Trial reportBMC pulmonary medicine2023
A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study.
Trial report in BMC pulmonary medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Machine learning model predicts acute kidney injury in pediatric patients after cardiac surgery: a systematic review and meta-analysis.BMC nephrology · 2026Pooled it
- Incidence and predictors of acute respiratory distress syndrome in sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Prediction models for the occurrence and mortality of sepsis-associated lung injury: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Prognostic significance of the blood urea nitrogen-to-serum albumin ratio in patients with septic shock.Experimental and therapeutic medicine · 2026Article
- The prognostic value of CT-quantified visceral and subcutaneous fat areas in sepsis.World journal of emergency medicine · 2026Article
- Correlation between urea-to-creatinine ratio and poor prognosis of intensive care unit patients with chronic obstructive pulmonary disease: a study based on the MIMIC-IV database.BMC pulmonary medicine · 2026Article
- Article
- Perioperative Blood Biomarkers of Infectious and Non-Infectious Postoperative Pulmonary Complications: A Narrative Review.Journal of clinical medicine · 2026Review
- Development and validation of a prognostic model for acute respiratory distress syndrome in critically Ill patients with intra-abdominal sepsis: a multicenter cohort study.Frontiers in medicine · 2026Article
- A nomogram for individualized prediction of acute respiratory distress syndrome in patients with severe traumatic brain injury: a retrospective cohort study.BMC pulmonary medicine · 2025Article
- A prediction model for 30-day mortality in patients with ARDS admitted to the intensive care unit.European journal of medical research · 2025Article
- Bio inspired feature selection and graph learning for sepsis risk stratification.Scientific reports · 2025Article
- SEX DIFFERENCES IN SEPSIS-RELATED ACUTE RESPIRATORY DISTRESS SYNDROME AND OTHER SHORT-TERM OUTCOMES AMONG CRITICALLY ILL PATIENTS WITH SEPSIS: A RETROSPECTIVE STUDY IN CHINA.Shock (Augusta, Ga.) · 2025Observational
- Predicting mortality and risk factors of sepsis related ARDS using machine learning models.Scientific reports · 2025Article
- Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19.Japanese journal of radiology · 2025Article
- Development and validation of a Nomogram for the prediction of patients with sepsis-induced multiple organ dysfunction syndrome.Pakistan journal of medical sciences · 2025Article
- Autophagy-related biomarkers identified in sepsis-induced ARDS through bioinformatics analysis.Scientific reports · 2025Article
- Early Prediction of ICU Mortality in Patients with Acute Hypoxemic Respiratory Failure Using Machine Learning: The MEMORIAL Study.Journal of clinical medicine · 2025Article
- Derivation and Validation of a Clinical and Endothelial Biomarker Risk Model to Predict Persistent Pediatric Sepsis-Associated Acute Respiratory Dysfunction.CHEST critical care · 2025Article
- Determinants of generalized anxiety and construction of a predictive model in patients with chronic obstructive pulmonary disease.World journal of psychiatry · 2025Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundThe risk of death in sepsis patients with acute respiratory distress syndrome (ARDS) was as high as 20-50%. Few studies focused on the risk identification of ARDS among sepsis patients. This study aimed to develop and validate a nomogram to predict the ARDS risk in sepsis patients based on the Medical Information Mart for Intensive Care IV database.
methodsA total of 16,523 sepsis patients were included and randomly divided into the training and testing sets with a ratio of 7:3 in this retrospective cohort study. The outcomes were defined as the occurrence of ARDS for ICU patients with sepsis. Univariate and multivariate logistic regression analyses were used in the training set to identify the factors that were associated with ARDS risk, which were adopted to establish the nomogram. The receiver operating characteristic and calibration curves were used to assess the predictive performance of nomogram.
resultsTotally 2422 (20.66%) sepsis patients occurred ARDS, with the median follow-up time of 8.47 (5.20, 16.20) days. The results found that body mass index, respiratory rate, urine output, partial pressure of carbon dioxide, blood urea nitrogen, vasopressin, continuous renal replacement therapy, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis might be predictors. The area under the curve of developed model were 0.811 (95% CI 0.802-0.820) in the training set and 0.812 (95% CI 0.798-0.826) in the testing set. The calibration curve showed a good concordance between the predicted and observed ARDS among sepsis patients.
conclusionWe developed a model incorporating thirteen clinical features to predict the ARDS risk in patients with sepsis. The model showed a good predictive ability by internal validation.
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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.