ArticleBMJ open respiratory research2023
Dynamic nomogram for predicting acute kidney injury in patients with community-acquired pneumonia.
Article in BMJ open respiratory research, 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, 4 citations in OpenAlex.
- Development and bootstrap-based internal validation of a dynamic model for predicting acute kidney injury after day-3 reassessment in children with severe community-acquired pneumonia.Translational pediatrics · 2026Article
- Treatment protocol for pneumonia with acute kidney injury: effects of ulinastatin combined with CRRT on inflammatory factors and renal function in patients.Frontiers in medicine · 2026Article
- Construction of machine learning-based models for screening the high-risk patients with gastric precancerous lesions.Chinese medicine · 2025Article
- Treatment with Ceftazidime-Avibactam for Lower Respiratory Tract Infections Caused by the Multidrug-Resistant Gram-Negative Bacteria in the Intensive Care Unit.Infection and drug resistance · 2025Article
- A dynamic online nomogram for predicts delayed postoperative bleeding after colorectal polyp surgery.Scientific reports · 2024Article
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6 authors at 1 institution in 1 country.
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Abstract
introductionAcute kidney injury (AKI) is a common complication in patients with community-acquired pneumonia (CAP) and negatively affects both short-term and long-term prognosis in patients with CAP. However, no study has been conducted on developing a clinical tool for predicting AKI in CAP patients. Therefore, this study aimed to develop a predictive tool based on a dynamic nomogram for AKI in CAP patients.
methodsThis retrospective study was conducted from January 2014 to May 2017, and data from adult inpatients with CAP at Nanjing First Hospital were analysed. Demographic data and clinical data were obtained. The least absolute shrinkage and selection operator (LASSO) regression model was used to select important variables, which were entered into logistic regression to construct the predictive model for AKI. A dynamic nomogram was based on the results of the logistic regression model. Calibration and discrimination were used to assess the performance of the dynamic nomogram. A decision curve analysis was used to assess clinical efficacy.
resultsA total of 2883 CAP patients were enrolled in this study. The median age was 76 years (IQR 63-84), and 61.3% were male. AKI developed in 827 (28.7%) patients. The LASSO regression analysis selected five important factors for AKI (albumin, acute respiratory failure, CURB-65 score, Cystatin C and white cell count), which were then entered into the logistic regression to construct the predictive model for AKI in CAP patients. The dynamic nomogram model showed good discrimination with an area under the receiver operating characteristics curve of 0.870 and good calibration with a Brier score of 0.129 and a calibration plot. The decision curve analysis showed that the dynamic nomogram prediction model had good clinical decision-making.
conclusionThis easy-to-use dynamic nomogram may help physicians predict AKI in patients with CAP.
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