A simple prognostic index based on admission vital signs data among patients with sepsis in a resource-limited setting
In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with r...
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Published in | Critical care (London, England) Vol. 19; no. 1; p. 86 |
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16.03.2015
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Abstract | In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables.
Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS).
We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates ≥30 cycles/minute; pulse rates ≥100 beats/minute; mean arterial pressures ≥110/<70 mmHg; temperatures ≥38.6/<35.6°C; and presence of altered mental state defined as Glasgow coma score ≤14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring ≥3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring ≥5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively.
Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings. |
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AbstractList | Introduction In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables. Methods Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS). Results We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates [greater than or equai to]30 cycles/minute; pulse rates [greater than or equai to]100 beats/minute; mean arterial pressures [greater than or equai to]110/<70 mmHg; temperatures [greater than or equai to]38.6/<35.6[degrees]C; and presence of altered mental state defined as Glasgow coma score [less than or equai to]14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring [greater than or equai to]3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring [greater than or equai to]5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively. Conclusion Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings. Abstract Introduction In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables. Methods Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS). Results We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates ≥30 cycles/minute; pulse rates ≥100 beats/minute; mean arterial pressures ≥110/<70 mmHg; temperatures ≥38.6/<35.6°C; and presence of altered mental state defined as Glasgow coma score ≤14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring ≥3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring ≥5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively. Conclusion Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings. In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables. Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS). We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates [greater than or equai to]30 cycles/minute; pulse rates [greater than or equai to]100 beats/minute; mean arterial pressures [greater than or equai to]110/<70 mmHg; temperatures [greater than or equai to]38.6/<35.6[degrees]C; and presence of altered mental state defined as Glasgow coma score [less than or equai to]14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring [greater than or equai to]3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring [greater than or equai to]5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively. Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings. INTRODUCTIONIn sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables.METHODSSubjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS).RESULTSWe included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates ≥30 cycles/minute; pulse rates ≥100 beats/minute; mean arterial pressures ≥110/<70 mmHg; temperatures ≥38.6/<35.6°C; and presence of altered mental state defined as Glasgow coma score ≤14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring ≥3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring ≥5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively.CONCLUSIONAmong patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings. Key messages * Existing prognostic indexes that use vital signs information incorporate this information in ways that are not always efficient * We used restricted cubic splines to assess how admission vital signs data predict mortality in patients with sepsis * Guided by these predictions, we incorporated treatment-modifiable routine vital signs into a prognostic index with reduced segmentation in the values of incorporated variables * The resulting prognostic index adequately predicted mortality in two cohorts of patients with sepsis * Future studies using a similar approach on post-admission vital signs data could develop indexes that can be used to monitor treatment, especially in resource-limited settings Abbreviations BP: blood pressure CI: confidence interval CVP: central venous pressure DBP: diastolic blood pressure GCS: Glasgow coma score HOTEL: hypotension, oxygen saturation, temperature, electrocardiographic abnormality, and loss of independence IQR: inter-quartile range IRC: Modified Early Warning Score MI: multiple imputations OR: odds ratio SBP: systolic blood pressure SIRS: systemic inflammatory response syndrome SSA: sub-Saharan Africa ScvO2: central venous oxygen saturation TOTAL: tachypnea, oxygen saturation, temperature, alertness, and loss of independence Declarations Acknowledgements Funding for data