Key result
A random forest machine learning algorithm using claims-based features accurately identified pulmonary arterial hypertension in the electronic medical record with an area under the curve of 0.96, sensitivity of 0.88, and specificity of 0.93.
Why the study?
Previous claims-based algorithms to identify pulmonary arterial hypertension had limited test characteristics, motivating development of a more accurate machine learning algorithm.
Does a machine learning algorithm using claims-based features accurately identify pulmonary arterial hypertension cases in an electronic medical record?
Observational (n=1,694)
No
Does a machine learning algorithm using claims-based features accurately identify pulmonary arterial hypertension cases in an electronic medical record?
Effect estimate: AUC 0.96
A machine learning algorithm using only claims-based features can accurately identify patients with pulmonary arterial hypertension in an electronic medical record.
BACKGROUND: Study of pulmonary arterial hypertension (PAH) in claims-based (CB) cohorts may facilitate understanding of disease epidemiology, however previous CB algorithms to identify PAH have had limited test characteristics. We hypothesized that machine learning algorithms (MLA) could accurately identify PAH in an CB cohort. METHODS: ICD-9/10 codes, CPT codes or PAH medications were used to screen an electronic medical record (EMR) for possible PAH. A subset (Development Cohort) was manually reviewed and adjudicated as PAH or "not PAH" and used to train and test MLAs. A second subset (Refinement Cohort) was manually reviewed and combined with the Development Cohort to make The Final Cohort, again divided into training and testing sets, with MLA characteristics defined on test set. The MLA was validated using an independent EMR cohort. RESULTS: 194 PAH and 786 "not PAH" in the Development Cohort trained and tested the initial MLA. In the Final Cohort test set, the final MLA sensitivity was 0.88, specificity was 0.93, positive predictive value was 0.89, and negative predictive value was 0.92. Persistence and strength of PAH medication use and CPT code for right heart catheterization were principal MLA features. Applying the MLA to the EMR cohort using a split cohort internal validation approach, we found 265 additional non-confirmed cases of suspected PAH that exhibited typical PAH demographics, comorbidities, hemodynamics. CONCLUSIONS: We developed and validated a MLA using only CB features that identified PAH in the EMR with strong test characteristics. When deployed across an entire EMR, the MLA identified cases with known features of PAH.
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Schuler et al. (2022) conducted an observational in Pulmonary arterial hypertension (n=1,694). Machine learning algorithm (Random Forest) vs. Manual chart review was evaluated on Diagnostic accuracy (Area under the curve) in the test set (AUC 0.96). A random forest machine learning algorithm using claims-based features accurately identified pulmonary arterial hypertension in the electronic medical record with an area under the curve of 0.96, sensitivity of 0.88, and specificity of 0.93.
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