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Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients

OBJECTIVE: This study aims to evaluate the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) II score on different days in predicting the mortality of critically ill patients to identify the best time point for the APACHE II score. METHODS: The demographic and clinical data are...

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Autores principales: Tian, Yao, Yao, Yang, Zhou, Jing, Diao, Xin, Chen, Hui, Cai, Kaixia, Ma, Xuan, Wang, Shengyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826444/
https://www.ncbi.nlm.nih.gov/pubmed/35155461
http://dx.doi.org/10.3389/fmed.2021.744907
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author Tian, Yao
Yao, Yang
Zhou, Jing
Diao, Xin
Chen, Hui
Cai, Kaixia
Ma, Xuan
Wang, Shengyu
author_facet Tian, Yao
Yao, Yang
Zhou, Jing
Diao, Xin
Chen, Hui
Cai, Kaixia
Ma, Xuan
Wang, Shengyu
author_sort Tian, Yao
collection PubMed
description OBJECTIVE: This study aims to evaluate the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) II score on different days in predicting the mortality of critically ill patients to identify the best time point for the APACHE II score. METHODS: The demographic and clinical data are retrieved from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. APACHE II scores on days 1, 2, 3, 5, 7, 14, and 28 of hospitalization are calculated, and their performance is evaluated using the area under the receiver operating characteristic (AUROC) analysis. The cut-off for defining the high risk of mortality is determined using Youden's index. The APACHE II score on day 3 is the best time point to predict hospital mortality of ICU patients. The Hosmer-Lemeshow goodness-of-fit test is then applied to evaluate the calibration of the day 3 APACHE II score. RESULTS: We recruited 6,374 eligible subjects from the MIMIC-IV database. Day 3 is the optimal time point for obtaining the APACHE II score to predict the hospital mortality of patients. The best cut-off for day 3 APACHE II score is 17. When APACHE II score ≥17, the sensitivity for the non-survivors and survivors is 92.8 and 82.2%, respectively, and the positive predictive value (PPV) is 23.1%. When APACHE II socre <17, the specificity for non-survivors and survivors is 90.1 and 80.2%, respectively, and the negative predictive value (NPV) is 87.8%. When day-3 APACHE II is used to predict the hospital mortality, the AUROC is 0.743 (P <0.001). In the ≥17 group, the sensitivity of non-survivors and survivors is 92.2 and 81.3%, respectively, and the PPV is 30.3%. In the <17 group, the specificity of non-survivors and survivors is 100.0 and 80.2%, respectively, and the NPV is 81.6%. The Hosmer-Lemeshow test indicated day-3 APACHE II has a high predicting the hospital mortality (X(2) = 6.198, P = 0.625, consistency = 79.4%). However, the day-1 APACHE II has a poor calibration in predicting the hospital mortality rate (X (2) = 294.898, P <0.001). CONCLUSION: Day-3 APACHE II score is an optimal biomarker to predict the outcomes of ICU patients; 17 is the best cut-off for defining patients at high risk of mortality.
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spelling pubmed-88264442022-02-10 Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients Tian, Yao Yao, Yang Zhou, Jing Diao, Xin Chen, Hui Cai, Kaixia Ma, Xuan Wang, Shengyu Front Med (Lausanne) Medicine OBJECTIVE: This study aims to evaluate the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) II score on different days in predicting the mortality of critically ill patients to identify the best time point for the APACHE II score. METHODS: The demographic and clinical data are retrieved from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. APACHE II scores on days 1, 2, 3, 5, 7, 14, and 28 of hospitalization are calculated, and their performance is evaluated using the area under the receiver operating characteristic (AUROC) analysis. The cut-off for defining the high risk of mortality is determined using Youden's index. The APACHE II score on day 3 is the best time point to predict hospital mortality of ICU patients. The Hosmer-Lemeshow goodness-of-fit test is then applied to evaluate the calibration of the day 3 APACHE II score. RESULTS: We recruited 6,374 eligible subjects from the MIMIC-IV database. Day 3 is the optimal time point for obtaining the APACHE II score to predict the hospital mortality of patients. The best cut-off for day 3 APACHE II score is 17. When APACHE II score ≥17, the sensitivity for the non-survivors and survivors is 92.8 and 82.2%, respectively, and the positive predictive value (PPV) is 23.1%. When APACHE II socre <17, the specificity for non-survivors and survivors is 90.1 and 80.2%, respectively, and the negative predictive value (NPV) is 87.8%. When day-3 APACHE II is used to predict the hospital mortality, the AUROC is 0.743 (P <0.001). In the ≥17 group, the sensitivity of non-survivors and survivors is 92.2 and 81.3%, respectively, and the PPV is 30.3%. In the <17 group, the specificity of non-survivors and survivors is 100.0 and 80.2%, respectively, and the NPV is 81.6%. The Hosmer-Lemeshow test indicated day-3 APACHE II has a high predicting the hospital mortality (X(2) = 6.198, P = 0.625, consistency = 79.4%). However, the day-1 APACHE II has a poor calibration in predicting the hospital mortality rate (X (2) = 294.898, P <0.001). CONCLUSION: Day-3 APACHE II score is an optimal biomarker to predict the outcomes of ICU patients; 17 is the best cut-off for defining patients at high risk of mortality. Frontiers Media S.A. 2022-01-26 /pmc/articles/PMC8826444/ /pubmed/35155461 http://dx.doi.org/10.3389/fmed.2021.744907 Text en Copyright © 2022 Tian, Yao, Zhou, Diao, Chen, Cai, Ma and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Tian, Yao
Yao, Yang
Zhou, Jing
Diao, Xin
Chen, Hui
Cai, Kaixia
Ma, Xuan
Wang, Shengyu
Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients
title Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients
title_full Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients
title_fullStr Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients
title_full_unstemmed Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients
title_short Dynamic APACHE II Score to Predict the Outcome of Intensive Care Unit Patients
title_sort dynamic apache ii score to predict the outcome of intensive care unit patients
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826444/
https://www.ncbi.nlm.nih.gov/pubmed/35155461
http://dx.doi.org/10.3389/fmed.2021.744907
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