ArticleBMC pulmonary medicine2024
Development and validation of a nomogram for assessing survival in acute exacerbation of chronic obstructive pulmonary disease patients.
Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Tackling Imbalanced Data in Chronic Obstructive Pulmonary Disease Diagnosis: An Ensemble Learning Approach with Synthetic Data Generation.Bioengineering (Basel, Switzerland) · 2026Article
- Construction and Comparison of Multiple Serum-Based Prognostic Models for Predicting the Prognosis of Acute Exacerbations of Chronic Obstructive Pulmonary Disease.Journal of inflammation research · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundEarly prediction of survival of hospitalized acute exacerbations of chronic obstructive pulmonary disease (AECOPD) patients is vital. We aimed to establish a nomogram to predict the survival probability of AECOPD patients.
methodsRetrospectively collected data of 4601 patients hospitalized for AECOPD. These patients were randomly divided into a training and a validation cohort at a 6:4 ratio. In the training cohort, LASSO-Cox regression analysis and multivariate Cox regression analysis were utilized to identify prognostic factors for in-hospital survival of AECOPD patients. A model was established based on 3 variables and visualized by nomogram. The performance of the model was assesed by AUC, C-index, calibration curve, decision curve analysis in both cohorts.
resultsCoexisting arrhythmia, invasive mechanical ventilation (IMV) usage and lower serum albumin values were found to be significantly associated with lower survival probability of AECOPD patients, and these 3 predictors were further used to establish a prediction nomogram. The C-indexes of the nomogram were 0.816 in the training cohort and 0.814 in the validation cohort. The AUC in the training cohort was 0.825 for 7-day, 0.807 for 14-day and 0.825 for 21-day survival probability, in the validation cohort this were 0.796 for 7-day, 0.831 for 14-day and 0.841 for 21-day. The calibration of the nomogram showed a good goodness-of-fit and decision curve analysis showed the net clinical benefits achievable at different risk thresholds were excellent.
conclusionWe established a nomogram based on 3 variables for predicting the survival probability of AECOPD patients. The nomogram showed good performance and was clinically useful.
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