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Endocrine

Endocrine/Head & Neck Poster Presentations 1

P23: Validation of a Novel Risk Prediction Model for Primary Aldosteronism

Thursday, March 21, 2024

9:36am – 9:42am ET

Location: C108

Oral Poster Presenter(s)

Julia A. Gasior, BS

Medical Student
Department of Surgery, Hospital of the University of Pennsylvania
Philadelphia, Pennsylvania, United States

Submitter(s)

Julia A. Gasior, BS

Medical Student
Department of Surgery, Hospital of the University of Pennsylvania
Philadelphia, Pennsylvania, United States

Author(s)

Julia A. Gasior, BS

Medical Student
Department of Surgery, Hospital of the University of Pennsylvania
Philadelphia, Pennsylvania, United States
Sara P. Ginzberg, MD MSHP (she/her/hers)

Resident, Department of Surgery
Hospital of the University of Pennsylvania, Pennsylvania, United States
DH

Daniel S. Herman, MD PhD

Physician
Department of Pathology and Laboratory Medicine, University of Pennsylvania, United States
SK

Saiesh Kalva, BA

Medical Student
Department of Surgery, Hospital of the University of Pennsylvania, United States
CW

Christopher J. Wirtalla, MBMI

Research Analyst
Department of Surgery, Hospital of the University of Pennsylvania, United States
Jesse E. Passman, MD MPH (he/him/his)

Surgery Resident
Department of Surgery, Hospital of the University of Pennsylvania
Philadelphia, Pennsylvania, United States
DC

Debbie L. Cohen, MD

Professor of Medicine, Division of Renal-Electrolyte and Hypertension
Hospital of the University of Pennsylvania, United States
JC

Jordana B. Cohen, MD MSCE

Professor of Medicine
Division of Renal-Electrolyte and Hypertension, Hospital of the University of Pennsylvania, United States
Heather Wachtel, MD (she/her/hers)

Associate Professor of Surgery
Department of Surgery, Hospital of the University of Pennsylvania
Philadelphia, Pennsylvania, United States

Disclosure(s):

Jesse E. Passman, MD MPH: No financial relationships to disclose

Heather Wachtel, MD: No financial relationships to disclose

Introduction: Primary aldosteronism (PA) affects up to 20% of patients with treatment-resistant hypertension (RH) yet remains significantly underdiagnosed, with only 1-3% of eligible patients receiving appropriate screening. Our group developed a PA risk prediction model using five clinical covariates in an initial study of patients at the Veterans Health Administration. In the present study, we validated our risk prediction model in an independent cohort.

Methods: Patients screened for PA across our health system were identified. Five clinical covariates identified in our prior study were extracted from the electronic health record. A risk score was calculated by assigning point values as follows: age < 50 years (1 point), obesity (1 point), RH (1 point), elevated serum sodium (2 points), and recent hypokalemia (3 points), for 0-8 possible points. The predicted and observed proportions of patients who screened positive for PA at each risk score were calculated.

Results: Of 1489 patients screened for PA, 76% met criteria for RH and 69% had hypokalemia. The mean age was 55 years (SD 12), 64% were female and 65% were Black. The majority (59%) were obese, 31% were younger than 50, and 31% had elevated serum sodium. The positive screening rate for PA was 15%. After accounting for data missingness, the risk prediction model was applied to 785 subjects. Rates of positive PA diagnosis increased significantly (p< 0.001) with each point increase in risk score (Fig. 1). Rates of PA varied from 0% (risk score of 1) to 27% in patients with a risk score of 8. Hypokalemia was the clinical covariate most strongly associated with PA (OR 7.7, 95% CI 3.4-17).

Conclusions: We validated the performance of our risk prediction model in an independent cohort, demonstrating that, in patients who underwent PA screening, rates of positive screen range from 0-27% depending on clinical characteristics. A model that incorporates these electronic health record-based data may be clinically useful in identifying patients at elevated risk of PA.

Learning Objectives:

Upon completion, participants will be able to describe the rationale for developing a novel risk prediction model for primary aldosteronism.
Upon completion, participants will be able to list the clinical covariates used for validation of this model.
Upon completion, participants will be able to describe the range of positive primary aldosteronism for each risk score in our model.