Clinical decision support tool for Co-management signalling. (May 2018) - Horta, Alexandra Bayão; Salgado, Cátia; Fernandes, Marta; Vieira, Susana; Sousa, João M.; Papoila, Ana Luísa; Xavier, Miguel
Highlights: Postoperative outcomes of high risk surgical patients have potential improve and measures to make it possible should be implemented. The benefits of CM have been demonstrated in many studies. Clinical care should integrate objective evidence with individual expertise. Collaboration of data scientists and medical doctors is crucial. The use of the decision support tool helps hospitals to explain and predict how health care resources are delivered and consumed. We used real-world patient data, routinely collected in the process of care, to develop a simple and usable tool for use on a bedside setting. Abstract: Introduction: Co-management between internists and surgeons of selected patients is becoming one of the pillars of modern clinical management in large hospitals. Defining the patients to be co-managed is essential. The aim of this study is to create a decision tool using real-world patient data collected in the preoperative period, to support the decision on which patients should have the co-management service offered. Methods: Data was collected from the electronic clinical health records of patients who had an International Classification of Diseases, 9th edition (ICD–9) code of colorectal surgery during the period between January 2012 and October 2014 in a 200 bed private teaching hospital in Lisbon. ICD–9 codes of colorectal surgery [48.5 and 48.6 (anterior rectal resection and abdominoperineal resection), 45.7 (partial colectomy), 45.8 (Total Colectomy), and 45.9 (Bowel Anastomosis)] were used. Only patients above 18 years old were considered. Patients with more than one procedure were excluded from the study. From these data the authors investigated the construction of predictive models using logistic regression and Takagi-Sugeno fuzzy modelling. Results: Data contains information obtained from the clinical records of a cohort of 344 adult patients. Data from 398 emergent and elective surgeries were collected, from which 54 were excluded because they were second procedures for the same patients. Four preoperative variables were identified as being the most predictive of co-management, in multivariable regression analysis. The final model performed well after being internally validated (0.81 AUC, 77% accuracy, 74% sensitivity, 78% specificity, 93% negative predictive value). The results indicate that the decision process can be more objective and potentially automated. Conclusions: The authors developed a prediction model based on preoperative characteristics, in order to support the decision for the co-management of surgical patients in the postoperative ward setting. The model is a simple bedside decision tool that uses only four numerical variables.