Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score

Berenguer, Juan; Borobia, Alberto M.; Ryan, Pablo; Rodríguez-Baño, Jesús; Bellón, José M.; Jarrín, Inmaculada; Carratalà, Jordi; Pachón, Jerónimo; Carcas, Antonio J.; Yllescas, María; Arribas, José Ramón

doi:10.1136/thoraxjnl-2020-216001

Cited by 75 publications

(72 citation statements)

References 29 publications

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“…The median (quartiles) of the CT score at ICU admission in the oxygen therapy group was 10 [ [7] , [8] , [9] , [10] , [11] , [12] ], which was significantly lower than that [18 [ [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] ]] in the MV group ( P < 0.0001). A significant negative correlation ( r = −0.6) was detected between SpO 2 on room air and the CT score ( P < 0.0001).…”

Section: Resultsmentioning

confidence: 93%

“…In the present study, we evaluated SpO 2 on the room air rather than on oxygen because the determination of FiO 2 remains inaccurate and influenced by minute ventilation, air leak, and breathing pattern in patients receiving oxygen through a facemask. Few studies had evaluated the validity of baseline SpO 2 as an important risk factor in COVID-10; however, most of these studies used SpO 2 as a component of multivariate prediction models [ [14] , [15] , [16] ] and did not use the need to ventilatory support as the primary outcome. Pre-hospital lowest SpO 2 had been evaluated for triaging of patients with COVID-19.…”

Section: Discussionmentioning

confidence: 99%

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Admission SpO2 and ROX index predict outcome in patients with COVID-19

Mukhtar

Rady

Hasanin

et al. 2021

The American Journal of Emergency Medicine

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Background This study aimed to evaluate the accuracy of pulse oximetry-derived oxygen saturation (SpO 2 ) on room air, determined at hospital admission, as a predictor for the need for mechanical ventilatory support in patients with Coronavirus Disease-2019 (COVID-19). Methods In this retrospective observational study, demographic and clinical details of the patients were obtained during ICU admission. SpO 2 and respiratory rate (RR) on room air were determined within the first 6 h of hospital admission. As all measurements were obtained on room air, we calculated the simplified respiratory rate‑oxygenation (ROX) index by dividing the SpO 2 by the RR. Based on the use of any assistance of mechanical ventilator (invasive or noninvasive), patients were divided into mechanical ventilation (MV) group and oxygen therapy group. The accuracy of the SpO 2 , CT score, and ROX index to predict the need to MV were determined using the Area under receiver operating curve (AUC). Results We included 72 critically ill patients who tested COVID-19-positive. SpO 2 on the room air could predict any MV requirement (AUC [95% confidence interval]: 0.9 [0.8–0.96], sensitivity: 70%, specificity 100%, cut-off value ≤78%, P < 0.001). Within the MV group, the use of noninvasive ventilation (NIV) was successful in 37 (74%) patients, whereas 13 patients (26%) required endotracheal intubation. The cut-off ROX value for predicting early NIV failure was ≤1.4, with a sensitivity of 85%, a specificity of 86%, and an AUC of 0.86 (95% confidence interval of 0.73–0.94, P < 0.0001). Conclusions A baseline SpO 2 ≤78% is an excellent predictor of MV requirement with a positive predictive value of 100%. Moreover, the ROX index measured within the first 6 h of hospital admission is a good indicator of early NIV failure.

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Section: Resultsmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Admission SpO2 and ROX index predict outcome in patients with COVID-19

Mukhtar

Rady

Hasanin

et al. 2021

The American Journal of Emergency Medicine

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“…In older adults, elevations in IL-6 and C-reactive protein (CRP) have been documented even prior to the onset of syndromal depression [63,64], suggesting that they are a "trait" rather than a "state" marker of depression. Similarly, the neutrophil-to-lymphocyte ratio, a marker of systemic inflammation, is elevated in patients with major depression even after antidepressant treatment [65,66], and this parameter has been identified as a predictor of COVID-19 mortality in prospective studies of hospitalized patients [67,68]. Second, recurrent or chronic depression is associated with a sedentary lifestyle characterized by less physical activity or exercise [69], less healthy dietary practices [70] and the use of substances, particularly alcohol [71].…”

