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. 2022 Nov:135:104214.
doi: 10.1016/j.jbi.2022.104214. Epub 2022 Oct 8.

Evaluation of an automated phenotyping algorithm for rheumatoid arthritis

Henry W Zheng  1 , Veena K Ranganath  2 , Lucas C Perry  2 , David A Chetrit  2 , Karla M Criner  2 , Angela Q Pham  2 , Richard Seto  2 , Sitaram Vangala  3 , David A Elashoff  3 , Alex A T Bui  4
Affiliations

Evaluation of an automated phenotyping algorithm for rheumatoid arthritis

Henry W Zheng et al. J Biomed Inform. 2022 Nov.

Abstract

To better understand the challenges of generally implementing and adapting computational phenotyping approaches, the performance of a Phenotype KnowledgeBase (PheKB) algorithm for rheumatoid arthritis (RA) was evaluated on a University of California, Los Angeles (UCLA) patient population, focusing on examining its performance on ambiguous cases. The algorithm was evaluated on a cohort of 4,766 patients, along with a chart review of 300 patients by rheumatologists against accepted diagnostic guidelines. The performance revealed low sensitivity towards specific subtypes of positive RA cases, which suggests revisions in features used for phenotyping. A close examination of select cases also indicated a significant portion of patients with missing data, drawing attention to the need to consider data integrity as an integral part of phenotyping pipelines, as well as issues around the usability of various codes for distinguishing cases. We use patterns in the PheKB algorithm's errors to further demonstrate important considerations when designing a phenotyping algorithm.

Keywords: Computational phenotyping; PheKB; Phenotyping algorithm; Rheumatoid arthritis.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1.
Fig. 1.
Histogram of raw PheKB algorithm scores of cases and controls. The red dotted line represents the PheKB positive cutoff score of 0.632. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2.
Fig. 2.
Histogram of PheKB algorithm scores of ACR criteria subset, separated by case/control (left) and by RA status based on the 2010 ACR/EULAR criteria (right). The red dotted line represents the PheKB positive cutoff score of 0.632. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
See this image and copyright information in PMC

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