Early evaluation of organ failure using MELD-XI in critically ill elderly COVID-19 patients

Bruno, Raphael Romano; Wernly, Bernhard; Hornemann, Johanna; Fjølner, Jesper; Artigas, Antonio; Pinto, Bernardo Bollen; Schefold, Joerg C.; Wolff, Georg; Baldia, Philipp Heinrich; Binneboessel, Stephan; Kelm, Malte; Beil, Michael; Sviri, Sigal; Heerden, Peter Vernon van; Szczeklik, Wojciech; Elhadi, Muhammed; Joannidis, Michael; Oeyen, Sandra; Kondili, Eumorfia; Wollborn, Jakob; Marsh, Brian; Andersen, Finn H.; Moreno, Rui; Leaver, Susannah; Boumendil, Ariane; Lange, Dylan W. de; Guidet, Bertrand; Jung, Christian

doi:10.3233/ch-219202

Cited by 8 publications

(7 citation statements)

References 38 publications

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“…With the last reported LVEF and classification into HFrEF, HFmrEF, and HFpEF, the study captures other prognostically important parameters in CHF. Our study shares methodological limitations with the other COVIPstudies 3,7,22,[32][33][34] : our study lacks a control group of younger COVID-19 patients for comparison or a comparable age cohort of patients who were not or could not be admitted to the ICU. Furthermore, the COVIP database does not store information on pre-ICU care and triage.…”

Section: Limitationsmentioning

confidence: 99%

Association of chronic heart failure with mortality in old intensive care patients suffering from Covid‐19

et al. 2022

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Aims Chronic heart failure (CHF) is a major risk factor for mortality in coronavirus disease 2019 (COVID‐19). This prospective international multicentre study investigates the role of pre‐existing CHF on clinical outcomes of critically ill old (≥70 years) intensive care patients with COVID‐19. Methods and results Patients with pre‐existing CHF were subclassified as having ischaemic or non‐ischaemic cardiac disease; patients with a documented ejection fraction (EF) were subclassified according to heart failure EF: reduced (HFrEF, n = 132), mild (HFmrEF, n = 91), or preserved (HFpEF, n = 103). Associations of heart failure characteristics with the 30 day mortality were analysed in univariate and multivariate logistic regression analyses. Pre‐existing CHF was reported in 566 of 3917 patients (14%). Patients with CHF were older, frailer, and had significantly higher SOFA scores on admission. CHF patients showed significantly higher crude 30 day mortality [60% vs. 48%, P < 0.001; odds ratio 1.87, 95% confidence interval (CI) 1.5–2.3] and 3 month mortality (69% vs. 56%, P < 0.001). After multivariate adjustment for confounders (SOFA, age, sex, and frailty), no independent association of CHF with mortality remained [adjusted odds ratio (aOR) 1.2, 95% CI 0.5–1.5; P = 0.137]. More patients suffered from pre‐existing ischaemic than from non‐ischaemic disease [233 vs. 328 patients (n = 5 unknown aetiology)]. There were no differences in baseline characteristics between ischaemic and non‐ischaemic disease or between HFrEF, HFmrEF, and HFpEF. Crude 30 day mortality was significantly higher in HFrEF compared with HFpEF (64% vs. 48%, P = 0.042). EF as a continuous variable was not independently associated with 30 day mortality (aOR 0.98, 95% CI 0.9–1.0; P = 0.128). Conclusions In critically ill older COVID‐19 patients, pre‐existing CHF was not independently associated with 30 day mortality. Trial registration number: NCT04321265.

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Section: Limitationsmentioning

confidence: 99%

Association of chronic heart failure with mortality in old intensive care patients suffering from Covid‐19

et al. 2022

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“…While our study was conducted, few studies were had already evaluated the relationship between in-hospital (short-term) mortality o, length of intensive care, length of hospital stay, and long-term mortality of COVID-19 and the MELD-XI score [26][27] . In one of these studies, a higher MELD-XI score was found to be associated with mortality in COVID-19 patients without liver disease, and it was stated that it could be a helpful tool for other risk modalities 26 .…”

Section: Discussionmentioning

confidence: 99%

“…In one of these studies, a higher MELD-XI score was found to be associated with mortality in COVID-19 patients without liver disease, and it was stated that it could be a helpful tool for other risk modalities 26 . In another study, it was shown that the MELD-XI score may be useful in predicting early mortality in the elderly and critically ill patients 27 .…”

