ArticleInternational urology and nephrology2023
Development and validation of a risk nomogram model for predicting pulmonary hypertension in patients with stage 3-5 chronic kidney disease.
Article in International urology and nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 2 citations in OpenAlex.
- Development and validation of a nomogram for predicting advanced chronic kidney disease.iScience · 2026Article
- Difference in risk profiles for pulmonary hypertension between non-elderly and elderly patients undergoing maintenance hemodialysis.BMC nephrology · 2026Article
- Article
- Explainable Machine Learning Model Based on Routine Admission Laboratory Tests for Predicting New-Onset Hypoalbuminemia in Hospitalized Older Patients with Acute Exacerbation of COPD.Clinical interventions in aging · 2026Article
- A Machine Learning-Based Model to Estimate the Risk of Pulmonary Hypertension in Chronic Kidney Disease Patients.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
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12 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThe occurrence of pulmonary arterial hypertension (PAH) can greatly affect the prognosis of patients with chronic kidney disease (CKD). We aimed to construct a nomogram to predict the probability of PAH development in patients with stage 3-5 CKD to guide early intervention and to improve prognosis.
methodsFrom August 2018 to December 2021, we collected the data of 1258 patients with stage 3-5 CKD hospitalized at the Affiliated Hospital of Xuzhou Medical University as a training set and 389 patients hospitalized at Zhongda Hospital as a validation set. These patients were divided into PAH and N-PAH groups with pulmonary arterial systolic pressure ≥ 35 mmHg as the cutoff. The results of univariate and multivariate logistic regression analyses were used to establish the nomogram. Then, areas under the receiver operating characteristic curve (AUC-ROCs), a calibration plot, and decision curve analysis (DCA) were used to validate the nomogram.
resultsThe nomogram included nine variables: age, diabetes mellitus, hemoglobin, platelet count, serum creatinine, left ventricular end-diastolic diameter, left atrial diameter, main pulmonary artery diameter and left ventricular ejection fraction. The AUC-ROCs of the training set and validation set were 0.801 (95% confidence interval (CI) 0.771-0.830) and 0.760 (95% CI 0.699-0.818), respectively, which showed good discriminative ability of the nomogram. The calibration diagram showed good agreement between the predicted and observed results. DCA also demonstrated that the nomogram could be clinically useful.
conclusionThe evaluation of the nomogram model for predicting PAH in patients with CKD based on risk factors showed its ideal efficacy. Thus, the nomogram can be used to screen for patients at high risk for PAH and has guiding value for the subsequent formulation of prevention strategies and clinical treatment.
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