We propose an ICD-10-CM version of the combined comorbidity score that includes codes identified by ICD-10-CA and GEMs. Compared with the original score, it has similar performance in predicting readmission in a population of United States commercially insured individuals.

Comorbidity scores are widely used to help address confounding bias in nonrandomized studies conducted within healthcare databases, but existing scores were developed to predict all-cause mortality in adults and may not be appropriate for use in pediatric studies. We developed and validated a pediatric comorbidity index, using healthcare utilization data from the tenth revision of the International Classification of Diseases. Within the MarketScan database, pediatric patients (<18 years) continuously enrolled between October 1, 2015-September 30, 2017 were identified. Logistic regression was used to predict the 1-year risk of hospitalization based on 27 predefined conditions and empirically-identified conditions derived from the most prevalent diagnoses among patients with the outcome. A single numerical index was created by assigning weights to each condition based on its beta coefficient. We conducted internal validation of the index and compared its performance to existing adult scores. The pediatric comorbidity index consisted of 24 conditions and achieved a c-statistic of 0.718 (95% confidence interval [CI] 0.714, 0.723). The index oasutperformed existing adult scores in a pediatric population (c-statistics ranging from 0.522 to 0.640). The pediatric comorbidity index provides a summary measure of disease burden and can be used for risk adjustment in epidemiologic studies of pediatric patients.

Introduction With increasing rates of opioid overdoses in the US, a surveillance tool to identify high-risk patients may help facilitate early intervention. Objective To develop an algorithm to predict overdose using routinely-collected healthcare databases. Methods Within a US commercial claims database (2011-2015), patients with �1 opioid prescription were identified. Patients were randomly allocated into the training (50%), validation (25%), or test set (25%). For each month of follow-up, pooled logistic regression was used to predict the odds of incident overdose in the next month based on patient history from the preceding 3-6 months (time-updated), using elastic net for variable selection. As secondary analyses, we explored whether using simpler models (few predictors, baseline only) or different analytic methods (random forest, traditional regression) influenced performance. Results We identified 5,293,880 individuals prescribed opioids; 2,682 patients (0.05%) had an overdose during follow-up (mean: 17.1 months). On average, patients who overdosed were younger and had more diagnoses and prescriptions. The elastic net model achieved good performance (c-statistic 0.887, 95% CI 0.872-0.902; sensitivity 80.2, specificity 80.1, PPV 0.21, NPV 99.9 at optimal cutpoint). It outperformed simpler models based on few predictors (c-statistic 0.825, 95% CI 0.808-0.843) and baseline predictors only (c-statistic 0.806, 95% CI 0.787-0.26). Different analytic techniques did not substantially influence performance. In