Identification of people with autosomal dominant polycystic kidney disease using routine data: a cross sectional study

BMC Nephrology

Simon de Lusignan¹  Richard Sandford²  Anand K Saggar³  Jeremy van Vlymen¹  Simon Jones¹  Andrew P McGovern¹ 

[1] Department of Health Care Management and Policy, University of Surrey, Guildford, UK;East Anglian Medical Genetics Service, Addenbrooke’s Hospital, Cambridge, UK;St George's University of London, London, UK

关键词: Primary care records;    Early detection;    Screening;    Autosomal dominant polycystic kidney disease;   

Others  :  1082566
DOI  :  10.1186/1471-2369-15-182

 received in 2014-06-13, accepted in 2014-11-10,  发布年份 2014

【 摘 要 】

Background

Autosomal dominant polycystic kidney disease (ADPKD) causes progressive renal damage and is a leading cause of end-stage renal failure. With emerging therapies it is important to devise a method for early detection. We aimed to identify factors from routine clinical data which can be used to distinguish people with a high likelihood of having ADPKD in a primary health care setting.

Method

A cross-sectional study was undertaken using data from the Quality Intervention in Chronic Kidney Disease trial extracted from 127 primary care practices in England. The health records of 255 people with ADPKD were compared to the general population. Logistic regression was used to identify clinical features which distinguish ADPKD. These clinical features were used to stratify individual risk using a risk score tool.

Results

Renal impairment, proteinuria, haematuria, a diastolic blood pressure over 90 mmHg and multiple antihypertensive medications were more common in ADPKD than the general population and were used to build a regression model (area under the receiver operating characteristic curve; 0.79). Age, gender, haemoglobin and urinary tract infections were not associated with ADPKD. A risk score (range −3 to +10) of ≥0 gave a sensitivity of 70.2% and specificity 74.9% of for detection.

Conclusions

Stratification of ADPKD likelihood from routine data may be possible. This approach could be a valuable component of future screening programs although further longitudinal analyses are needed.

【 授权许可】

   
2014 McGovern et al.; licensee BioMed Central Ltd.

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