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Several countries have implemented lockdowns to control their COVID-19 epidemic. However, questions like “where” and “when” still require answers. We assessed the impact of national and regional lockdowns considering the French first epidemic wave of COVID-19 as a case study. In a regional lockdown scenario aimed at preventing intensive care units (ICU) saturation, almost all French regions would have had to implement a lockdown within 10 days and 96% of ICU capacities would have been used. For slowly growing epidemics, with a lower reproduction number, the expected delays between regional lockdowns increase. However, the public health costs associated with these delays tend to grow with time. In a quickly growing pandemic wave, defining the timing of lockdowns at a regional rather than national level delays by a few days the implementation of a nationwide lockdown but leads to substantially higher morbidity, mortality, and stress on the healthcare system.

Acute viral respiratory infections (AVRIs) rank among the most common causes of hospitalisation worldwide, imposing significant healthcare burdens and driving the development of pharmacological treatments. However, inconsistent outcome reporting across clinical trials limits evidence synthesis and its translation into clinical practice. A core outcome set (COS) for pharmacological treatments in hospitalised adults with AVRIs is essential to standardise trial outcomes and improve research comparability. To develop an internationally agreed COS for pharmacological treatments in hospitalised adults ≥18 years with acute viral respiratory infections (COSAVRI) through stakeholder agreement. This protocol follows a four-stage development process in accordance with Core Outcome Set Handbook guidelines. Stage 1 comprises a rapid scoping review of randomised controlled trials (2015-2025) to systematically catalogue patient-relevant outcomes reported in pharmacological AVRI treatment studies. Semi-automated screening and data extraction will employ machine learning and large language models, with human verification. Stage 2 involves an online Real-Time Delphi survey with international stakeholders, including healthcare professionals, researchers, patients/caregivers, and policymakers, to prioritise identified outcomes using a 9-point scale. Stage 3 consists of structured online consensus meetings utilising anonymous electronic voting to finalise the COS. Stage 4 focuses on dissemination and implementation through academic publications, conferences, and stakeholder engagement. COSAVRI will provide a standardised minimum set of outcomes for measuring and reporting in future pharmacological trials involving hospitalised adults with AVRIs. This initiative will enhance evidence synthesis, reduce research waste, support regulatory decision-making, and improve pandemic preparedness by facilitating the rapid deployment of harmonised outcomes in trial protocols.

Platform trials (PTs) are gaining popularity in clinical research due to their innovative and flexible methodologies. The objective was to determine the characteristics, methodological, and statistical practices in PTs. We identified PTs from trial registries and bibliographic databases up to August 2024. Eligible PTs were randomized controlled trials studying multiple interventions within a single population, with flexibility to add or drop arms. Data were extracted on trial status, design, statistical methods, and reporting practices. We identified 189 PTs. Most focused on infectious diseases (77, including 57 for COVID-19) and oncology (68). PT initiation peaked during the COVID-19 pandemic but has since stabilized at 84 active PTs, with 25 in planning. A complete master protocol was available for 47% (89/189) of PTs. Bayesian designs featured in 58/189 PTs vs. 56/189 frequentist trials, 20/189 trials utilizing both (unclear in 55/189 PTs). Overall, 25/111 trials (23%) were designed without a predetermined target sample size, all of which were Bayesian. Among these, 16 were explicitly reported as “perpetual” trials. The number of interim analyses was predetermined in 18% (10/57) of Bayesian trials vs. 58% (28/48) of frequentist trials. Simulations to evaluate operating characteristics were used in 93% (39/42) of Bayesian trials. Simulation reports were available in 67% (26/39) of cases, and the procedures were detailed for 62% (24/39) of trials. Only two trials shared the simulation code. PTs remain popular and increasingly diverse. Efforts to enhance transparency and reporting, especially in complex Bayesian PTs, are essential to ensure reliability and broader acceptance. •Bayesian designs were as common as frequentist designs.•Simulations to evaluate operating characteristics were used in 93% of Bayesian trials.•Simulation reports were available in only 67% of cases in Bayesian trials.•Simulation procedures were detailed in only 62% of cases in Bayesian trials.•Efforts are needed to enhance transparency and reporting of platform trial features.

To the Editor: Hsiao and colleagues (Dec. 14 issue) 1 report the results of a study of influenza vaccine implementation. We are concerned that intracluster correlations were ignored at the hospital, week, household, and within-patient (repeated vaccination) levels. Supplementary analyses that were stratified according to the first two potential correlations ignore interactions (which would occur, e.g., if a nursing home organizes group visits every other week) and ignore other potential correlations. Mixed-effects models may provide more accurate assessments. 2 To the Editor: In their article about recombinant or standard-dose inactivated influenza vaccines in adults under 65 years of age, Hsiao et al. . . .

