ArticleBMJ open2025
Development and validation of a risk prediction model for chronic kidney disease among adult hypertensive patients having follow-up at University of Gondar Comprehensive Specialised Hospital, Ethiopia: a retrospective cohort study.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveChronic kidney disease (CKD) arises due to uncontrolled hypertension (HTN). HTN significantly increases the risk of complications in vital organs, mainly the kidneys. If hypertensive individuals receive early intervention, the majority of these complications and deaths from CKD can be avoided. Having a clinically applicable tool to predict the future risk of those complications can prevent early disability and premature mortality. However, to this day, there is a lack of a validated risk prediction model specifically designed for CKD of hypertensive patients in Ethiopia. We aimed to develop a risk prediction model for CKD among hypertensive patients at the University of Gondar Comprehensive Specialised Hospital (UoGCSH), Ethiopia. STUDY
designA retrospective follow-up study was conducted from 1 January 2012 to 30 December 2021. The Least Absolute Shrinkage and Selection Operator regression methods were used to select predictors. The performance of the models was assessed using the Area Under the Curve and calibration plots. The internal validity of the model was evaluated using bootstrapping methods, and the model was presented as a nomogram. Decision curve analysis was conducted to assess the net benefit of the prediction model in clinical and public health contexts.
settingData from patients' medical records were collected via the Kobo Toolbox in the UoGCSH. PARTICIPANT: We followed a total of 1120 Patients diagnosed with HTN.
resultsThe incidence of CKD among adult hypertensive patients was 19.82% (95% CI 17.59% to 22.26%). In the multivariable logistic regression analysis, age, residency, baseline blood pressure status, type of HTN, family history of HTN, baseline serum creatinine levels, proteinuria at baseline and dyslipidaemia were identified as statistically significant predictors of CKD. The nomogram demonstrated a discriminatory power of 91.98% (95% CI 90.09% to 93.88%) and a calibration p value of 0.327. The sensitivity and specificity of the prediction model were 80.63% (95% CI 74.81% to 85.61%) and 87.97% (95% CI 85.66% to 90.03%), respectively. The developed nomogram has a greater net benefit than using the treat-all or treat-none strategies when the threshold probability of the patient is increased.
conclusionThe nomogram demonstrated excellent discrimination and calibration in identifying hypertensive patients at high risk of CKD. This predictive model offers clinicians a valuable tool for early identification of high-risk individuals, enabling timely interventions, personalised counselling and optimised management through close monitoring to prevent disease progression.
Indexed as
Identifiers
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.