Y MA- MJL N2Q N9A NAPCQ NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P9S PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R4E R89 R9I RHV RIG RNI ROL RPX RRX RSV RZK S16 S1Z S26 S27 S28 S37 S3B SAP SCLPG SDE SDH SDM SHX SISQX SJYHP SMD SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZ9 SZN T13 T16 TEORI TSG TSK TSV TT1 TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WJK WK8 WOW X7M YCJ YLTOR Z45 Z5O Z7U Z7X Z82 Z83 Z86 Z87 Z8O Z8V Z8W Z8Z Z91 ZGI ZMTXR ZOVNA ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ACMFV ACSTC ADHKG AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT ABRTQ CGR CUY CVF ECM EIF NPM PJZUB PPXIY AEIIB PMFND 7XB 8FD 8FK FR3 K9. M7Z P64 PKEHL PQEST PQUKI PRINS PUEGO 7X8 5PM |
| ID | FETCH-LOGICAL-c645t-136c813dad9f6e2a4bed1492276abde66f5307c0984c0f8a1bad333a766beca83 |
| IEDL.DBID | C6C |
| ISSN | 0342-4642 1432-1238 |
| IngestDate | Thu Aug 21 14:04:04 EDT 2025 Thu Jul 10 23:47:48 EDT 2025 Sat Aug 23 14:54:35 EDT 2025 Tue Jun 17 21:39:22 EDT 2025 Thu Jun 12 23:25:02 EDT 2025 Tue Jun 10 20:47:53 EDT 2025 Mon Jul 21 05:57:36 EDT 2025 Tue Jul 01 03:51:16 EDT 2025 Thu Apr 24 23:08:22 EDT 2025 Fri Feb 21 02:35:11 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Sepsis Septic shock Systematic review Machine learning Prediction Meta-analysis |
| Language | English |
| License | Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c645t-136c813dad9f6e2a4bed1492276abde66f5307c0984c0f8a1bad333a766beca83 |
| Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-3 ObjectType-Evidence Based Healthcare-1 ObjectType-Article-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 ObjectType-Undefined-4 |
| ORCID | 0000-0002-4056-1692 |
| OpenAccessLink | https://doi.org/10.1007/s00134-019-05872-y |
| PMID | 31965266 |
| PQID | 2376506671 |
| PQPubID | 48752 |
| PageCount | 18 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7067741 proquest_miscellaneous_2343503941 proquest_journals_2376506671 gale_infotracmisc_A723927719 gale_infotracgeneralonefile_A723927719 gale_infotracacademiconefile_A723927719 pubmed_primary_31965266 crossref_citationtrail_10_1007_s00134_019_05872_y crossref_primary_10_1007_s00134_019_05872_y springer_journals_10_1007_s00134_019_05872_y |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2020-03-01 |
| PublicationDateYYYYMMDD | 2020-03-01 |
| PublicationDate_xml | – month: 03 year: 2020 text: 2020-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: United States – name: Heidelberg |
| PublicationTitle | Intensive care medicine |
| PublicationTitleAbbrev | Intensive Care Med |
| PublicationTitleAlternate | Intensive Care Med |
| PublicationYear | 2020 |
| Publisher | Springer Berlin Heidelberg Springer Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer – name: Springer Nature B.V |
| References | Alsolamy, Al Salamah, Al Thagafi (CR39) 2014; 14 Thottakkara, Ozrazgat-Baslanti, Hupf (CR16) 2016; 11 Liu, Faes, Kale (CR61) 2019; 1 Liu, Fielding-Singh, Greene (CR5) 2017; 196 Talisa, Yende, Seymour, Angus (CR10) 2018; 9 Moss, Lake, Calland (CR36) 2016; 44 Brown, Jones, Kuttler (CR28) 2016; 16 Desautels, Calvert, Hoffman (CR54) 2016; 4 Brown, Jones, Kuttler (CR47) 2016; 16 Singer, Deutschman, Seymour (CR19) 2016; 315 Thiel, Rosini, Shannon (CR29) 2010; 5 McInnes, Moher, Thombs (CR18) 2018; 319 Møller, Alhazzani, Shankar-Hari (CR37) 2019; 45 Yu, Beam, Kohane (CR13) 2018; 2 Fleischmann, Scherag, Adhikari (CR1) 2016; 193 Ferrer, Martin-Loeches, Phillips (CR7) 2014; 42 Delahanty, Alvarez, Flynn (CR63) 2018 Nemati, Holder, Razmi (CR55) 2018; 46 Makam, Nguyen, Auerbach (CR38) 2015; 10 Shankar-Hari, Datta, Wilson, Assi, Stephen, Weir, Rennie, Antonelli, Bateman, Felton, Warner, Judge, Keenan, Wang, Burpee, Brown, Lewis, Mare, Roy, Wright, Hulme, Dimmick, Gray, Rossi, Simpson, Conway Morris, Walsh (CR43) 2018; 44 Mao, Jay, Hoffman (CR71) 2018; 8 Van, Khojandi, Mohammed (CR69) 2019; 122 McCoy, Das (CR31) 2017; 6 Kam, Kim (CR53) 2017; 89 Beam, Kohane (CR12) 2018; 319 Haug, Ferraro (CR62) 2016 Seymour, Liu, Iwashyna (CR34) 2016; 315 Rhodes, Evans, Alhazzani (CR6) 2017; 43 Giannini, Ginestra, Chivers (CR51) 2019 Mao, Jay, Hoffman (CR25) 2018; 8 Rhee, Dantes, Epstein (CR2) 2017; 318 Horng, Sontag, Halpern (CR35) 2017; 12 He, Garcia (CR60) 2009; 21 Van Wyk, Khojandi, Kamaleswaran (CR70) 