Renal and major clinical outcomes and their determinants after nephrectomy in patients with pre-existing chronic kidney disease: A retrospective cohort study.

The consequences of partial nephrectomy (PN) compared to radical nephrectomy (RN) are less documented in patients with pre-existing chronic kidney disease (CKD) or with solitary kidney (SK). We assessed renal outcomes, and their determinants, after PN or RN in a retrospective cohort of patients with moderate-to-severe CKD (RN-CKD and PN-CKD) or SK (PN-SK). All surgical procedures conducted between 2013 and 2018 in our institution in patients with pre-operative estimated glomerular filtration rate (eGFR)<60 mL/min/1.73m2 or with SK were included. The primary outcome was a composite criterion including CKD progression or major adverse cardio-vascular events (MACE) or death, assessed one year after surgery. Predictors of the primary outcome were determined using multivariate analyses. A total of 173 procedures were included (67 RN, and 106 PN including 27 SK patients). Patients undergoing RN were older, with larger tumors. Preoperative eGFR was not significantly different between the groups. One year after surgery, PN-CKD was associated with lower rate of the primary outcome compared to RN-CKD (43% vs 71% p = 0.007). In multivariate analysis, independent risk factors for the primary outcome were postoperative AKI (stage 1 to stage 3 ranging from OR = 8.68, 95% CI 3.23-23.33, to OR = 28.87, 95% CI 4.77-167.61), larger tumor size (OR = 1.21 per cm, 95% CI 1.02-1.45), while preoperative eGFR, age, sex, diabetes mellitus, and hypertension were not. Postoperative AKI after PN or RN was the major independent determinant of worse outcomes (CKD progression, MACE, or death) one year after surgery.

Multiple xenoestrogen air pollutants and breast cancer risk: Statistical approaches to investigate combined exposures effect.

Studies suggested that exposure to air pollutants, with endocrine disrupting (ED) properties, have a key role in breast cancer (BC) development. Although the population is exposed simultaneously to a mixture of multiple pollutants and ED pollutants may act via common biological mechanisms leading to synergic effects, epidemiological studies generally evaluate the effect of each pollutant separately. We aimed to assess the complex effect of exposure to a mixture of four xenoestrogen air pollutants (benzo-[a]-pyrene (BaP), cadmium, dioxin (2,3,7,8-Tétrachlorodibenzo-p-dioxin TCDD)), and polychlorinated biphenyl 153 (PCB153)) on the risk of BC, using three recent statistical methods, namely weighted quantile sum (WQS), quantile g-computation (QGC) and Bayesian kernel machine regression (BKMR). The study was conducted on 5222 cases and 5222 matched controls nested within the French prospective E3N cohort initiated in 1990. Annual average exposure estimates to the pollutants were assessed using a chemistry transport model, at the participants' residence address between 1990 and 2011. We found a positive association between the WQS index of the joint effect and the risk of overall BC (adjusted odds ratio (OR) = 1.10, 95% confidence intervals (CI): 1.03-1.19). Similar results were found for QGC (OR = 1.11, 95%CI: 1.03-1.19). Despite the association did not reach statistical significance in the BKMR model, we observed an increasing trend between the joint effect of the four pollutants and the risk of BC, when fixing other chemicals at their median concentrations. BaP, cadmium and PCB153 also showed positive trends in the multi-pollutant mixture, while dioxin showed a modest inverse trend. Despite we found a clear evidence of a positive association between the joint exposure to pollutants and BC risk only from WQS and QGC regression, we observed a similar suggestive trend using BKMR. This study makes a major contribution to the understanding of the joint effects of air pollution.

Place of concordance-discordance model in evaluating NGS performance.

