Fever as a predictor of adverse outcomes in COVID-19.
Ontology highlight
ABSTRACT: Introduction
There are little data on outcomes of COVID-19 patients with the presence of fever compared to the presence of symptoms. We examined the associations between symptomology, presence of fever, and outcomes of a COVID-19 cohort.Methods
Between 23rd January to 30th April 2020, 554 COVID-19 patients were admitted to a tertiary hospital in Singapore. They were allocated into 4 groups based on symptomology and fever - Group 1: asymptomatic and afebrile, Group 2: symptomatic but afebrile, Group 3: febrile but asymptomatic, Group 4: symptomatic and febrile. The primary outcomes were intensive care unit (ICU) admissions and mortality. The composite end-point included ICU admissions, mortality or any COVID-19 related end-organ involvement.Results
There were differences in ferritin (p?=?0.003), C-reactive protein (CRP) levels (p?ConclusionIn this COVID-19 cohort, presence of fever was a predictor of adverse outcomes. This has implications on the management of febrile but asymptomatic COVID-19 patients.
SUBMITTER: Ws Chew N
PROVIDER: S-EPMC7928573 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Publications
Fever as a predictor of adverse outcomes in COVID-19.
Chew N W NW Ngiam J N JN Tham S M SM Lim Z Y ZY Li T Y W TYW Cen S S Yap E S ES Tambyah P A PA Santosa A A Cross G B GB Sia C-H CH
QJM : monthly journal of the Association of Physicians 20211201 10
<h4>Background/introduction</h4>There are little data on outcomes of COVID-19 patients with the presence of fever compared to the presence of symptoms.<h4>Aim</h4>We examined the associations between symptomology, presence of fever and outcomes of a COVID-19 cohort.<h4>Design and methods</h4>Between 23 January and 30 April 2020, 554 COVID-19 patients were admitted to a tertiary hospital in Singapore. They were allocated into four groups based on symptomology and fever-Group 1: asymptomatic and a ...[more]
Similar Datasets
Project description:BackgroundSubclinical coronary artery calcification (CAC) is a risk factor for adverse cardiovascular events, but studies investigating its association with outcomes in hospitalized patients with COVID-19 are limited.MethodsThis was a retrospective study of 457 patients without history of clinical coronary artery disease (CAD) who underwent chest CT imaging during COVID-19 hospitalization at MCW/Froedtert-affiliated hospitals from July 1, 2020 to July 1, 2021. Visually estimated CAC (yes/no) and CAC burden (none/mild/moderate/severe) were recorded from radiology reports. Unadjusted and adjusted regression models were used to assess associations between CAC and hospital length of stay (LOS), ICU admission, mechanical ventilation, and mortality.ResultsThe mean age was 63.1 ± 15.3 years. Presence of CAC was associated with mechanical ventilation (p = 0.01), ICU admission (p = 0.02), in-hospital or 30-day mortality (p < 0.01), and hospital LOS (p < 0.001). Compared to no CAC, hospital LOS was increased for mild (p = 0.01) and severe CAC (p = 0.02) after adjustment for covariates. Severe CAC was also associated with increased ICU admission (OR 3.97; p = 0.002) and mechanical ventilation (OR 3.08; p = 0.03) after adjustment. In unadjusted analysis, in-hospital or 30-day mortality increased with magnitude of CAC severity, with HR 2.43 (p = 0.003) for mild and HR 3.70 (p = 0.002) for severe CAC. However, associations with mortality were not significant after adjustment.ConclusionsCAC is associated with increased ICU admission, mechanical ventilation, hospital LOS, and in-hospital or 30-day mortality for patients hospitalized with COVID-19. Patients with severe CAC, and without clinical history of CAD, represent a high-risk population for morbidity and mortality.
