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 heart failure and acute ischemic stroke (AIS).

Background:

Not Applicable

Design/Methods:

The 2015-2019 NIS was queried using ICD-10 PCS coding to identify patients with both heart failure and AIS. A machine learning clustering analysis evaluated the population based on 50 comorbidities, complications and clinical covariates. Optimal number of clusters was determined using the Davies-Bouldin Index and Calinski-Harabasz Index. 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:

Machine learning analysis identified 83,280 patients and segmented them into four patient clusters, ranging from 3,379 to 56,881 patients. Mortality rate in Cluster 1 was the lowest at 8.14%. Clusters 2-4 demonstrated higher mortality rates of 9.38%, 31.08% and 37.59% respectively [OR Range 1.24-8.11, p<0.001]. Cluster comorbidity profiles are visualized in a heatmap. Cluster 1 displays the highest prevalence of arrhythmia and hyperlipidemia, while Cluster 4 has the highest prevalence of acute kidney failure, myocardial infarction, and sepsis. Compared to Cluster 1, risk of non-routine discharge was highest in Cluster 4 [OR 8.55, p<0.001]. Kruskall-Wallis H-testing and post-hoc pairwise comparison of length-of-stay distributions showed significant (p<0.001) differences between all clusters.

Conclusions:

This analysis distinguishes four groups with distinct comorbidity profiles. This allows for the assessment of different phenotypic comorbidity presentations of patients with varying mortality, discharge disposition, and length-of-stay, offering a foundation for further analysis to inform future clinical decision-making.