Is creatinine an independent risk factor for predicting adverse outcomes in COVID-19 patients?
Ontology highlight
ABSTRACT:
SUBMITTER: Wu J
PROVIDER: S-EPMC7744867 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, enzyme-linked immunosorbent assay (ELISA) and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the intensive care unit (ICU), requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within 6 days post-symptom onset and sometimes within 1 day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patient’s comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 likelihood ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention.
2021-04-21 | GSE172471 | GEO
Project description:BackgroundAlthough hyperuricemia frequently associates with respiratory diseases, patients with severe coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) can show marked hypouricemia. Previous studies on the association of serum uric acid with risk of adverse outcomes related to COVID-19 have produced contradictory results. The precise relationship between admission serum uric acid and adverse outcomes in hospitalized patients is unknown.MethodsData of patients affected by laboratory-confirmed COVID-19 and admitted to Leishenshan Hospital were retrospectively analyzed. The primary outcome was composite and comprised events, such as intensive care unit (ICU) admission, mechanical ventilation, or mortality. Logistic regression analysis was performed to explore the association between serum concentrations of uric acid and the composite outcome, as well as each of its components. To determine the association between serum uric acid and in-hospital adverse outcomes, serum uric acid was also categorized by restricted cubic spline, and the 95% confidence interval (CI) was used to estimate odds ratios (OR).ResultsThe study cohort included 1854 patients (mean age, 58 years; 52% women). The overall mean ± SD of serum levels of uric acid was 308 ± 96 µmol/L. Among them, 95 patients were admitted to ICU, 75 patients received mechanical ventilation, and 38 died. In total, 114 patients reached composite end-points (have either ICU admission, mechanical ventilation or death) during hospitalization. Compared with a reference group with estimated baseline serum uric acid of 279-422 µmol/L, serum uric acid values ≥ 423 µmol/L were associated with an increased risk of composite outcome (OR, 2.60; 95% CI, 1.07- 6.29) and mechanical ventilation (OR, 3.01; 95% CI, 1.06- 8.51). Serum uric acid ≤ 278 µmol/L was associated with an increased risk of the composite outcome (OR, 2.07; 95% CI, 1.18- 3.65), ICU admission (OR, 2.18; 95% CI, 1.17- 4.05]), and mechanical ventilation (OR, 2.13; 95% CI, 1.06- 4.28), as assessed by multivariate analysis.ConclusionsThis study shows that the association between admission serum uric acid and composite outcome of COVID-19 patients was U-shaped. In particular, we found that compared with baseline serum uric acid levels of 279-422 µmol/L, values ≥ 423 µmol/L were associated with an increased risk of composite outcome and mechanical ventilation, whereas levels ≤ 278 µmol/L associated with increased risk of composite outcome, ICU admission and mechanical ventilation.
| S-EPMC8134697 | biostudies-literature
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.
| S-EPMC7460960 | biostudies-literature
Project description:BackgroundThis study sought to investigate incidence and risk factors for acute kidney injury (AKI) in hospitalized COVID-19.MethodsIn this retrospective study, we enrolled 823 COVID-19 patients with at least two evaluations of renal function during hospitalization from four hospitals in Wuhan, China between February 2020 and April 2020. Clinical and laboratory parameters at the time of admission and follow-up data were recorded. Systemic renal tubular dysfunction was evaluated via 24-h urine collections in a subgroup of 55 patients.ResultsIn total, 823 patients were enrolled (50.5% male) with a mean age of 60.9 ± 14.9 years. AKI occurred in 38 (40.9%) ICU cases but only 6 (0.8%) non-ICU cases. Using forward stepwise Cox regression analysis, we found eight independent risk factors for AKI including decreased platelet level, lower albumin level, lower phosphorus level, higher level of lactate dehydrogenase (LDH), procalcitonin, C-reactive protein (CRP), urea, and prothrombin time (PT) on admission. For every 0.1 mmol/L decreases in serum phosphorus level, patients had a 1.34-fold (95% CI 1.14-1.58) increased risk of AKI. Patients with hypophosphatemia were likely to be older and with lower lymphocyte count, lower serum albumin level, lower uric acid, higher LDH, and higher CRP. Furthermore, serum phosphorus level was positively correlated with phosphate tubular maximum per volume of filtrate (TmP/GFR) (Pearson r = 0.66, p < .001) in subgroup analysis, indicating renal phosphate loss via proximal renal tubular dysfunction.ConclusionThe AKI incidence was very low in non-ICU patients as compared to ICU patients. Hypophosphatemia is an independent risk factor for AKI in patients hospitalized for COVID-19 infection.