| S-EPMC9993728 | biostudies-literature
Project description:BackgroundSystemic inflammation elicited by a cytokine storm is considered a hallmark of coronavirus disease 2019 (COVID-19). This study aims to assess the validity and clinical utility of the lymphocyte-to-C-reactive protein (CRP) ratio (LCR), typically used for gastric carcinoma prognostication, versus the neutrophil-to-lymphocyte ratio (NLR) for predicting in-hospital outcomes in COVID-19.MethodsA retrospective cohort study was performed to determine the association of LCR and NLR with the need for invasive mechanical ventilation (IMV), dialysis, upgrade to an intensive care unit (ICU) and mortality. Independent t-test and multivariate logistic regression analysis were performed to calculate mean differences and adjusted odds ratios (aORs) with its 95% confidence interval (CI), respectively.ResultsThe mean age for NLR patients was 63.6 versus 61.6, and for LCR groups, it was 62.6 versus 63.7 years, respectively. The baseline comorbidities across all groups were comparable except that the higher LCR group had female predominance. The mean NLR was significantly higher for patients who died during hospitalization (19 vs. 7, P ≤ 0.001) and those requiring IMV (12 vs. 7, P = 0.01). Compared to alive patients, a significantly lower mean LCR was observed in patients who did not survive hospitalization (1,011 vs. 632, P = 0.04). For patients with a higher NLR (> 10), the unadjusted odds of mortality (odds ratios (ORs) 11.0, 3.6 - 33.0, P < 0.0001) and need for IMV (OR 3.3, 95% CI 1.4 - 7.7, P = 0.008) were significantly higher compared to patients with lower NLR. By contrast, for patients with lower LCR (< 100), the odds of in-hospital all-cause mortality were significantly higher compared to patients with a higher LCR (OR 0.2, 0.06 - 0.47, P = 0.001). The aORs controlled for baseline comorbidities and medications mirrored the overall results, indicating a genuinely significant correlation between these biomarkers and outcomes.ConclusionsA high NLR and decreased LCR value predict higher odds of in-hospital mortality. A high LCR at presentation might indicate impending clinical deterioration and the need for IMV.
| S-EPMC7331862 | biostudies-literature
Project description: Not available
| S-BSST1269 | biostudies-other
Project description:PurposeWhile fever may be a presenting symptom of COVID-19, fever at hospital admission has not been identified as a predictor of mortality. However, hyperthermia during critical illness among ventilated COVID-19 patients in the ICU has not yet been studied. We sought to determine mortality predictors among ventilated COVID-19 ICU patients and we hypothesized that fever in the ICU is predictive of mortality.Materials and methodsWe conducted a retrospective cohort study of 103 ventilated COVID-19 patients admitted to the ICU between March 14 and May 27, 2020. Final follow-up was June 5, 2020. Patients discharged from the ICU or who died were included. Patients still admitted to the ICU at final follow-up were excluded.Results103 patients were included, 40 survived and 63(61.1%) died. Deceased patients were older {66 years[IQR18] vs 62.5[IQR10], (p = 0.0237)}, more often male {48(68%) vs 22(55%), (p = 0.0247)}, had lower initial oxygen saturation {86.0%[IQR18] vs 91.5%[IQR11.5], (p = 0.0060)}, and had lower pH nadir than survivors {7.10[IQR0.2] vs 7.30[IQR0.2] (p < 0.0001)}. Patients had higher peak temperatures during ICU stay as compared to hospital presentation {103.3°F[IQR1.7] vs 100.0°F[IQR3.5], (p < 0.0001)}. Deceased patients had higher peak ICU temperatures than survivors {103.6°F[IQR2.0] vs 102.9°F[IQR1.4], (p = 0.0008)}. Increasing peak temperatures were linearly associated with mortality. Febrile patients who underwent targeted temperature management to achieve normothermia did not have different outcomes than those not actively cooled. Multivariable analysis revealed 60% and 75% higher risk of mortality with peak temperature greater than 103°F and 104°F respectively; it also confirmed hyperthermia, age, male sex, and acidosis to be predictors of mortality.ConclusionsThis is one of the first studies to identify ICU hyperthermia as predictive of mortality in ventilated COVID-19 patients. Additional predictors included male sex, age, and acidosis. With COVID-19 cases increasing, identification of ICU mortality predictors is crucial to improve risk stratification, resource management, and patient outcomes.
