The Prognostic Value of Olfactory Dysfunction in Patients with COVID-19: The COVIDORA Study.

BACKGROUND: Among all studies describing COVID-19 clinical features during the first wave of the pandemic, only a few retrospective studies have assessed the correlation between olfac-tory dysfunction (OD) and the evolution of disease severity. The main aim was to assess whether OD is a predictive factor of COVID-19 severity based on the patient's medical management (outpa-tient care, standard hospital admission, and ICU admission). METHODS: A national, prospective, mul-ticenter cohort study was conducted in 20 public hospitals and a public center for COVID-19 screen-ing. During the first wave of the pandemic, from 6 April to 11 May 2020, all patients tested positive for COVID-19 confirmed by RT-PCR underwent two follow-up ENT consultations within 10 days of symptom onset. The main outcome measures were the evolution of medical management (out-patient care, standard hospital admission, and ICU admission) at diagnosis and along the clinical course of COVID-19 disease. RESULTS: Among 481 patients included, the prevalence of OD was 60.7%, and it affected mostly female patients (74.3%) under 65 years old (92.5%), with fewer comor-bidities than patients with normal olfactory function. Here, 99.3% (290/292) of patients with OD presented with non-severe COVID-19 disease. Patients reporting OD were significantly less hospi-talized than the ones managed as outpatients, in either a standard medical unit or an ICU. Conclu-sions: As regards the clinical course of COVID-19 disease, OD could predict a decreased risk of hospitalization during the first wave of the pandemic.

External validation of an artificial intelligence solution for the detection of elbow fractures and joint effusions in children.

PURPOSE: The purpose of this study was to conduct an external validation of an artificial intelligence (AI) solution for the detection of elbow fractures and joint effusions using radiographs from a real-life cohort of children. MATERIALS AND METHODS: This single-center retrospective study was conducted on 758 radiographic sets (1637 images) obtained from consecutive emergency room visits of 712 children (mean age, 7.27 ± 3.97 [standard deviation] years; age range, 7 months and 10 days to 15 years and 10 months), referred for a trauma of the elbow. For each set, fracture and/or effusion detection by eleven senior radiologists (reference standard) and AI solution was recorded. Diagnostic performance of the AI solution was measured via four different approaches: fracture detection (presence/absence of fracture as binary variable), fracture enumeration, fracture localization and lesion detection (fracture and/or a joint effusion used as constructed binary variable). RESULTS: The sensitivity of the AI solution for each of the four approaches was >89%. Greatest sensitivity of the AI solution was obtained for lesion detection (95.0%; 95% confidence interval: 92.1-96.9). The specificity of the AI solution ranged between 63% (for lesion detection) and 77% (for fracture detection). For all four approaches, the negative predictive values were >92% and the positive predictive values ranged between 54% (for fracture enumeration and localization) and 73% (for lesion detection). Specificity was lower for plastered children for all approaches (P < 0.001). CONCLUSION: The AI solution demonstrates high performances for detecting elbow's fracture and/or joint effusion in children. However, in our context of use, 8% of the radiographic sets ruled-out by the algorithm concerned children with a genuine traumatic elbow lesion.

Ear and upper airway clinical outcome measures for use in primary ciliary dyskinesia research: a scoping review.

BACKGROUND: Primary ciliary dyskinesia (PCD) is a rare genetic disorder characterised by pulmonary, otological and sino-nasal manifestations. Well-defined clinical outcome measures are needed in such rare diseases research to improve follow-up and treatments. Pulmonary outcome measures have recently been described. The aim of this study was to identify ear and upper airway outcome measures that could be used for longitudinal follow-up of individuals with PCD. METHODS: A scoping review was performed by systematically searching MEDLINE, Embase and Cochrane Database of Systematic Reviews online databases for studies published from January 1996 to March 2022 that included at least 10 adult or paediatric PCD patients and reported ear and upper airway outcomes. RESULTS: 33 studies (1794 patients) were included. 10 ear and upper airway outcomes were reported. 17 studies reported audiometry, 16 reported otoscopic findings, and 13 reported rhinoscopic findings and sinus imaging. Health-related quality of life questionnaires were performed in seven studies. There was a high variability in definitions and measurement of outcomes between studies. CONCLUSIONS: This scoping review highlights the lack of data regarding ear and upper airway outcomes in PCD. It also reports a high heterogeneity in outcome definitions or measures. We provide well-founded specific suggestions to standardise ear and upper airway outcome definitions and reporting for future PCD research studies.

Deconstructing forearm casting task by videos with step-by-step simulation teaching improved performance of medical students: is making working student's memory work better similar to a process of artificial intelligence or just an improvement of the prefrontal cortex homunculus?

