Allyson R Cochran1, Kyle M Raub2, Keith J Murphy3, David A Iannitti3, Dionisios Vrochides3. 1. Department of Surgery, Division of Hepatopancreatobiliary Surgery, Carolinas Medical Center, Charlotte, NC, USA. Electronic address: allyson.Cochran@carolinashealthcare.org. 2. Office of Clinical and Translational Research, Carolinas HealthCare System Charlotte, NC, USA. 3. Department of Surgery, Division of Hepatopancreatobiliary Surgery, Carolinas Medical Center, Charlotte, NC, USA.
Abstract
BACKGROUND: Prediction models are increasingly being used with clinical practice guidelines to inform decision making. Enhanced Recovery After Surgery (ERAS®) protocols are standardized care pathways that incorporate evidence-based practices to improve patient outcomes. Predictive analytics incorporated within a data management system, such as Research Electronic Data Capture (REDCap), may help clinicians estimate risk probabilities and track compliance with standardized care practices. METHODS: Predictive models were developed from retrospective data on 400 patients who underwent pancreaticoduodenectomy from 2008 through 2014. The REDCap was programmed to display predictive analytics and create a data tracking system that met ERAS guidelines. Based on predictive scores for serious complication, 30-day readmission, and 30-day mortality, we developed targeted interventions to decrease readmissions and postoperative laboratory tests. RESULTS: Predictive models demonstrated a receiver-operating characteristic area (ROC) ranges of 641-856. After implementing the REDCap platform, the readmission rate for high-risk patients decreased 15.8% during the initial three months following ERAS implementation. Based on predictive outputs, patients with a low-risk score received a limited set of postoperative laboratory tests. Targeted interventions to decrease hospital readmission for high-risk patients included home care orders and post-discharge instructions. CONCLUSIONS: The REDCap platform offers hospitals a practical option to display predictive analytics and create a data tracking program that meets ERAS guidelines. Prediction models programmed into REDCap offer clinicians a support tool to assess the probability of patient outcomes. Risk calculations based on predictive scores enabled clinicians to titrate postoperative laboratory tests and develop post-discharge home care orders.
BACKGROUND: Prediction models are increasingly being used with clinical practice guidelines to inform decision making. Enhanced Recovery After Surgery (ERAS®) protocols are standardized care pathways that incorporate evidence-based practices to improve patient outcomes. Predictive analytics incorporated within a data management system, such as Research Electronic Data Capture (REDCap), may help clinicians estimate risk probabilities and track compliance with standardized care practices. METHODS: Predictive models were developed from retrospective data on 400 patients who underwent pancreaticoduodenectomy from 2008 through 2014. The REDCap was programmed to display predictive analytics and create a data tracking system that met ERAS guidelines. Based on predictive scores for serious complication, 30-day readmission, and 30-day mortality, we developed targeted interventions to decrease readmissions and postoperative laboratory tests. RESULTS: Predictive models demonstrated a receiver-operating characteristic area (ROC) ranges of 641-856. After implementing the REDCap platform, the readmission rate for high-risk patients decreased 15.8% during the initial three months following ERAS implementation. Based on predictive outputs, patients with a low-risk score received a limited set of postoperative laboratory tests. Targeted interventions to decrease hospital readmission for high-risk patients included home care orders and post-discharge instructions. CONCLUSIONS: The REDCap platform offers hospitals a practical option to display predictive analytics and create a data tracking program that meets ERAS guidelines. Prediction models programmed into REDCap offer clinicians a support tool to assess the probability of patient outcomes. Risk calculations based on predictive scores enabled clinicians to titrate postoperative laboratory tests and develop post-discharge home care orders.