| S-EPMC7738811 | biostudies-literature
Project description:BackgroundThe coronavirus disease 2019 (COVID-19) is a recently emerged respiratory infectious disease with kidney injury as a part of the clinical complications. However, the dynamic change of kidney function and its association with COVID-19 prognosis are largely unknown.MethodsIn this multicenter retrospective cohort study, we analyzed clinical characteristics, medical history, laboratory tests, and treatment data of 12,413 COVID-19 patients. The patient cohort was stratified according to the severity of the outcome into three groups: non-severe, severe, and death.FindingsThe prevalence of elevated blood urea nitrogen (BUN), elevated serum creatinine (Scr), and decreased blood uric acid (BUA) at admission was 6.29%, 5.22%, and 11.66%, respectively. The trajectories showed the elevation in BUN and Scr levels, as well as a reduction in BUA level for 28 days after admission in death cases. Increased all-cause mortality risk was associated with elevated baseline levels of BUN and Scr and decreased levels of BUA.ConclusionsThe dynamic changes of the three kidney function markers were associated with different severity and poor prognosis of COVID-19 patients. BUN showed a close association with and high potential for predicting adverse outcomes in COVID-19 patients for severity stratification and triage.FundingThis study was supported by grants from the National Key R&D Program of China (2016YFF0101504), the National Science Foundation of China (81630011, 81970364, 81970070, 81970011, 81870171, and 81700356), the Major Research Plan of the National Natural Science Foundation of China (91639304), the Hubei Science and Technology Support Project (2019BFC582, 2018BEC473, and 2017BEC001), and the Medical Flight Plan of Wuhan University.
| S-EPMC7531337 | biostudies-literature
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.
| S-EPMC7935934 | biostudies-literature
Project description:Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Instead, using plasma proteomes quantifying 302 plasma protein groups at 387 timepoints in 57 critically ill patients on invasive mechanical ventilation, we found 14 proteins that showed trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81, n=49). We tested the established predictor on an independent validation cohort (AUROC of 1.0, n=24). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.
2022-03-03 | PXD029009 | Pride
Project description:ObjectivesBNT-162b2, mRNA-1273, and Ad26.COV2.S vaccines data regarding adverse events (AEs) are scarce. In this report, we aimed to describe fatal and non-fatal possible AEs after COVID-19 vaccine administration.MethodsAn observational multicenter study investigating the causes of emergency department visits and hospital admissions within 10 days of COVID-19 vaccination. Patients who received first or second doses of COVID-19 vaccines and presented to the emergency department (ED), as well as those admitted to the hospitals or intensive care units (ICUs) were included. Causes of ED, hospital, and ICU admissions and discharges were collected based on the International Classification of Diseases, Tenth Revision (ICD-10) coding system.ResultsBetween December 2020 and March 2021, 1842 patients visited the ED within 10 days of COVID-19 vaccine administration. The mean age was 70.3 years. Overall, 1221 patients presented after the first dose of the vaccine and 653 after the second dose. Trauma (14.9%), hypertensive emergency/urgency (7.8%), generalized pain and arthralgia (5.7%), and chest pain (4.4%) were the most common causes of presentation to the ED. Of all ED presentations, mortality rate was at 2.2% (41 patients) with a median follow-up time of 68.0 days, versus 2.6% in unvaccinated ED patients. Postvaccination acute hypoxemic respiratory failure (46.3%), septic shock (24.4%), and cardiogenic shock (12.2%) were the most common causes of death.ConclusionAlthough reported AEs are not necessarily caused by the vaccination, this study provides further information about possible AEs after COVID-19 immunization, especially those requiring hospital admission. This study also supports prior data that serious AEs post vaccination are much lower than primary COVID-19 infections. Further studies are needed to investigate causalities between vaccines and reported AEs across all age groups.
