Objective To develop a semi-Markov model of hypertension specifically using estimates from primary analysis of Medicare data, and calibrate and validate using external trial and observational data.

Methods A semi-Markov model of hypertension with ten disease states was developed using clinical expertise and literature review: hypertension, myocardial infarction (MI), congestive heart failure (CHF), stroke, transient ischemic attack (TIA), other cardiovascular disease (CVD), chronic CVD, early-stage chronic kidney disease (ES CKD), late-stage CKD (LS CKD), and death. Most transition probabilities and hazard ratios for excess mortality were obtained from analysis of Medicare data (2018–2021). Transition probabilities and hazard ratios were calibrated against findings from the Systolic Hypertension in the Elderly Program (SHEP) trial and post-trial follow-up studies. Iterative grid search using Latin Hypercube Sampling was used for calibration, followed by a recalibration using Nelder-Mead simplex method with common random numbers. External validation was performed using mortality data after MI and stroke.

Results Final monthly transition probabilities from hypertension to other states were: other CVD (0.026), ES CKD (0.010), CHF (0.004), stroke (0.004), TIA (0.002), and MI (0.002). Final hazard ratios were: MI (3.29), CHF (3.99), stroke (3.79), TIA (1.39), other CVD (3.84), chronic CVD (1.39), ES CKD (1.24), and LS CKD (4.16). In the external validation, mortality rates per 100 person-years among stroke survivors were 7.8 (modeled) and 8.2 (observed).

Conclusion Our model was reasonably calibrated except for CHF incidence and stroke-related mortality. External validation showed the model performed well over longer timeframe, but discrepancies were observed for shorter periods.

The authors have declared no competing interest.

No external funding.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study was deemed Not Human Subject Research by University of Arkansas for Medical Sciences Institutional Review Board (#276216).

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