ArticleFrontiers in public health2025
Development and validation of a machine learning based early warning scoring system for high altitude polycythemia.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: High-altitude polycythemia (HAPC) lacks a lifestyle-focused risk-stratification tool among lifelong high-altitude residents. Here we aimed to develop and validate a novel machine-learning predictive scoring system for HAPC using readily modifiable lifestyle variables in this population. Methods: In a high altitude cohort (≥4,500 m, Results: Logistic regression achieved the best balance (AUC 0.848, sensitivity 0.81, specificity 0.79). Low SpO Conclusion: This score equips frontline health workers in extremely high-altitude, resource-scarce settings to rapidly pinpoint high-risk residents and initiate low-cost lifestyle interventions, thereby curbing the incidence of chronic altitude-related illnesses, easing local medical burdens, and improving overall quality of life for native high-altitude populations. Trial Registration: ChiCTR2100047945.
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