Dataset Information

Machine learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure.

ABSTRACT:

Aims

Models predicting mortality in heart failure (HF) patients are often limited with regard to performance and applicability. The aim of this study was to develop a reliable algorithm to compute expected in-hospital mortality rates in HF cohorts on a population level based on administrative data comparing regression analysis with different machine learning (ML) models.

Methods and results

Inpatient cases with primary International Statistical Classification of Diseases and Related Health Problems (ICD-10) encoded discharge diagnosis of HF non-electively admitted to 86 German Helios hospitals between 1 January 2016 and 31 December 2018 were identified. The dataset was randomly split 75%/25% for model development and testing. Highly unbalanced variables were removed. Four ML algorithms were applied, and all algorithms were tuned using a grid search with multiple repetitions. Model performance was evaluated by computing receiver operating characteristic areas under the curve. In total, 59 125 cases (69.8% aged 75 years or older, 51.9% female) were investigated, and in-hospital mortality was 6.20%. Areas under the curve of all ML algorithms outperformed regression analysis in the testing dataset with values of 0.829 [95% confidence interval (CI) 0.814-0.843] for logistic regression, 0.875 (95% CI 0.863-0.886) for random forest, 0.882 (95% CI 0.871-0.893) for gradient boosting machine, 0.866 (95% CI 0.854-0.878) for single-layer neural networks, and 0.882 (95% CI 0.872-0.893) for extreme gradient boosting. Brier scores demonstrated a good calibration especially of the latter three models.

Conclusions

We introduced reliable models to calculate expected in-hospital mortality based only on administrative routine data using ML algorithms. A broad application could supplement quality measurement programs and therefore improve future HF patient care.

SUBMITTER: Konig S

PROVIDER: S-EPMC8318394 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveThe predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.DesignA retrospective cohort study.Setting and participantsData were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed.MethodsPatients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. 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.ResultsAmong the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (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.ConclusionsOur nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

Dataset Information

Machine learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure.

Aims

Methods and results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Machine ​learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure.

Aims

Methods and results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Machine learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure.