Using Comorbidity Statistical Modeling to Predict Inpatient Mortality: Insights Into the Burden on Hospitalized Patients

Background The expenditures of the United States for healthcare are the highest in the world. Assessment of inpatient disease classifications associated with death can provide useful information for risk stratification, outcome prediction, and comparative analyses to understand the most resource-intensive chronic illnesses. This project aims to adapt a comorbidity index model to the National Inpatient Sample (NIS) database of 2020 to predict one-year mortality for patients admitted with select International Classification of Diseases, 10th Edition (ICD-10) codes of diagnoses. Methodology A retrospective cohort study analyzed mortality with comorbidity using the Charlson comorbidity index model (CCI) in a sample population of an estimated 5,533,477 adult inpatients (individuals aged ≥18 years) obtained from the National Inpatient Database for 2020. A multivariate logistic regression model was constructed with in-hospital mortality as the outcome variable and identifying predictor variables as defined by the Clinical Classifications Software Refined Variables (CCSR) codes for selected ICD-10 diagnoses. Descriptive statistics and the base logistic regression analyses were conducted using SAS statistical software version 9.4 (SAS Institute, Cary, NC, USA). To avoid overpowering, a subsample (n = 100,000) was randomly selected from the original dataset. The initial CCI assigned weights to ICD-10 diagnoses based on the associated risk of death, and conditions with the greatest collective weights were included in a subsequent backward stepwise logistic regression model. Results The results of the base CCI regression analysis revealed 16 chronic conditions with P-values <0.20. Anemia (1,567,081, 28.32%), pulmonary disease (asthma, chronic obstructive pulmonary disease [COPD], pneumoconiosis; 1,210,892, 21.88%), and diabetes without complications (1,077,239, 19.47%) were the three most prevalent conditions associated with inpatient mortality. Results of the backward stepwise regression analysis revealed that severe liver disease/hepatic failure (adjusted odds ratio [aOR] 10.50; 95% confidence interval [CI] 10.40-10.59), acute myocardial infarction (aOR 2.85; 95% CI 2.83-2.87) and malnutrition (aOR 2.15, 95% CI 2.14-2.16) were three most important risk factors and had the highest impact on inpatient mortality (P-value <0.0001). The concordance statistic (c-statistic) or the area under the curve (AUC) for the final model was 0.752. Conclusions The CCI model proved to be a valuable approach in categorizing morbidity classifications associated with the greatest risk of death using a national sample of hospitalized patients in 2020. Study findings provide an objective approach to compare patient populations that bear important implications for healthcare system improvements, clinician treatment approaches, and ultimately decision decision-makers poised to influence advanced models of care and prevention strategies that limit disease progression and improve patient outcomes.