2018; 2018 Giannini, Ginestra, Chivers (CR30) 2019 Serafim, Gomes, Salluh, Póvoa (CR40) 2018; 153 Khojandi, Tansakul, Li (CR64) 2018; 57 Hooper, Weavind, Wheeler (CR50) 2012; 40 Shashikumar, Li, Clifford, Nemati (CR68) 2017; 38 CR14 CR58 CR57 Shimabukuro, Barton, Feldman (CR32) 2017; 4 Bihorac, Ozrazgat-Baslanti, Ebadi (CR17) 2019; 269 Vincent (CR9) 2016; 13 Nguyen, Mwakalindile, Booth (CR48) 2014; 2 Nachimuthu, Haug (CR15) 2012; 2012 Fleischmann-Struzek, Thomas-Rüddel, Schettler (CR44) 2018; 13 Hiensch, Poeran, Saunders-Hao (CR41) 2017; 45 Calvert, Price, Chettipally (CR52) 2016; 74 Whiting, Rutjes, Westwood (CR22) 2011; 155 de Grooth, Postema, Loer (CR11) 2018; 44 Calvert, Desautels, Chettipally (CR33) 2016; 8 Jones (CR46) 2012; 12 CR23 Shashikumar, Stanley, Sadiq (CR67) 2017; 50 Kwong, Colopy, Weber (CR24) 2019; 2 Nelson, Smith, Jared, Younger (CR49) 2011; 57 CR66 Álvaro-Meca, Jiménez-Sousa, Micheloud (CR3) 2018; 16 CR21 CR65 CR20 Parlato, Philippart, Rouquette (CR42) 2018; 44 Barton, Chettipally, Zhou (CR27) 2019; 109 Core Team (CR26) 2019 Scherpf, Gräßer, Malberg, Zaunseder (CR59) 2019; 113 Seymour, Gesten, Prescott (CR4) 2017; 376 Kumar, Roberts, Wood (CR8) 2006; 34 Henry, Hager, Pronovost, Saria (CR56) 2015; 7 Bouza, Lopez-Cuadrado, Amate-Blanco (CR45) 2016; 20 A Kumar (5872_CR8) 2006; 34 Manu Shankar-Hari (5872_CR43) 2018; 44 SP Shashikumar (5872_CR68) 2017; 38 C Fleischmann (5872_CR1) 2016; 193 P Thottakkara (5872_CR16) 2016; 11 JL Nelson (5872_CR49) 2011; 57 MH Møller (5872_CR37) 2019; 45 R Ferrer (5872_CR7) 2014; 42 A Bihorac (5872_CR17) 2019; 269 A Khojandi (5872_CR64) 2018; 57 R Core Team (5872_CR26) 2019 A Álvaro-Meca (5872_CR3) 2018; 16 TJ Moss (5872_CR36) 2016; 44 Katharine E. Henry (5872_CR56) 2015; 7 J-L Vincent (5872_CR9) 2016; 13 SM Brown (5872_CR47) 2016; 16 SQ Nguyen (5872_CR48) 2014; 2 A McCoy (5872_CR31) 2017; 6 SK Nachimuthu (5872_CR15) 2012; 2012 HM Giannini (5872_CR51) 2019 M Scherpf (5872_CR59) 2019; 113 F Van Wyk (5872_CR70) 2018; 2018 5872_CR57 5872_CR14 S Nemati (5872_CR55) 2018; 46 5872_CR58 Q Mao (5872_CR71) 2018; 8 SM Brown (5872_CR28) 2016; 16 CW Seymour (5872_CR34) 2016; 315 CW Seymour (5872_CR4) 2017; 376 DW Shimabukuro (5872_CR32) 2017; 4 K-H Yu (5872_CR13) 2018; 2 C Barton (5872_CR27) 2019; 109 JS Calvert (5872_CR52) 2016; 74 VX Liu (5872_CR5) 2017; 196 H-J de Grooth (5872_CR11) 2018; 44 5872_CR66 5872_CR23 MT Kwong (5872_CR24) 2019; 2 HJ Kam (5872_CR53) 2017; 89 Wyk F Van (5872_CR69) 2019; 122 PF Whiting (5872_CR22) 2011; 155 M Singer (5872_CR19) 2016; 315 J Calvert (5872_CR33) 2016; 8 A Rhodes (5872_CR6) 2017; 43 M Parlato (5872_CR42) 2018; 44 P Haug (5872_CR62) 2016 R Serafim (5872_CR40) 2018; 153 5872_CR20 5872_CR21 5872_CR65 VB Talisa (5872_CR10) 2018; 9 MDF McInnes (5872_CR18) 2018; 319 C Bouza (5872_CR45) 2016; 20 SW Thiel (5872_CR29) 2010; 5 R Hiensch (5872_CR41) 2017; 45 SP Shashikumar (5872_CR67) 2017; 50 C Fleischmann-Struzek (5872_CR44) 2018; 13 AN Makam (5872_CR38) 2015; 10 Q Mao (5872_CR25) 2018; 8 S Horng (5872_CR35) 2017; 12 MH Hooper (5872_CR50) 2012; 40 S Alsolamy (5872_CR39) 2014; 14 HM Giannini (5872_CR30) 2019 AL Beam (5872_CR12) 2018; 319 Haibo He (5872_CR60) 2009; 21 T Desautels (5872_CR54) 2016; 4 X Liu (5872_CR61) 2019; 1 RJ Delahanty (5872_CR63) 2018 C Rhee (5872_CR2) 2017; 318 M Jones (5872_CR46) 2012; 12 |
| References_xml | – volume: 45 start-page: 1459 year: 2019 end-page: 1461 ident: CR37 article-title: Focus on sepsis publication-title: Intensive Care Med doi: 10.1007/s00134-019-05680-4 – volume: 10 start-page: 396 year: 2015 end-page: 402 ident: CR38 article-title: Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: a systematic review publication-title: J Hosp Med doi: 10.1002/jhm.2347 – volume: 4 start-page: e28 year: 2016 ident: CR54 article-title: Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach publication-title: JMIR Med Inf doi: 10.2196/medinform.5909 – volume: 44 start-page: 1639 year: 2016 end-page: 1648 ident: CR36 article-title: Signatures of subacute potentially catastrophic illness in the ICU: model development and validation publication-title: Crit Care Med doi: 10.1097/CCM.0000000000001738 – volume: 8 start-page: 50 year: 2016 end-page: 55 ident: CR33 article-title: High-performance detection and early prediction of septic shock for alcohol-use