P3183 - Development and Validation of a Machine Learning<br>Risk-Prediction Model for In-Hospital Mortality from C. difficile: National Inpatient Sample Study

Umesh Bhagat, MD, Ankit Agrawal, MD
Cleveland Clinic Foundation, Cleveland, OH

Introduction: Clostridioides difficile (Cdiff) infection carries increased mortality and morbidity in United States. Due to the scarcity of a prognostic scoring system of Cdiff, we aimed to develop a novel machine learning based risk score model to predict in-hospital mortality in Cdiff patients

Methods: The National Inpatient Sample (NIS) 2016-2020 database was used to identify all adult patients (18 years of age) with Cdiff, using ICD-10 code A04.7. Relevant clinical characteristics and outcomes were extracted. The primary endpoint was in-hospital all-cause mortality. The dataset was randomly split into 3 subsets: training, validation, and testing, with a ratio of 0,7, 0.1, and 0.2, respectively. The risk score was generated using the Autoscore package in R software, a machine learning based tool for automatic clinical score generation. The performance was evaluated using area under receiver-operative characteristics curve (AUC) with 95% confidence intervals (95%CI)

Results: Amongst 288,791 Cdiff patients identified [age 64.8318.77 years, female 164,611 (57%)], there were 17.602 (6.09%) primary outcome events. Out of the top 20 covariates seen in the Parsimony plot, we used eight for our risk score (0-100): age, all cause shock, comorbidity burden, ICU admission, acute respiratory failure, race, female sex, and acute kidney injury (Figure 1A). The AUC for the derivation and validation cohort was 0.8698 (0.861-0.8786) and 0.8644 (0.8582-0.8706), respectively (Figure 1B and 1C).

Discussion: We developed and internally validated a machine learning based risk score model to predict outcomes in CDiff patients, achieving a high AUC of 0.8644 (0.8582-0.8706). Risk score models guide in risk stratification and management and large future studies are needed to externally evaluate our results



Disclosures:


Umesh Bhagat, MD, Ankit Agrawal, MD. P3183 - Development and Validation of a Machine Learning<br>Risk-Prediction Model for In-Hospital Mortality from <i>C. difficile</i>: National Inpatient Sample Study, ACG 2024 Annual Scientific Meeting Abstracts. Philadelphia, PA: American College of Gastroenterology.