| S-EPMC8514147 | biostudies-literature
Project description:BackgroundWidespread lockdowns imposed during the coronavirus disease 2019 crisis may impact birth outcomes.ObjectiveThis study aimed to evaluate the association between the COVID-19 lockdown and the risk of adverse birth outcomes in Botswana.Study designIn response to the coronavirus disease 2019 crisis, Botswana enforced a lockdown that restricted movement within the country. We used data from an ongoing nationwide birth outcomes surveillance study to evaluate adverse outcomes (stillbirth, preterm birth, small-for-gestational-age fetuses, and neonatal death) and severe adverse outcomes (stillbirth, very preterm birth, very-small-for-gestational-age fetuses, and neonatal death) recorded prelockdown (January 1, 2020-April 2, 2020), during lockdown (April 3, 2020-May 7, 2020), and postlockdown (May 8, 2020-July 20, 2020). Using difference-in-differences analyses, we compared the net change in each outcome from the prelockdown to lockdown periods in 2020 relative to the same 2 periods in 2017-2019 with the net change in each outcome from the prelockdown to postlockdown periods in 2020 relative to the same 2 periods in 2017-2019.ResultsIn this study, 68,448 women delivered a singleton infant in 2017-2020 between January 1 and July 20 and were included in our analysis (mean [interquartile range] age of mothers, 26 [22-32] years). Across the included calendar years and periods, the risk of any adverse outcome ranged from 27.92% to 31.70%, and the risk of any severe adverse outcome ranged from 8.40% to 11.38%. The lockdown period was associated with a 0.81 percentage point reduction (95% confidence interval, -2.95% to 1.30%) in the risk of any adverse outcome (3% relative reduction) and a 0.02 percentage point reduction (95% confidence interval, -0.79% to 0.75%) in the risk of any severe adverse outcome (0% relative reduction). The postlockdown period was associated with a 1.72 percentage point reduction (95% confidence, -3.42% to 0.02%) in the risk of any adverse outcome (5% relative reduction) and a 1.62 percentage point reduction (95% confidence interval, -2.69% to -0.55%) in the risk of any severe adverse outcome (14% relative reduction). Reductions in adverse outcomes were largest among women with human immunodeficiency virus and among women delivering at urban delivery sites, driven primarily by reductions in preterm birth and small-for-gestational-age fetuses.ConclusionAdverse birth outcomes decreased from the prelockdown to postlockdown periods in 2020, relative to the change during the same periods in 2017-2019. Our findings may provide insights into associations between mobility and birth outcomes in Botswana and other low- and middle-income countries.
| S-EPMC7817370 | biostudies-literature
Project description:BackgroundGenetic deficiencies of immune system, referred to as inborn errors of immunity (IEI), serve as a valuable model to study human immune responses. In a multicenter prospective cohort, we evaluated the outcome of SARS-CoV-2 infection among IEI subjects and analyzed genetic and immune characteristics that determine adverse COVID-19 outcomes.MethodsWe studied 34 IEI patients (19M/15F, 12 [min: 0.6-max: 43] years) from six centers. We diagnosed COVID-19 infection by finding a positive SARS-CoV-2 PCR test (n = 25) and/or a lung tomography scoring (CORADS) ≥4 (n = 9). We recorded clinical and laboratory findings prospectively, fitted survival curves, and calculated fatality rates for the entire group and each IEI subclass.ResultsNineteen patients had combined immune deficiency (CID), six with predominantly antibody deficiency (PAD), six immune dysregulation (ID), two innate immune defects, and one in the autoinflammatory class. Overall, 23.5% of cases died, with disproportionate fatality rates among different IEI categories. PAD group had a relatively favorable outcome at any age, but CIDs and IDs were particularly vulnerable. At admission, presence of dyspnea was an independent risk for COVID-related death (OR: 2.630, 95% CI; 1.198-5.776, p < .001). Concerning predictive roles of laboratory markers at admission, deceased subjects compared to survived had significantly higher CRP, procalcitonin, Troponin-T, ferritin, and total-lung-score (p = .020, p = .003, p = .014, p = .013, p = .020; respectively), and lower absolute lymphocyte count, albumin, and trough IgG (p = .012, p = .022, p = .011; respectively).ConclusionOur data disclose a highly vulnerable IEI subgroup particularly disadvantaged for COVID-19 despite their youth. Future studies should address this vulnerability and consider giving priority to these subjects in SARS-Cov-2 therapy trials.
| S-EPMC8441734 | biostudies-literature