disorder patients publication-title: Ann Med Surg doi: 10.1016/j.amsu.2016.04.023 – volume: 319 start-page: 388 year: 2018 end-page: 396 ident: CR18 article-title: Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies the PRISMA-DTA statement publication-title: JAMA J Am Med Assoc doi: 10.1001/jama.2017.19163 – year: 2016 ident: CR62 article-title: Using a semi-automated modeling environment to construct a Bayesian, sepsis diagnostic system publication-title: BCB '16 doi: 10.1145/2975167.2985841 – volume: 45 start-page: 1091 year: 2017 end-page: 1100 ident: CR41 article-title: Impact of an electronic sepsis initiative on antibiotic use and health care facility–onset clostridium difficile infection rates publication-title: Am J Infect Control doi: 10.1016/j.ajic.2017.04.005 – volume: 11 start-page: e0155705 year: 2016 ident: CR16 article-title: Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications publication-title: PLoS One doi: 10.1371/journal.pone.0155705 – volume: 50 start-page: 739 year: 2017 end-page: 743 ident: CR67 article-title: Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics publication-title: J Electrocardiol doi: 10.1016/j.jelectrocard.2017.08.013 – volume: 38 start-page: 2235 year: 2017 end-page: 2248 ident: CR68 article-title: Multiscale network representation of physiological time series for early prediction of sepsis publication-title: Physiol Meas doi: 10.1088/1361-6579/aa9772 – ident: CR58 – volume: 8 start-page: 1 year: 2018 end-page: 11 ident: CR25 article-title: Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU publication-title: BMJ Open doi: 10.1136/bmjopen-2017-017833 – volume: 9 start-page: 1502 year: 2018 ident: CR10 article-title: Arguing for adaptive clinical trials in sepsis publication-title: Front Immunol doi: 10.3389/fimmu.2018.01502 – volume: 44 start-page: 1836 issue: 11 year: 2018 end-page: 1848 ident: CR43 article-title: Early PREdiction of sepsis using leukocyte surface biomarkers: the ExPRES-sepsis cohort study publication-title: Intensive Care Medicine doi: 10.1007/s00134-018-5389-0 – ident: CR21 – volume: 13 start-page: e1002022 year: 2016 ident: CR9 article-title: The clinical challenge of sepsis identification and monitoring publication-title: PLoS Med doi: 10.1371/journal.pmed.1002022 – volume: 44 start-page: 1061 year: 2018 end-page: 1070 ident: CR42 article-title: Circulating biomarkers may be unable to detect infection at the early phase of sepsis in ICU patients: the CAPTAIN prospective multicenter cohort study publication-title: Intensive Care Med doi: 10.1007/s00134-018-5228-3 – volume: 16 start-page: 31 year: 2016 ident: CR47 article-title: Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department publication-title: BMC Emerg Med doi: 10.1186/s12873-016-0095-0 – volume: 46 start-page: 547 year: 2018 end-page: 553 ident: CR55 article-title: An interpretable machine learning model for accurate prediction of sepsis in the ICU publication-title: Crit Care Med doi: 10.1097/CCM.0000000000002936 – volume: 153 start-page: 646 year: 2018 end-page: 655 ident: CR40 article-title: A comparison of the quick-SOFA and systemic inflammatory response syndrome criteria for the diagnosis of sepsis and prediction of mortality: a systematic review and meta-analysis publication-title: Chest doi: 10.1016/J.CHEST.2017.12.015 – volume: 7 start-page: 299ra122 issue: 299 year: 2015 end-page: 299ra122 ident: CR56 article-title: A targeted real-time early warning score (TREWScore) for septic shock publication-title: Science Translational Medicine doi: 10.1126/scitranslmed.aab3719 – volume: 2012 start-page: 653 year: 2012 end-page: 662 ident: CR15 article-title: Early detection of sepsis in the emergency department using dynamic Bayesian networks publication-title: AMIA Annu Symp Proc AMIA Symp – volume: 2 start-page: e343 year: 2014 ident: CR48 article-title: Automated electronic medical record sepsis detection in the emergency department publication-title: PeerJ doi: 10.7717/peerj.343 – volume: 14 start-page: 105 year: 2014 ident: CR39 article-title: Diagnostic accuracy of a screening electronic alert tool for severe sepsis and septic shock in the emergency department publication-title: BMC Med Inform Decis Mak doi: 10.1186/s12911-014-0105-7 – volume: 57 start-page: 500 year: 2011 end-page: 