scholarly journals Development and validation of the OUTCoV score to predict the risk of hospitalisation among patients with SARS-CoV-2 infection in ambulatory settings: a prospective cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e044242
Author(s):  
Frederique Jacquerioz ◽  
Stéphanie Baggio ◽  
Angele Gayet-Ageron ◽  
François Chappuis ◽  
Laurent Getaz ◽  
...  
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
University Hospital ◽  
Ambulatory Setting ◽  
Likelihood Ratios ◽  
Predictive Values ◽  
Rule Out

ObjectivesTo develop and validate a rule-out prediction model for the risk of hospitalisation among patients with SARS-CoV-2 infection in the ambulatory setting to derive a simple score to determine outpatient follow-up.DesignProspective cohort study.SettingSwiss university hospital.Participants1459 individuals with a positive result for SARS-CoV-2 infection between 2 March and 23 April 2020.MethodsWe applied the rule of 10 events per variable to construct our multivariable model and included a maximum of eight covariates. We assessed the model performance in terms of discrimination and calibration and performed internal validation to estimate the statistical optimism of the final model. The final prediction model included age, fever, dyspnoea, hypertension and chronic respiratory disease. To develop the OUTCoV score, we assigned points for each predictor that were proportional to the coefficients of the regression equation. Sensitivity, specificity, positive and negative likelihood ratios were estimated, including positive and negative predictive values in different thresholds.Main outcome measureThe primary outcome was COVID-19-related hospitalisation.ResultsThe OUTCoV score ranged from 0 to 7.5 points. The two threshold parameters with optimal rule-out and rule-in characteristics for the risk of hospitalisation were 3 and 5.5, respectively. Outpatients with a score <3 (997/1459; 68.3%) had no follow-up as at low risk of hospitalisation (1.8%; 95% CI 1.1 to 2.8). For a score ≥5.5 (20/1459; 1.4%), the hospitalisation risk was higher (30%; 95% CI 11.9 to 54.3).ConclusionsThe OUTCoV score allows to rule out two-thirds of outpatients with SARS-CoV-2 infection presenting a low hospitalisation risk and to identify those at high risk that require careful follow-up to assess the need for hospitalisation. The model provides a simple decision-making tool for an effective allocation of resources to maintain quality care for outpatient populations.

Long-term predictors of remission in patients treated for medication-overuse headache at a specialized headache center: A prospective cohort study

Cephalalgia ◽  
2016 ◽  
Vol 38 (2) ◽  
pp. 265-273 ◽  
Author(s):  
Jasna J Zidverc-Trajkovic ◽  
Tatjana Pekmezovic ◽  
Zagorka Jovanovic ◽  
Aleksandra Pavlovic ◽  
Milija Mijajlovic ◽  
...  
Keyword(s):  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Medication Overuse ◽  
Medication Overuse Headache ◽  
Predictive Values ◽  
One Year ◽  
The One

Objective To evaluate long-term predictors of remission in patients with medication-overuse headache (MOH) by prospective cohort study. Background Knowledge regarding long-term predictors of MOH outcome is limited. Methods Two hundred and forty MOH patients recruited from 2000 to 2005 were included in a one-year follow-up study and then subsequently followed until 31 December 2013. The median follow-up was three years (interquartile range, three years). Predictive values of selected variables were assessed by the Cox proportional hazard regression model. Results At the end of follow-up, 102 (42.5%) patients were in remission. The most important predictors of remission were lower number of headache days per month before the one-year follow-up (HR-hazard ratio = 0.936, 95% confidence interval (CI) 0.884–0.990, p = 0.021) and efficient initial drug withdrawal (HR = 0.136, 95% CI 0.042–0.444, p = 0.001). Refractory MOH was observed in seven (2.9%) and MOH relapse in 131 patients (54.6%). Conclusions Outcome at the one-year follow-up is a reliable predictor of MOH long-term remission.

scholarly journals The impact of adding fetal MRI to sonographically diagnosed intrauterine ventriculomegaly: a prospective cohort study

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Mostafa Mahmod ◽  
Sara M Ragaee ◽  
Soha T Hamed ◽  
Ahmed Abbas ◽  
Mohamed A.F. Mourad
Keyword(s):  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Fetal Mri ◽  
Added Value ◽  
University Hospital ◽  
Mri Findings ◽  
Sonographic Examination ◽  
Predictive Values ◽  
The Impact

