J Biomed Inform - The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions.

Tópicos

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Resumo

CKGROUND: Hospital readmission risk prediction remains a motivated area of investigation and operations in light of the hospital readmissions reduction program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance.OBJECTIVES: To study the effects of varying three factors of model development in the prediction of risk based on health record data: (1) reason for readmission (primary readmission diagnosis); (2) available data and data types (e.g. visit history, laboratory results, etc); (3) cohort selection.METHODS: Regularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison in cohort selection testing. Calibration by model refitting to outcome prevalence.RESULTS: Predicting readmission risk across multiple reasons for readmission resulted in ROC areas ranging from 0.92 for readmission for congestive heart failure to 0.71 for syncope and 0.68 for all-cause readmission. Visit history and laboratory tests contributed the most predictive value; contributions varied by readmission diagnosis. Cohort definition affected performance for both parametric and nonparametric algorithms. Compared to all patients, limiting the cohort to patients whose index admission and readmission diagnoses matched resulted in a decrease in average ROC from 0.78 to 0.55 (difference in ROC 0.23, p value 0.01). Calibration plots demonstrate good calibration with low mean squared error.CONCLUSION: Targeting reason for readmission in risk prediction impacted discriminatory performance. In general, laboratory data and visit history data contributed the most to prediction; data source contributions varied by reason for readmission. Cohort selection had a large impact on model performance, and these results demonstrate the difficulty of comparing results across different studies of predictive risk modeling.

Resumo Limpo

ckground hospit readmiss risk predict remain motiv area investig oper light hospit readmiss reduct program cms multipl model risk report variabl discriminatori perform remain unclear design factor affect performanceobject studi effect vari three factor model develop predict risk base health record data reason readmiss primari readmiss diagnosi avail data data type eg visit histori laboratori result etc cohort selectionmethod regular regress lasso generat predict readmiss risk use preval sampl support vector machin svm use comparison cohort select test calibr model refit outcom prevalenceresult predict readmiss risk across multipl reason readmiss result roc area rang readmiss congest heart failur syncop allcaus readmiss visit histori laboratori test contribut predict valu contribut vari readmiss diagnosi cohort definit affect perform parametr nonparametr algorithm compar patient limit cohort patient whose index admiss readmiss diagnos match result decreas averag roc differ roc p valu calibr plot demonstr good calibr low mean squar errorconclus target reason readmiss risk predict impact discriminatori perform general laboratori data visit histori data contribut predict data sourc contribut vari reason readmiss cohort select larg impact model perform result demonstr difficulti compar result across differ studi predict risk model

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