Chest computed tomography (CT) is a useful tool for the diagnosis of coronavirus disease-2019 (COVID-19), although its exact value for predicting critical illness remains unclear. This study ...evaluated the efficacy of chest CT to predict disease progression, pulmonary complications, and viral positivity duration.
A single-center cohort study was conducted by consecutively including hospitalized patients with confirmed COVID-19. The chest CT patterns were described and a total severity score was calculated. The predictive accuracy of the severity score was evaluated using the receiver operating characteristic analysis, while a Cox proportional hazards regression model was implemented to identify the radiological features that are linked to prolonged duration of viral positivity.
Overall, 42 patients were included with 10 of them requiring intensive care unit admission. The most common lesions were ground glass opacities (92.9%), consolidation (66.7%), and crazy-paving patterns (61.9%). The total severity score significantly correlated with inflammatory and respiratory distress markers, as well as with admission CURB-65 and PSI/PORT scores. It was estimated to predict critical illness with a sensitivity and specificity of 75% and 70%, respectively. Time-to-event analysis indicated that patients without ground-glass opacities presented significantly shorter median viral positivity (16 vs. 27 days).
Chest CT severity score positively correlates with markers of COVID-19 severity and presents promising efficacy in predicting critical illness. It is suggested that ground-glass opacities are linked to prolonged viral positivity. Further studies should confirm the efficacy of the severity score and elucidate the long-term pulmonary effects of COVID-19.
•Chest CT imaging correlates with Covid-19 severity markers.•CT severity score may predict critical illness and ICU admission.•Ground glass opacities may correlate with prolonged SARS-CoV-2 viral positivity.
This study aimed to investigate the association between plasma adiponectin levels and 5-year survival after first-ever ischemic stroke.
Plasma adiponectin measured within 24 hours after first-ever ...ischemic stroke was related to 5-year outcome. The Kaplan-Meier technique was applied in survival analysis, and the Cox proportional hazards model was used to evaluate the relationship between risk factors and prognosis.
The probabilities of death were 92.8%, 52.5%, and 10.5% (P<0.001) for patients stratified according to tertiles of adiponectin (<4 microg/mL, 4 to 7 microg/mL, and >7 microg/mL, respectively). The relative risk of death was 8.1 (95% CI, 3.1, 24.5; P<0.001) for individuals with adiponectin levels in the lowest tertile compared with the upper tertile. Adiponectin <4 microg/mL (hazard ratio HR, 5.2; 95% CI, 2.1, 18.4; P<0.001), score >15 in the National Institutes of Health Stroke Scale (HR, 3.6; 95% CI, 1.7, 15.9; P<0.001), and coronary heart disease (HR, 2.9; 95% CI, 1.5, 12.3; P<0.001) were independently associated with mortality.
Low plasma adiponectin is related to an increased risk of 5-year mortality after first-ever ischemic stroke, independently of other adverse predictors.
Abstract Objective The present study aimed to develop and evaluate a simple diagnostic model that could aid physicians to discriminate between infectious and non-infectious causes of fever of unknown ...origin (FUO). Design/Setting/Subjects Patients with classical FUO were studied in two distinct, prospective, observational phases. In the derivation phase that lasted from 1992 to 2000, 33 variables regarding demographic characteristics, history, symptoms, signs, and laboratory profile were recorded and considered in a logistic regression analysis using the diagnosis of infection as a dependent variable. In the validation phase, the discriminatory capacity of a score based on the derived predictors of infection was calculated for FUO patients assessed from 2001 to 2007. Results Data from 112 individuals (mean age 56.5 ± 11.2 years) were analyzed in the derivation cohort. The final diagnoses included infections, malignancies, non-infectious inflammatory diseases, and miscellaneous conditions in 30.4%, 10.7%, 33% and 5.4% of subjects, whereas 20.5% of cases remained undiagnosed. C-reactive protein > 60 mg/L (odds ratio 6.0 95% confidence intervals 2.5, 9.8), eosinophils < 40/mm3 (4.1 2.0, 7.3) and ferritin < 500 μg/L (2.5 1.3, 5.2) were independently associated with diagnosis of infection. Among the 100 patients of the validation cohort, the presence of ≥ 2 of the above factors predicted infection with sensitivity, specificity, and positive and negative predictive values of 91.4%, 92.3%, 86.5%, and 95.2%, respectively. Conclusions The combination of C-reactive protein, ferritin and eosinophil count may be useful in discriminating infectious from non-infectious causes in patients hospitalised for classical FUO.
