ArticleChronic respiratory disease
Connective tissue disease-associated interstitial lung disease risk of readmission and all-cause mortality: Interpretable machine learning approach.
Article in Chronic respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
ObjectiveConnective tissue disease (CTD) encompasses a group of autoimmune disorders, with interstitial lung disease (ILD) being the most common form of pulmonary involvement. The primary focus of this study was to employ machine learning for the identification of blood-based biomarkers in individuals afflicted with CTD-ILD. Additionally, the study aimed to assess the potential association of these biomarkers with the likelihood of hospital readmissions and all-cause mortality within a 1-year period among CTD-ILD patients.MethodsA total of 210 patients were included in the study, with 147 patients allocated to the training set and 63 patients assigned to the test set. Univariate logistic regression, LASSO regression, and multivariable logistic regression analyses were executed to discern the risk factors associated with readmission within 1 year among CTD-ILD. Logistic regression, support vector machine, and XGBoost were utilized to build the model. The global and local interpretation of the model was conducted using SHAP. The efficacy of model was evaluated using the ROC curve and DCA. Furthermore, the predictive values of inflammatory indicators were compared for their ability to forecast all-cause mortality in CTD-ILD patients.ResultsLow albumin levels, high CA125, and CYFRA 21-1 were identified as significant factors associated with patient readmissions. The XGBoost model demonstrated the highest efficacy in both the training and test sets, achieving an AUC of 0.857 (95% CI 0.832-0.879) and 0.788 (95% CI 0.706-0.833), respectively. SHAP analysis indicated that low albumin had the most significant impact on the model outcomes. Among the 1-year all-cause deaths of CTD-ILD patients, the neutrophil-to-lymphocyte ratio (NLR) was the most potent predictor in univariate analysis. A model combining albumin, CA125, and CYFRA 21-1 with NLR was constructed, achieving an AUC of 0.944 (95% CI 0.915-0.964).ConclusionElevated levels of CA125, CYFRA 21-1, and NLR, along with lower albumin levels, were predictive of a poor prognosis in CTD-ILD patients.
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