Quantitative assessment of fibrosis-4 score and adverse clinical outcomes in patients with COVID-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Tang K
PROVIDER: S-EPMC9244247 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We perform shotgun transcriptome sequencing of human RNA obtained from nasopharyngeal swabs of patients with COVID-19, and identify a molecular signature associated with disease severity
2020-12-19 | GSE162835 | GEO
Project description:AimsMidwall myocardial fibrosis on cardiovascular magnetic resonance (CMR) is a marker of early ventricular decompensation and adverse outcomes in aortic stenosis (AS). We aimed to develop and validate a novel clinical score using variables associated with midwall fibrosis.Methods and resultsOne hundred forty-seven patients (peak aortic velocity (Vmax) 3.9 [3.2,4.4] m/s) underwent CMR to determine midwall fibrosis (CMR cohort). Routine clinical variables that demonstrated significant association with midwall fibrosis were included in a multivariate logistic score. We validated the prognostic value of the score in two separate outcome cohorts of asymptomatic patients (internal: n = 127, follow-up 10.3 [5.7,11.2] years; external: n = 289, follow-up 2.6 [1.6,4.5] years). Primary outcome was a composite of AS-related events (cardiovascular death, heart failure, and new angina, dyspnoea, or syncope). The final score consisted of age, sex, Vmax, high-sensitivity troponin I concentration, and electrocardiographic strain pattern [c-statistic 0.85 (95% confidence interval 0.78-0.91), P < 0.001; Hosmer-Lemeshow χ(2) = 7.33, P = 0.50]. Patients in the outcome cohorts were classified according to the sensitivity and specificity of this score (both at 98%): low risk (probability score <7%), intermediate risk (7-57%), and high risk (>57%). In the internal outcome cohort, AS-related event rates were >10-fold higher in high-risk patients compared with those at low risk (23.9 vs. 2.1 events/100 patient-years, respectively; log rank P < 0.001). Similar findings were observed in the external outcome cohort (31.6 vs. 4.6 events/100 patient-years, respectively; log rank P < 0.001).ConclusionWe propose a clinical score that predicts adverse outcomes in asymptomatic AS patients and potentially identifies high-risk patients who may benefit from early valve replacement.
| S-EPMC4761400 | biostudies-literature
Project description:ObjectivesTo develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19.MethodsWe included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients.ResultsThere was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness.ConclusionsWe presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation.Key points• Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.
| S-EPMC8046645 | biostudies-literature
Project description:BackgroundRecent studies have demonstrated a complex interplay between comorbid cardiovascular disease, COVID-19 pathophysiology, and poor clinical outcomes. Coronary artery calcification (CAC) may therefore aid in risk stratification of COVID-19 patients.MethodsNon-contrast chest CT studies on 180 COVID-19 patients ≥ age 21 admitted from March 1, 2020 to April 27, 2020 were retrospectively reviewed by two radiologists to determine CAC scores. Following feature selection, multivariable logistic regression was utilized to evaluate the relationship between CAC scores and patient outcomes.ResultsThe presence of any identified CAC was associated with intubation (AOR: 3.6, CI: 1.4-9.6) and mortality (AOR: 3.2, CI: 1.4-7.9). Severe CAC was independently associated with intubation (AOR: 4.0, CI: 1.3-13) and mortality (AOR: 5.1, CI: 1.9-15). A greater CAC score (UOR: 1.2, CI: 1.02-1.3) and number of vessels with calcium (UOR: 1.3, CI: 1.02-1.6) was associated with mortality. Visualized coronary stent or coronary artery bypass graft surgery (CABG) had no statistically significant association with intubation (AOR: 1.9, CI: 0.4-7.7) or death (AOR: 3.4, CI: 1.0-12).ConclusionCOVID-19 patients with any CAC were more likely to require intubation and die than those without CAC. Increasing CAC and number of affected arteries was associated with mortality. Severe CAC was associated with higher intubation risk. Prior CABG or stenting had no association with elevated intubation or death.
| S-EPMC7875715 | biostudies-literature
Project description:To identify the association between the kinetics of viral load and clinical outcome in severe coronavirus disease 2019 (COVID-19) patients, a retrospective study was performed by involved 188 hospitalized severe COVID-19 patients in the LOTUS China trial. Among the collected 578 paired throat swab (TS) and anal swab (AS) samples, viral RNA was detected in 193 (33.4%) TS and 121 (20.9%) AS. A higher viral RNA load was found in TS than that of AS, with means of 1.0 × 106 and 2.3 × 105 copies/ml, respectively. In non-survivors, the viral RNA in AS was detected earlier than that in survivors (median of 14 days vs 19 days, P = 0.007). The positivity and viral load in AS were higher in non-survivors than that of survivors at week 2 post symptom onset (P = 0.006). A high initial viral load in AS was associated with death (OR 1.368, 95% CI 1.076-1.741, P = 0.011), admission to the intensive care unit (OR 1.237, 95% CI 1.001-1.528, P = 0.049) and need for invasive mechanical ventilation (OR 1.340, 95% CI 1.076-1.669, P = 0.009). Our findings indicated viral replication in extrapulmonary sites should be monitored intensively during antiviral therapy.
