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

Hezborn M Magacha1, Sheryl M Strasser2, Shimini Zheng3

  • 1Internal Medicine, Quillen College of Medicine, East Tennessee State University, Johnson City, USA.

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Summary
This summary is machine-generated.

This study adapted the Charlson Comorbidity Index (CCI) to predict one-year mortality in hospitalized patients using 2020 US data. Severe liver disease, acute myocardial infarction, and malnutrition were key predictors of inpatient mortality.

Keywords:
age-adjusted charlson comorbidity indexcharlson comorbidity indexinpatient mortalitymortalitypredicting mortality

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Area of Science:

  • Health Services Research
  • Epidemiology
  • Biostatistics

Background:

  • US healthcare expenditures are the highest globally.
  • Assessing inpatient disease classifications linked to mortality aids risk stratification and understanding chronic illnesses.
  • Predicting mortality is crucial for resource allocation and patient outcome improvement.

Purpose of the Study:

  • To adapt the Charlson Comorbidity Index (CCI) model for the 2020 National Inpatient Sample (NIS) database.
  • To predict one-year mortality for adult inpatients using International Classification of Diseases, 10th Edition (ICD-10) codes.
  • To identify key chronic conditions and risk factors associated with inpatient mortality.

Main Methods:

  • Retrospective cohort study of 5,533,477 adult inpatients from the 2020 NIS database.
  • Utilized the Charlson Comorbidity Index (CCI) model and multivariate logistic regression.
  • Employed backward stepwise logistic regression on a subsample (n=100,000) to identify significant predictors of mortality.

Main Results:

  • Anemia, pulmonary disease (including COPD), and diabetes were most prevalent among inpatients.
  • Severe liver disease/hepatic failure (aOR 10.50), acute myocardial infarction (aOR 2.85), and malnutrition (aOR 2.15) were the strongest predictors of inpatient mortality.
  • The final model achieved a concordance statistic (AUC) of 0.752.

Conclusions:

  • The adapted CCI model effectively categorizes morbidity linked to mortality risk in hospitalized patients.
  • Findings offer an objective method for comparing patient populations and improving healthcare systems.
  • Results have implications for treatment approaches, care models, and prevention strategies to enhance patient outcomes.