INTRODUCTION: Ideally, evaluating NGS performance requires a gold standard; in its absence, concordance between replicates is often used as substitute standard. However, the appropriateness of the concordance-discordance criterion has been rarely evaluated. This study analyzes the relationship between the probability of discordance and the probability of error under different conditions. METHODS: This study used a conditional probability approach under conditional dependence then conditional independence between two sequencing results and compares the probabilities of discordance and error in different theoretical conditions of sensitivity, specificity, and correlation between replicates, then on real results of sequencing genome NA12878. The study examines also covariate effects on discordance and error using generalized additive models with smooth functions. RESULTS: With 99% sensitivity and 99.9% specificity under conditional independence, the probability of error for a positive concordant pair of calls is 0.1%. With additional hypotheses of 0.1% prevalence and 0.9 correlation between replicates, the probability of error for a positive concordant pair is 47.4%. With real data, the estimated sensitivity, specificity, and correlation between tests for variants are around 98.98%, 99.996%, and 93%, respectively, and the error rate for positive concordant calls approximates 2.5%. In covariate effect analyses, the effects' functional form are close between discordance and error models, though the parts of deviance explained by the covariates differ between discordance and error models. CONCLUSION: With conditional independence of two sequencing results, the concordance-discordance criterion seems acceptable as substitute standard. However, with high correlation, the criterion becomes questionable because a high percentage of false concordant results appears among concordant results.

Phosphatidylserine enriched with polyunsaturated n-3 fatty acid supplementation for attention-deficit hyperactivity disorder in children and adolescents with epilepsy: A randomized placebo-controlled trial.

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is a frequent comorbidity in children with epilepsy, which management mostly relies on the usual treatments of ADHD, especially methylphenidate. Supplementation with polyunsaturated n-3 Fatty Acid (PUFA) has been proposed as an alternative therapeutic approach in ADHD without epilepsy but has never been evaluated in epilepsy-associated ADHD. METHODS: A multicenter double blind randomized placebo-controlled trial evaluating supplementation with PUFA, in eicosapentaenoic- and docosahexaenoic-acid form, conjugated to a phospholipid vector (PS-Omega3) in children aged >6 and <16-years old, and suffering from any type of epilepsy and ADHD (inattentive or combined type) according to DSM-V. After a 4-week baseline period, patients were allocated (1:1) either to placebo group or to PS-Omega 3 group and entered a 12 week-double-blind treatment period which was followed by a 12 week-open-label treatment period. The primary outcome was the reduction of the ADHD-rating scale IV attention-deficit subscore after 12 weeks of treatment. RESULTS: The study was stopped early because of lack of eligible participants and the expected sample size was not reached. Seventy-four patients were randomized, 44 in PS-Omega3, and 30 in the placebo group. The reduction after 12 weeks of treatment in the inattention subscore of the ADHD-IV scale was -1.57 in the PS-Omega3 group, and -2.90 in the placebo group (p = 0.33, α = 5%). Results were similar after 24 weeks of treatment and for all other ADHD-related secondary outcomes, with no difference between placebo and PS-Omega3. CONCLUSION: Our study remaining underpowered, no formal conclusion about the effect of Ps-Omega3 could be drawn. However, our data strongly suggested that the PS-Omega 3 formulation used in the current study did not improve ADHD symptoms in children with epilepsy. PLAIN LANGUAGE SUMMARY: Supplementation with polyunsaturated n-3 Fatty Acid (PUFA) has been proposed in ADHD but has never been evaluated in patients with both epilepsy and ADHD. To address this issue, we conducted a multicenter double blind randomized placebo-controlled trial evaluating supplementation with PUFA in children with epilepsy and ADHD. The study was stopped early because of lack of eligible participants, hampering formal conclusion. However, the evolution of the ADHD symptoms at 12 and 24 weeks did not differ between placebo and PUFA supplementation, strongly suggesting that PUFA did not improve ADHD symptoms in children with epilepsy.

Integrating imaging-based classification and transcriptomics for quality assessment of human oocytes according to their reproductive efficiency.