504 ident: CR49 article-title: Prospective trial of real-time electronic surveillance to expedite early care of severe sepsis publication-title: Ann Emerg Med doi: 10.1016/j.annemergmed.2010.12.008 – volume: 13 start-page: e0198847 year: 2018 ident: CR44 article-title: Comparing the validity of different ICD coding abstraction strategies for sepsis case identification in German claims data publication-title: PLoS One doi: 10.1371/journal.pone.0198847 – ident: CR57 – volume: 109 start-page: 79 year: 2019 end-page: 84 ident: CR27 article-title: Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2019.04.027 – volume: 376 start-page: 2235 year: 2017 end-page: 2244 ident: CR4 article-title: Time to treatment and mortality during mandated emergency care for sepsis publication-title: N Engl J Med doi: 10.1056/NEJMoa1703058 – volume: 2 start-page: 31 year: 2019 end-page: 40 ident: CR24 article-title: The efficacy and effectiveness of machine learning for weaning in mechanically ventilated patients at the intensive care unit: a systematic review publication-title: Bio-Design Manuf doi: 10.1007/s42242-018-0030-1 – volume: 113 start-page: 103395 year: 2019 ident: CR59 article-title: Predicting sepsis with a recurrent neural network using the MIMIC III database publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2019.103395 – volume: 1 start-page: e271 year: 2019 end-page: e297 ident: CR61 article-title: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis publication-title: Lancet Digit Heal doi: 10.1016/s2589-7500(19)30123-2 – volume: 5 start-page: 19 year: 2010 end-page: 25 ident: CR29 article-title: Early prediction of septic shock in hospitalized patients publication-title: J Hosp Med doi: 10.1002/jhm.530 – volume: 12 start-page: e0174708 year: 2017 ident: CR35 article-title: Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning publication-title: PLoS One doi: 10.1371/journal.pone.0174708 – volume: 21 start-page: 1263 year: 2009 end-page: 1284 ident: CR60 article-title: Learning from imbalanced data publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2008.239 – volume: 193 start-page: 259 year: 2016 end-page: 272 ident: CR1 article-title: Assessment of global incidence and mortality of hospital-treated sepsis. current estimates and limitations publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201504-0781OC – volume: 8 start-page: e017833 year: 2018 ident: CR71 article-title: Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU publication-title: BMJ Open doi: 10.1136/bmjopen-2017-017833 – volume: 155 start-page: 529 year: 2011 ident: CR22 article-title: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies publication-title: Ann Intern Med doi: 10.7326/0003-4819-155-8-201110180-00009 – volume: 2018 start-page: 1 year: 2018 end-page: 9 ident: CR70 article-title: Improving prediction performance using hierarchical analysis of real-time data : a sepsis case study publication-title: IEEE J Biomed Health Inf doi: 10.1109/JBHI.2019.2894570 – ident: CR66 – volume: 318 start-page: 1241 year: 2017 ident: CR2 article-title: Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009–2014 publication-title: JAMA doi: 10.1001/jama.2017.13836 – volume: 12 start-page: 501 year: 2012 end-page: 503 ident: CR46 article-title: NEWSDIG: the national early warning score development and implementation group publication-title: Clin Med doi: 10.7861/clinmedicine.12-6-501 – volume: 196 start-page: 856 year: 2017 end-page: 863 ident: CR5 article-title: The timing of early antibiotics and hospital mortality in sepsis publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201609-1848OC – volume: 16 start-page: 1 year: 2016 end-page: 7 ident: CR28 article-title: Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department publication-title: BMC Emerg Med doi: 10.1186/s12873-016-0095-0 – ident: CR14 – volume: 89 start-page: 248 year: 2017 end-page: 255 ident: CR53 article-title: Learning representations for the early detection of sepsis with deep neural networks publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2017.08.015 – volume: 20 start-page: 313 year: 2016 ident: CR45 article-title: Use of explicit ICD9-CM codes to identify adult severe sepsis: impacts on epidemiological estimates publication-title: Crit Care doi: 10.1186/s13054-016-1497-9 – year: 2019 ident: CR30 article-title: A machine learning algorithm to predict severe sepsis and septic shock publication-title: Crit Care Med doi: 10.1097/ccm.0000000000003891 – volume: 269 start-page: 652 year: 2019 end-page: 662 ident: CR17 article-title: My surgery risk publication-title: Ann Surg doi: 10.1097/SLA.0000000000002706 – volume: 4 start-page: e000234 year: 2017 ident: CR32 article-title: Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial publication-title: BMJ Open Respir Res doi: 10.1136/bmjresp-2017-000234 – ident: CR23 – volume: 40 start-page: 2096 year: 2012 end-page: 2101 ident: CR50 article-title: Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit publication-title: Crit Care Med doi: 10.1097/CCM.0b013e318250a887 – volume: 42 start-page: 1749 year: 2014 end-page: 1755 ident: CR7 article-title: Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour publication-title: Crit Care Med doi: 10.1097/CCM.0000000000000330 – volume: 315 start-page: 801 year: 2016 ident: CR19 article-title: The third international consensus definitions for sepsis and septic shock (sepsis-3) publication-title: JAMA doi: 10.1001/jama.2016.0287 – year: 2019 ident: CR51 article-title: A machine learning algorithm to predict severe sepsis and septic shock: development, implementation, and impact on clinical practice publication-title: Crit Care Med doi: 10.1097/CCM.0000000000003891 – ident: CR65 – volume: 16 start-page: 4 year: 2018 ident: CR3 article-title: Epidemiological trends of sepsis in the twenty-first century (2000–2013): an analysis of incidence, mortality, and associated costs in Spain publication-title: Popul Health Metr doi: 10.1186/s12963-018-0160-x – volume: 2 start-page: 719 year: 2018 end-page: 731 ident: CR13 article-title: Artificial intelligence in healthcare publication-title: Nat Biomed Eng doi: 10.1038/s41551-018-0305-z – volume: 6 start-page: e000158 year: 2017 ident: CR31 article-title: Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units publication-title: BMJ open Qual doi: 10.1136/bmjoq-2017-000158 – volume: 34 start-page: 1589 year: 2006 end-page: 1596 ident: CR8 article-title: Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock publication-title: Crit Care Med doi: 10.1097/01.CCM.0000217961.75225.E9 – volume: 315 start-page: 762 year: 2016 ident: CR34 article-title: Assessment of clinical criteria for sepsis publication-title: JAMA doi: 10.1001/jama.2016.0288 – year: 2018 ident: CR63 article-title: Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis publication-title: Ann Emerg Med doi: 10.1016/j.annemergmed.2018.11.036 – volume: 44 start-page: 311 year: 2018 end-page: 322 ident: CR11 article-title: Unexplained mortality differences between septic shock trials: a systematic analysis of population characteristics and control-group mortality rates publication-title: Intensive Care Med doi: 10.1007/s00134-018-5134-8 – volume: 122 start-page: 55 year: 2019 end-page: 62 ident: CR69 article-title: A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier publication-title: Int J Med Inform doi: 10.1016/j.ijmedinf.2018.12.002 – volume: 43 start-page: 304 year: 2017 end-page: 377 ident: CR6 article-title: Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016 publication-title: Intensive Care Med doi: 10.1007/s00134-017-4683-6 – volume: 319 start-page: 1317 year: 2018 ident: CR12 article-title: Big data and machine learning in health care publication-title: JAMA doi: 10.1001/jama.2017.18391 – year: 2019 ident: CR26 publication-title: R: a language and environment for statistical computing – volume: 74 start-page: 69 year: 2016 end-page: 73 ident: CR52 article-title: A computational approach to early sepsis detection publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2016.05.003 – ident: CR20 – volume: 57 start-page: 185 year: 2018 end-page: 193 ident: CR64 article-title: Prediction of sepsis and in-hospital mortality using electronic health records publication-title: Methods Inf Med doi: 10.3414/ME18-01-0014 – volume: 46 start-page: 547 year: 2018 ident: 5872_CR55 publication-title: Crit Care Med doi: 10.1097/CCM.0000000000002936 – year: 2019 ident: 5872_CR30 publication-title: Crit Care Med doi: 10.1097/ccm.0000000000003891 – volume: 74 start-page: 69 year: 2016 ident: 5872_CR52 