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      The incremental value of aspartate aminotransferase/alanine aminotransferase ratio combined with CURB-65 in predicting treatment outcomes in hospitalized adult community-acquired pneumonia patients with type 2 diabetes mellitus

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          Abstract

          Background

          The features of community-acquired pneumonia (CAP) patients with type 2 diabetes mellitus (T2DM) differ from those without. This study aims to spot a routinely tested parameter with discriminative, predictive and prognostic value to enhance CURB-65’s prognostic accuracy in CAP patients with T2DM.

          Methods

          We retrospectively studied consecutive CAP patients from 2020 to 2021, comparing laboratory parameters between patients with and without T2DM. Receiver operating characteristic (ROC) curve analysis, univariate and multivariate logistic regression were used to identify key parameters. The area under the ROC curve (AUC), Fagan’s nomogram, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) evaluated the added predictive accuracy.

          Results

          A total of 720 patients were included, comprising 180 diabetic CAP patients and 540 non-diabetic controls after matching for age, gender, and comorbidities through propensity score matching. In diabetic CAP patients, the aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio showed the highest AUC (0.676, 95% CI, 0.575–0.776) among laboratory parameters with different distributions between the groups. AST/ALT was also identified as an independent predictor of poor treatment outcome (OR = 3.672, 95% CI, 1.455–9.268, p = 0.006). Adding AST/ALT to CURB-65 slightly increased the AUC, but remarkably enhanced NRI and IDI (AUC, 0.756 vs. 0.782, p = 0.017; continuous NRI, 0.635, 95% CI, 0.304–0.966, p < 0.001; categorical NRI, 0.175, 95% CI, 0.044–0.307, p = 0.009; IDI, 0.043, 95% CI, 0.006–0.080, p = 0.021). An AST/ALT ratio of ≥ 1.625 conferred a 74% post-test probability of poor treatment outcome, while < 1.625 predicted 21%. AST/ALT also predicted outcomes for all the CAP patients enrolled (OR = 1.771, 95% CI, 1.231–2.549, p = 0.002). Predictive accuracy improved after incorporating AST/ALP into CURB-65 in these population (AUC, 0.615 vs. 0.645, p = 0.038; continuous NRI, 0.357, 95% CI, 0.196–0.517, p < 0.001; categorical NRI, 0.264, 95% CI, 0.151–0.376, p < 0.001; IDI, 0.019, 95% CI, 0.008–0.029, p < 0.001).

          Conclusions

          AST/ALT was identified as a discriminative, predictive and prognostic factor for CAP patients with T2DM. The integration of AST/ALT into CURB-65 enhanced outcome prediction for both diabetic and non-diabetic CAP patients.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12890-025-03488-1.

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          Most cited references30

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          • Abstract: found
          • Article: not found

          Risk Factors, Mortality, and Cardiovascular Outcomes in Patients with Type 2 Diabetes

          Patients with diabetes are at higher risk for death and cardiovascular outcomes than the general population. We investigated whether the excess risk of death and cardiovascular events among patients with type 2 diabetes could be reduced or eliminated.
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            Trends in Prevalence of Diabetes and Control of Risk Factors in Diabetes Among US Adults, 1999-2018

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              Is Open Access

              TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

              The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective of whether regression modelling or machine learning methods have been used. The new checklist supersedes the TRIPOD 2015 checklist, which should no longer be used. This article describes the development of TRIPOD+AI and presents the expanded 27 item checklist with more detailed explanation of each reporting recommendation, and the TRIPOD+AI for Abstracts checklist. TRIPOD+AI aims to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. Complete reporting will facilitate study appraisal, model evaluation, and model implementation.

                Author and article information

                Contributors
                lsq254244679@126.com
                Journal
                BMC Pulm Med
                BMC Pulm Med
                BMC Pulmonary Medicine
                BioMed Central (London )
                1471-2466
                17 January 2025
                17 January 2025
                2025
                : 25
                : 26
                Affiliations
                [1 ]Department of Endocrinology, People’s Hospital of Nanchuan, Chongqing, 408400 People’s Republic of China
                [2 ]Department of Respiratory and Critical Care Medicine, People’s Hospital of Nanchuan, Chongqing, 408400 People’s Republic of China
                [3 ]Department of Public Health, People’s Hospital of Nanchuan, Chongqing, 408400 People’s Republic of China
                Article
                3488
                10.1186/s12890-025-03488-1
                11742778
                39825277
                22cfc710-c60f-4985-9cae-37607a9879c5
                © The Author(s) 2025

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 23 February 2024
                : 7 January 2025
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2025

                Respiratory medicine
                community-acquired pneumonia (cap),type 2 diabetes mellitus (t2dm),prognostic model,aspartate aminotransferase/alanine aminotransferase ratio (ast/alt),cubr-65

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