PURPOSE: Utilising non-invasive imaging parameters to assess human oocyte fertilisation, development and implantation; and their influence on transcriptomic profiles. METHODS: A ranking tool was designed using imaging data from 957 metaphase II stage oocytes retrieved from 102 patients undergoing ART. Hoffman modulation contrast microscopy was conducted with an Olympus IX53 microscope. Images were acquired prior to ICSI and processed using ImageJ for optical density and grey-level co-occurrence matrices texture analysis. Single-cell RNA sequencing of twenty-three mature oocytes classified according to their competence was performed. RESULT(S): Overall fertilisation, blastulation and implantation rates were 73.0%, 62.6% and 50.8%, respectively. Three different algorithms were produced using binary logistic regression methods based on "optimal" quartiles, resulting in an accuracy of prediction of 76.6%, 67% and 80.7% for fertilisation, blastulation and implantation. Optical density, gradient, inverse difference moment (homogeneity) and entropy (structural complexity) were the parameters with highest predictive properties. The ranking tool showed high sensitivity (68.9-90.8%) but with limited specificity (26.5-62.5%) for outcome prediction. Furthermore, five differentially expressed genes were identified when comparing "good" versus "poor" competent oocytes. CONCLUSION(S): Imaging properties can be used as a tool to assess differences in the ooplasm and predict laboratory and clinical outcomes. Transcriptomic analysis suggested that oocytes with lower competence may have compromised cell cycle either by non-reparable DNA damage or insufficient ooplasmic maturation. Further development of algorithms based on image parameters is encouraged, with an increased balanced cohort and validated prospectively in multicentric studies.

Performance comparisons between clustering models for reconstructing NGS results from technical replicates.

To improve the performance of individual DNA sequencing results, researchers often use replicates from the same individual and various statistical clustering models to reconstruct a high-performance callset. Here, three technical replicates of genome NA12878 were considered and five model types were compared (consensus, latent class, Gaussian mixture, Kamila-adapted k-means, and random forest) regarding four performance indicators: sensitivity, precision, accuracy, and F1-score. In comparison with no use of a combination model, i) the consensus model improved precision by 0.1%; ii) the latent class model brought 1% precision improvement (97%-98%) without compromising sensitivity (= 98.9%); iii) the Gaussian mixture model and random forest provided callsets with higher precisions (both >99%) but lower sensitivities; iv) Kamila increased precision (>99%) and kept a high sensitivity (98.8%); it showed the best overall performance. According to precision and F1-score indicators, the compared non-supervised clustering models that combine multiple callsets are able to improve sequencing performance vs. previously used supervised models. Among the models compared, the Gaussian mixture model and Kamila offered non-negligible precision and F1-score improvements. These models may be thus recommended for callset reconstruction (from either biological or technical replicates) for diagnostic or precision medicine purposes.

Impact of Neuroeffector Adrenergic Receptor Polymorphisms on Incident Ventricular Fibrillation During Acute Myocardial Ischemia.

Background Cardiac adrenergic receptor gene polymorphisms have the potential to influence risk of developing ventricular fibrillation (VF) during ST-segment-elevation myocardial infarction, but no previous study has comprehensively investigated those most likely to alter norepinephrine release, signal transduction, or biased signaling. Methods and Results In a case-control study, we recruited 953 patients with ST-segment-elevation myocardial infarction without previous cardiac history, 477 with primary VF, and 476 controls without VF, and genotyped them for ADRB1 Arg389Gly and Ser49Gly, ADRB2 Gln27Glu and Gly16Arg, and ADRA2C Ins322-325Del. Within each minor allele-containing genotype, haplotype, or 2-genotype combination, patients with incident VF were compared with non-VF controls by odds ratios (OR) of variant frequencies referenced against major allele homozygotes. Of 156 investigated genetic constructs, 19 (12.2%) exhibited significantly (P<0.05) reduced association with incident VF, and none was associated with increased VF risk except for ADRB1 Gly389 homozygotes in the subset of patients not receiving ß-blockers. ADRB1 Gly49 carriers (prevalence 23.0%) had an OR (95% CI) of 0.70 (0.49-0.98), and the ADRA2C 322-325 deletion (Del) carriers (prevalence 13.5%) had an OR of 0.61 (0.39-0.94). When present in genotype combinations (8 each), both ADRB1 Gly49 carriers (OR, 0.67 [0.56-0.80]) and ADRA2C Del carriers (OR, 0.57 [0.45- 0.71]) were associated with reduced VF risk. Conclusions In ST-segment-elevation myocardial infarction, the adrenergic receptor minor alleles ADRB1 Gly49, whose encoded receptor undergoes enhanced agonist-mediated internalization and ß-arrestin interactions leading to cardioprotective biased signaling, and ADRA2C Del322-325, whose receptor causes disinhibition of norepinephrine release, are associated with a lower incidence of VF. Registration URL: https://clinicaltrials.gov; Unique identifier: NCT00859300.

Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M.

Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert.

Individualized Net Benefit estimation and meta-analysis using generalized pairwise comparisons in N-of-1 trials.

BACKGROUND: The Net Benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes and thresholds of clinical relevance. We extended Δ to N-of-1 trials, with a focus on patient-level and population-level Δ. METHODS: We developed a Δ estimator at the individual level as an extension of the stratum-specific Δ, and at the population-level as an extension of the stratified Δ. We performed a simulation study mimicking PROFIL, a series of 38 N-of-1 trials testing sildenafil in Raynaud's phenomenon, to assess the power for such an analysis with realistic data. We then reanalyzed PROFIL using GPC. This reanalysis was finally interpreted in the context of the main analysis of PROFIL which used Bayesian individual probabilities of efficacy. RESULTS: Simulations under the null showed good size of the test for both individual and population levels. The test lacked power when being simulated from the true PROFIL data, even when increasing the number of repetitions up to 140 days per patient. PROFIL individual-level estimated Δ were well correlated with the probabilities of efficacy from the Bayesian analysis while showing similarly wide confidence intervals. Population-level estimated Δ was not significantly different from zero, consistently with the previous Bayesian analysis. CONCLUSION: GPC can be used to estimate individual Δ which can then be aggregated in a meta-analytic way in N-of-1 trials. GPC ability to easily incorporate patient preferences allow for more personalized treatment evaluation, while needing much less computing time than Bayesian modeling.

Lymph Node Ratio Improves Prediction of Overall Survival in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy: A National Cancer Database Analysis.

OBJECTIVE: This study aimed to propose a revised ypN (r-ypN) classification based on lymph node ratio (LNR) and to examine its prognostic value in postneoadjuvant esophageal cancer. BACKGROUND: A new postneoadjuvant pathologic (ypTNM) staging classification has been introduced for esophageal cancer. However, the ypN classification currently defined by the number of positive lymph nodes is influenced by the extent of lymphadenectomy. METHODS: Data on 7195 esophageal cancer patients receiving neoadjuvant chemoradiation were extracted from the National Cancer Database (NCDB). Four r-ypN stages were defined by 3 LNR thresholds (0%, 10%, and 20% using X-tile software). A revised ypTNM (r-ypTNM) classification was developed by solely changing N categories. Kaplan-Meier method and Cox proportional hazards models were used for survival analyses. Akaike information criterion (AIC) and Harrell's concordance index ( C -index) were used to compare the predictive performance of the current and the revised classification. External validation was performed using an independent cohort from the NEOCRTEC5010 clinical trial. RESULTS: Both ypN ( P <0.001) and r-ypN ( P <0.001) were independent prognostic factors of overall survival (OS) for esophageal cancer patients. Kaplan-Meier curves demonstrated a better discrimination with r-ypN than ypN categories. Within each ypN category (except ypN3), OS was significantly different comparing r-ypN strata; however, there were no differences between ypN strata within each r-ypN category (except r-ypN3). r-ypN (AIC: 60752 vs 60782; C -index: 0.591 vs 0.587) and r-ypTNM (AIC: 60623 vs 60628; C -index: 0.613 vs 0.610) showed better predictive performance than the current staging system, with a lower AIC (better calibration) and higher C -index (improved discrimination). This advantage was also confirmed by external validation using the NEOCRTEC5010 cohort. CONCLUSIONS: LNR showed better performance than ypN in predicting OS of esophageal cancer patients after neoadjuvant chemoradiation and may be an improvement on the current staging system.

