Risk assessment of morbidities after right hemicolectomy based on the National Clinical Database in Japan.
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ABSTRACT: Objective:Nationwide databases are expected to provide critical data to improve medical practice. The present study used such data to develop risk models for clinically important outcomes after right hemicolectomy based on preoperative risk factors. Methods:Japan's National Clinical Database (NCD) identified 38 030 cases of right hemicolectomy in the years 2011 and 2012. These were used to analyze correlations between mortality and eight selected clinical outcomes of interest by Pearson's correlation coefficient (r). To construct risk models for the eight selected clinical outcomes, 80% of all the examined cases were extracted randomly as a development cohort, and preoperative risk factors for each clinical outcome were identified using a forward stepwise selection method. Morbidities predicted from the risk models were used to find areas under the receiver operator curves among the remaining 20% of the testing cohort. Results:The following clinical outcomes were identified as highly associated with operative mortality: systemic sepsis (r = .360), renal failure (r = .341), unplanned intubation (r = .316) and central nervous system (CNS) occurrences (r = .301). Risk models containing up to 21 preoperative variables were constructed for these eight postoperative clinical outcomes. Predictive values of the eight models were as follows: surgical site infections (0.634), anastomotic leakage (0.656), systemic sepsis (0.816), pneumonia (0.846), unplanned intubation (0.838), renal failure (0.883), CNS occurrences (0.833) and transfusion >5 units (0.846). Conclusions:This study indicated that the NCD-generated risk models for six of the eight selected critical postoperative outcomes had high discrimination among right hemicolectomy patients. These risk models can accurately identify high-risk patients prior to surgery.
SUBMITTER: Yoshida T
PROVIDER: S-EPMC5980426 | biostudies-literature | 2018 May
REPOSITORIES: biostudies-literature
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