ArticleRisk management and healthcare policy2021
Development of a Nomogram Model for Predicting the Risk of In-Hospital Death in Patients with Acute Kidney Injury.
Article in Risk management and healthcare policy, 2021. 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, 5 citations in OpenAlex.
- Using machine learning to predict the risk of short-term and long-term death in acute kidney injury patients after commencing CRRT.BMC nephrology · 2024Article
- A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury.Scientific reports · 2024Article
- Constructing and validating a predictive nomogram for osteoporosis risk among Chinese single-center male population using the systemic immune-inflammation index.Scientific reports · 2024Article
- Development and Validation of a Nomogram to Predict the Risk of Special Uterine Leiomyoma Pathological Types or Leiomyosarcoma in Postmenopausal Women: A Retrospective Study.Risk management and healthcare policy · 2024Article
- Risk Factors for Death Among Veterans Following Acute Kidney Injury.The American journal of medicine · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
objectiveTo analyze the risk factors of in-hospital death in patients with acute kidney injury (AKI) in the intensive care unit (ICU), and to develop a personalized risk prediction model.
methodsThe clinical data of 137 AKI patients hospitalized in the ICU of Anhui provincial hospital from January 2018 to December 2020 were retrospectively analyzed. Patients were divided into two groups: those that survived to discharge ("survival" group, 100 cases) and those that died while in hospital ("death" group, 37 cases), and risk factors for in-hospital death analyzed.
resultsThe in-hospital mortality of AKI patients in the ICU was 27.01% (37/137). A multivariate logistic regression analysis indicated age, mechanical ventilation and vasoactive drugs were significant risk factors for in-hospital death in AKI patients, and a nomogram risk prediction model was developed. The Harrell's C-index of the nomogram model was 0.891 (95% CI: 0.837-0.945), and the area under the receiver operating characteristic (ROC) curve was 0.886 (95% CI: 0.823-0.936) after internal validation, indicating that the nomogram model had good discrimination. The Hosmer-Lemeshow goodness of fit test and calibration curve indicated the predicted probability of the nomogram model was consistent with the actual frequency of death in ICU patients with AKI. The decision curve analysis (DCA) showed that the clinical net benefit level of the nomogram model is highest when the probability threshold of AKI is between 0.01 and 0.75.
conclusionPatients in the ICU with AKI had high in-hospital mortality and were affected by a variety of risk factors. The nomogram prediction model based on the risk factors of AKI showed good prediction efficiency and clinical applicability, which could help medical staff in the ICU to identify AKI patients with high-risk, allowing early prevention, detection and intervention, and reducing the risk of in-hospital deaths in ICU patients with AKI.
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