Abstract Details

Title Machine Learning Clustering Algorithm Analysis of Dementia and Acute Ischemic Stroke in 53,000 Patients

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P5 - Poster Session 5 (8:00 AM-9:00 AM)

Poster/Presentation
Number 3-006

Objective

We aim to identify patient groups with unique comorbidity profiles in the 2015-2019 National Inpatient Sample (NIS), and assess the contributions of these profiles to mortality, discharge disposition, and length-of-stay in the sample of patients with dementia and acute ischemic stroke (AIS).

Background Not Applicable

Design/Methods The 2015-2019 NIS was assessed using ICD-10 PCS/CM codes to identify patients with dementia and concomitant AIS. A machine learning clustering analysis evaluated the population based on 50 comorbidities, complications and clinical covariates. Optimal clustering was determined using the Davies-Bouldin Index (DBI) and Calinski-Harabasz Index (CHI). Between-cluster multivariate regression analysis was performed to assess risk of mortality and non-routine discharge. Kruskal-Wallis H-Testing was performed to assess variance in length-of-stay between clusters. Statistical analysis was performed using Python.

Results 53,411 patients were clustered into 4 groups ranging from 1096 to 41,557 patients. Mortality ranged from 6.01% in Cluster 1 to 19.80% in Cluster 4. Clusters 2-4 each displayed significantly higher rates of mortality [OR Range 1.32-3.86, p<0.001] relative to Cluster 1. These results are visualized in a heatmap. Cluster 1 had the greatest prevalence of dyslipidemia amongst groups. Cluster 4 had the greatest prevalence of sepsis, aspiration pneumonia, and arrhythmia. Risk of non-routine discharge was lowest in Cluster 2 [OR 1.21, p<0.001] and highest in Cluster 4 [OR 4.77, p<0.001] when compared to Cluster 1. Kruskal-Wallis H-testing and post-hoc pairwise comparison of length-of-stay variances showed significant (p<0.001) differences between all clusters with the greatest test statistics occurring when comparing group 4 to all others.

Conclusions This analysis distinguishes four groups with unique comorbidity profiles. This clustering approach enables the assessment of different comorbidity presentations of patients with varying mortality, discharge disposition, and length-of-stay, offering a foundation for further analysis to inform clinical decision-making.

Authors/Disclosures
Ariel Sacknovitz, Medical Student PRESENTER	Mr. Sacknovitz has nothing to disclose.
Aryan Malhotra	Mr. Malhotra has nothing to disclose.
Adam C. Kiss	Mr. Kiss has nothing to disclose.
Hao Yu	Mr. Yu has nothing to disclose.
Rafay Khan	Mr. Khan has nothing to disclose.
Mateusz Faltyn, CTO	Mr. Faltyn has nothing to disclose.
Fawaz Al-Mufti, MD (Westchester Medical Center at New York Medical College)	Dr. Al-Mufti has received personal compensation in the range of $0-$499 for serving as a Consultant for Stryker. Dr. Al-Mufti has received personal compensation in the range of $0-$499 for serving as a Consultant for Cerenovus. Dr. Al-Mufti has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for Revalesio .

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