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2016.05.003 – ident: 5872_CR14 doi: 10.1109/ICHI.2018.00033 – volume: 20 start-page: 313 year: 2016 ident: 5872_CR45 publication-title: Crit Care doi: 10.1186/s13054-016-1497-9 – volume: 376 start-page: 2235 year: 2017 ident: 5872_CR4 publication-title: N Engl J Med doi: 10.1056/NEJMoa1703058 – volume: 122 start-page: 55 year: 2019 ident: 5872_CR69 publication-title: Int J Med Inform doi: 10.1016/j.ijmedinf.2018.12.002 – volume: 50 start-page: 739 year: 2017 ident: 5872_CR67 publication-title: J Electrocardiol doi: 10.1016/j.jelectrocard.2017.08.013 – volume: 2018 start-page: 1 year: 2018 ident: 5872_CR70 publication-title: IEEE J Biomed Health Inf doi: 10.1109/JBHI.2019.2894570 – volume: 2 start-page: 719 year: 2018 ident: 5872_CR13 publication-title: Nat Biomed Eng doi: 10.1038/s41551-018-0305-z – volume: 44 start-page: 1061 year: 2018 ident: 5872_CR42 publication-title: Intensive Care Med doi: 10.1007/s00134-018-5228-3 – volume: 44 start-page: 1836 issue: 11 year: 2018 ident: 5872_CR43 publication-title: Intensive Care Medicine doi: 10.1007/s00134-018-5389-0 – ident: 5872_CR23 – volume: 2012 start-page: 653 year: 2012 ident: 5872_CR15 publication-title: AMIA Annu Symp Proc AMIA Symp – volume: 318 start-page: 1241 year: 2017 ident: 5872_CR2 publication-title: JAMA doi: 10.1001/jama.2017.13836 – ident: 5872_CR65 – volume: 196 start-page: 856 year: 2017 ident: 5872_CR5 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201609-1848OC – year: 2018 ident: 5872_CR63 publication-title: Ann Emerg Med doi: 10.1016/j.annemergmed.2018.11.036 – volume: 10 start-page: 396 year: 2015 ident: 5872_CR38 publication-title: J Hosp Med doi: 10.1002/jhm.2347 – volume: 2 start-page: 31 year: 2019 ident: 5872_CR24 publication-title: Bio-Design Manuf doi: 10.1007/s42242-018-0030-1 – volume: 1 start-page: e271 year: 2019 ident: 5872_CR61 publication-title: Lancet Digit Heal doi: 10.1016/s2589-7500(19)30123-2 – volume: 44 start-page: 311 year: 2018 ident: 5872_CR11 publication-title: Intensive Care Med doi: 10.1007/s00134-018-5134-8 – volume: 21 start-page: 1263 year: 2009 ident: 5872_CR60 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2008.239 – volume: 38 start-page: 2235 year: 2017 ident: 5872_CR68 publication-title: Physiol Meas doi: 10.1088/1361-6579/aa9772 – volume: 193 start-page: 259 year: 2016 ident: 5872_CR1 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201504-0781OC – volume: 4 start-page: e000234 year: 2017 ident: 5872_CR32 publication-title: BMJ Open Respir Res doi: 10.1136/bmjresp-2017-000234 – volume: 44 start-page: 1639 year: 2016 ident: 5872_CR36 publication-title: Crit Care Med doi: 10.1097/CCM.0000000000001738 – volume: 45 start-page: 1459 year: 2019 ident: 5872_CR37 publication-title: Intensive Care Med doi: 10.1007/s00134-019-05680-4 – ident: 5872_CR58 doi: 10.1109/SIEDS.2015.7116970 – volume: 8 start-page: 50 year: 2016 ident: 5872_CR33 publication-title: Ann Med Surg doi: 10.1016/j.amsu.2016.04.023 – volume: 40 start-page: 2096 year: 2012 ident: 5872_CR50 publication-title: Crit Care Med doi: 10.1097/CCM.0b013e318250a887 – volume: 16 start-page: 31 year: 2016 ident: 5872_CR47 publication-title: BMC Emerg Med doi: 10.1186/s12873-016-0095-0 – volume: 16 start-page: 1 year: 2016 ident: 5872_CR28 publication-title: BMC Emerg Med doi: 10.1186/s12873-016-0095-0 – ident: 5872_CR57 doi: 10.1109/ICHI.2018.00068 – volume: 57 start-page: 185 year: 2018 ident: 5872_CR64 publication-title: Methods Inf Med doi: 10.3414/ME18-01-0014 – volume: 11 start-page: e0155705 year: 2016 ident: 5872_CR16 publication-title: PLoS One doi: 10.1371/journal.pone.0155705 – volume: 319 start-page: 1317 year: 2018 ident: 5872_CR12 publication-title: JAMA doi: 10.1001/jama.2017.18391 – volume: 14 start-page: 105 year: 2014 ident: 5872_CR39 publication-title: BMC Med Inform Decis Mak doi: 10.1186/s12911-014-0105-7 – volume: 42 start-page: 1749 year: 2014 ident: 5872_CR7 publication-title: Crit Care Med doi: 10.1097/CCM.0000000000000330 – volume: 109 start-page: 79 year: 2019 ident: 5872_CR27 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2019.04.027 – volume-title: R: a language and environment for statistical computing year: 2019 ident: 5872_CR26 – year: 2019 ident: 5872_CR51 publication-title: Crit Care