Risk Adjustment of ICD-10-CM Coded Potential Inpatient Complications Using Administrative Data

Objective:To risk-adjust the Potential Inpatient Complication (PIC) measure set and propose a method to identify large deviations between observed and expected PIC counts. Data Sources:Acute inpatient stays from the Premier Healthcare Database from January 1, 2019 to December 31, 2021. Study Design:In 2014, the PIC list was developed to identify a broader set of potential complications that can occur as a result of care decisions. Risk adjustment for 111 PIC measures is performed across 3 age-based strata. Using patient-level risk factors and PIC occurrences, PIC-specific probabilities of occurrence are estimated through multivariate logistic regression models. Poisson Binomial cumulative mass function estimates identify deviations between observed and expected PIC counts across levels of patient-visit aggregation. Area under the curve (AUC) estimates are used to demonstrate PIC predictive performance in an 80:20 derivation-validation split framework. Data collection/Extraction methods:We used N=3,363,149 administrative hospitalizations between 2019 and 2021 from the Premier Healthcare Database. Principal Findings:PIC-specific model predictive performance was strong across PICs and age strata. Average area under the curve estimates across PICs were 0.95 (95% CI: 0.93-0.96), 0.91 (95% CI: 0.90-0.93), and 0.90 (95% CI: 0.89-0.91) for the neonate and infant, pediatric, and adult strata, respectively. Conclusions:The proposed method provides a consistent quality metric that adjusts for the population's case mix. Age-specific risk stratification further addresses currently ignored heterogeneity in PIC prevalence across age groups. Finally, the proposed aggregation method identifies large PIC-specific deviations between observed and expected counts, flagging areas with a potential need for quality improvements.

Automated summary

The objective of this study is to risk-adjust the Potential Inpatient Complication (PIC) measure set and propose a method to identify large deviations between observed and expected PIC counts. Patient-level risk factors and PIC occurrences were used to estimate PIC-specific probabilities of occurrence through multivariate logistic regression models. The proposed method provides a consistent quality metric that adjusts for the population's case mix and identifies potential areas for quality improvements.