Abstract
Background: Risk stratification of COVID-19 patients is fundamental to improving prognosis and selecting the right treatment. We hypothesized that a combination of lung ultrasound (LUZ-score), biomarkers (sST2), and clinical models (PANDEMYC score) could be useful to improve risk stratification. Methods: This was a prospective cohort study designed to analyze the prognostic value of lung ultrasound, sST2, and PANDEMYC score in COVID-19 patients. The primary endpoint was in-hospital death and/or admission to the intensive care unit. The total length of hospital stay, increase of oxygen flow, or escalated medical treatment during the first 72 h were secondary endpoints. Results: a total of 144 patients were included; the mean age was 57.5 ± 12.78 years. The median PANDEMYC score was 243 (52), the median LUZ-score was 21 (10), and the median sST2 was 53.1 ng/mL (30.9). Soluble ST2 showed the best predictive capacity for the primary endpoint (AUC = 0.764 (0.658–0.871); p = 0.001), towards the PANDEMYC score (AUC = 0.762 (0.655–0.870); p = 0.001) and LUZ-score (AUC = 0.749 (0.596–0.901); p = 0.002). Taken together, these three tools significantly improved the risk capacity (AUC = 0.840 (0.727–0.953); p ≤ 0.001). Conclusions: The PANDEMYC score, lung ultrasound, and sST2 concentrations upon admission for COVID-19 are independent predictors of intra-hospital death and/or the need for admission to the ICU for mechanical ventilation. The combination of these predictive tools improves the predictive power compared to each one separately. The use of decision trees, based on multivariate models, could be useful in clinical practice.
Highlights
Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) causes COVID-19 disease [1,2]
We have previously shown the predictive value of lung ultrasound (LUS) [17] and biochemical biomarkers [18]
It is a prediction model for COVID-19 patients, based on basic clinical and laboratory data at admission, that has been demonstrated to predict in-hospital death of COVID-19 patients [9]. This model was chosen based on the following arguments: first, its creation was based on a cohort of patients from our same country and with similar characteristics; second, score is calculated from nine variables that are easy to obtain in routine clinical practice, even if some of them are missing; and third, the tool shows excellent power in predicting a hard target such as in-hospital death (AUC = 0.88) [9]
Summary
Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) causes COVID-19 disease [1,2]. A viral picture occurs that later gives way to a pro-inflammatory state [3], influenced by cytokine storm and thrombotic phenomena [6]. This situation can last for months in some cases, which is called post-COVID-19 syndrome [7,8]. Risk-stratification tools for COVID-19 were initially based on the analysis of baseline clinical characteristics through retrospective cohort studies during the first pandemic wave [1,9].
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