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Multistate Model of Chronic Wounds, Amputations, and Mortality: Cohort Study of a State-wide Registry. | LitMetric

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Article Abstract

Objective: Use a new patient registry to characterize and predict progression of lower-extremity chronic wounds to amputation and/or death, while considering infection history.

Background: Chronic wounds are a common health issue causing amputation and death. The Regenstrief Chronic Wound Registry, created from one of the U.S.'s largest health information exchanges, can give insights into their epidemiology.

Methods: This retrospective cohort study analyzed 52,916 patients first diagnosed with lower-extremity chronic wounds during 2011-2021, sourced from the registry of 152,237 chronic wound patients. Disease progression was modeled using a multistate model, tracking transitions from initial diagnosis to minor amputation, major amputation, and/or death. Cox proportional-hazards models assessed associations between time-dependent covariates, including infection history and wound type, and each state transition.

Results: Three-year pooled mortality rates after wound diagnosis, minor amputation, and major amputation were 10.71%, 13.38%, and 19.47%, respectively. The multistate model showed these associations: diabetic foot ulcer and increased minor amputation; venous ulcer and decreased amputation or death; pressure ulcer, decreased minor amputation, and increased death; osteomyelitis, increased amputation, and decreased death before major amputation; sepsis and increased death before major amputation. Simulated patients' individualized risks were estimated-e.g., probabilities of major amputation for diabetic White patients within three years after minor amputation, with and without prior osteomyelitis diagnosis, were 3.7% and 14.9% respectively.

Conclusions: A multistate model, applied to a state-wide chronic wound patient registry, reveals insights into chronic wound disease progression, enables personalized prognoses for patients with chronic wounds, and has the potential to improve clinical decision-making.

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http://dx.doi.org/10.1097/SLA.0000000000006761DOI Listing

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