Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database

Objective: The predictors of in-hospital mortality for intensive care
units (ICU)-admitted HF patients remain poorly characterized.We aimed to
develop and validate a prediction model for all-cause in-hospital
mortality among ICU-admitted HF patients. Design: A retrospective cohort
study. Setting and Participants: Data were extracted from the MIMIC-III
database. Data on 1,177 heart failure patients were analysed. Methods:
Patients meeting the inclusion criteria were identified from the MIMIC-III
database and randomly divided into derivation and validation groups.
Independent risk factors for in-hospital mortality were screened using
XGBoost and LASSO regression models in the derivation sample.
Multivariable logistic regression analysis was used to build prediction
models. Discrimination, calibration, and clinical usefulness of the
predicting model were assessed using the C-index, calibration plot, and
decision curve analysis. After pairwise comparison, the best performing
model was chosen to build a nomogram according to the regression
coefficients. Results: Among the 1,177 admissions, in-hospital mortality
was 13.52%. In both groups, the XGBoost, LASSO regression, and GWTG-HF
risk score models showed acceptable discrimination. The XGBoost and LASSO
regression models also showed good calibration. In pairwise comparison,
the prediction effectiveness was higher with the XGBoost and LASSO
regression models than with the GWTG-HF risk score model (P<0.05).
The XGBoost model was chosen as our final model for its more concise and
wider net benefit threshold probability range and was presented as the
nomogram. Conclusions: Our nomogram enabled good prediction of in-hospital
mortality in ICU-admitted HF patients, which may help clinical
decision-making for such patients.