Richard Bright Renal Service, Southmead Hospital, Bristol, BS10 5NB, UK.
UK Renal Registry, The Renal Association, Bristol, UK.
BMC Nephrol. 2021 Mar 17;22(1):95. doi: 10.1186/s12882-021-02301-5.
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.
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.
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.
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.
常规医疗保健数据集越来越多地用于检查健康结果，这些数据集包含临床和行政信息。但这些数据的准确性尚未明确界定。我们使用英国的常规医疗保健数据集，将接受移植和移植结局测量研究的记录与经过培训的研究护士收集的信息进行比较，以此来研究晚期慢性肾病患者的诊断记录准确性。
我们将 Access to Transplant 和 Transplant Outcome Measures 研究的记录与医院入院统计数据集相链接。使用国际疾病分类（ICD-10）和人口普查和统计局的手术操作分类（OPCS-4）代码从医院数据中识别医疗条件。计算了一系列诊断的灵敏度、特异性、阳性预测值和阴性预测值。
在开始进行肾脏替代治疗之前，96%的个体都有合并症信息。从常规医疗保健数据集中识别出的个别医疗条件的准确性存在差异。灵敏度和阳性预测值范围从糖尿病的 97.7%和 90.4%到缺血性心脏病的 82.6%和 82.9%，到肝病的 44.2%和 28.4%。
常规医疗保健数据集准确地捕捉了晚期慢性肾病患者的某些情况。它们有可能用于临床和流行病学研究，但不太可能作为识别所有合并症的单一资源。
