Introducing a new prognostic instrument for long-term mortality prediction in COPD patients: the CADOT index

Abstract Background: The release of hormones from the adrenal gland is vital in acute and chronic illnesses such as chronic obstructive pulmonary disease (COPD) involving recurrent exacerbations. Using a metabolomic approach, we aim to investigate associations of different adrenal hormone metabolites with short- and long-term mortality in COPD patients. Methods: We prospectively followed 172 COPD patients (median age 75 years, 62% male) from a previous Swiss multicenter trial. At baseline, we measured levels of a comprehensive spectrum of adrenal hormone metabolites, including glucocorticoid, mineralocorticoid and androgen hormones by liquid chromatography coupled with tandem mass spectrometry (MS). We calculated Cox regression models adjusted for gender, age, comorbidities and previous corticosteroid therapy. Results: Mortality was 6.4% after 30 days and increased to 61.6% after 6 years. Higher initial androgen hormones predicted lower long-term mortality with significant results for dehydroepiandrosterone (DHEA) [adjusted hazard ratio (HR), 0.82; 95% confidence interval (CI), 0.70–0.98; p=0.026] and dehydroepiandrosterone sulfate (DHEA-S) (adjusted HR, 0.68; 95% CI, 0.50–0.91; p=0.009). An activation of stress hormones (particularly cortisol and cortisone) showed a time-dependent effect with higher levels pointing towards higher mortality at short term, but lower mortality at long term. Activation of the mineralocorticoid axis tended to be associated with increased short-term mortality (adjusted HR of aldosterone, 2.76; 95% CI, 0.79–9.65; p=0.111). Conclusions: Independent of age, gender, corticosteroid exposure and exacerbation type, adrenal hormones are associated with mortality at short and long term in patients with COPD exacerbation with different time-dependent effects of glucocorticoids, androgens and mineralocorticoids. A better physiopathological understanding of the causality of these effects may have therapeutic implications.

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Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

PLoS ONE ◽

10.1371/journal.pone.0254894 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0254894

Author(s):

Firdaus Aziz ◽

Sorayya Malek ◽

Khairul Shafiq Ibrahim ◽

Raja Ezman Raja Shariff ◽

Wan Azman Wan Ahmad ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Risk Score ◽

Fasting Blood Glucose ◽

Mortality Prediction ◽

Machine Learning Algorithms ◽

Timi Score ◽

Short And Long Term ◽

Long Term Mortality

Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

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