Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry.

BACKGROUND: Machine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk. METHODS: We included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD). RESULTS: During mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p < 0.01 for ML-Remove vs remaining methods). Stress TPD (AUC 0.698) and visual diagnosis (0.681) had the lowest AUCs. CONCLUSION: Missing values reduce the accuracy of ML models when predicting MACE risk. Removing variables with missing values and retraining the model may yield superior patient-level prediction performance.

Practicing internal medicine onboard the USNS COMFORT in the aftermath of the Haitian earthquake.

On 12 January 2010, a 7.0-magnitude earthquake devastated the island nation of Haiti, leading to the world's largest humanitarian effort in over 6 decades. The catastrophe caused massive destruction of homes and buildings and overwhelmed the Haitian health care system. The United States responded immediately with a massive relief effort, sending U.S. military forces and civilian volunteers to Haiti's aid and providing a tertiary care medical center aboard the USNS COMFORT hospital ship. The COMFORT offered sophisticated medical care to a geographically isolated population and helped to transfer resource-intensive patients from other treatment facilities. Working collaboratively with the surgical staff, ancillary services, and nursing staff, internists aboard the COMFORT were integral to supporting the mission of the hospital ship and provided high-level care to the casualties. This article provides the perspective of the U.S. Navy internists who participated in the initial response to the Haitian earthquake disaster onboard the COMFORT.