TY - JOUR
T1 - Clinical decision support tool for Co-management signalling
AU - Horta, Alexandra Bayão
AU - Salgado, Cátia
AU - Fernandes, Marta
AU - Vieira, Susana
AU - Sousa, João M.
AU - Papoila, Ana Luísa
AU - Xavier, Miguel
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Co-management
KW - Decision support tool
KW - Failure to rescue
KW - High risk patients
KW - Internal Medicine
KW - Multistage modelling
UR - http://www.scopus.com/inward/record.url?scp=85042691172&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2018.02.014
DO - 10.1016/j.ijmedinf.2018.02.014
M3 - Article
C2 - 29602434
AN - SCOPUS:85042691172
SN - 1386-5056
VL - 113
SP - 56
EP - 62
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
ER -