Background The electronic health record (EHR), utilized to apply statistical methodology, assists provider decision-making, including during the care of chronic kidney disease (CKD) patients. When estimated glomerular filtration (eGFR) decreases, the rate of that change adds meaning to a patient’s single eGFR and may represent severity of renal injury. Since the cumulative sum chart technique (CUSUM), often used in quality control and surveillance, continuously checks for change in a series of measurements, we selected this statistical tool to detect clinically relevant eGFR decreases and developed CUSUMGFR.
Methods In a retrospective analysis we applied CUSUMGFR to signal identification of eventual ESKD patients prior to diagnosis date. When the patient signaled by reaching a specified threshold CUSUMGFR value, days from CUSUMGFR signal date to ESKD diagnosis date were measured, along with the corresponding eGFR measurement at the signal.
Results Signaling occurred 790 days prior to ESKD diagnosis date with sensitivity of 0.830 and specificity of 0.910. Mean days prior to ESKD diagnosis were significantly greater in Black patients (875 ), and in patients with hypertension (849 ), diabetes (940 ), cardiovascular disease (1037 ), and hypercholesterolemia (971 ). Sensitivity and specificity did not vary by sociodemographic and clinical risk factors.
Conclusions CUSUMGFR correctly identified nearly 25% of CKD patients destined for ESKD when eGFR was > 60 ml/min/1.73 m2. If utilized in an EHR, signaling patients could focus providers’ efforts to slow or prevent progression to ESKD.