Trial reportClinical and translational science2023
Construction and evaluation of a risk prediction model for pulmonary infection-associated acute kidney injury in intensive care units.
Trial report in Clinical and translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed, 1 citations in OpenAlex.
- Construction and evaluation of a risk prediction model for pulmonary infection-associated acute kidney injury in intensive care units.Clinical and translational science · 2023Trial
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute kidney injury (AKI) is one of the common complications of pulmonary infections. However, nomograms predicting the risk of early-onset AKI in patients with pulmonary infections have not been comprehensively researched. In this study, 3278 patients with pulmonary infection were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. These patients were randomly divided into training and validation cohorts, with the training cohort used for model building and the validation cohort used for validation. Independent risk factors for patients with pulmonary infection were determined using the least absolute shrinkage and selection operator (LASSO) method and forward stepwise logistic regression, which revealed that 11 independent risk factors for AKI in patients with pulmonary infections were congestive heart failure (CHF), hypertension, diabetes, transcutaneous oxygen saturation (SpO2), 24-h urine output, white blood cells (WBC), serum creatinine (Scr), prothrombin time (PT), potential of hydrogen (PH), vasopressor use, and mechanical ventilation (MV) use. The nomogram was then constructed and validated. The area under the receiver operating characteristic curve (AUC) values of the nomogram were 0.770 (95% CI = 0.789-0.807) in the training cohort and 0.724 (95% CI = 0.754-0.784) in the validation cohort. High AUC values indicated the good discriminative ability of the nomogram, while the calibration curves and Hosmer-Lemeshow test results indicated that the nomogram was well-calibrated. Improvements in net reclassification index (NRI) and integrated discrimination improvement (IDI) values indicate that our nomogram was superior to the Simplified Acute Physiology Score (SAPS) II scoring system, and the decision-curve analysis (DCA) curves indicate that the nomogram has good clinical application. We established a risk-prediction model for AKI in patients with pulmonary infection, which has good discriminative power and is superior to the SAPS II scoring system. This model can provide clinical reference information for patients with this type of disease in the intensive care unit.
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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 Socratic graph.