Accuracy of routinely collected comorbidity data in patients undergoing colectomy: a retrospective study.

Objectives: This paper aimed to determine the baseline accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of routinely collected comorbidity data in patients undergoing any types of colectomy.

Methods: All patients aged >18 who underwent right hemicolectomy, left hemicolectomy, sigmoid colectomy, subtotal colectomy, or total colectomy between 1 January 2015 and 1 November 2016 were identified. The following comorbidities were considered: hypertension, ischemic heart disease (IHD), diabetes, asthma, chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CVD), chronic kidney disease (CKD), and hypercholesterolemia. The comorbidity data from clinical notes were compared with corresponding data in hospital episode statistics (HES) database in order to calculate accuracy, sensitivity, specificity, PPV, and NPV of HES codes for comorbidities. In order to assess the agreement between clinical notes and HES data, we also calculated Cohen's kappa index value as a more robust measure of agreement.

Results: Overall, 267 patients comprising 2136 comorbidity codes were included. Overall, HES codes for comorbidities in patients undergoing colectomy had substandard accuracy 94% (kappa 0.542), sensitivity (39%), and NPV (89%). The HES codes were 100% specific with PPV of 100%. The results were consistent when individual comorbidities were analyzed separately.

Conclusions: Our results demonstrated that HES comorbidity codes in patients undergoing colectomy are specific with good positive predictive value; however, they have substandard accuracy, sensitivity, and negative predictive value. Better documentation of comorbidities in admission clerking proforma may help to improve the quality of source documents for coders, which in turn may improve the accuracy of coding.