collection for the source cohort studies was provided by the University of Virginia, Charlottesville, Center for Global Health - Pfizer Initiative in International Health. In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital mortality among patients with sepsis. In particular, we assessed whether vital signs data can be incorporated into a prognostic index with reduced segmentation in the values of included variables. Subjects were patients with sepsis hospitalized in Uganda, who participated in two cohort studies. Using restricted cubic splines of admission vital signs data, we predicted probability of in-hospital death in the development cohort and used this information to construct a simple prognostic index. We assessed the performance of the index in a validation cohort and compared its performance to that of the Modified Early Warning Score (MEWS). We included 317 patients (167 in the development cohort and 150 in the validation cohort). Based on how vital signs predicted mortality, we created a prognostic index giving a score of 1 for: respiratory rates ≥30 cycles/minute; pulse rates ≥100 beats/minute; mean arterial pressures ≥110/<70 mmHg; temperatures ≥38.6/<35.6°C; and presence of altered mental state defined as Glasgow coma score ≤14; 0 for all other values. The proposed index (maximum score = 5) predicted mortality comparably to MEWS. Patients scoring ≥3 on the index were 3.4-fold (95% confidence interval (CI) 1.6 to 7.3, P = 0.001) and 2.3-fold (95% CI 1.1 to 4.7, P = 0.031) as likely to die in hospital as those scoring 0 to 2 in the development and validation cohorts respectively; those scoring ≥5 on MEWS were 2.5-fold (95% CI 1.2 to 5.3, P = 0.017) and 1.8-fold (95% CI 0.74 to 4.2, P = 0.204) as likely to die as those scoring 0 to 4 in the development and validation cohorts respectively. Among patients with sepsis, a prognostic index incorporating admission vital signs data with reduced segmentation in the values of included variables adequately predicted mortality. Such an index may be more easily implemented when triaging acutely-ill patients. Future studies using a similar approach may develop indexes that can be used to monitor treatment among acutely-ill patients, especially in resource-limited settings. |
ArticleNumber | 86 |
Audience | Academic |
Author | Asiimwe, Stephen B Ssekitoleko, Richard Abdallah, Amir |
Author_xml | – sequence: 1 givenname: Stephen B surname: Asiimwe fullname: Asiimwe, Stephen B email: asiimwesteve@gmail.com, asiimwesteve@gmail.com organization: Department of Epidemiology and Biostatistics, University of California San Francisco, 550 16th Street, Second Floor, Mission Hall: Global Health and Clinical Sciences Building, San Francisco, CA, 94158-2549, USA. asiimwesteve@gmail.com – sequence: 2 givenname: Amir surname: Abdallah fullname: Abdallah, Amir email: dr.amir83@yahoo.co.uk, dr.amir83@yahoo.co.uk organization: Department of Medicine, Mbarara University of Science and Technology, P.O. Box 1410, Mbarara, Uganda. dr.amir83@yahoo.co.uk – sequence: 3 givenname: Richard surname: Ssekitoleko fullname: Ssekitoleko, Richard email: sekirchrd@yahoo.com organization: Department of Medicine, College of Health Sciences, Makerere University, P.O. Box 7072, Kampala, Uganda. sekirchrd@yahoo.com |
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CitedBy_id | crossref_primary_10_4269_ajtmh_20_1624 crossref_primary_10_1016_j_ijmedinf_2018_03_014 crossref_primary_10_1186_s12879_015_1151_1 crossref_primary_10_1016_j_ijnurstu_2017_09_003 crossref_primary_10_4269_ajtmh_17_0682 crossref_primary_10_1016_j_jemermed_2019_11_020 crossref_primary_10_1371_journal_pone_0170152 crossref_primary_10_1111_anae_16048 crossref_primary_10_1136_bmjopen_2021_055752 crossref_primary_10_1007_s00134_016_4381_9 crossref_primary_10_1007_s10586_018_2208_x crossref_primary_10_1186_s13012_017_0654_0 crossref_primary_10_1097_CCE_0000000000000067 crossref_primary_10_1111_ijcp_14044 crossref_primary_10_3390_medicina59081438 |
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Snippet | In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict in-hospital... Abstract Introduction In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs... Introduction In sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data... Key messages * Existing prognostic indexes that use vital signs information incorporate this information in ways that are not always efficient * We used... INTRODUCTIONIn sub-Saharan Africa, vital signs are a feasible option for monitoring critically ill patients. We assessed how admission vital signs data predict... |
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SubjectTerms | Adult Complications and side effects Critical care Critical Illness - mortality Diagnosis Female Health aspects Health Resources Hospital Mortality Humans Infection Intensive care Low income groups Male Measurement Medical research Medicine, Experimental Middle Aged Monitoring, Physiologic - methods Mortality Prognosis Prospective Studies Risk factors Sepsis Sepsis - mortality Sub-Saharan Africa Uganda Vital signs Vital Signs - physiology |
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Title | A simple prognostic index based on admission vital signs data among patients with sepsis in a resource-limited setting |
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