The link between type 2 diabetes (T2D) and the severe outcomes of COVID-19 has raised concerns about the optimal management of patients with T2D. This study aimed to investigate the clinical ...characteristics and outcomes of T2D patients hospitalized with COVID-19 and explore the potential associations between chronic T2D treatments and adverse outcomes. This was a multicenter prospective cohort study of T2D patients hospitalized with COVID-19 in Greece during the third wave of the pandemic (February-June 2021). Among the 354 T2D patients included in this study, 63 (18.6%) died during hospitalization, and 16.4% required ICU admission. The use of DPP4 inhibitors for the chronic management of T2D was associated with an increased risk of in-hospital death (adjusted odds ratio (adj. OR) 2.639, 95% confidence interval (CI) 1.148-6.068,
= 0.022), ICU admission (adj. OR = 2.524, 95% CI: 1.217-5.232,
= 0.013), and progression to ARDS (adj. OR = 2.507, 95% CI: 1.278-4.916,
= 0.007). Furthermore, the use of DPP4 inhibitors was significantly associated with an increased risk of thromboembolic events (adjusted OR of 2.249, 95% CI: 1.073-4.713,
= 0.032) during hospitalization. These findings highlight the importance of considering the potential impact of chronic T2D treatment regiments on COVID-19 and the need for further studies to elucidate the underlying mechanisms.
Resistin (RSN) is an adipocytokine involved in insulin resistance, obesity and atherosclerosis. This study aimed to investigate the association between plasma RSN and outcome after ischemic stroke.
...RSN measured within 24 h after the event was related to functional outcome and 5-year survival in 211 subjects with first-ever atherothrombotic ischemic stroke. Prognosis was assessed by the Kaplan Meier and the Cox techniques.
The probabilities of death were 80.4%, 46.2% and 15.7% (
p
<
0.001) for patients stratified according to tertiles of RSN (>
30 ng/mL, 20–30 ng/mL and <
20 ng/mL, respectively). The proportion of dependency (modified Rankin Scale score ≥
3) was greater in 5-year survivors with RSN in the upper tertile (6/11 54.5%) compared to the middle (20/56 35.7%) and the lowest tertile (8/43 18.6%;
p
<
0.01). C-reactive protein levels (hazard ratio HR 3.96 95% CI 2.06, 8.91;
p
<
0.001), coronary heart disease (2.69 1.62, 6.23;
p
<
0.001), RSN levels (2.12 1.31, 5.08
p
<
0.001), National Institute of Health Stroke Scale score (2.02 1.23, 4.49;
p
<
0.01) and age (1.84 1.19, 3.93;
p
<
0.01) were independent predictors of death.
High plasma RSN appears to be associated with increased risk of 5-year mortality or disability after atherothrombotic ischemic stroke, independently of other adverse predictors.
Antimicrobial drug resistance rates in Greece are among the highest in Europe. The prevalence of carbapenemresistant Gram-negative species has increased considerably, including endemic strains in ...intensive care units. Pandrug-resistant Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa are sporadically reported. Methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus rates are also high in Greek hospitals. Multidrug resistance increases risk of mortality, hospitalization duration and costs, and undermines the medical system. Administrative responses initiated include action plans, monitoring systems, and guidelines. Common terminology among involved parties for defining and grading resistance is required. Multidrug-resistant microorganisms challenge clinical laboratories; uniform recommendations towards detection of resistance mechanisms need to be established. Prospective multicenter outcome studies comparing antibiotic regimens and containment methods are needed. Because new antimicrobials against Gram-negative pathogens are not foreseeable, judicious use of the existing and strict adherence to infection control best practice might restrain resistance spread. Awareness of resistance patterns and organisms prevailing locally by reporting laboratories and treating physicians is important.
Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a ...data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.
Objectives
Coronavirus disease‐19 (COVID‐19) is associated with various clinical manifestations, ranging from asymptomatic infection to critical illness. The aim of this study is to evaluate the ...clinical and laboratory characteristics of hospitalised COVID‐19 patients and construct a predictive model for the discrimination of patients at risk of disease progression.
Methods
A single‐centre cohort study was conducted including consecutively patients with COVID‐19. Demographic, clinical and laboratory findings were prospectively collected at admission. The primary outcome of interest was the intensive care unit admission. A risk model was constructed by applying a Cox's proportional hazard's model with elastic net penalty. Its diagnostic performance was assessed by receiver operating characteristic analysis and was compared with conventional pneumonia severity scores.
Results
From a total of 67 patients 15 progressed to critical illness. The risk score included patients’ gender, presence of hypertension and diabetes mellitus, fever, shortness of breath, serum glucose, aspartate aminotransferase, lactate dehydrogenase, C‐reactive protein and fibrinogen. Its predictive accuracy was estimated to be high (area under the curve: 97.1%), performing better than CURB‐65, CRB‐65 and PSI/PORT scores. Its sensitivity and specificity were estimated to be 92.3% and 93.3%, respectively, at the optimal threshold of 1.6.
Conclusions
A10‐variable risk score was constructed based on clinical and laboratory characteristics in order to predict critical illness amongst hospitalised COVID‐19 patients, achieving better discrimination compared with traditional pneumonia severity scores. The proposed risk model should be externally validated in independent cohorts in order to ensure its prognostic efficacy.
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