Background: Several countries are implementing COVID-19 booster vaccination campaigns. The objective of this study was to model the impact of different primary and booster vaccination strategies. Methods: We used a compartmental model fitted to hospital admission data in France to analyze the impact of primary and booster vaccination strategies on morbidity and mortality, assuming waning of immunity and various levels of virus transmissibility during winter. Results: Strategies prioritizing primary vaccinations were systematically more effective than strategies prioritizing boosters. Regarding booster strategies targeting different age groups, their effectiveness varied with immunity and virus transmissibility levels. If the waning of immunity affects all adults, people aged 30 to 49 years should be boosted in priority, even for low transmissibility levels. Conclusions: Increasing the primary vaccination coverage should remain a priority. If a plateau has been reached, boosting the immunity of younger adults could be the most effective strategy, especially if SARS-CoV-2 transmissibility is high.

Background Alongside the recent worldwide expansion of hypervirulent Klebsiella pneumoniae (KP) infections, the available literature regarding cases of community acquired pneumonias (KP-CAP) remains scarce but reports a strikingly high and early mortality. We performed a retrospective multicenter study (7 ICU in France) between 2015 and 2019, comparing prognosis and severity of KP-CAP versus Streptococcus pneumoniae - CAP (SP-CAP). Methods For each KP-CAP, three SP-CAP admitted in ICUs within the same center and within the same 6-month window were selected. When available, KP strains were studied, and bacterial virulence was genetically assessed for virulence factors. The primary outcome was in-hospital mortality. Associations between clinical outcomes and type of infection were tested using univariate and multivariate logistic regressions, adjusted for pairing variables. Results Twenty-seven KP-CAP and 81 SP-CAP were included. Respective in-hospital mortality rates were 59% ( n  = 16) and 17% ( n  = 14, p  < 0.001), despite adequate antibiotic therapy. KP-CAP median time from admission to death was 26.9 h [IQR 5.75–44 h] and were significantly associated with higher rates of multiple organ failures (93% vs. 42%, p  < 0.001), disseminated intravascular coagulation (12% vs. 1.3%, p  = 0.046), septic shock (median lactate on ICU admission 4.60 vs. 2.90 mmol/L, p  = 0.030) and kidney failure (KDIGO-3: 87% vs. 44%, p  < 0.001). Interestingly, alcoholism was the only identified predisposing factor of KP-CAP. Severity on ICU admission (2-fold higher for KP-CAP) was the only factor associated with mortality in a multivariate analysis. Conclusion We described a strong association between KP-CAP infection and higher and earlier mortality when compared to SP-CAP. Moreover, alcoholism was the sole predisposing factor associated with KP-CAP infection. These findings should raise awareness of clinicians involved in the management of severe CAP about this microbiological etiology. Future prospective studies are needed to confirm these results and to design strategies to improve the prognosis of such infections.

Background: With the continuous expansion of available biomedical data, efficient and effective information retrieval has become of utmost importance. Semantic expansion of queries using synonyms may improve information retrieval. Objective: The aim of this study was to automatically construct and evaluate expanded PubMed queries of the form “preferred term”[MH] OR “preferred term”[TIAB] OR “synonym 1”[TIAB] OR “synonym 2”[TIAB] OR …, for each of the 28,313 Medical Subject Heading (MeSH) descriptors, by using different semantic expansion strategies. We sought to propose an innovative method that could automatically evaluate these strategies, based on the three main metrics used in information science (precision, recall, and F-measure). Methods: Three semantic expansion strategies were assessed. They differed by the synonyms used to build the queries as follows: MeSH synonyms, Unified Medical Language System (UMLS) mappings, and custom mappings (Catalogue et Index des Sites Médicaux de langue Française [CISMeF]). The precision, recall, and F-measure metrics were automatically computed for the three strategies and for the standard automatic term mapping (ATM) of PubMed. The method to automatically compute the metrics involved computing the number of all relevant citations (A), using National Library of Medicine indexing as the gold standard (“preferred term”[MH]), the number of citations retrieved by the added terms (”synonym 1“[TIAB] OR ”synonym 2“[TIAB] OR …) (B), and the number of relevant citations retrieved by the added terms (combining the previous two queries with an “AND” operator) (C). It was possible to programmatically compute the metrics for each strategy using each of the 28,313 MeSH descriptors as a “preferred term,” corresponding to 239,724 different queries built and sent to the PubMed application program interface. The four search strategies were ranked and compared for each metric. Results: ATM had the worst performance for all three metrics among the four strategies. The MeSH strategy had the best mean precision (51%, SD 23%). The UMLS strategy had the best recall and F-measure (41%, SD 31% and 36%, SD 24%, respectively). CISMeF had the second best recall and F-measure (40%, SD 31% and 35%, SD 24%, respectively). However, considering a cutoff of 5%, CISMeF had better precision than UMLS for 1180 descriptors, better recall for 793 descriptors, and better F-measure for 678 descriptors. Conclusions: This study highlights the importance of using semantic expansion strategies to improve information retrieval. However, the performances of a given strategy, relatively to another, varied greatly depending on the MeSH descriptor. These results confirm there is no ideal search strategy for all descriptors. Different semantic expansions should be used depending on the descriptor and the user’s objectives. Thus, we developed an interface that allows users to input a descriptor and then proposes the best semantic expansion to maximize the three main metrics (precision, recall, and F-measure).

