Dataset Information

Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.

ABSTRACT:

Purpose

Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a prediction model to help identify future patients and assess the impact of acute PE on hospital outcomes.

Methods

We included 758 patients who were treated for asthma exacerbation and underwent a computed tomographic pulmonary angiography (CTA) during the same encounter at a university-based hospital between June 2011 and October 2018. We compared clinical characteristics of patients with and without acute PE and developed a machine learning prediction model to classify the PE status based on the clinical variables. We used multivariable regression analysis to evaluate the impact of acute PE on hospital outcomes.

Results

Twenty percent of the asthma exacerbation patients who underwent CTA had an acute PE. Factors associated with acute PE included previous history of PE, high CHA₂DS₂-VASc score, hyperlipidemia, history of deep vein thrombosis, malignancy, chronic systemic corticosteroids use, high body mass index and atrial fibrillation. Using these factors, we developed a random forest machine learning prediction model which had an 88% accuracy in classifying the acute PE status of the patients (area under the receiver operating characteristic curve = 0.899; 95% confidence interval: 0.885-0.913). Acute PE in asthma exacerbation was associated with longer hospital stay and intensive care unit stay.

Conclusion

It is important to consider acute PE, a potentially life-threatening event, in the setting of asthma exacerbation especially when other risk factors are present.

SUBMITTER: Alzghoul BN

PROVIDER: S-EPMC7711330 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: In patients with chronic obstructive pulmonary disease (COPD), acute exacerbations affect patients' health and can lead to death. This study was aimed to develop a prediction model for in-hospital mortality in patients with acute exacerbations of COPD (AECOPD). Method: A retrospective study was performed in patients hospitalized for AECOPD between 2015 and 2019. Patients admitted between 2015 and 2017 were included to develop model and individuals admitted in the following 2 years were included for external validation. We analyzed variables that were readily available in clinical practice. Given that death was a rare outcome in this study, we fitted Firth penalized logistic regression. C statistic and calibration plot quantified the model performance. Optimism-corrected C statistic and slope were estimated by bootstrapping. Accordingly, the prediction model was adjusted and then transformed into risk score. Result: Between 2015 and 2017, 1,096 eligible patients were analyzed, with a mean age of 73 years and 67.8% male. The in-hospital mortality was 2.6%. Compared to survivors, non-survivors were older, more admitted from emergency, more frequently concomitant with respiratory failure, pneumothorax, hypoxic-hypercarbic encephalopathy, and had longer length of stay (LOS). Four variables were included into the final model: age, respiratory failure, pneumothorax, and LOS. In internal validation, C statistic was 0.9147, and the calibration slope was 1.0254. Their optimism-corrected values were 0.90887 and 0.9282, respectively, indicating satisfactory discrimination and calibration. When externally validated in 700 AECOPD patients during 2018 and 2019, the model demonstrated good discrimination with a C statistic of 0.8176. Calibration plot illustrated a varying discordance between predicted and observed mortality. It demonstrated good calibration in low-risk patients with predicted mortality rate ≤10% (P = 0.3253) but overestimated mortality in patients with predicted rate >10% (P < 0.0001). The risk score of 20 was regarded as a threshold with an optimal Youden index of 0.7154. Conclusion: A simple prediction model for AECOPD in-hospital mortality has been developed and externally validated. Based on available data in clinical setting, the model could serve as an easily used instrument for clinical decision-making. Complications emerged as strong predictors, underscoring an important role of disease management in improving patients' prognoses during exacerbation episodes.