ABSTRACT
BackgroundMore contagious SARS-CoV-2 virus variants, breakthrough infections, waning immunity, and differential access to COVID-19 vaccines account for the worst yet numbers of hospitalization and deaths during the COVID-19 pandemic, particularly in resource-limited countries. There is an urgent need for clinically valuable, generalizable, and parsimonious triage tools assisting appropriate allocation of hospital resources during the pandemic. We aimed to develop and extensively validate a machine learning-based tool for accurately predicting the clinical outcome of hospitalized COVID-19 patients. MethodsCODOP was built using modified stable iterative variable selection and linear regression with lasso regularisation. To avoid generalization problems, CODOP was trained and tested with three time-sliced and geographically distinct cohorts encompassing 40 511 blood-based analyses of COVID-19 patients from more than 110 hospitals in Spain and the USA during 2020-21. We assessed the discriminative ability of the model using the Area Under the Receiving Operative Curve (AUROC) as well as horizon and Kaplan-Meier risk stratification analyses. To reckon the fluctuating pressure levels in hospitals through the pandemic, we offer two online CODOP calculators suited for undertriage or overtriage scenarios. We challenged their generalizability and clinical utility throughout an evaluation with datasets gathered in five hospitals from three Latin American countries. FindingsCODOP uses 12 clinical parameters commonly measured at hospital admission and associated with the pathophysiology of COVID-19. CODOP reaches high discriminative ability up to nine days before clinical resolution (AUROC: 0{middle dot}90-0{middle dot}96, 95% CI 0{middle dot}879-0{middle dot}970), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. The two CODOP online calculators predicted the clinical outcome of the majority of patients (73-100% sensitivity and 84-100% specificity) from the distinctive Latin American evaluation cohort. InterpretationThe high predictive performance of CODOP in geographically disperse patient cohorts and the easiness-of-use, strongly suggest its clinical utility as a global triage tool, particularly in resource-limited countries. FundingThe Max Planck Society. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe have searched PubMed for articles about the existence of in-hospital COVID-19 mortality predictive models, using the search terms "coronavirus", "COVID-19", "risk", "death", "mortality", and "prediction", focusing on studies published between March 1, 2020 and 31 August, 2021. The studies we identified generally used small-medium size cohorts of patients that are geographically restricted to small regions of the developed world (many times, to the same city). We havent found studies that challenged their models in cohorts of patients from distinct health system populations, particularly from resource-limited countries. Further, all previous models are rigid by not acknowledging the fluctuating availability of hospital resources during the pandemic (e.g., beds, oxygen supply). These and other limitations have been pointed out by expert reviews indicating that published in-hospital COVID-19 mortality predictive models are subject to high risk of bias, report an over-optimistic performance, and have limited clinical value in assisting daily triage decisions. A parsimonious, accurate and extensively validated model is yet to be developed. Added value of this studyWe analysed clinical data from different cohorts totalling 21 607 COVID-19 patients treated in more than 110 hospitals in Spain and the USA during three different pandemic waves extending from February 2020 to April 2021. The new CODOP in-hospital mortality prediction model is based on 11 blood biochemistry parameters (representing main biological pathways involved in the pathogenesis of SARS-CoV-2) plus Age, all of them commonly measured upon hospitalization, even in resource-limited countries. CODOP accurately predicted mortality risk up to nine days before clinical resolution (AUROC: 0{middle dot}90-0{middle dot}96, 95% CI 0{middle dot}879-0{middle dot}970), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. As a unique characteristic, we offer two online CODOP calculator subtypes (https://gomezvarelalab.em.mpg.de/codop/) tailored to overtriage and undertriage scenarios. The online calculators were able to correctly predict the clinical outcome of the majority of patients of five independent evaluation cohorts gathered hospitals of three Latin American countries from March 7th 2020 to June 7th 2021. Implications of all the available evidenceWe present here a highly accurate, parsimonious and extensively validated COVID-19 in-hospital mortality prediction model, derived from working with the largest number and the most geographically extended representation of patients and health systems to date. The rigorous analytical methods, the generalizability of the model in distinct world regions, and its flexibility to reckon with the changing availability of hospital resources point to CODOP as a clinically useful tool potentially improving the outcome prediction and the management of COVID-19 hospitalized patients.