Predicted Impacts of Booster, Immunity Decline, Vaccination Strategies, and Non-Pharmaceutical Interventions on COVID-19 Outcomes in France.

The major economic and health consequences of COVID-19 called for various protective measures and mass vaccination campaigns. A previsional model was used to predict the future impacts of various measure combinations on COVID-19 mortality over a 400-day period in France. Calibrated on previous national hospitalization and mortality data, an agent-based epidemiological model was used to predict individual and combined effects of booster doses, vaccination of refractory adults, and vaccination of children, according to infection severity, immunity waning, and graded non-pharmaceutical interventions (NPIs). Assuming a 1.5 hospitalization hazard ratio and rapid immunity waning, booster doses would reduce COVID-19-related deaths by 50-70% with intensive NPIs and 93% with moderate NPIs. Vaccination of initially-refractory adults or children ≥5 years would half the number of deaths whatever the infection severity or degree of immunity waning. Assuming a 1.5 hospitalization hazard ratio, rapid immunity waning, moderate NPIs and booster doses, vaccinating children ≥12 years, ≥5 years, and ≥6 months would result in 6212, 3084, and 3018 deaths, respectively (vs. 87,552, 64,002, and 48,954 deaths without booster, respectively). In the same conditions, deaths would be 2696 if all adults and children ≥12 years were vaccinated and 2606 if all adults and children ≥6 months were vaccinated (vs. 11,404 and 3624 without booster, respectively). The model dealt successfully with single measures or complex combinations. It can help choosing them according to future epidemic features, vaccination extensions, and population immune status.

Human Papilloma Virus Vaccination in Patients with Rheumatic Diseases in France: A Study of Vaccination Coverage and Drivers of Vaccination.

Objectives: To describe human papillomavirus (HPV) vaccination practices in adolescent girls with systemic lupus erythematosus (SLE) and juvenile idiopathic arthritis (JIA) and to identify barriers to and motivators for vaccination. Methods: Cross-sectional, multicenter study on girls aged 9 to 19 years and their accompanying adults. The measurement criteria were the proportion of girls who were vaccinated against HPV, compliance with the vaccination schedule, factors associated with vaccination, and reasons for vaccination and non-vaccination through a self-administered questionnaire. Results: Seventy-one patients (16 with SLE and 55 with JIA) were included with a mean age of 13 years old (rank 11−18). According to parental questioning, 39% of patients were vaccinated against HPV or in progress (44% and 38% of SLE and JIA, respectively). This rate was 82% for the 22 patients ≥ 15 years of age. The vaccine was administered as often by a general practitioner (39%) as by a hospital pediatrician (also 39%). Two factors were significantly associated with vaccination: Older age (OR 53.68, 95% CI 5.85−429.29, p < 0.001) and previous hepatitis B vaccination (OR 4.97, 95% CI 1.03−24.01, p = 0.040). Recommendation of the vaccine by a health professional and fear of HPV-related diseases were the main facilitators. Lack of knowledge about the vaccine, lack of recommendation by a health professional, and fear of vaccine side effects were the main barriers. Conclusions: HPV vaccination coverage remains insufficient among patients with autoimmune disease. Education and awareness of health professionals about HPV infections are crucial elements in vaccine acceptance.

Prognostic Impact of Microscopic Extra-Thyroidal Extension (mETE) on Disease Free Survival in Patients with Papillary Thyroid Carcinoma (PTC).

