Quality indicators for the detection and management of chronic kidney disease in primary care in Canada derived from a modified Delphi panel approach

Primary Care Screening and Comorbidity Management in Rheumatoid Arthritis in Ontario, Canada

“…Third, there is a potential for misclassification of patient diagnoses and incomplete data capture, given the secondary nature of our data source. However, we previously conducted rigorous approaches to the validation of identifying patients with RA , hypertension , ischemic heart disease , and diabetes mellitus within our data source, with a PPV exceeding 90% and specificity exceeding 99% for each condition. The sensitivity for identifying diabetes mellitus and hypertension was excellent (91% and 83%, respectively); however, our approaches to identifying RA and ischemic heart disease were less sensitive (74% and 75%, respectively), but the lower sensitivity was necessary to maximize the PPV.…”

“…Patients not identified by the algorithm were classified as non‐RA patients, and 10 non‐RA patients were matched on age (+/− 1 year) and sex to each RA patient. We used previously validated algorithms to identify patients with hypertension , ischemic heart disease , and diabetes mellitus among patients with and without RA. The case ascertainment period involved respectively identifying these chronic conditions over all patient entries, since the inception of the patient record in the EMR.…”

“…The feasibility assessment incorporated data availability and accuracy (e.g., what data elements are available, where the data are located, and whether that information is known to be valid), data standards (e.g., the data elements are of sufficient quality to permit a structured data query), and operational issues (e.g., how the data elements are captured). To assess the concurrent and content validity of the quality measures, we incorporated results from criterion validity assessments from our previous research . The concurrent validity was previously independently assessed for specific quality measures derived from EMR data, by determining whether the information from the computer‐generated EMR query was similar to the information that a human abstractor obtained by reading the front end of the EMR.…”

Medication Prescribing Quality in Australian Primary Care Patients with Chronic Kidney Disease

“…The prescribing of an ACEI or ARB in patients with CKD with albuminuria was slightly lower in Australian general practice compared to that reported in other developed nations [24][25][26][27]. Studies from different provinces of Canada [24][25][26] investigating prescribing in CKD patients reported rates of 74% to 80% for ACEI or ARB prescribing, while a study conducted in the Netherlands found prescribing in 78% and 82% of non-diabetes and diabetes patients, respectively [11]. The reason for the low rate of ACEI/ARB prescribing could be non-concordance to Australian CKD treatment guidelines, including not referring patients to nephrology care [3].…”

“…Despite strong evidence for the efficacy of ACEI/ARB to reduce proteinuria and slow progression of CKD to ESKD, less than 70% of Australian adult patients with CKD stages 3-5 with diabetes and microalbuminuria were receiving an ACEI or ARB. The prescribing of an ACEI or ARB in patients with CKD with albuminuria was slightly lower in Australian general practice compared to that reported in other developed nations [24][25][26][27]. Studies from different provinces of Canada [24][25][26] investigating prescribing in CKD patients reported rates of 74% to 80% for ACEI or ARB prescribing, while a study conducted in the Netherlands found prescribing in 78% and 82% of non-diabetes and diabetes patients, respectively [11].…”