PURPOSE: To compare two teaching methods of a forearm cast in medical students through simulation, the traditional method (Trad) based on a continuous demonstration of the procedure and the task deconstruction method (Decon) with the procedure fragmenting into its constituent parts using videos. METHODS: During simulation training of the below elbow casting technique, 64 medical students were randomized in two groups. Trad group demonstrated the entire procedure without pausing. Decon group received step-wise teaching with educational videos emphasizing key components of the procedure. Direct and video evaluations were performed immediately after training (day 0) and at six months. Performance in casting was assessed using a 25-item checklist, a seven item global rating scale (GRS Performance), and a one item GRS (GRS Final Product). RESULTS: Fifty-two students (Trad n = 24; Decon n = 28) underwent both day zero and six month assessments. At day zero, the Decon group showed higher performance via video evaluation for OSATS (p = 0.035); GRS performance (p < 0.001); GRS final product (p < 0.001), and for GRS performance (p < 0.001) and GRS final product (p = 0.011) via direct evaluation. After six months, performance was decreased in both groups with ultimately no difference in performance between groups via both direct and video evaluation. Having done a rotation in orthopaedic surgery was the only independent factor associated to higher performance. CONCLUSIONS: The modified video-based version simulation led to a higher performance than the traditional method immediately after the course and could be the preferred method for teaching complex skills.

Impact of the 1st Wave of the COVID-19 Pandemic and Lockdown on In Utero Transfer Activity in the Paris Area, France.

BACKGROUND: To assess changes in the number and profile of in utero transfer requests during the first lockdown. METHODS: An observational, retrospective, cohort study. All pregnant women, from the Paris area (France), for whom a request for in utero transfer to the transfer unit was made during the first lockdown in France (from 17 March to 10 May 2020) or during a mirror period (years 2016 to 2019) were included. We compared the numbers and proportions of various indications for in utero transfer, the rates of in utero transfer acceptance and the proportion of outborn deliveries. RESULTS: 206 transfer requests were made during the lockdown versus 227, 236, 204 and 228 in 2016, 2017, 2018 and 2019, respectively. The relative proportion of requests for threatened preterm births and for fetal growth restriction decreased from 45% in the mirror period to 37% and from 8 to 3%, respectively. The transfer acceptance rates and outborn deliveries did not differ between time periods. CONCLUSIONS: Although a reduction in in utero transfer requests was observed for certain indications, the first lockdown was not associated with a decrease in acceptance rates nor in an increase in outborn births of pregnancies with a high risk of prematurity in the Paris area.

External validation of a commercially available deep learning algorithm for fracture detection in children.

PURPOSE: The purpose of this study was to conduct an external validation of a fracture assessment deep learning algorithm (Rayvolve®) using digital radiographs from a real-life cohort of children presenting routinely to the emergency room. MATERIALS AND METHODS: This retrospective study was conducted on 2634 radiography sets (5865 images) from 2549 children (1459 boys, 1090 girls; mean age, 8.5 ± 4.5 [SD] years; age range: 0-17 years) referred by the pediatric emergency room for trauma. For each set was recorded whether one or more fractures were found, the number of fractures, and their location found by the senior radiologists and the algorithm. Using the senior radiologist diagnosis as the standard of reference, the diagnostic performance of deep learning algorithm (Rayvolve®) was calculated via three different approaches: a detection approach (presence/absence of a fracture as a binary variable), an enumeration approach (exact number of fractures detected) and a localization approach (focusing on whether the detected fractures were correctly localized). Subgroup analyses were performed according to the presence of a cast or not, age category (0-4 vs. 5-18 years) and anatomical region. RESULTS: Regarding detection approach, the deep learning algorithm yielded 95.7% sensitivity (95% CI: 94.0-96.9), 91.2% specificity (95% CI: 89.8-92.5) and 92.6% accuracy (95% CI: 91.5-93.6). Regarding enumeration and localization approaches, the deep learning algorithm yielded 94.1% sensitivity (95% CI: 92.1-95.6), 88.8% specificity (95% CI: 87.3-90.2) and 90.4% accuracy (95% CI: 89.2-91.5) for both approaches. Regarding age-related subgroup analyses, the deep learning algorithm yielded greater sensitivity and negative predictive value in the 5-18-years age group than in the 0-4-years age group for the detection approach (P < 0.001 and P = 0.002) and for the enumeration and localization approaches (P = 0.012 and P = 0.028). The high negative predictive value was robust, persisting in all of the subgroup analyses, except for patients with casts (P = 0.001 for the detection approach and P < 0.001 for the enumeration and localization approaches). CONCLUSION: The Rayvolve® deep learning algorithm is very reliable for detecting fractures in children, especially in those older than 4 years and without cast.

Cluster of Coronavirus Disease 2019 (COVID-19) in the French Alps, February 2020.