Background: This study assessed the risk of reduced disease-free survival (DFS) and poor clinical outcome in patients with papillary thyroid carcinomas (PTC) with microscopic extra-thyroidal extension (mETE), as compared to PTC patients without mETE. Methods: Retrospective analysis of a prospective database of patients treated by total thyroidectomy and radioactive iodine (RAI) with a five-year follow-up and tumors < 40 mm. In total, 303 patients were analyzed: 30.7% presented tumors with mETE, and 69.3% without. mETE was defined as extra-thyroidal invasion without skeletal muscle involvement. The primary outcome, DFS, was defined as the interval between initial treatment and any subsequent PTC-related treatment. The second outcome was the clinical status at five years. Results: In univariate analyses, the five-year DFS was significantly lower for tumors with mETE (62.4% versus 88.1%, p < 0.001). In multivariate analysis, mETE and massive lymph node involvement (LNI) were independent prognostic factors, associated respectively with a hazard ratio of 2.55 (95% CI 1.48−4.40) and 8.94 (95% CI 4.92−16.26). mETE was significantly associated with a pejorative clinical outcome at five years, i.e., biochemical/indeterminate response and structural persistence (Respectively OR 1.83 (95% CI 0.83; 4.06) and OR 4.92 (95% CI 1.87; 12.97)). Conclusion: Our results suggest that mETE is an independent poor prognosis factor of reduced DFS and predictive of poor clinical outcome.

Protocol for a prospective quasi-experimental study on SARS-CoV-2 transmission during outdoor sports events in France: the COVID-ESO project.

INTRODUCTION: The spread of SARS-CoV-2 and its variants in the community remains a major concern despite the application of control measures including the banning of mass sporting events. The circulation of SARS-CoV-2 within the general population, and potentially within the population practicing outdoor sports activities, suggests contexts conducive to the transmission of the virus. We hypothesise that outdoor sports events (OSEs) do not present a higher risk of SARS-CoV-2 contamination. The objective of the COVID-ESO project is to measure if individuals participating in OSE present a similar risk of SARS-CoV-2 transmission compared with individuals not participating in OSE, in France. METHODS AND ANALYSIS: The COVID-ESO project is a prospective, quasi-experimental study to be conducted in volunteer individuals likely to participate in OSE. Six events are targeted across France to be included. Three sport trials will be eligible for the study: running, cycling and triathlon. Each individual participating in the OSE will choose one of his or her usual training partner to be eligible for the unexposed control group. Individuals will be matched (1:1) on age, sex and the district of residence. Individuals assigned to the exposed group will participate in the OSE, whereas individuals assigned to the unexposed group will not participate in the OSE. All individuals will be asked to perform saliva tests on the day of the event and 7 days after the event. A questionnaire including sociodemographic, clinical and exposure data to SARS-CoV-2 will be sent by email for both groups on the day before the event and 7 days after the event. Differences in SARS-CoV-2 infection rates between the exposed versus the unexposed group will be analysed by fitting a conditional logistic regression model, adjusted for potential confounders. As the sport events unfold, data will be analyzed by performing sequential meta-analyses. ETHICS AND DISSEMINATION: This protocol has been approved by the ethical committee. Ethical approval has been obtained for the Clinical research and committee of South West of France, 10 June 2021. COMITE DE PROTECTION DES PERSONNES DU SUD-OUEST ET OUTRE-MER 4 under the reference number 21.03.23.71737/CPP2021-04-045 a COVID/2021-A00845-36. Findings generated from this study will be shared to national health and sport authorities.

FlauBERT vs. CamemBERT: Understanding patient's answers by a French medical chatbot.

In a number of circumstances, obtaining health-related information from a patient is time-consuming, whereas a chatbot interacting efficiently with that patient might help saving health care professional time and better assisting the patient. Making a chatbot understand patients' answers uses Natural Language Understanding (NLU) technology that relies on 'intent' and 'slot' predictions. Over the last few years, language models (such as BERT) pre-trained on huge amounts of data achieved state-of-the-art intent and slot predictions by connecting a neural network architecture (e.g., linear, recurrent, long short-term memory, or bidirectional long short-term memory) and fine-tuning all language model and neural network parameters end-to-end. Currently, two language models are specialized in French language: FlauBERT and CamemBERT. This study was designed to find out which combination of language model and neural network architecture was the best for intent and slot prediction by a chatbot from a French corpus of clinical cases. The comparisons showed that FlauBERT performed better than CamemBERT whatever the network architecture used and that complex architectures did not significantly improve performance vs. simple ones whatever the language model. Thus, in the medical field, the results support recommending FlauBERT with a simple linear network architecture.