Med doi: 10.1097/CCM.0000000000003891 – volume: 269 start-page: 652 year: 2019 ident: 5872_CR17 publication-title: Ann Surg doi: 10.1097/SLA.0000000000002706 – ident: 5872_CR21 – volume: 315 start-page: 762 year: 2016 ident: 5872_CR34 publication-title: JAMA doi: 10.1001/jama.2016.0288 – volume: 12 start-page: 501 year: 2012 ident: 5872_CR46 publication-title: Clin Med doi: 10.7861/clinmedicine.12-6-501 – volume: 315 start-page: 801 year: 2016 ident: 5872_CR19 publication-title: JAMA doi: 10.1001/jama.2016.0287 – volume: 34 start-page: 1589 year: 2006 ident: 5872_CR8 publication-title: Crit Care Med doi: 10.1097/01.CCM.0000217961.75225.E9 – volume: 8 start-page: 1 year: 2018 ident: 5872_CR25 publication-title: BMJ Open doi: 10.1136/bmjopen-2017-017833 – ident: 5872_CR66 doi: 10.1109/ICHI.2018.00032 – volume: 9 start-page: 1502 year: 2018 ident: 5872_CR10 publication-title: Front Immunol doi: 10.3389/fimmu.2018.01502 – volume: 57 start-page: 500 year: 2011 ident: 5872_CR49 publication-title: Ann Emerg Med doi: 10.1016/j.annemergmed.2010.12.008 – volume: 153 start-page: 646 year: 2018 ident: 5872_CR40 publication-title: Chest doi: 10.1016/J.CHEST.2017.12.015 – volume: 89 start-page: 248 year: 2017 ident: 5872_CR53 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2017.08.015 – volume: 6 start-page: e000158 year: 2017 ident: 5872_CR31 publication-title: BMJ open Qual doi: 10.1136/bmjoq-2017-000158 – volume: 16 start-page: 4 year: 2018 ident: 5872_CR3 publication-title: Popul Health Metr doi: 10.1186/s12963-018-0160-x – volume: 45 start-page: 1091 year: 2017 ident: 5872_CR41 publication-title: Am J Infect Control doi: 10.1016/j.ajic.2017.04.005 – volume: 113 start-page: 103395 year: 2019 ident: 5872_CR59 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2019.103395 – volume: 43 start-page: 304 year: 2017 ident: 5872_CR6 publication-title: Intensive Care Med doi: 10.1007/s00134-017-4683-6 – volume: 319 start-page: 388 year: 2018 ident: 5872_CR18 publication-title: JAMA J Am Med Assoc doi: 10.1001/jama.2017.19163 – volume: 12 start-page: e0174708 year: 2017 ident: 5872_CR35 publication-title: PLoS One doi: 10.1371/journal.pone.0174708 – volume: 7 start-page: 299ra122 issue: 299 year: 2015 ident: 5872_CR56 publication-title: Science Translational Medicine doi: 10.1126/scitranslmed.aab3719 – ident: 5872_CR20 – volume: 8 start-page: e017833 year: 2018 ident: 5872_CR71 publication-title: BMJ Open doi: 10.1136/bmjopen-2017-017833 – volume: 2 start-page: e343 year: 2014 ident: 5872_CR48 publication-title: PeerJ doi: 10.7717/peerj.343 – volume: 5 start-page: 19 year: 2010 ident: 5872_CR29 publication-title: J Hosp Med doi: 10.1002/jhm.530 – year: 2016 ident: 5872_CR62 publication-title: BCB '16 doi: 10.1145/2975167.2985841 – volume: 155 start-page: 529 year: 2011 ident: 5872_CR22 publication-title: Ann Intern Med doi: 10.7326/0003-4819-155-8-201110180-00009 – volume: 13 start-page: e0198847 year: 2018 ident: 5872_CR44 publication-title: PLoS One doi: 10.1371/journal.pone.0198847 – volume: 13 start-page: e1002022 year: 2016 ident: 5872_CR9 publication-title: PLoS Med doi: 10.1371/journal.pmed.1002022 – volume: 4 start-page: e28 year: 2016 ident: 5872_CR54 publication-title: JMIR Med Inf doi: 10.2196/medinform.5909 |
| SSID | ssj0017651 |
| Score | 2.690662 |
| SecondaryResourceType | review_article |
| Snippet | Purpose
Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have... Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged.... Purpose Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have... PurposeEarly clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have... |
| SourceID | pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 383 |
| SubjectTerms | Accuracy Analysis Anesthesiology Critical Care Medicine Diagnostic systems Diagnostic tests Diagnostic Tests, Routine Emergency medical care Emergency medical services Emergency Medicine Humans Infection Intensive Intensive care Learning algorithms Machine Learning Medical diagnosis Medical tests Medicine Medicine & Public Health Meta-analysis Multivariate analysis Pain Medicine Pediatrics Pneumology/Respiratory System Predictions Quality assessment Quality control Real time Retrospective Studies Reviews Sepsis Sepsis - diagnosis Septic shock Shock, Septic Systematic Review |