BACKGROUND: On 7 February 2020, French Health authorities were informed of a confirmed case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in an Englishman infected in Singapore who had recently stayed in a chalet in the French Alps. We conducted an investigation to identify secondary cases and interrupt transmission. METHODS: We defined as a confirmed case a person linked to the chalet with a positive reverse-transcription polymerase chain reaction sample for SARS-CoV-2. RESULTS: The index case stayed 4 days in the chalet with 10 English tourists and a family of 5 French residents; SARS-CoV-2 was detected in 5 individuals in France, 6 in England (including the index case), and 1 in Spain (overall attack rate in the chalet: 75%). One pediatric case, with picornavirus and influenza A coinfection, visited 3 different schools while symptomatic. One case was asymptomatic, with similar viral load as that of a symptomatic case. Seven days after the first cases were diagnosed, 1 tertiary case was detected in a symptomatic patient with from the chalet a positive endotracheal aspirate; all previous and concurrent nasopharyngeal specimens were negative. Additionally, 172 contacts were monitored; all contacts tested for SARS-CoV-2 (N = 73) were negative. CONCLUSIONS: The occurrence in this cluster of 1 asymptomatic case with similar viral load as a symptomatic patient suggests transmission potential of asymptomatic individuals. The fact that an infected child did not transmit the disease despite close interactions within schools suggests potential different transmission dynamics in children. Finally, the dissociation between upper and lower respiratory tract results underscores the need for close monitoring of the clinical evolution of suspected cases of coronavirus disease 2019.

Virological and immunological impact of integrase inhibitor-based regimens initiated during primary HIV-1 infection.

DESIGN: Current international guidelines recommend either boosted protease inhibitor (PI/r)-based or integrase inhibitors (INSTI)-based regimens during primary HIV infection (PHI), even though the latter have only demonstrated their superiority at the chronic stage. We compared the effectiveness of INSTI-based versus PI/r-based combined antiretroviral therapy (cART) initiated during PHI. METHODS: This study was conducted among patients who initiated cART between 2013 and 2017, using data from the ANRS-PRIMO cohort and the Dat'AIDS study. Cumulative proportions of patients reaching viral suppression (HIV-1 RNA <50 copies/ml) were calculated using Turnbull's estimator for interval-censored data. CD4 cells and CD4/CD8 ratio increases were estimated using mixed linear models. Results were adjusted for the data source. RESULTS: Among the 712 study patients, 299 received an INSTI-based cART. Patients' baseline characteristics were similar between groups. Viral suppression was reached more rapidly in INSTI-treated versus PI/r-treated patients (P < 0.01), with cumulative proportions of 32 versus 6% at 4 weeks, 72 versus 31% at 12 weeks, 91 versus 78% at 24 weeks and about 95% in both groups at 48 weeks. At 4 weeks, INSTI-treated patients had gained on average 40 CD4 cells/µl (P = 0.05) over PI/r-treated ones; mean CD4 counts were similar in the two groups at 48 weeks. The CD4/CD8 ratio followed the same pattern. Results were similar when restricted to a comparison between dolutegravir-based versus darunavir-based cART. CONCLUSION: On the basis of this study and available literature, we recommend the use of INSTI-based cART for treatment initiation during PHI, as it leads to faster viral suppression and immune restoration.

Trajectories of Biological Values and Vital Parameters: An Observational Cohort Study of Adult Patients with Sickle Cell Disease Hospitalized for a Non-Complicated Vaso-Occlusive Crisis.

Hospital admission of patients with sickle-cell disease (SCD) presenting with a vaso-occlusive crisis (VOC) can be justified by pain refractory to usual outpatient care and/or the occurrence of a complication. Yet, the trajectories of vital parameters and standard biomarkers throughout a non-complicated VOC has not been established. In this observational cohort study, we describe the course of routine parameters throughout 329 hospital stays for non-complicated VOC. We used a new spline-based approach to study and visualize non-specific time-dependent variables extracted from the hospital clinical data warehouse. We identified distinct trends during the VOC for hemoglobin level, leukocytes count, C-Reactive Protein (CRP) level and temperature. Hemoglobin decreased after admission and rarely returned to steady state levels before discharge. White blood cell counts were elevated at admission before immediately decreasing, whereas eosinophils increased slowly throughout the first five days of the stay. In over 95% of non-complicated VOC-related stays, the CRP value was below 100 mg/L within the first day following admission and above normal after 48 hours, and the temperature was below 38 °C throughout the entire stay. Knowing the typical trajectories of these routine parameters during non-complicated VOC may urge the clinicians to be more vigilant in case of deviation from these patterns.

Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework.

BACKGROUND: Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variable selection aspects separately. Second, methods are either compared within a binary outcome setting (where we want to predict whether the readmission will occur within an arbitrarily chosen delay or not) or within a survival analysis setting (where the outcomes are directly the censored times), but not both. In this paper, we propose a comparison methodology to weight up those different settings both in terms of prediction and variables selection, while incorporating advanced machine learning strategies. METHODS: Using a high-dimensional case study on a sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the binary outcome setting, we consider logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting (GB) and neural network (NN); while on the survival analysis setting, we consider the Cox Proportional Hazards (PH), the CURE and the C-mix models. We also propose a method using Gaussian Processes to extract meaningfull structured covariates from longitudinal data. RESULTS: Among all assessed statistical methods, the survival analysis ones obtain the best results. In particular the C-mix model yields the better performances in both the two considered settings (AUC =0.94 in the binary outcome setting), as well as interesting interpretation aspects. There is some consistency in selected covariates across methods within a setting, but not much across the two settings. CONCLUSIONS: It appears that learning withing the survival analysis setting first (so using all the temporal information), and then going back to a binary prediction using the survival estimates gives significantly better prediction performances than the ones obtained by models trained "directly" within the binary outcome setting.