| S-EPMC8462927 | biostudies-literature
Project description:Conflicting results have been obtained through meta-analyses for the role of obesity as a risk factor for adverse outcomes in patients with coronavirus disease-2019 (COVID-19), possibly due to the inclusion of predominantly multimorbid patients with severe COVID-19. Here, we aimed to study obesity alone or in combination with other comorbidities as a risk factor for short-term all-cause mortality and other adverse outcomes in Mexican patients evaluated for suspected COVID-19 in ambulatory units and hospitals in Mexico. We performed a retrospective observational analysis in a national cohort of 71 103 patients from all 32 states of Mexico from the National COVID-19 Epidemiological Surveillance Study. Two statistical models were applied through Cox regression to create survival models and logistic regression models to determine risk of death, hospitalisation, invasive mechanical ventilation, pneumonia and admission to an intensive care unit, conferred by obesity and other comorbidities (diabetes mellitus (DM), chronic obstructive pulmonary disease, asthma, immunosuppression, hypertension, cardiovascular disease and chronic kidney disease). Models were adjusted for other risk factors. From 24 February to 26 April 2020, 71 103 patients were evaluated for suspected COVID-19; 15 529 (21.8%) had a positive test for SARS-CoV-2; 46 960 (66.1%), negative and 8614 (12.1%), pending results. Obesity alone increased adjusted mortality risk in positive patients (hazard ratio (HR) = 2.7, 95% confidence interval (CI) 2.04-2.98), but not in negative and pending-result patients. Obesity combined with other comorbidities further increased risk of death (DM: HR = 2.79, 95% CI 2.04-3.80; immunosuppression: HR = 5.06, 95% CI 2.26-11.41; hypertension: HR = 2.30, 95% CI 1.77-3.01) and other adverse outcomes. In conclusion, obesity is a strong risk factor for short-term mortality and critical illness in Mexican patients with COVID-19; risk increases when obesity is present with other comorbidities.
| S-EPMC8134888 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has seen a large surge in case numbers over several waves, and has critically strained the health care system, with a significant number of cases requiring hospitalization and ICU admission. This study used a decision tree modeling approach to identify the most important predictors of severe outcomes among COVID-19 patients.MethodsWe identified a retrospective population-based cohort (n = 140,182) of adults who tested positive for COVID-19 between 5th March 2020 and 31st May 2021. Demographic information, symptoms and co-morbidities were extracted from a communicable disease and outbreak management information system and electronic medical records. Decision tree modeling involving conditional inference tree and random forest models were used to analyze and identify the key factors(s) associated with severe outcomes (hospitalization, ICU admission and death) following COVID-19 infection.ResultsIn the study cohort, nearly 6.37% were hospitalized, 1.39% were admitted to ICU and 1.57% died due to COVID-19. Older age (>71Y) and breathing difficulties were the top two factors associated with a poor prognosis, predicting about 50% of severe outcomes in both models. Neurological conditions, diabetes, cardiovascular disease, hypertension, and renal disease were the top five pre-existing conditions that altogether predicted 29% of outcomes. 79% of the cases with poor prognosis were predicted based on the combination of variables. Age stratified models revealed that among younger adults (18-40 Y), obesity was among the top risk factors associated with adverse outcomes.ConclusionDecision tree modeling has identified key factors associated with a significant proportion of severe outcomes in COVID-19. Knowledge about these variables will aid in identifying high-risk groups and allocating health care resources.