| S-EPMC7782020 | biostudies-literature
Project description:The vast majority of SARS-CoV-2 infections are uncomplicated and do not require hospitalization, but these infections contribute to ongoing transmission. There remains a critical need to identify host immune biomarkers predictive of virologic and clinical outcomes in planning future treatment studies of COVID-19. We recently completed a randomized clinical trial of Pegylated PegIinterferon Lambda for treatment of SARS-CoV-2 infected patients conducted in the Stanford COVID-19 CTRU. Leveraging longitudinal samples and data from this trial, we define early immunebaseline and infection-induced signatures that predict the duration of viral shedding, resolution of symptoms, and immunologic memory.
2021-10-01 | GSE178967 | GEO
Project description:ObjectivesFrailty can be used as a predictor of adverse outcomes in people with coronavirus disease 2019 (COVID-19). The aim of the study was to analyse the prognostic value of two different frailty scores in patients hospitalised for COVID-19.Material and methodsThis retrospective cohort study included adult (≥18 years) inpatients with COVID-19 and took place from 3 March to 2 May 2020. Patients were categorised by Clinical Frailty Score (CFS) and Hospital Frailty Risk Score (HFRS). The primary outcome was in-hospital mortality, and secondary outcomes were tocilizumab treatment, length of hospital stay, admission in intensive care unit (ICU) and need for invasive mechanical ventilation. Results were analysed by multivariable logistic regression and expressed as odds ratios (ORs), adjusting for age, sex, kidney function and comorbidity.ResultsOf the 290 included patients, 54 were frail according to the CFS (≥5 points; prevalence 18.6%, 95% confidence interval [CI]: 14.4-23.7) vs 65 by HFRS (≥5 points; prevalence: 22.4%, 95% CI 17.8-27.7). Prevalence of frailty increased with age according to both measures: 50-64 years, CFS 1.9% vs HFRS 12.3%; 65-79 years, CFS 31.5% vs HFRS 40.0%; and ≥80 years, CFS 66.7% vs HFRS 40.0% (P < .001). CFS-defined frailty was independently associated with risk of death (OR 3.67, 95% CI 1.49-9.04) and less treatment with tocilizumab (OR 0.28, 95% CI 0.08-0.93). HFRS-defined frailty was independently associated with length of hospital stay over 10 days (OR 2.89, 95% CI 1.53-5.44), ICU admission (OR 4.18, 95% CI 1.84-9.52) and invasive mechanical ventilation (OR 5.93, 95% CI 2.33-15.10).ConclusionIn the spring 2020 wave of the COVID-19 pandemic in Spain, CFS-defined frailty was an independent predictor for death, while frailty as measured by the HFRS was associated with length of hospital stay over 10 days, ICU admission and use of invasive mechanical ventilation.
| S-EPMC8420333 | biostudies-literature
Project description:BackgroundEarly reports on cancer patients with coronavirus disease 2019 (COVID-19) corroborated speculation that cancer patients are at increased risk for becoming infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and developing severe COVID-19. However, cancer patients are a heterogeneous population and their corresponding risk may be different.AimTo compare COVID-19 presentation in patients with active malignancy to those with a history of cancer to determine the impact of cancer status on COVID-19 outcomes in the two groups.Methods and resultsOf the 6724 patients who were hospitalized at NYU Langone Health (3/16/20-7/31/20) and tested positive for SARS-CoV-2, 580 had either active cancer (n = 221) or a history of cancer (n = 359). We compared the baseline clinicodemographic characteristics and hospital courses of the two groups. We studied the relationship between cancer status and the rate of admission to the intensive care unit (ICU), use of invasive mechanical ventilation (IMV), and all-cause mortality. The two groups had similar laboratory results associated with COVID-19 infection, incidence of venous thromboembolism, and incidence of severe COVID-19. Active cancer status was not associated with the rate of ICU admission (p = .307) or use of IMV (p = .236), but was significantly associated with worse all-cause mortality in both univariate and multivariate analysis with odds ratios of 1.48 (95% confidence interval [CI]: 1.04-2.09; p = .028) and 1.71 (95% CI: 1.12-2.63; p = .014), respectively.ConclusionActive cancer patients had worse survival outcomes compared to patients with a history of cancer despite similar COVID-19 disease characteristics in the two groups. Our data suggest that cancer care should continue with minimal interruptions during the pandemic to bring about response and remission as soon as possible.
| S-EPMC8420395 | 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
Project description:BackgroundWe sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak.MethodsSingle-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed event ratio (E/O). Discrimination was assessed by C-statistics (AUC).ResultsIn the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (E/O: 1.01, AUC: 0.76); for critical illness (E/O 1.03, AUC: 0.79); and for death (E/O: 1.63, AUC=0.93). Among 30 predictors, the top five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate.ConclusionsCoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.
| S-EPMC7325189 | biostudies-literature