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Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase.

ABSTRACT: Circulatory shock is a life-threatening disease that accounts for around one-third of all admissions to intensive care units (ICU). It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. In this study, the ShockOmics European project original database is used to extract attributes capable of predicting mortality due to shock in the ICU. Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. Selected features were later used to build Bayesian Networks, revealing causal relationships between features and ICU outcome. The main result is a subset of predictive features that includes well-known indicators such as the SOFA and APACHE II scores, but also less commonly considered ones related to cardiovascular function assessed through echocardiograpy or shock treatment with pressors. Importantly, certain selected features are shown to be most predictive at certain time-steps. This means that, as shock progresses, different attributes could be prioritized. Clinical traits obtained at 24h. from ICU admission are shown to accurately predict cardiogenic and septic shock mortality, suggesting that relevant life-saving decisions could be made shortly after ICU admission.

SUBMITTER: Aushev A

PROVIDER: S-EPMC6245679 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase.

Aushev Alexander A Ripoll Vicent Ribas VR Vellido Alfredo A Aletti Federico F Pinto Bernardo Bollen BB Herpain Antoine A Post Emiel Hendrik EH Medina Eduardo Romay ER Ferrer Ricard R Baselli Giuseppe G Bendjelid Karim K

PloS one 20181120 11

Circulatory shock is a life-threatening disease that accounts for around one-third of all admissions to intensive care units (ICU). It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. In this study, the ShockOmics European project original database is used to extract attributes capable of predicting mortality due to shock in the ICU. Missing data imputation techniques and ma ...[more]

PMID: 30457997

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Project description:BackgroundClinical outcome of ischemic cardiogenic shock (CS) requiring extracorporeal membrane oxygenation is highly variable, necessitating appropriate assessment of prognosis. However, a systemic predictive model estimating the mortality of refractory ischemic CS is lacking. The PRECISE (Prediction of In-Hospital Mortality for Patients With Refractory Ischemic Cardiogenic Shock Requiring Veno-Arterial Extracorporeal Membrane Oxygenation Support) score was developed to predict the prognosis of refractory ischemic CS due to acute myocardial infarction.Methods and resultsData were obtained from the multicenter CS registry RESCUE (Retrospective and Prospective Observational Study to Investigate Clinical Outcomes and Efficacy of Left Ventricular Assist Device for Korean Patients With Cardiogenic Shock) that consists of 322 patients with acute myocardial infarction complicated by refractory ischemic CS requiring extracorporeal membrane oxygenation support. Fifteen parameters were selected to assess in-hospital mortality. The developed model was validated internally and externally using an independent external cohort (n=138). Among 322 patients, 138 (42.9%) survived postdischarge. Fifteen predictors were included for model development: age, diastolic blood pressure, hypertension, chronic kidney disease, peak lactic acid, serum creatinine, lowest left ventricular ejection fraction, vasoactive inotropic score, shock to extracorporeal membrane oxygenation insertion time, extracorporeal cardiopulmonary resuscitation, use of intra-aortic balloon pump, continuous renal replacement therapy, mechanical ventilator, successful coronary revascularization, and staged percutaneous coronary intervention. The PRECISE score yielded a high area under the receiver-operating characteristic curve (0.894 [95% CI, 0.860-0.927]). External validation and calibration resulted in competent sensitivity (area under the receiver-operating characteristic curve, 0.895 [95% CI, 0.853-0.930]).ConclusionsThe PRECISE score demonstrated high predictive performance and directly translates into the expected in-hospital mortality rate. The PRECISE score may be used to support clinical decision-making in ischemic CS (www.theprecisescore.com).RegistrationURL: https://www.clinicaltrials.gov; Unique identifier: NCT02985008.

Project description:ObjectiveThe management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day mortality of elderly patients with CS.MethodsWe extracted data from the Medical Information Mart for Intensive Care III version 1.4 (MIMIC-III) as the training set and the data of validation sets were collected from the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. Three models, including the cox regression model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and the CoxBoost model, were established using the training set. Through the comparison of area under the receiver operating characteristic (ROC) curve (AUC), C index, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and median improvement in risk score, the best model was selected. Then for external validation, compared the best model with the simplified acute physiology score II (SAPSII) and the CardShock risk score.ResultsA total of 919 patients were included in the study, of which 804 patients were in the training set and 115 patients were in the verification set. Using the training set, we built three models: the cox regression model including 6 predictors, the LASSO regression model including 4 predictors, and the CoxBoost model including 16 predictors. Among them, the CoxBoost model had good discrimination [AUC: 0.730; C index: 0.6958 (0.6657, 0.7259)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of other models were all<0. In the validation set, the CoxBoost model was also well-discriminated [AUC: 0.770; C index: 0.7713 (0.6751, 0.8675)]. Compared with the CoxBoost model, the NRI, IDI, and median improvement in risk score of SAPS II and the CardShock risk score were all < 0. And we constructed a dynamic nomogram to visually display the model.ConclusionIn conclusion, this study showed that in predicting the 30-day mortality of elderly CS patients, the CoxBoost model was superior to the Cox regression model, LASSO regression model, SAPS II, and the CardShock risk score.

Dataset Information

Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase.

Publications

Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase.

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