Section: Mechanisms Linking Depression and Obesity With Covid-19 Mortalitymentioning

confidence: 99%

Cross-National Variations in COVID-19 Mortality: The Role of Diet, Obesity and Depression

Rajkumar

2021

Diseases

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Background: The COVID-19 pandemic has been characterized by wide variations in mortality across nations. Some of this variability may be explained by medical comorbidities such as obesity and depression, both of which are strongly correlated with dietary practices such as levels of sugar and seafood consumption. Methods: COVID-19 mortality indices for 156 countries were obtained from the Johns Hopkins University’s data aggregator. Correlations between these variables and (a) per capita consumption of sugar and seafood, and (b) country-wise prevalence of depression and obesity were examined. Results: Sugar consumption (r = 0.51, p < 0.001) and prevalence of obesity (r = 0.66, p < 0.001) and depression (r = 0.56, p < 0.001) were positively correlated with crude mortality rates, while seafood consumption was negatively correlated with the infection fatality rate (r = −0.28, p = 0.015). These effects were significant even after correcting for potential confounders. The associations with depression and obesity remained significant upon multivariate regression. Conclusions: Both obesity and depression, which are associated with inflammatory dysregulation, may be related to cross-national variations in COVID-19 mortality, while seafood consumption may be protective. These findings have implications in terms of protecting vulnerable individuals during the current pandemic.

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“…In contrast to prior models, we excluded patients with palliative goals of care, for whom invasive mechanical ventilation was not offered to ensure our model did not predict patients who were expected to succumb, or ineligible for the highest level of critical care. (9)(10)(11)13,14,(20)(21)(22)(23)(24) This avoids the potential for self-fulfilling prophecy bias, whereby the prognostic model predicts the outcome that occurred as a result of a decision to withhold life-sustaining measures. (25)(26)(27) Prior models were derived or validated during the early pandemic while COVID-19 testing was restricted to those with severe disease, and did not include consecutive eligible patients, both of which may have resulted in selection bias.…”

Section: Explanation Of the Findingsmentioning

confidence: 99%

Derivation and Validation of a Clinical Score to Predict Death Among Non-Palliative Covid-19 Patients Presenting to Emergency Departments: The Ccedrrn Covid Mortality Score

Hohl

Rosychuk

Archambault

et al. 2021

Preprint

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Background: Predicting mortality from coronavirus disease 2019 (COVID-19) using information available when patients present to the Emergency Department (ED) can inform goals-of-care decisions and assist with ethical allocation of critical care resources. Methods: We conducted an observational study to develop and validate a clinical score to predict ED and in-hospital mortality among consecutive non-palliative COVID-19 patients. We recruited from 44 hospitals participating in the Canadian COVID-19 ED Rapid Response Network (CCEDRRN) between March 1, 2020 and January 31, 2021. We randomly assigned hospitals to derivation or validation, and pre-specified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort, and examined its performance in predicting ED and in-hospital mortality in a validation cohort. Results: Of 8,761 eligible patients, 618 (7·01%) died. The score included age, sex, type of residence, arrival mode, chest pain, severe liver disease, respiratory rate, and level of respiratory support. The area under the curve was 0·92 (95% confidence intervals [CI] 0·91—0·93) in derivation and 0·92 (95%CI 0·89—0·93) in validation. The score had excellent calibration. Above a score of 15, the observed mortality was 81·0% (81/100) with a specificity of 98·8% (95%CI 99·5—99·9%). Interpretation: The CCEDRRN COVID Mortality Score is a simple score that accurately predicts mortality with variables that are available on patient arrival without the need for diagnostic tests.

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Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score

Cited by 75 publications

References 29 publications

Admission SpO2 and ROX index predict outcome in patients with COVID-19

Admission SpO2 and ROX index predict outcome in patients with COVID-19

Cross-National Variations in COVID-19 Mortality: The Role of Diet, Obesity and Depression

Derivation and Validation of a Clinical Score to Predict Death Among Non-Palliative Covid-19 Patients Presenting to Emergency Departments: The Ccedrrn Covid Mortality Score

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