{"title":"Coagulation and inflammatory markers independently predict in-hospital mortality in aspiration pneumonia patients undergoing bronchoalveolar lavage.","authors":"Tao Ren, Weimin Jiang","doi":"10.62347/BAQD6951","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the prognostic value of serum coagulation and inflammatory markers for in-hospital mortality in patients with aspiration pneumonia (AP) undergoing bronchoalveolar lavage, and to develop a predictive model.</p><p><strong>Methods: </strong>This retrospective study included 220 AP patients admitted to XianJu People's Hospital between January 2022 and October 2024. Data on demographics, coagulation parameters, inflammatory markers, and in-hospital outcomes were collected. Multivariate logistic regression was used to identify independent predictors of mortality, and a nomogram was constructed based on significant variables.</p><p><strong>Results: </strong>Among the 220 patients, 42 (19.1%) died during hospitalization. Multivariate logistic regression identified age (OR = 1.057, P = 0.006), fibrinogen (FIB; OR = 1.456, P = 0.002), D-dimer (OR = 2.414, P < 0.001), leukocyte count (OR = 1.128, P = 0.027), and procalcitonin (PCT; OR = 9.240, P < 0.001) as independent predictors of in-hospital mortality. The nomogram model incorporating these variables demonstrated good discriminative ability with an area under the curve of 0.835. Calibration plots and decision curve analysis further confirmed the model's accuracy and clinical utility.</p><p><strong>Conclusion: </strong>Age, FIB, D-dimer, leukocyte count, and PCT are independent predictors of in-hospital mortality in AP patients undergoing bronchoalveolar lavage. The nomogram based on these markers shows strong predictive performance and may facilitate individualized risk assessment and clinical decision-making.</p>","PeriodicalId":7731,"journal":{"name":"American journal of translational research","volume":"17 6","pages":"4601-4611"},"PeriodicalIF":1.6000,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261143/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of translational research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.62347/BAQD6951","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: To evaluate the prognostic value of serum coagulation and inflammatory markers for in-hospital mortality in patients with aspiration pneumonia (AP) undergoing bronchoalveolar lavage, and to develop a predictive model.
Methods: This retrospective study included 220 AP patients admitted to XianJu People's Hospital between January 2022 and October 2024. Data on demographics, coagulation parameters, inflammatory markers, and in-hospital outcomes were collected. Multivariate logistic regression was used to identify independent predictors of mortality, and a nomogram was constructed based on significant variables.
Results: Among the 220 patients, 42 (19.1%) died during hospitalization. Multivariate logistic regression identified age (OR = 1.057, P = 0.006), fibrinogen (FIB; OR = 1.456, P = 0.002), D-dimer (OR = 2.414, P < 0.001), leukocyte count (OR = 1.128, P = 0.027), and procalcitonin (PCT; OR = 9.240, P < 0.001) as independent predictors of in-hospital mortality. The nomogram model incorporating these variables demonstrated good discriminative ability with an area under the curve of 0.835. Calibration plots and decision curve analysis further confirmed the model's accuracy and clinical utility.
Conclusion: Age, FIB, D-dimer, leukocyte count, and PCT are independent predictors of in-hospital mortality in AP patients undergoing bronchoalveolar lavage. The nomogram based on these markers shows strong predictive performance and may facilitate individualized risk assessment and clinical decision-making.