Do routine hospital data accurately record comorbidity in advanced kidney disease populations? A record linkage cohort study

Ailish Nimmo, Retha Steenkamp, Rommel Ravanan, Dominic Taylor, Ailish Nimmo, Retha Steenkamp, Rommel Ravanan, Dominic Taylor

Abstract

Background: Routine healthcare datasets capturing clinical and administrative information are increasingly being used to examine health outcomes. The accuracy of such data is not clearly defined. We examine the accuracy of diagnosis recording in individuals with advanced chronic kidney disease using a routine healthcare dataset in England with comparison to information collected by trained research nurses.

Methods: We linked records from the Access to Transplant and Transplant Outcome Measures study to the Hospital Episode Statistics dataset. International Classification of Diseases (ICD-10) and Office for Population Censuses and Surveys Classification of Interventions and Procedures (OPCS-4) codes were used to identify medical conditions from hospital data. The sensitivity, specificity, positive and negative predictive values were calculated for a range of diagnoses.

Results: Comorbidity information was available in 96% of individuals prior to starting kidney replacement therapy. There was variation in the accuracy of individual medical conditions identified from the routine healthcare dataset. Sensitivity and positive predictive values ranged from 97.7 and 90.4% for diabetes and 82.6 and 82.9% for ischaemic heart disease to 44.2 and 28.4% for liver disease.

Conclusions: Routine healthcare datasets accurately capture certain conditions in an advanced chronic kidney disease population. They have potential for use within clinical and epidemiological research studies but are unlikely to be sufficient as a single resource for identifying a full spectrum of comorbidities.

Keywords: Chronic kidney disease; Comorbidity; Record linkage; Routine healthcare datasets; Secondary care.

Conflict of interest statement

The authors, other than RS, received funding from the National Institute for Health Research (NIHR) under the Programme Grants for Applied Research scheme (RP-PG-0109-10116) for completion of the ATTOM study. RS declares no conflicts of interest.

Figures

Fig. 1
Fig. 1
Flow chart depicting individuals included in the study. There were 69 individuals without an admitted patient care episode prior to study recruitment, but 67 of these had a subsequent admitted patient care episode after recruitment
Fig. 2
Fig. 2
Prevalence of comorbidities derived from study and hospital datasets
Fig. 3
Fig. 3
Forest plot displaying sensitivity (%) with 95% confidence intervals for individual comorbidities derived from hospital data. Comorbidities are ordered by prevalence. ES: effect size, represents sensitivity (%)
Fig. 4
Fig. 4
Forest plot displaying positive predictive values (%) with 95% confidence intervals for individual comorbidities derived from hospital data. Comorbidities are ordered by prevalence. ES: effect size, represents positive predictive value (%)

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