ArticleMedical care2015
Impact of automated reporting of estimated glomerular filtration rate in the veterans health administration.
Article in Medical care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Chronic Kidney Disease in Brazil: Current Status and Recommended Improvements.Kidney diseases (Basel, Switzerland) · 2024Review
- Chronic kidney disease recognition amongst physicians and advanced practice providers.Renal failure · 2021Article
- Article
- Estimation of renal function in adult outpatients with normal serum creatinine.BMC research notes · 2019Article
- Early recognition of multiple sclerosis using natural language processing of the electronic health record.BMC medical informatics and decision making · 2017Article
- Prevalence and recognition of chronic kidney disease in Stockholm healthcare.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundEarly detection and treatment of chronic kidney disease (CKD) is important for slowing progression to renal failure and preventing cardiovascular events, but CKD is often not recognized and patients are referred to nephrologists too late for timely management. Automated laboratory reporting of estimated glomerular filtration rate (eGFR) has been introduced in many health systems to improve CKD recognition, but its impact on large, US-based health systems remains unclear. RESEARCH
designRetrospective time-series study examined change in renal care services and CKD recognition across VA health care system facilities in 2000-2009. Hierarchical generalized linear models were used to estimate immediate and long-term impacts of eGFR reporting across facilities on monthly rates of outpatient CKD diagnoses, utilization of CKD diagnostic tests (urine microalbumin and kidney ultrasound), and outpatient nephrology visits.
resultsRates of CKD recognition through diagnoses in patient medical records changed an average of 11.4 additional diagnosed patients per 10,000 in the general outpatient population per month, with sustained long-term increases in CKD diagnoses (P<0.001). Diagnostic microalbumin and kidney ultrasound testing increased significantly, with long-term increases in microalbumin testing (P<0.001) and short-term increases in kidney ultrasound (P=0.01-0.04) rates across the VHA. There was no significant change in nephrology consultation rates.
conclusionsAutomated eGFR reporting was associated with moderate system-level improvements in documentation of CKD diagnoses and use of diagnostic tests, but had no impact on nephrology consultation. To effectively reduce the large burden of disease and its associated complications, further strategies are needed to identify and provide timely treatment to those with CKD.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.