Dynamic prediction of time to wound healing at routine wound care visits

Thomassen, D.; Amesz, S.F.; Stol, N.P.; Cessie, S. le; Steyerberg, E.W.

doi:doi:10.1089/wound.2024.0069

Objective: Having a wound decreases patients’ quality of life and brings uncertainty, especially if the wound does not show a healing tendency. The objective of this study was to develop and validate a model to dynamically predict time to wound healing at subsequent routine wound care visits.Approach: A dynamic prediction model was developed in a cohort of wounds treated by nurse practitioners between 2017 and 2022. Potential predictors were selected based on literature, expert opinion, and availability in the routine care setting. To assess performance for future wound care visits, the model was validated in a new cohort of wounds visited in early 2023. Reporting followed TRIPOD guidelines.Results: We analyzed data from 92,098 visits, corresponding to 14,248 wounds and 7,221 patients. At external validation, discriminative performance of our developed model was comparable with internal validation ...Show moreObjective: Having a wound decreases patients’ quality of life and brings uncertainty, especially if the wound does not show a healing tendency. The objective of this study was to develop and validate a model to dynamically predict time to wound healing at subsequent routine wound care visits.Approach: A dynamic prediction model was developed in a cohort of wounds treated by nurse practitioners between 2017 and 2022. Potential predictors were selected based on literature, expert opinion, and availability in the routine care setting. To assess performance for future wound care visits, the model was validated in a new cohort of wounds visited in early 2023. Reporting followed TRIPOD guidelines.Results: We analyzed data from 92,098 visits, corresponding to 14,248 wounds and 7,221 patients. At external validation, discriminative performance of our developed model was comparable with internal validation (concordance statistic = 0.70 [95% confidence interval 0.69, 0.71]), and the model remained well calibrated. Strong predictors were wound-level characteristics and indicators of the healing process so far (e.g., wound surface area).Innovation: Going beyond previous prediction studies in the field, the developed model dynamically predicts the remaining time to wound healing for many wound types at subsequent wound care visits, in line with the dynamic nature of wound care. In addition, the model was externally validated and showed stable performance.Conclusion: The developed model can potentially contribute to patient satisfaction and reduce uncertainty around wound healing times when implemented in practice. When the predicted time of wound healing remains high, practitioners can consider adapting their wound management.Show less

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Dynamic prediction of time to wound healing at routine wound care visits