Machine learning predicting mortality in sarcoidosis patients admitted for acute heart failure

ABSTRACT:

Background

Sarcoidosis with cardiac involvement, although rare, has a worse prognosis than sarcoidosis involving other organ systems.

Objective

We used a large dataset to train machine learning models to predict in-hospital mortality among sarcoidosis patients admitted with heart failure (HF).

Method

Utilizing the National Inpatient Sample, we identified 4659 patients hospitalized with a primary diagnosis of HF. In this cohort, we identified patients with a secondary diagnosis of sarcoidosis using International Statistical Classification of Disease, Tenth Revision (ICD-10) codes. Patients were separated into a training group and a testing group in a 7:3 ratio. Least absolute shrinkage and selection operator regression was used to select variables to prevent model

SUBMITTER: Dai Q 

PROVIDER: S-EPMC9795270 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature