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Cystatin C is associated with adverse COVID-19 outcomes in diverse populations.

ABSTRACT: COVID-19 has highly variable clinical courses. The search for prognostic host factors for COVID-19 outcome is a priority. We performed logistic regression for ICU admission against a polygenic score (PGS) for Cystatin C (CyC) production in patients with COVID-19. We analyzed the predictive value of longitudinal plasma CyC levels in an independent cohort of patients hospitalized with COVID-19. In four cohorts spanning European and African ancestry populations, we identified a significant association between CyC-production PGS and odds of critical illness (n cases=2,319), with the strongest association captured in the UKB cohort (OR 2.13, 95% CI 1.58-2.87, p=7.12e-7). Plasma proteomics from an independent cohort of hospitalized COVID-19 patients (n cases = 131) demonstrated that CyC production was associated with COVID-specific mortality (p=0.0007). Our findings suggest that CyC may be useful for stratification of patients and it has functional role in the host response to COVID-19.

SUBMITTER: Kleeman SO

PROVIDER: S-EPMC9428108 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Cystatin C is associated with adverse COVID-19 outcomes in diverse populations.

Kleeman Sam O SO Cordioli Mattia M Timmers Paul R H J PRHJ Khan Atlas A Tober-Lau Pinkus P Kurth Florian F Demichev Vadim V Meyer Hannah V HV Wilson James F JF Ralser Markus M Kiryluk Krzysztof K Ganna Andrea A Baillie Kenneth K Janowitz Tobias T

iScience 20220831 10

COVID-19 has highly variable clinical courses. The search for prognostic host factors for COVID-19 outcome is a priority. We performed logistic regression for ICU admission against a polygenic score (PGS) for Cystatin C (CyC) production in patients with COVID-19. We analyzed the predictive value of longitudinal plasma CyC levels in an independent cohort of patients hospitalized with COVID-19. In four cohorts spanning European and African ancestry populations, we identified a significant associat ...[more]

PMID: 36062073

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Project description:The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients' past medical records (before their COVID-19 infection). MLHO's architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.

Project description:IntroductionSymptoms from post COVID-19 condition (PCC) can impair functioning in working-age adults. However, there is uncertainty about the relationship between PCC and work outcomes. This study aimed to assess health-related adverse work outcomes in individuals with a PCC history compared with others who had COVID-19.MethodsThis was a cross-sectional study in which participants in British Columbia (BC) completed an online questionnaire. Participants comprised adults who tested positive for SARS-CoV-2 by PCR at least 2 years before questionnaire completion and were working age (18-64) at the time of infection. PCC status was determined by self-report. The health-related adverse work outcomes evaluated included change in occupation or employer, reduced workload, increased sick days compared with before COVID-19, early retirement and indefinite sick leave. Analyses were weighted to reflect the characteristics of individuals who had COVID-19 in BC. Propensity score overlap weighting was used to adjust for relevant sociodemographic and clinical covariates.ResultsAmong 1106 participants, 966 (87.3%) were employed when they contracted SARS-CoV-2 and included in analyses. Of these participants, 47.8% were female, the median age was 37 and 46.9% had a PCC history. Compared with other individuals who had COVID-19, those with a PCC history were more likely to have had a health-related adverse work outcome (46.5% vs 24.9%; adjusted OR (aOR) 2.6 (95% CI 1.7 to 4.0)) and reported a greater number of sick days in the first 2 years since contracting SARS-CoV-2 (adjusted mean difference 43 days (95% CI 20 to 65)). The risk of a health-related adverse work outcome was especially high in those with post-COVID-19 fatigue (aOR 4.6 (95% CI 2.7 to 7.9)), fever (aOR 4.0 (95% CI 1.7 to 9.6)), weakness (aOR 3.8 (95% CI 2.2 to 6.3)), palpitations (aOR 3.3 (95% CI 2.0 to 5.0)) and brain fog (aOR 3.2 (95% CI 2.0 to 5.0)).ConclusionsPCC is associated with health-related adverse work outcomes. This is an important consideration for clinicians, employers and health system leaders.

Project description:BackgroundLittle is known about the association between acute mental changes and adverse outcomes in hospitalized adults with COVID-19.ObjectivesTo investigate the occurrence of delirium in hospitalized patients with COVID-19 and explore its association with adverse outcomes.DesignLongitudinal observational study.SettingTertiary university hospital dedicated to the care of severe cases of COVID-19 in São Paulo, Brazil.ParticipantsA total of 707 patients, aged 50 years or older, consecutively admitted to the hospital between March and May 2020.MeasurementsWe completed detailed reviews of electronic medical records to collect our data. We identified delirium occurrence using the Chart-Based Delirium Identification Instrument (CHART-DEL). Trained physicians with a background in geriatric medicine completed all CHART-DEL assessments. We complemented our baseline clinical information using telephone interviews with participants or their proxy. Our outcomes of interest were in-hospital death, length of stay, admission to intensive care, and ventilator utilization. We adjusted all multivariable analyses for age, sex, clinical history, vital signs, and relevant laboratory biomarkers (lymphocyte count, C-reactive protein, glomerular filtration rate, D-dimer, and albumin).ResultsOverall, we identified delirium in 234 participants (33%). On admission, 86 (12%) were delirious. We observed 273 deaths (39%) in our sample, and in-hospital mortality reached 55% in patients who experienced delirium. Delirium was associated with in-hospital death, with an adjusted odds ratio of 1.75 (95% confidence interval = 1.15-2.66); the association held both in middle-aged and older adults. Delirium was also associated with increased length of stay, admission to intensive care, and ventilator utilization.ConclusionDelirium was independently associated with in-hospital death in adults aged 50 years and older with COVID-19. Despite the difficulties for patient care during the pandemic, clinicians should routinely monitor delirium when assessing severity and prognosis of COVID-19 patients.

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Cystatin C is associated with adverse COVID-19 outcomes in diverse populations.

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Cystatin C is associated with adverse COVID-19 outcomes in diverse populations.

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