Abstract Details

Title Machine Learning Clustering and Subgroup Analysis of 12,000 Patients with Lacunar Stroke

Topic Cerebrovascular Disease and Interventional Neurology

Presentation(s) P6 - Poster Session 6 (11:45 AM-12:45 PM)

Poster/Presentation
Number 14-013

Objective This study aims to identify unique clinical profiles in patients with lacunar stroke in the National Inpatient Sample (NIS) by leveraging machine learning-based clustering algorithms.

Background Not Applicable

Design/Methods The 2015-2019 NIS was queried using ICD-10 PCS coding to identify patients with lacunar stroke. A machine learning clustering algorithm evaluated the population based on 50 comorbidities, complications and clinical covariates. Optimal number of clusters was determined using the Davies-Bouldin Index (DBI) and Calinski-Harabasz Index (CHI). Between-cluster multivariate logistic 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 A total of 12,083 patients were included in this study. Composite DBI and CHI scoring determined the optimal number of clusters to be seven, with sizes ranging from 58-9437 patients. Mortality ranged from 1.07 % in Cluster 1 to 27.93% in Cluster 7. Clusters 3,4,5,6 and 7 each displayed significantly higher rates of mortality [OR Range 2.38 - 35.83, p<0.001] relative to Cluster 1. Individual cluster profiles are visualized in a heatmap. Cluster 1 had the greatest prevalence of hyperlipidemia amongst groups. Cluster 4 had the greatest prevalence of sepsis, arrhythmia, and aspiration pneumonia. Risk of non-routine discharge was highest in Cluster 7 [OR 11.21, p<0.001]. Kruskal-Wallis H-Testing and post-hoc pairwise comparison of length of stay distributions showed significant (p<0.001) differences between all clusters except 2 and 3, 5 and 6 and 6 and 7 with the greatest test statistics occurring when comparing group 1 to all other groups.

Conclusions Clustering analysis of patients with lacunar stroke identified 7 distinct groups. This clustering approach enables a nuanced understanding of comorbidity interactions, further informing clinical decision-making.

Authors/Disclosures
Ariel Sacknovitz, Medical Student PRESENTER	Mr. Sacknovitz has nothing to disclose.
Adam C. Kiss	Mr. Kiss has nothing to disclose.
Rafay Khan	Mr. Khan has nothing to disclose.
Samy Khessib, BS, MSIII	Mr. Khessib has nothing to disclose.
Hao Yu	Mr. Yu 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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