key: cord-0793139-qvozmvwz authors: Utrero-Rico, Alberto; Ruiz-Hornillos, Javier; González-Cuadrado, Cecilia; Rita, Claudia Geraldine; Almoguera, Berta; Minguez, Pablo; Herrero-González, Antonio; Fernández-Ruiz, Mario; Carretero, Octavio; Taracido-Fernández, Juan Carlos; López-Rodriguez, Rosario; Corton, Marta; Aguado, José María; Villar, Luisa María; Ayuso-García, Carmen; Paz-Artal, Estela; Laguna-Goya, Rocio title: Interleukin-6-based mortality prediction model for COVID-19: validation and update in multicentre and second wave cohorts date: 2021-03-01 journal: J Allergy Clin Immunol DOI: 10.1016/j.jaci.2021.02.021 sha: 32f3fe51ea4e78a489885e55bf05499de1050b03 doc_id: 793139 cord_uid: qvozmvwz Background Coronavirus disease 2019 (COVID-19) is a highly variable condition. Validated tools to assist in the early detection of patients at high risk of mortality can help guide medical decisions. Objective To validate externally, as well as in patients from the second pandemic wave in Europe, our previously developed mortality prediction model for hospitalized COVID-19 patients. Methods Three validation cohorts were generated: two external with 185 and 730 patients from the first wave and one internal with 119 patients from the second wave. The probability of death was calculated for all subjects using our prediction model, which includes SpO2/FiO2 ratio, neutrophil-to-lymphocyte ratio, LDH, interleukin-6 and age. Discrimination and calibration were evaluated in the validation cohorts. The prediction model was updated by re-estimating individual risk factor effects in the overall cohort (N=1477). Results The mortality prediction model showed good performance in the external validation cohorts 1 and 2, and in the second wave validation cohort 3 (AUC 0.94, 0.86 and 0.86, respectively), with excellent calibration (calibration slope 0.86, 0.94 and 0.79; intercept 0.05, 0.03 and 0.10, respectively). The updated model accurately predicted mortality in the overall cohort (AUC 0.91), which included patients from both the first and second COVID-19 waves. The updated model was also useful to predict fatal outcome in patients without respiratory distress at the time of evaluation. Conclusions This is the first COVID-19 mortality prediction model validated in patients from the first and second pandemic waves. The COR+12 online calculator is freely available to facilitate its implementation (https://utrero-rico.shinyapps.io/COR12_Score/) The COVID-19 outbreak started in December 2019 and since then has caused more than 132 77 million infections and over a million and a half deaths 1 . In Europe, infections by 133 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have occurred in two 134 waves: a first wave from February to June 2020, and a second wave which started in 135 August 2020 and peaked in November 2020 2, 3 . derived complications such as thrombosis 11 , is responsible for the disease severity. 149 However, there are still many uncertainties about the pathophysiology of COVID-19. 150 Until we have a better understanding of this disease, the availability of tools that allow 151 risk stratification of infected patients can be useful for optimising therapeutic 152 management and improving patient prognosis. During the first wave of COVID-19 cases in Europe, we developed a prediction model 155 which estimates the probability of death in hospitalized patients with COVID-19, based 156 on five parameters taken at, or soon after, hospital admission: peripheral blood oxygen (https://utrero-rico.shinyapps.io/COR12_Score/). We have now externally validated this 163 prediction model in two independent cohorts from two different tertiary hospitals 164 (validation cohorts 1 and 2). In addition, we have internally validated the model in a 165 prospective patient cohort recruited during the second wave (validation cohort 3). 166 Finally, the model has been updated with the overall sum of patients from the 167 development and validation cohorts. The variables included in the prediction model (SpO 2 /FiO 2 , N/L ratio, LDH, IL-6 and 230 age) were taken at hospital admission or in the first 4 days of the hospitalization. Most 231 measurements were taken at the ER in the external validation cohort 2 and the second 232 wave validation cohort 3, while measurements were taken with a median of 2 days from 233 hospital admission in the external validation cohort 1 (Table 1) . (Table 1) . 294 Nonetheless, in the validation cohorts, all the variables were significantly increased in 295 patients who died compared with those who survived, except for SpO 2 /FiO 2 in the 296 second wave validation cohort 3, which was relatively high in non-survivors (Table 2) . Overall, patients who died were significantly older and had significantly lower 298 SpO 2 /FiO 2 and higher levels of N/L ratio, LDH and IL-6, than patients who survived. We applied the model to the patients in both external cohorts and found, as expected, 309 that the probability of dying was significantly higher in non-survivors, followed by 310 patients who survived after intensive care, followed by patients who survived without 311 the need for intensive care (Figure 3) . The Youden's index-based cut-off generated Second wave COVID-19 pandemics in 472 Europe: a temporal playbook Infection fatality risk for SARS-CoV-2 in community 478 dwelling population of Spain: nationwide seroepidemiological study Factors associated with COVID-19-related death using OpenSAFELY An inflammatory cytokine signature predicts COVID-19 severity and 485 survival CoV-2 infection and COVID-19 mortality during an 488 outbreak investigation in a skilled nursing facility Asymptomatic SARS-CoV-2 Infection in Nursing Homes Longitudinal analyses reveal immunological misfiring in severe COVID-19. 494 Comprehensive mapping of immune perturbations 497 associated with severe COVID-19 Acute respiratory distress syndrome: the Berlin Definition New Guideline for the Length of hospital stay (days) (Median IL-6 (pg/mL) h (Median a Interquartile range; b Acute respiratory distress syndrome; c Real time reverse transcription polymerase chain reaction; d Intensive care unit; e Peripheral blood oxygen saturation/fraction of inspired oxygen ratio; f Neutrophil-to-lymphocyte ratio; g Lactate dehydrogenase 462 We would like to thank all patients, nurses and medical colleagues who contributed to 463 this study.