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Discrimination and Calibration of Predictive Scores of Mortality in Ecmo for Patients with Covid-19
Critical Care Medicine ; 51(1 Supplement):450, 2023.
Article in English | EMBASE | ID: covidwho-2190634
ABSTRACT

INTRODUCTION:

The criteria for the COVID-19 patients' selection that benefit most from ECMO therapy are yet to be defined. In this study, we evaluate the predictive performance of the ECMO mortality predictive models in patients with COVID-19. METHOD(S) A retrospective study was performed in two high-complexity hospitals between March 18, 2020, and December 31, 2021. We included patients over 18 years old with COVID-19 infection confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) who received V-V ECMO due to COVID-19-related ARDS. We evaluated the predictive performance (discrimination, calibration, and accuracy) of death prediction of the following predictive models i) Prediction of Death due to Severe ARDS in V-V ECMO score (PRESERVE);ii) The Respiratory Extracorporeal Membrane Oxygenation Survival Score (RESP) score;iii) Prediction of Survival on ECMO Therapy- Score (PRESET) score, to predict death. Also, we perform a cost-benefit analysis using the health-related quality of life reported by the CESAR TRIAL and the US life expectancy. Besides, we add the mortality predicted probability calculated with the best predictive model to the cost-benefit analysis. Therefore, the cost/QALY formula was cost/QALY = cost / age-specific life expectancy*health utilitiesz.ast;survival probability. RESULT(S) We included 38 adult patients who received ECMO due to COVID-19. The PRESET score had the highest discrimination (AUROCs 0.81 [CI95% 0.67-0.94]) and the best calibration (Hosmer-Lemeshow test, p=0.6). The optimal threshold for this score was 7 (sensitivity 67%, specificity 89%, accuracy 78%). The cost per QALY in the USA, adjusted to life expectancy, was higher than UDS 100,000 in patients older than 45 years with a PRESET>10. CONCLUSION(S) The PRESET score had the highest predictive performance and could help in the patient's selection that benefits most from this resource-demanding and highly invasive therapy. Also, the addition of the costbenefit analysis output can help decide which patient to place on ECMO therapy, especially in low-resource settings.
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Full text: Available Collection: Databases of international organizations Database: EMBASE Type of study: Prognostic study Language: English Journal: Critical Care Medicine Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: EMBASE Type of study: Prognostic study Language: English Journal: Critical Care Medicine Year: 2023 Document Type: Article