Predictors of Mortality and the Development of a Risk Score for Gallbladder Adenocarcinoma: A Population-Based Analysis Using Healthcare Cost and Utilization Project National Inpatient Database (HCUP-NIS) Data.

Objective This study aims to identify factors predictive of mortality in patients with gallbladder adenocarcinoma and to develop a risk score to predict poor outcomes using data from the Using Healthcare Cost and Utilization Project National Inpatient Database (HCUP-NIS) database between 2016 and 2020. Methods We conducted a retrospective cohort study analyzing 8596 patients diagnosed with gallbladder adenocarcinoma. Data were extracted using the International Classification of Diseases (ICD) 10th Edition Clinical Modification (CM) code C23. Variables analyzed included age, gender, hospital division, race, income quartile, and APRDRG mortality risk. Logistic regression was utilized to determine the predictors of mortality and develop a risk-scoring system. Descriptive statistics and Chi-squared tests assessed the relationship between variables and mortality, with p-values indicating significance. Results The study population had a mean age of 68.3 years, with 66.6% female patients. The overall mortality rate was 7.2%. Key predictors of mortality included higher All Patients Refined Diagnosis Related Groups (APR DRG) risk of mortality (p<0.001), age (p=0.04), and female gender (p=0.033). Race and hospital division were significantly associated with mortality (p<0.001 and p=0.015, respectively). A logistic regression model incorporating these variables yielded an area under the receiver operating characteristics curve of 0.82, indicating good discriminative ability. The developed risk score categorized patients into low, medium, and high-risk groups, with corresponding mortality rates of 0.88%, 5.28%, and 17.78%. Conclusion This study identified critical predictors of mortality in gallbladder adenocarcinoma patients, with APR DRG risk of mortality and age being the most significant. The developed risk score effectively stratifies patients by risk, potentially guiding clinical decision-making and improving patient outcomes.