Med Decis Making - Adaptation of clinical prediction models for application in local settings.

Tópicos

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Resumo

CKGROUND: When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions.OBJECTIVE: To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice.METHODS: A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use.RESULTS: Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation.CONCLUSIONS: Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.

Resumo Limpo

ckground plan use valid predict model new patient adequ perform guarante exampl chang clinic practic time differ case mix origin valid popul may result inaccur risk predictionsobject demonstr clinic inform can direct updat predict model develop strategi handl miss predictor valu clinic practicemethod previous deriv valid predict model postop nausea vomit updat use data set patient updat consist chang definit exist predictor reestim regress coeffici predictor ad new predictor model updat model valid new seri patient furthermor sever imput model consid handl realtim miss valu possibl miss predictor valu anticip actual model useresult differ clinic practic local popul origin deriv popul guid updat strategi predict model predict accuraci updat model better c statist calibr slope origin model c statist calibr slope inclus logist variabl imput model besid observ patient characterist contribut strategi deal miss predictor valu time risk calculationconclus extens knowledg local clinic process provid crucial inform guid process adapt predict model new clinic practic

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