ArticleBMC pulmonary medicine2022
Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection.
Article in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 3 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 3 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- Association between neutrophil to lymphocyte ratio and the mortality of patients with sepsis: an update systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Shared genetic correlations between kidney diseases and sepsis.Frontiers in endocrinology · 2024Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- A prediction model for predicting the risk of acute respiratory distress syndrome in sepsis patients: a retrospective cohort study.BMC pulmonary medicine · 2023Trial
- Nomogram for predicting inadequate treatment response in vertebral osteomyelitis.Annals of medicine · 2026Article
- Development and Internal Validation of a Web-Based Dynamic Nomogram for Dynamic Risk Stratification of Early Bacterial Infection in Liver Transplant Recipients.Annals of transplantation · 2026Article
- Development and Validation of a Nomogram for Predicting Sepsis Risk in Patients with Non-Ventilator Hospital-Acquired Pneumonia.Biomedicines · 2026Article
- Platelet membrane-camouflaged matrine biomimetic nanoparticles for efficient acute lung injury therapy.iScience · 2026Article
- Predictive Value of Platelet-Based Indexes for Mortality in Sepsis.Biomedicines · 2026Article
- Hematocrit and 28-day mortality in mechanically ventilated septic patients: a prospective observational study.Frontiers in medicine · 2026Article
- Lactylated chitinase-3-like protein 1 promotes the development of sepsis.Central-European journal of immunology · 2026Article
- Development and Validation of a 28-Day Mortality Prediction Model for Patients with Sepsis Complicated by Autoimmune Diseases Using Two Machine Learning Methods.Journal of inflammation research · 2026Article
- Influencing Factors of New-Onset Atrial Fibrillation in Sepsis Patients and Construction of a Nomogram Prediction Model: Based on LASSO Regression.International journal of general medicine · 2026Article
- Nomograms to predict outcome for patients undergoing venoarterial extracorporeal membrane oxygenation treatment for septic shock.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025Article
- Development and validation of a diagnostic prediction model for acute liver injury in sepsis based on serum ferritin level.European journal of medical research · 2025Article
- Factors linked to lung cancer in MIMIC-IV database.Journal of thoracic disease · 2025Article
- Article
- Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.BMC medical informatics and decision making · 2025Article
- Construction and verification of a nomogram model for the risk of death in sepsis patients.Scientific reports · 2025Article
- Neutrophil-to-Lymphocyte Ratio Predicts Dialysis Timing & Prognosis in Critically Ill Patients.Health science reports · 2025Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundLung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection.
methodsData were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis.
resultsThe risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713-0.773) and 0.746 (95% C.I.: 0.699-0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets.
conclusionOur new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.
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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.