Acute viral respiratory infections (AVRIs) rank among the most common causes of hospitalisation worldwide, imposing significant healthcare burdens and driving the development of pharmacological treatments. However, inconsistent outcome reporting across clinical trials limits evidence synthesis and its translation into clinical practice. A core outcome set (COS) for pharmacological treatments in hospitalised adults with AVRIs is essential to standardise trial outcomes and improve research comparability. To develop an internationally agreed COS for pharmacological treatments in hospitalised adults [greater than or equal to]18 years with acute viral respiratory infections (COSAVRI) through stakeholder agreement. This protocol follows a four-stage development process in accordance with Core Outcome Set Handbook guidelines. Stage 1 comprises a rapid scoping review of randomised controlled trials (2015-2025) to systematically catalogue patient-relevant outcomes reported in pharmacological AVRI treatment studies. Semi-automated screening and data extraction will employ machine learning and large language models, with human verification. Stage 2 involves an online Real-Time Delphi survey with international stakeholders, including healthcare professionals, researchers, patients/caregivers, and policymakers, to prioritise identified outcomes using a 9-point scale. Stage 3 consists of structured online consensus meetings utilising anonymous electronic voting to finalise the COS. Stage 4 focuses on dissemination and implementation through academic publications, conferences, and stakeholder engagement.

We investigated whether baseline levels of biomarkers related to endotheliopathy, thromboinflammation, and fibrosis were associated with clinical outcomes in hospitalized COVID-19 patients. We analyzed the associations between baseline levels of 21 biomarkers and time to hospital discharge and change in NEWS-2 score in patients from DisCoVeRy trial. We fitted multivariate models adjusted for baseline ISARIC 4C score, disease severity, D-dimer values, and treatment regimen. Between March 22 and June 29, 2020, 603 participants were randomized; 454 had a sample collected at baseline and analyzed. The backward selection of multivariate models showed that higher baseline levels of soluble suppressor of tumorigenicity 2 (sST2) and nucleosomes were statistically associated with a lower chance of hospital discharge before day 29 (sST2: aHR 0.24, 95% CI [0.15–0.38], p  < 10 −9 ; nucleosomes: aHR 0.62, 95% CI [0.48–0.81], p  < 10 −3 ). Likewise, higher levels of baseline sST2 were statistically associated with lower changes in the NEWS-2 score between baseline and day 15 (adjusted beta 4.47, 95% CI [2.65–6.28], p  < 10 −5 ). Moreover, we evaluated sST2 involvement in a confirmation cohort (SARCODO study, 103 patients) and found that elevated baseline sST2 levels were significantly associated with lower rates of hospital discharge before day 29 and a higher model performance (AUC at day 29 of 92%) compared to models without sST2. sST2 emerged as an independent predictor of clinical outcomes in two large cohort of hospitalized COVID-19 patients, warranting further investigation to elucidate its role in disease progression and potential as a therapeutic target.

Acute viral respiratory infections (AVRIs) rank among the most common causes of hospitalisation worldwide, imposing significant healthcare burdens and driving the development of pharmacological treatments. However, inconsistent outcome reporting across clinical trials limits evidence synthesis and its translation into clinical practice. A core outcome set (COS) for pharmacological treatments in hospitalised adults with AVRIs is essential to standardise trial outcomes and improve research comparability. To develop an internationally agreed COS for pharmacological treatments in hospitalised adults [greater than or equal to]18 years with acute viral respiratory infections (COSAVRI) through stakeholder agreement. This protocol follows a four-stage development process in accordance with Core Outcome Set Handbook guidelines. Stage 1 comprises a rapid scoping review of randomised controlled trials (2015-2025) to systematically catalogue patient-relevant outcomes reported in pharmacological AVRI treatment studies. Semi-automated screening and data extraction will employ machine learning and large language models, with human verification. Stage 2 involves an online Real-Time Delphi survey with international stakeholders, including healthcare professionals, researchers, patients/caregivers, and policymakers, to prioritise identified outcomes using a 9-point scale. Stage 3 consists of structured online consensus meetings utilising anonymous electronic voting to finalise the COS. Stage 4 focuses on dissemination and implementation through academic publications, conferences, and stakeholder engagement.