Dataset Information

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.

ABSTRACT: Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.

SUBMITTER: Ovcharenko E

PROVIDER: S-EPMC9967447 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.

Ovcharenko Evgeny E Kutikhin Anton A Gruzdeva Olga O Kuzmina Anastasia A Slesareva Tamara T Brusina Elena E Kudasheva Svetlana S Bondarenko Tatiana T Kuzmenko Svetlana S Osyaev Nikolay N Ivannikova Natalia N Vavin Grigory G Moses Vadim V Danilov Viacheslav V Komossky Egor E Klyshnikov Kirill K

Journal of cardiovascular development and disease 20230123 2

Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and grad ...[more]

PMID: 36826535

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Project description:ObjectiveThis 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.MethodsThe 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.ResultsWe 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).ConclusionDay-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.

Project description:Background and objectiveAcute Physiology and Chronic Health Evaluation (APACHE) III score has been widely used for prediction of clinical outcomes in mixed critically ill patients. However, it has not been validated in patients with sepsis-associated acute lung injury (ALI). The aim of the study was to explore the calibration and predictive value of APACHE III in patients with sepsis-associated ALI.MethodThe study was a secondary analysis of a prospective randomized controlled trial investigating the efficacy of rosuvastatin in sepsis-associated ALI (Statins for Acutely Injured Lungs from Sepsis, SAILS). The study population was sepsis-related ALI patients. The primary outcome of the current study was the same as in the original trial, 60-day in-hospital mortality, defined as death before hospital discharge, censored 60 days after enrollment. Discrimination of APACHE III was assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC) with its 95% CI. Hosmer-Lemeshow goodness-of-fit statistic was used to assess the calibration of APACHE III. The Brier score was reported to represent the overall performance of APACHE III in predicting outcome.Main resultsA total of 745 patients were included in the study, including 540 survivors and 205 non-survivors. Non-survivors were significantly older than survivors (59.71 ± 16.17 vs 52.00 ± 15.92 years, p < 0.001). The primary causes of ALI were also different between survivors and non-survivors (p = 0.017). Survivors were more likely to have the cause of sepsis than non-survivors (21.2% vs. 15.1%). APACHE III score was higher in non-survivors than in survivors (106.72 ± 27.30 vs. 88.42 ± 26.86; p < 0.001). Discrimination of APACHE III to predict mortality in ALI patients was moderate with an AUC of 0.68 (95% confidence interval: 0.64-0.73).Conclusionthis study for the first time validated the discrimination of APACHE III in sepsis associated ALI patients. The result shows that APACHE III score has moderate predictive value for in-hospital mortality among adults with sepsis-associated acute lung injury.

Dataset Information

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.

Publications

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.

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