Development and validation of a prognostic nomogram model for ICU patients with alcohol-associated cirrhosis
The prognosis of patients with alcohol-associated cirrhosis (ALC) admitted to the intensive care unit (ICU) is poor. We developed and validated a nomogram (NIALC) for ICU patients with ALC. Predictors of mortality were defined by a machine learning method in a cohort of 394 ICU patients with ALC fro...
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| Published in | Digestive and liver disease Vol. 55; no. 4; pp. 498 - 504 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.04.2023
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| Abstract | The prognosis of patients with alcohol-associated cirrhosis (ALC) admitted to the intensive care unit (ICU) is poor. We developed and validated a nomogram (NIALC) for ICU patients with ALC.
Predictors of mortality were defined by a machine learning method in a cohort of 394 ICU patients with ALC from the Medical Information Mart for Intensive Care database. Then the nomogram (NIALC) was constructed and evaluated using the AUC. The MELD, MELD-sodium, Child–Pugh, and CLIF-SOFA scores were then compared with NIALC. Two datasets of 394 and 501 ICU patients with ALC were utilized for model validation.
In-hospital mortality was 41% and 21% in the training and external validation sets. Predictors included were blood urea nitrogen, total bilirubin, prothrombin time, serum creatinine, lactate, partial thromboplastin time, phosphate, mean arterial pressure, lymphocytes, fibrinogen, and albumin. The AUCs for the NIALC were 0.767 and 0.760 in the two validation cohorts, which were better than those of the MELD, MELD-sodium, Child–Pugh, and CLIF-SOFA.
We developed a nomogram for ICU patients with ALC, which demonstrated better discriminative ability than previous prognostic scores. This nomogram could be conveniently used to facilitate the individualized prediction of death in ICU patients with ALC. |
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| AbstractList | The prognosis of patients with alcohol-associated cirrhosis (ALC) admitted to the intensive care unit (ICU) is poor. We developed and validated a nomogram (NIALC) for ICU patients with ALC.
Predictors of mortality were defined by a machine learning method in a cohort of 394 ICU patients with ALC from the Medical Information Mart for Intensive Care database. Then the nomogram (NIALC) was constructed and evaluated using the AUC. The MELD, MELD-sodium, Child–Pugh, and CLIF-SOFA scores were then compared with NIALC. Two datasets of 394 and 501 ICU patients with ALC were utilized for model validation.
In-hospital mortality was 41% and 21% in the training and external validation sets. Predictors included were blood urea nitrogen, total bilirubin, prothrombin time, serum creatinine, lactate, partial thromboplastin time, phosphate, mean arterial pressure, lymphocytes, fibrinogen, and albumin. The AUCs for the NIALC were 0.767 and 0.760 in the two validation cohorts, which were better than those of the MELD, MELD-sodium, Child–Pugh, and CLIF-SOFA.
We developed a nomogram for ICU patients with ALC, which demonstrated better discriminative ability than previous prognostic scores. This nomogram could be conveniently used to facilitate the individualized prediction of death in ICU patients with ALC. The prognosis of patients with alcohol-associated cirrhosis (ALC) admitted to the intensive care unit (ICU) is poor. We developed and validated a nomogram (NIALC) for ICU patients with ALC.BACKGROUNDThe prognosis of patients with alcohol-associated cirrhosis (ALC) admitted to the intensive care unit (ICU) is poor. We developed and validated a nomogram (NIALC) for ICU patients with ALC.Predictors of mortality were defined by a machine learning method in a cohort of 394 ICU patients with ALC from the Medical Information Mart for Intensive Care database. Then the nomogram (NIALC) was constructed and evaluated using the AUC. The MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA scores were then compared with NIALC. Two datasets of 394 and 501 ICU patients with ALC were utilized for model validation.METHODSPredictors of mortality were defined by a machine learning method in a cohort of 394 ICU patients with ALC from the Medical Information Mart for Intensive Care database. Then the nomogram (NIALC) was constructed and evaluated using the AUC. The MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA scores were then compared with NIALC. Two datasets of 394 and 501 ICU patients with ALC were utilized for model validation.In-hospital mortality was 41% and 21% in the training and external validation sets. Predictors included were blood urea nitrogen, total bilirubin, prothrombin time, serum creatinine, lactate, partial thromboplastin time, phosphate, mean arterial pressure, lymphocytes, fibrinogen, and albumin. The AUCs for the NIALC were 0.767 and 0.760 in the two validation cohorts, which were better than those of the MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA.RESULTSIn-hospital mortality was 41% and 21% in the training and external validation sets. Predictors included were blood urea nitrogen, total bilirubin, prothrombin time, serum creatinine, lactate, partial thromboplastin time, phosphate, mean arterial pressure, lymphocytes, fibrinogen, and albumin. The AUCs for the NIALC were 0.767 and 0.760 in the two validation cohorts, which were better than those of the MELD, MELD-sodium, Child-Pugh, and CLIF-SOFA.We developed a nomogram for ICU patients with ALC, which demonstrated better discriminative ability than previous prognostic scores. This nomogram could be conveniently used to facilitate the individualized prediction of death in ICU patients with ALC.CONCLUSIONWe developed a nomogram for ICU patients with ALC, which demonstrated better discriminative ability than previous prognostic scores. This nomogram could be conveniently used to facilitate the individualized prediction of death in ICU patients with ALC. |
| Author | Lu, Yining Zheng, Luyan Wu, Jie Zheng, Min |
| Author_xml | – sequence: 1 givenname: Luyan surname: Zheng fullname: Zheng, Luyan – sequence: 2 givenname: Yining surname: Lu fullname: Lu, Yining – sequence: 3 givenname: Jie surname: Wu fullname: Wu, Jie email: zjwujie@zju.edu.cn – sequence: 4 givenname: Min orcidid: 0000-0001-6159-9879 surname: Zheng fullname: Zheng, Min email: minzheng@zju.edu.cn |
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| Keywords | Alcohol-associated cirrhosis Nomogram Intensive care unit Machine learning |
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| SubjectTerms | Alcohol-associated cirrhosis Critical Care Humans Intensive care unit Intensive Care Units Lactic Acid Liver Cirrhosis - complications Liver Cirrhosis, Alcoholic - complications Machine learning Nomogram Nomograms Prognosis Retrospective Studies |
| Title | Development and validation of a prognostic nomogram model for ICU patients with alcohol-associated cirrhosis |
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