Section: Discussionmentioning

confidence: 99%

Comparison of inflammation-based parameters and MELD-XI score with 4C mortality score in predicting in-hospital mortality in COVID-19

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Özer

et al. 2022

Cukurova Medical Journal

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Purpose: In this study, we compared the roles of inflammatory parameters such as neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), C-reactive protein/lymphocyte ratio (CLR), monocyte/lymphocyte ratio (MLR), neutrophil/platelet ratio (NPR), neutrophil/monocyte ratio (NMR), CRP/albumin ratio (CAR), BUN/albumin ratio (BAR), MELD-XI score and 4C mortality score in predicting in-hospital mortality risk in COVID-19. Materials and Methods: A total of 117 patients over 18 years old with a PCR-confirmed diagnosis of COVID-19 between June 2020 and February 2021 were retrospectively included. The roles of parameters for independently predicting in-hospital mortality were determined and compared with each other using appropriate statistical methods. Results: Age, chronic kidney disease, diabetes mellitus, acute kidney injury, and length of hospital stay, urea, creatinine, LDH, AST, ferritin, D-dimer, CRP, albumin, Hb, CLR, BAR, CAR, MELD-XI score, and 4C mortality score were significantly correlated to in-hospital mortality. However, only the 4C mortality score and AST independently predicted in-hospital mortality in COVID-19 [OR 2.08 (%95 CI 1.06-2.36), for 4C mortality score, and OR 1.05 (%95 CI 1.00-1.10), for AST]. Conclusion: Unlike other mortality-related inflammatory parameters, the 4C mortality score and AST were independent and strong predictors of mortality in hospitalized COVID-19 patients.

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“…The present study has some methodological limitations in common with the other COVIPstudies [29][30][31][32][33], such as COVIP does not contain a control group of younger COVID-19…”

Section: Limitationsmentioning

confidence: 99%

COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients

et al. 2022

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Background COVID-19 remains a complex disease in terms of its trajectory and the diversity of outcomes rendering disease management and clinical resource allocation challenging. Varying symptomatology in older patients as well as limitation of clinical scoring systems have created the need for more objective and consistent methods to aid clinical decision making. In this regard, machine learning methods have been shown to enhance prognostication, while improving consistency. However, current machine learning approaches have been limited by lack of generalisation to diverse patient populations, between patients admitted at different waves and small sample sizes. Objectives We sought to investigate whether machine learning models, derived on routinely collected clinical data, can generalise well i) between European countries, ii) between European patients admitted at different COVID-19 waves, and iii) between geographically diverse patients, namely whether a model derived on the European patient cohort can be used to predict outcomes of patients admitted to Asian, African and American ICUs. Methods We compare Logistic Regression, Feed Forward Neural Network and XGBoost algorithms to analyse data from 3,933 older patients with a confirmed COVID-19 diagnosis in predicting three outcomes, namely: ICU mortality, 30-day mortality and patients at low risk of deterioration. The patients were admitted to ICUs located in 37 countries, between January 11, 2020, and April 27, 2021. Results The XGBoost model derived on the European cohort and externally validated in cohorts of Asian, African, and American patients, achieved AUC of 0.89 (95% CI 0.89–0.89) in predicting ICU mortality, AUC of 0.86 (95% CI 0.86–0.86) for 30-day mortality prediction and AUC of 0.86 (95% CI 0.86–0.86) in predicting low-risk patients. Similar AUC performance was achieved also when predicting outcomes between European countries and between pandemic waves, while the models showed high calibration quality. Furthermore, saliency analysis showed that FiO2 values of up to 40% do not appear to increase the predicted risk of ICU and 30-day mortality, while PaO2 values of 75 mmHg or lower are associated with a sharp increase in the predicted risk of ICU and 30-day mortality. Lastly, increase in SOFA scores also increase the predicted risk, but only up to a value of 8. Beyond these scores the predicted risk remains consistently high. Conclusion The models captured both the dynamic course of the disease as well as similarities and differences between the diverse patient cohorts, enabling prediction of disease severity, identification of low-risk patients and potentially supporting effective planning of essential clinical resources. Trial registration number NCT04321265.

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Early evaluation of organ failure using MELD-XI in critically ill elderly COVID-19 patients

Cited by 8 publications

References 38 publications

Association of chronic heart failure with mortality in old intensive care patients suffering from Covid‐19

Association of chronic heart failure with mortality in old intensive care patients suffering from Covid‐19

Comparison of inflammation-based parameters and MELD-XI score with 4C mortality score in predicting in-hospital mortality in COVID-19

COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients

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