| S-EPMC9160794 | biostudies-literature
Project description:BACKGROUND:The World Health Organization has declared coronavirus disease 2019 (COVID-19) a public health emergency of global concern. Updated analysis of cases might help identify the risk factors of illness severity. RESULTS:The median age was 63 years, and 44.9% were severe cases. Severe patients had higher APACHE II (8.5 vs. 4.0) and SOFA (2 vs. 1) scores on admission. Among all univariable parameters, lymphocytes, CRP, and LDH were significantly independent risk factors of COVID-19 severity. LDH was positively related both with APACHE II and SOFA scores, as well as P/F ratio and CT scores. LDH (AUC = 0.878) also had a maximum specificity (96.9%), with the cutoff value of 344.5. In addition, LDH was positively correlated with CRP, AST, BNP and cTnI, while negatively correlated with lymphocytes and its subsets. CONCLUSIONS:This study showed that LDH could be identified as a powerful predictive factor for early recognition of lung injury and severe COVID-19 cases. METHODS:We extracted data regarding 107 patients with confirmed COVID-19 from Renmin Hospital of Wuhan University. The degree of severity of COVID-19 patients (severe vs. non-severe) was defined at the time of admission according to American Thoracic Society guidelines for community acquired pneumonia.
| S-EPMC7343511 | biostudies-literature
Project description:Allogeneic hematopoietic cell transplantation (HCT) has been increasingly used in the setting of FMS-like tyrosine kinase-3 (FLT3)-mutated AML. However, its role in conferring durable relapse-free intervals remains in question. Herein we sought to investigate FLT3 mutational status on transplant outcomes. We conducted a retrospective cohort study of 262 consecutive AML patients who underwent first-time allogeneic HCT (2008-2014), of whom 171 had undergone FLT3-ITD (internal tandem duplication) mutational testing. FLT3-mutated AML was associated with nearly twice the relapse risk (RR) compared with those without FLT3 mutation 3 years post-HCT (63% vs 37%, P<0.001) and with a shorter median time to relapse (100 vs 121 days). FLT3 mutational status remained significantly associated with this outcome after controlling for patient, disease and transplant-related risk factors (P<0.05). Multivariate analysis showed a significant association of FLT3 mutation with increased 3-year RR (hazard ratio (HR) 3.63, 95% confidence interval (CI): 2.13, 6.19, P<0.001) and inferior disease-free survival (HR 2.05, 95% CI: 1.29, 3.27, P<0.01) and overall survival (HR 1.92, 95% CI: 1.14, 3.24, P<0.05). These data demonstrate high risk of early relapse after allogeneic HCT for FLT3-mutated AML that translates into adverse disease-free and overall survival outcomes. Additional targeted and coordinated interventions are needed to maintain durable remission after allogeneic HCT in this high-risk population.
| S-EPMC4720584 | biostudies-literature
Project description:Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.
| S-EPMC9157510 | biostudies-literature
Project description:BackgroundThe primary goal of the presented cross-sectional observational study was to determine the clinical and demographic risk factors for adverse coronavirus disease 2019 (COVID-19) outcomes in the Pakistani population.MethodsWe examined the individuals (n = 6331) that consulted two private diagnostic centers in Lahore, Pakistan, for COVID-19 testing between May 1, 2020, and November 30, 2020. The attending nurse collected clinical and demographic information. A confirmed case of COVID-19 was defined as having a positive result through real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay of nasopharyngeal swab specimens.ResultsRT-PCR testing was positive in 1094 cases. Out of which, 5.2% had severe, and 20.8% had mild symptoms. We observed a strong association of COVID-19 severity with the number and type of comorbidities. The severity of the disease intensified as the number of comorbidities increased. The most vulnerable groups for the poor outcome are patients with diabetes and hypertension. Increasing age was also associated with PCR positivity and the severity of the disease.ConclusionsMost cases of COVID-19 included in this study developed mild symptoms or were asymptomatic. Risk factors for adverse outcomes included older age and the simultaneous presence of comorbidities.
| S-EPMC8357125 | biostudies-literature