Subject(s)
COVID-19 , Breakthrough PainABSTRACT
Background: More contagious SARS-CoV-2 virus variants, breakthrough infections, waning immunity, and differential access to COVID-19 vaccines account for the worst yet numbers of hospitalization and deaths during the COVID-19 pandemic, particularly in resource-limited countries. There is an urgent need for clinically valuable, generalizable, and parsimonious triage tools assisting appropriate allocation of hospital resources during the pandemic. We aimed to develop and extensively validate a machine learning-based tool for accurately predicting the clinical outcome of hospitalized COVID-19 patients.Methods: CODOP was built using modified stable iterative variable selection and linear regression with lasso regularisation. To avoid generalization problems, CODOP was trained and tested with three time-sliced and geographically distinct cohorts encompassing 40 511 blood-based analyses of COVID-19 patients from more than 110 hospitals in Spain and the USA during 2020-21. We assessed the discriminative ability of the model using the Area Under the Receiving Operative Curve (AUROC) as well as horizon and Kaplan-Meier risk stratification analyses. To reckon the fluctuating pressure levels in hospitals through the pandemic, we offer two online CODOP calculators suited for undertriage or overtriage scenarios. We challenged their generalizability and clinical utility throughout an evaluation with datasets gathered in five hospitals from three Latin American countries. Findings: CODOP uses 12 clinical parameters commonly measured at hospital admission and associated with the pathophysiology of COVID-19. CODOP reaches high discriminative ability up to nine days before clinical resolution (AUROC: 0·90-0·96, 95% CI 0·879-0·970), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. The two CODOP online calculators predicted the clinical outcome of the majority of patients (73-100% sensitivity and 84-100% specificity) from the distinctive Latin American evaluation cohort.Interpretation: The high predictive performance of CODOP in geographically disperse patient cohorts and the easiness-of-use, strongly suggest its clinical utility as a global triage tool, particularly in resource-limited countries.Funding: The Max Planck Society.Declaration of Interest: The authors declare no conflict of interest.Ethical Approval: This study was approved by the Provincial Research Ethics Committee of Málaga (Spain) and the Institutional Research Ethics Committees of each participating hospital.
Subject(s)
COVID-19ABSTRACT
Aim: To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods: Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results: As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p=0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.219, 95%CI 0.069-0.693, p=0.01). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions: Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality.
Subject(s)
COVID-19 , SepsisABSTRACT
ObjectivesA decrease in blood cell counts, especially lymphocytes and eosinophils, has been described in patients with severe SARS-CoV-2 (COVID-19), but there is no knowledge of the potential role of their recovery in these patients prognosis. This article aims to analyse the effect of blood cell depletion and blood cell recovery on mortality due to COVID-19. DesignThis work is a multicentre, retrospective, cohort study of 9,644 hospitalised patients with confirmed COVID-19 from the Spanish Society of Internal Medicines SEMI-COVID-19 Registry. SettingThis study examined patients hospitalised in 147 hospitals throughout Spain. ParticipantsThis work analysed 9,644 patients (57.12% male) out of a cohort of 12,826 patients [≥]18 years of age hospitalised with COVID-19 in Spain included in the SEMI-COVID-19 Registry as of 29 May 2020. Main outcome measuresThe main outcome measure of this work is the effect of blood cell depletion and blood cell recovery on mortality due to COVID-19. Univariate analysis was performed to determine possible predictors of death and then multivariate analysis was carried out to control for potential confounders. ResultsAn increase in the eosinophil count on the seventh day of hospitalisation was associated with a better prognosis, including lower mortality rates (5.2% vs 22.6% in non-recoverers, OR 0.234 [95% CI, 0.154 to 0.354]) and lower complication rates, especially regarding to development of acute respiratory distress syndrome (8% vs 20.1%, p=0.000) and ICU admission (5.4% vs 10.8%, p=0.000). Lymphocyte recovery was found to have no effect on prognosis. Treatment with inhaled or systemic glucocorticoids was not found to be a confounding factor. ConclusionEosinophil recovery in patients with COVID-19 is a reliable marker of a good prognosis that is independent of prior treatment. This finding could be used to guide discharge decisions.
Subject(s)
COVID-19ABSTRACT
Background. Spain has been one of the countries most affected by the COVID-19 pandemic. Objective. To create a registry of patients with COVID-19 hospitalized in Spain in order to improve our knowledge of the clinical, diagnostic, therapeutic, and prognostic aspects of this disease. Methods. A multicentre retrospective cohort study, including consecutive patients hospitalized with confirmed COVID-19 throughout Spain. Epidemiological and clinical data, additional tests at admission and at seven days, treatments administered, and progress at 30 days of hospitalization were collected from electronic medical records. Results. Up to April 30th 2020, 6,424 patients from 109 hospitals were included. Their median age was 69.1 years (range: 18-102 years) and 56.9% were male. Prevalences of hypertension, dyslipidemia, and diabetes mellitus were 50.2%, 39.7%, and 18.7%, respectively. The most frequent symptoms were fever (86.2%) and cough (76.5%). High values of ferritin (72.4%), lactate dehydrogenase (70.2%), and D-dimer (61.5%), as well as lymphopenia (52.6%), were frequent. The most used antiviral drugs were hydroxychloroquine (85.7%) and lopinavir/ritonavir (62.4%). 31.5% developed respiratory distress. Overall mortality rate was 21.1%, with a marked increase with age (50-59 years: 4.2%, 60-69 years: 9.1%, 70-79 years: 21.4%, 80-89 years: 42.5%, [≥] 90 years: 51.1%). Conclusions. The SEMI-COVID-19 Network provides data on the clinical characteristics of patients with COVID-19 hospitalized in Spain. Patients with COVID-19 hospitalized in Spain are mostly severe cases, as one in three patients developed respiratory distress and one in five patients died. These findings confirm a close relationship between advanced age and mortality.