| SummonAdditionalLinks | – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Nb9QwELWgSIhLVT6bUpCREBzAIrETO-kFVYiqQlpOVNqb5dhOqQTZdLN72H_PjOPNkhX07OcoyYzHz_b4DSFvZWNFVRvHskYKBuuNhtVAY5n3HMWOUuM8nujOvsvLq_zbvJjHDbc-plVuY2II1G5hcY_8E2ZvFJiRmX3ubhlWjcLT1VhC4z55gNJl6NVqPi64MugRKuaJnLMciHa8NBOuziH3wfyLiqVFqTjbTCam_fD81_y0nzu5d4Aa5qWLI3IYCSU9HzzgMbnn2yfk4SwemT8l3SykS3oa60NcU6CpFGgf7ZYIQsPQRUN73_U3_Rk1dCfuTIeLLdS0jv72K8NMlDDBDm5I0kMY0NUVNdaul8ZunpGri68_vlyyWGaBWZkXWIxe2jITzriqkZ6bvPYO1k2cK2lq56VsCggENq3K3KZNaTIwrRDCKCnBAUwpnpODdtH6Y0IzD_TNqdLUNs8FT-uyAT7jeVNZWNm5IiHZ9h9rGzXIsRTGLz2qJwe7aLCLDnbRm4R8GPt0gwLHnej3aDqNwxOebE28ZQDvh0JX-lxxYIRKZVVC3k2Q14PM97-ApxMgjD87bd56iY7jv9c7b03Im7EZe2JOW-sXa8QAVU1FlQPmxeBU4wdiYCyAOyVETdxtBKAq-LSlvfkZ1MEVagLiMz9uHXP3Wv__byd3f8VL8ojjRkNIvjslB6vl2r8CNraqX4ch9wc4TzGq priority: 102 providerName: ProQuest |
| Title | Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy |
| URI | https://link.springer.com/article/10.1007/s00134-019-05872-y https://www.ncbi.nlm.nih.gov/pubmed/31965266 https://www.proquest.com/docview/2376506671 https://www.proquest.com/docview/2343503941 https://pubmed.ncbi.nlm.nih.gov/PMC7067741 |
| Volume | 46 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pb9MwFLbYJiEuiPEzMCojITiApcRO7IRbW7VMoFYIUamcIsd2tkmQVv1x6H_Pe4mbLdWYtEtz8GcrzfOLP-c9f4-Q97I0Iiu0ZVEpBYP9RskKoLHMOY5iR6G2DiO6k6k8n8Xf5sncy-TgWZiD-D2KfUYC8yQyFiap4mx3RE6SSCgs0zCUwzZioGRdahEV7VgMpNofkLl9jM4idPgqvrEWHeZJHgRL6zVo_IQ89uSR9htrn5IHrnpKHk58ePwZWU7q1EhHfS2ICwqUlALFo8sVgtAIdFHStVuur9ZfqKbXQs60OcRCdWXpX7fRTHu5Euxgm4Q8hAE13VBtzHalze45mY1Hv4bnzJdUYEbGCRaelyaNhNU2K6XjOi6chT0S50rqwjopywSc3oRZGpuwTHUEZhRCaCUlGFun4gU5rhaVe0Vo5ICqWZXqwsSx4GGRlsBdHC8zA7s4mwQk2j_j3Hi9cSx78SdvlZJru-Rgl7y2S74LyKe2z7JR27gT_RFNl6MrwshG-xMFcH8oapX3FQf2p1SUBeRDB3nRSHrfBjzrAMHXTLd5P0ty7-vrHPOKEswVjgLyrm3Gnpi_VrnFFjFAS0ORxYB52Uyq9g_iSzABnhQQ1ZluLQAVwLst1dVlrQSuUP8Px_y8n5jXt_X_5_b6fvA35BHHjwx14t0ZOd6stu4tMLFN0SNHaq565KQ_HgymeP36-_sIroPR9MfPXu2g8Dvj_X-5uTFt |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELZKkYAL4k2ggJF4HMAicRI7QUKoAqot7fbUSntzHdsplSC7bHaF9k_xG5lxHktW0FvPHlt2Zmx_E898Q8gLUZo4L7RlUSliBv5GyQqAscw5jmRHobYOX3THR2J0knydpJMt8rvLhcGwyu5M9Ae1nRr8R_4OozdSjMiMPs5-Mqwaha-rXQmNxiwO3OoXuGz1h_3PoN-XnO99Of40Ym1VAWZEkmLtdWGyKLba5qVwXCeFs-AmcC6FLqwTokzB7k2YZ4kJy0xHsJI4jrUUAtarsxjGvUKuwsUborMnJ72DF8EMfYW-OOEsAWDfJun4VD3EWhjvkbMwzSRnq8FFuHkd_HUfbsZqbjzY-ntw7xa52QJYuttY3G2y5ao75Nq4faK_S2ZjH57paFuP4owCLKYAM-lsjkJoCHRa0trN6vP6PdV0TSZNm0QaqitLf7iFZrqlTMEOtgkKRDGAxwuqjVnOtVndIyeXooD7ZLuaVu4hoZEDuGhlpguTJDEPi6wE_OR4mRvwJG0akKj7xsq0nOdYeuO76tmavV4U6EV5vahVQN70fWYN48eF0q9RdQqPAxjZ6DarAeaHxFpqV3JAoFJGeUBeDSTPGlrxfwnuDARhv5thc2clqj1varXeHQF53jdjT4yhq9x0iTIAjcM4T0DmQWNU_QLxIE4BqwVEDsytF0AW8mFLdf7Ns5FL5CDEMd92hrme1v-_26OLV_GMXB8djw_V4f7RwWNyg-NPDh_4t0O2F_OlewJIcFE89duPktPL3u9_AN7xb10 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VrVRxQbxZKGAkHgewmjiJkyAhVGhXLWVXFaJSb6ljO6USZJd9CO1f49cxkzhZsoLeevbYsjMz9ufM-BuA57LQQZorw_1CBhzvGwXPEcZyawWRHXnKWIroDkfy4CT8dBqdbsDv5i0MpVU2e2K1UZuxpn_kO5S9EVFGpr9TuLSI473B-8lPThWkKNLalNOoTeTILn_h9W327nAPdf1CiMH-148H3FUY4FqGEdVhlzrxA6NMWkgrVJhbg1cGIWKpcmOlLCL0Ae2lSai9IlE-rioIAhVLiWtXSYDjXoPNmG5FPdj8sD86_tLGMHC-Vb2-IBQ8RJjvnuxUD_cIeVH2R8q9KIkFX3aOxfXD4a_TcT1zcy18W52Kg5tww8FZtlvb3y3YsOVt2Bq6gP0dmAyrZE3LXHWKc4YgmSHoZJMpCZFZsHHBZnYyu5i9ZYqtqKVZ_ayGqdKwH3auuHIEKtTB1CmCJIZgec6U1oup0su7cHIlKrgHvXJc2gfAfIvg0cSJynUYBsLLkwLRlBVFqvFeaaI--M03zrRjQKdCHN-zlru50kuGeskqvWTLPrxu-0xq_o9LpV-R6jLaHHBkrdwbB5wf0Wxlu7FAPBrHftqHlx3J85pk_F-C2x1B9H7dbW6sJHO7zyxb-UofnrXN1JMy6ko7XpAMAmUvSEOUuV8bVbtA2pYjRG59iDvm1goQJ3m3pbz4VnGTx8RISGO-aQxzNa3_f7eHl6_iKWyhr2efD0dHj-C6oD8eVRbgNvTm04V9jLBwnj9x_sfg7Kpd_g9ldnT4 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+learning+for+the+prediction+of+sepsis%3A+a+systematic+review+and+meta-analysis+of+diagnostic+test+accuracy&rft.jtitle=Intensive+care+medicine&rft.au=Fleuren%2C+Lucas+M&rft.au=Klausch%2C+Thomas+L.+T&rft.au=Zwager%2C+Charlotte+L&rft.au=Schoonmade%2C+Linda+J&rft.date=2020-03-01&rft.pub=Springer&rft.issn=0342-4642&rft.volume=46&rft.issue=3&rft.spage=383&rft_id=info:doi/10.1007%2Fs00134-019-05872-y&rft.externalDocID=A723927719 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0342-4642&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0342-4642&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0342-4642&client=summon |