Objective: Intrauterine fetal ventriculomegaly (IVM) is one of the most commonly detected fetal anomalies. Prenatal diagnosis in IVM is considered a challenge with a significant impact on management. The current study aims to evaluate the added value of performing fetal MRI to sonographically diagnosed IVM. Methods: A prospective cohort study was conducted at a tertiary University Hospital in the period between January 2017 and March 2019. We included pregnant women with a single fetus sonographically diagnosed IVM (symmetrical or asymmetrical). First, a basic obstetric sonographic examination was done, followed by a detailed (2D/3D) fetal CNS anomaly scan for the detection of other associated anomalies. A fetal MRI brain scan was performed for all cases. Results: Sixty women were included in the study. Of the 60 fetuses with IVM, additional findings were seen on MRI in 14 cases (23%), and most of these findings were identified in fetuses with severe IVM (about 50%). No additional abnormalities were identified in fetuses of less than 24 weeks gestation. Callosal and septum pellucidum lesions (29%), along with posterior fossa abnormalities (28%) and cortical malformations (21%) accounted for the most common additional significant fetal MRI findings. Fetal MRI sensitivity, specificity, and positive and negative predictive values in correlation with those of prenatal ultrasound turned out to be notably higher, approaching nearly 100 %. Conclusions: Fetal MRI for sonographically diagnosed moderate or severe IVM is recommended to guide clinical management.

scholarly journals Long-Term Symptoms among Hospitalized COVID-19 Patients 48 Weeks after Discharge—A Prospective Cohort Study

2021 ◽  
Vol 10 (22) ◽  
pp. 5298
Author(s):  
Martin Mølhave ◽  
Steffen Leth ◽  
Jesper Damsgaard Gunst ◽  
Søren Jensen-Fangel ◽  
Lars Østergaard ◽  
...  
Keyword(s):  
Cohort Study ◽  
Infectious Diseases ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
University Hospital ◽  
Follow Up Studies ◽  
One Year ◽  
Term Data

Follow-up studies of COVID-19 survivors have been performed to characterize persistence of long-term symptoms, but data are scarce on one year of follow-up. This study provides data from 48 weeks of follow-up after discharge. All patients discharged from the Department of Infectious Diseases at Aarhus University Hospital, Denmark between 1 March and 1 July 2020 were followed for 48 weeks. In total, 45 of 66 eligible patients were interviewed after 48 weeks. The median age was 57 (IQR 51–70) years, the majority were female (53%) and Caucasian (87%). Median BMI was 28.1 (IQR 24.8–32.6) kg/m2. One or more comorbidities were registered among 62% of the patients. In total, 39 out of 45 (87%) interviewed patients reported persistence of at least one symptom 48 weeks after hospitalization with COVID-19. Most frequently reported symptoms were fatigue, dyspnea, and concentration difficulties. This study provides new long-term data following COVID-19, contributing to the accumulating data of COVID-19 sequelae. Many patients suffer long-term sequelae and further research is urgently needed to gain further knowledge of the duration and therapeutic options.

scholarly journals Predictive value of the neutrophil to lymphocyte ratio for disease deterioration and serious adverse outcomes in patients with COVID-19: a prospective cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhi-Yong Zeng ◽  
Shao-Dan Feng ◽  
Gong-Ping Chen ◽  
Jiang-Nan Wu
Keyword(s):  
Cohort Study ◽  
Hospital Admission ◽  
Clinical Outcomes ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Characteristic Curve ◽  
Adverse Outcomes ◽  
Neutrophil To Lymphocyte Ratio ◽  
Predictive Values ◽  
Lymphocyte Ratio

Abstract Background Early identification of patients who are at high risk of poor clinical outcomes is of great importance in saving the lives of patients with novel coronavirus disease 2019 (COVID-19) in the context of limited medical resources. Objective To evaluate the value of the neutrophil to lymphocyte ratio (NLR), calculated at hospital admission and in isolation, for the prediction of the subsequent presence of disease progression and serious clinical outcomes (e.g., shock, death). Methods We designed a prospective cohort study of 352 hospitalized patients with COVID-19 between January 9 and February 26, 2020, in Yichang City, Hubei Province. Patients with an NLR equal to or higher than the cutoff value derived from the receiver operating characteristic curve method were classified as the exposed group. The primary outcome was disease deterioration, defined as an increase of the clinical disease severity classification during hospitalization (e.g., moderate to severe/critical; severe to critical). The secondary outcomes were shock and death during the treatment. Results During the follow-up period, 51 (14.5%) patients’ conditions deteriorated, 15 patients (4.3%) had complicated septic shock, and 15 patients (4.3%) died. The NLR was higher in patients with deterioration than in those without deterioration (median: 5.33 vs. 2.14, P < 0.001), and higher in patients with serious clinical outcomes than in those without serious clinical outcomes (shock vs. no shock: 6.19 vs. 2.25, P < 0.001; death vs. survival: 7.19 vs. 2.25, P < 0.001). The NLR measured at hospital admission had high value in predicting subsequent disease deterioration, shock and death (all the areas under the curve > 0.80). The sensitivity of an NLR ≥ 2.6937 for predicting subsequent disease deterioration, shock and death was 82.0% (95% confidence interval, 69.0 to 91.0), 93.3% (68.0 to 100), and 92.9% (66.0 to 100), and the corresponding negative predictive values were 95.7% (93.0 to 99.2), 99.5% (98.6 to 100) and 99.5% (98.6 to 100), respectively. Conclusions The NLR measured at admission and in isolation can be used to effectively predict the subsequent presence of disease deterioration and serious clinical outcomes in patients with COVID-19.

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