RESUMEN
Predictive modeling for postdischarge outcomes of inpatient care has been suboptimal. This study evaluated whether admission numerical laboratory data added to administrative models from New York and Minnesota hospitals would enhance the prediction accuracy for 90-day postdischarge deaths without readmission (PD-90) and 90-day readmissions (RA-90) following inpatient care for cardiac patients. Risk-adjustment models for the prediction of PD-90 and RA-90 were designed for acute myocardial infarction, percutaneous cardiac intervention, coronary artery bypass grafting, and congestive heart failure. Models were derived from hospital claims data and were then enhanced with admission laboratory predictive results. Case-level discrimination, goodness of fit, and calibration were used to compare administrative models (ADM) and laboratory predictive models (LAB). LAB models for the prediction of PD-90 were modestly enhanced over ADM, but negligible benefit was seen for RA-90. A consistent predictor of PD-90 and RA-90 was prolonged length of stay outliers from the index hospitalization.
Asunto(s)
Cardiopatías/patología , Reclamos Administrativos en el Cuidado de la Salud/estadística & datos numéricos , Anciano , Anciano de 80 o más Años , Puente de Arteria Coronaria/mortalidad , Puente de Arteria Coronaria/estadística & datos numéricos , Cardiopatías/mortalidad , Insuficiencia Cardíaca/mortalidad , Insuficiencia Cardíaca/patología , Humanos , Tiempo de Internación/estadística & datos numéricos , Modelos Estadísticos , Infarto del Miocardio/mortalidad , Infarto del Miocardio/patología , Alta del Paciente/estadística & datos numéricos , Intervención Coronaria Percutánea/mortalidad , Intervención Coronaria Percutánea/estadística & datos numéricos , Valor Predictivo de las Pruebas , Factores de Riesgo , Resultado del TratamientoRESUMEN
Numerical laboratory data at admission have been proposed for enhancement of inpatient predictive modeling from administrative claims. In this study, predictive models for inpatient/30-day postdischarge mortality and for risk-adjusted prolonged length of stay, as a surrogate for severe inpatient complications of care, were designed with administrative data only and with administrative data plus numerical laboratory variables. A comparison of resulting inpatient models for acute myocardial infarction, congestive heart failure, coronary artery bypass grafting, and percutaneous cardiac interventions demonstrated improved discrimination and calibration with administrative data plus laboratory values compared to administrative data only for both mortality and prolonged length of stay. Improved goodness of fit was most apparent in acute myocardial infarction and percutaneous cardiac intervention. The emergence of electronic medical records should make the addition of laboratory variables to administrative data an efficient and practical method to clinically enhance predictive modeling of inpatient outcomes of care.