Incidence and risk factors for bilateral proximal femoral fractures.

BACKGROUND: Proximal femoral fractures (PFFs) are a public health issue due to their high frequency. The frequency of a second PFF on the other side is estimated at 10%. This estimation is controversial, however, and the risk factors have not been evaluated in a large population of French patients. The objective of this retrospective case-control study was to determine: (1) the incidence of second PFFs and (2) their risk factors. HYPOTHESIS: The incidence of second PFFs is >2% after 1 year and >5% after 3 years. MATERIAL AND METHODS: We conducted a case-control study in a population of consecutive patients managed surgically for PPF at the Lyon Sud Hospital between 2013 and 2014. We analysed the following clinical factors: age, sex, body mass index (BMI), institutionalisation, the Parker score, the American Society of Anesthesiologists score (ASA), comorbidities, and the use of psychoactive drugs. RESULTS: We included 474 PFFs (trochanter, n=240 and neck, n=234) of which 36 were bilateral. The contralateral fracture occurred within 1 year of the first fracture in 6/474 (1.3%) cases and within 3 years in all 36 cases (7.6%). The case-control study comprised 49 cases with bilateral PFF and 161 controls with no second hip fracture within 3 years. Risk factors for a second hip fracture were age older than 90 years (odds ratio [OR]=5.44; 95% confidence interval [95%CI], 112-2642 (p=0.002)) and a history of heart disease (OR, 2.18; 95%CI, 1.06-4.47 [p=0.03]). A Parker score≥6 was protective (OR, 0.84; 95%CI, 0.71-0.99 [p=0.03]). Mortality after 3 years was 42% (201/474), and 13% (63/474) of patients were lost to follow-up. DISCUSSION: Age older than 90 years, a Parker score below 6, and a history of heart disease are risk factors for a second PFF within 3 years after the first PFF. LEVEL OF EVIDENCE: III; case-control study.

The impact of left truncation of exposure in environmental case-control studies: evidence from breast cancer risk associated with airborne dioxin.

In epidemiology, left-truncated data may bias exposure effect estimates. We analyzed the bias induced by left truncation in estimating breast cancer risk associated with exposure to airborne dioxins. Simulations were run with exposure estimates from a Geographic Information System (GIS)-based metric and considered two hypotheses for historical exposure, three scenarios for intra-individual correlation of annual exposures, and three exposure-effect models. For each correlation/model combination, 500 nested matched case-control studies were simulated and data fitted using a conditional logistic regression model. Bias magnitude was assessed by estimated odds-ratios (ORs) versus theoretical relative risks (TRRs) comparisons. With strong intra-individual correlation and continuous exposure, left truncation overestimated the Beta parameter associated with cumulative dioxin exposure. Versus a theoretical Beta of 4.17, the estimated mean Beta (5%; 95%) was 73.2 (67.7; 78.8) with left-truncated exposure and 4.37 (4.05; 4.66) with lifetime exposure. With exposure categorized in quintiles, the TRR was 2.0, the estimated ORQ5 vs. Q1 2.19 (2.04; 2.33) with truncated exposure versus 2.17 (2.02; 2.32) with lifetime exposure. However, the difference in exposure between Q5 and Q1 was 18× smaller with truncated data, indicating an important overestimation of the dose effect. No intra-individual correlation resulted in effect dilution and statistical power loss. Left truncation induced substantial bias in estimating breast cancer risk associated with exposure with continuous and categorical models. With strong intra-individual exposure correlation, both models detected associations, but categorical models provided better estimates of effect trends. This calls for careful consideration of left truncation-induced bias in interpreting environmental epidemiological data.

Coffee consumption and seizure frequency in patients with drug-resistant focal epilepsy.

