A novel approach selected small sets of diagnosis codes with high prediction performance in large healthcare datasets.

OBJECTIVE: To examine an approach for selecting small sets of diagnosis codes with high prediction performance in large datasets of electronic medical records.

STUDY DESIGN AND SETTING: Modelling study using national hospital and mortality records for patients with myocardial infarction (n=200 119), hip fracture (n=169 646), or colorectal cancer surgery (n=56 515) in England in 2015-17. One-year mortality was predicted from ICD-10 codes recorded for at least 0.5% of patients using logistic regression ('full' models). An approximation method was used to select fewer codes that explained at least 95% of variation in full model predictions ('reduced' models).

RESULTS: One-year mortality was 17.2% (34 520) after myocardial infarction, 27.2% (46 115) after hip fracture, and 9.3% (5273) after colorectal surgery. Full models included 202, 257, and 209 ICD-10 codes in these populations. C-statistics for these models were 0.884 (95% CI 0.882, 0.886), 0.798 (0.795, 0.800), 0.810 (0.804, 0.817). Reduced models included 18, 33, and 41 codes and had c-statistics of 0.874 (95% CI 0.872, 0.876), 0.791 (0.788, 0.793), 0.807 (0.801, 0.813). Performance was also similar when measured using Brier scores. All models were well calibrated.

CONCLUSION: Our approach selected small sets of diagnosis codes that predicted patient outcomes comparably to large, comprehensive sets of codes.