OBJECTIVE: To assess the relation between coffee consumption and seizure frequency in patients with drug-resistant focal epilepsy. METHODS: Cross-sectional analysis of data collected in the SAVE study, which included patients with drug-resistant focal epilepsy during long-term EEG monitoring. Patients in whom both coffee consumption and data about seizure frequency, including focal to bilateral tonic-clonic seizures (FBTCS), were available were selected. Coffee consumption was collected using a standardized self-report questionnaire and classified into four groups: none, rare (from less than 1 cup/week to up 3 cups/week), moderate (from 4 cups/week to 3 cups/day), and high (more than 4 cups/day). RESULTS: Six hundred and nineteen patients were included. There was no relation between coffee consumption and total seizure frequency (p = 0.902). In contrast, the number of FBTCS reported over the past year was significantly associated with usual coffee consumption (p = 0.029). Specifically, number of FBCTS in patients who reported moderate coffee consumption was lower than in others. In comparison with patients with moderate coffee consumption, the odds ratio (95%CI) for reporting at least 1 FBTCS per year was 1.6 (1.03-2.49) in patients who never take coffee, 1.62 (1.02-2.57) in those with rare consumption and 2.05 (1.24-3.4) in those with high consumption. Multiple ordinal logistic regression showed a trend toward an association between coffee consumption and number of FBTCS (p = 0.08). CONCLUSIONS AND RELEVANCE: Our data suggest that effect of coffee consumption on seizures might depend on dose with potential benefits on FBTCS frequency at moderate doses. These results will have to be confirmed by prospective studies.

Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models.

BACKGROUND AND OBJECTIVE: Machine learning and deep learning models are very powerful in predicting the presence of a disease. To achieve good predictions, those models require a certain amount of data to train on, whereas this amount i) is generally limited and difficult to obtain; and, ii) increases with the complexity of the interactions between the outcome (disease presence) and the model variables. This study compares the ways training dataset size and interactions affect the performance of those prediction models. METHODS: To compare the two influences, several datasets were simulated that differed in the number of observations and the complexity of the interactions between the variables and the outcome. A few logistic regressions and neural networks were trained on the simulated datasets and their performance evaluated by cross-validation and compared using accuracy, F1 score, and AUC metrics. RESULTS: Models trained on simulated datasets without interactions provided good results: AUC close to 0.80 with either logistic regression or neural networks. Models trained on simulated dataset with order 2 interactions led also to AUCs close to 0.80 with either logistic regression or neural networks. Models trained on simulated datasets with order 4 interactions led to AUC close to 0.80 with neural networks and 0.85 with penalized logistic regressions. Whatever the interaction order, increasing the dataset size did not significantly affect model performance, especially that of machine learning models. CONCLUSION: Machine learning models were the less influenced by the dataset size but needed interaction terms to achieve good performance, whereas deep learning models could achieve good performance without interaction terms. Conclusively, with the considered scenarios, well-specified machine learning models outperformed deep learning models.

Expected Evolution of COVID-19 Epidemic in France for Several Combinations of Vaccination Strategies and Barrier Measures.

The outbreak of the SARS-CoV-2 virus, enhanced by rapid spreads of variants, has caused a major international health crisis, with serious public health and economic consequences. An agent-based model was designed to simulate the evolution of the epidemic in France over 2021 and the first six months of 2022. The study compares the efficiencies of four theoretical vaccination campaigns (over 6, 9, 12, and 18 months), combined with various non-pharmaceutical interventions. In France, with the emergence of the Alpha variant, without vaccination and despite strict barrier measures, more than 600,000 deaths would be observed. An efficient vaccination campaign (i.e., total coverage of the French population) over six months would divide the death toll by 10. A vaccination campaign of 12, instead of 6, months would slightly increase the disease-related mortality (+6%) but require a 77% increase in ICU bed-days. A campaign over 18 months would increase the disease-related mortality by 17% and require a 244% increase in ICU bed-days. Thus, it seems mandatory to vaccinate the highest possible percentage of the population within 12, or better yet, 9 months. The race against the epidemic and virus variants is really a matter of vaccination strategy.