In target trial emulation (TTE), marginal structural models (MSMs) can be used to characterise per-protocol treatment effects over time. The MSM parameters are often estimated by inverse probability weighting (IPW), with weights estimated by maximum likelihood. However, IPW-based estimators can be unstable in small samples and are sensitive to misspecification of the weight models. An alternative method for estimating the MSM parameters is longitudinal targeted maximum likelihood estimation (LTMLE). LTMLE is double robust and potentially more efficient than IPW. Nevertheless, LTMLE also relies on inverse probability weights and may therefore share the instability of IPW-based estimators. We propose joint calibrated LTMLE, which integrates LTMLE with joint calibrated weights tailored for per-protocol effect estimation in TTE. This calibration of weights improves finite-sample performance by enforcing covariate balance in both the treatment and censoring processes simultaneously. Simulations show that the proposed method has improved efficiency and robustness to weight model misspecification, compared to standard LTMLE. We illustrate the method using a case study to evaluate the effect of highly active antiretroviral therapy on CD4 cell count among HIV-positive women.

To examine the associations between clinical characteristics and the occurrence of ischemic stroke (IS), and to describe the clinical features and outcomes of IS in patients with eosinophilic granulomatosis with polyangiitis (EGPA). In this single-center retrospective cohort study, 38 EGPA patients were stratified by ischemic stroke occurrence. Analyses included logistic regression and Kaplan–Meier survival analysis. IS occurred in 8 patients (21.1

BACKGROUND:Neuropsychiatric SLE (NPSLE) is a clinically challenging subset of SLE, marked by heterogeneous central nervous system involvement. Diagnosis relies on clinical symptoms and exclusionary criteria, lacking objective biomarker. PURPOSE:To investigate metabolic patterns of intracerebral lesions and identify diagnostic biomarkers for NPSLE using translocator protein (TSPO) positron emission tomography (PET)/magnetic resonance (MR). METHODS:A retrospective analysis was conducted on 19 patients with NPSLE and 10 patients with non-NPSLE, who underwent [18F] DPA-714 PET/MRI. Diagnoses of SLE and NPSLE followed American College of Rheumatology (ACR) classification and Systemic Lupus International Collaborating Clinics (SLICC) Model B criteria. T2-weighted MRI lesions served as regions of interest (ROI), coregistered to PET for cross-modality quantitative analysis. The maximum uptake (SUVmax) and mean uptake (SUVmean) of brain lesions for each patient was measured. Group differences in SUVmax and SUVmean were compared. Clinical associations were conducted using Pearson correlation, and differentiation between non-NPSLE and NPSLE was performed by logistic regression analysis. RESULTS:SUVmax was significantly higher in the NPSLE group than in the non-NPSLE group (p<0.01), while there was no significant difference in SUVmean (p>0.05). SUVmax was correlated with clinical assessment scores (SLICC/ACR: r=0.43, p=0.02; modified Rankin Scale: r=0.41, p=0.04; SLE Disease Activity Index: r=0.41, p=0.03), and no significant correlation was found for SUVmean. In logistic regression analysis, only the model based on SUVmax alone was significant (p=0.01). In ROC analysis, the area under the curve (AUC) of SUVmax (0.83) was higher than that of SUVmean (0.68), and Model 4 (SUVmax+SUVmean + Interaction) showed the best diagnostic performance (AUC=0.94). CONCLUSIONS:Patients with NPSLE and non-NPSLE showed distinct TSPO uptake in brain lesions, indicating different pathophysiology. TSPO PET/MR may serve as a potential imaging biomarker for differentiating NPSLE, providing insights for clinical diagnosis and mechanistic stratification in SLE.

Effective, safe, and accessible antivirals are needed to treat pregnant/postpartum (obstetric) patients with COVID-19, a group at high risk for severe disease. Newer antivirals (nirmatrelvir/ritonavir, remdesivir) are expensive and less accessible in low- and middle-income countries, including Brazil. Oseltamivir, a cheap, widely available, pregnancy-safe anti-influenza medication, was used off-label for COVID-19 in Brazil. The primary outcome was comparing in-hospital all-cause mortality in hospitalised obstetric patients with COVID-19 treated with oseltamivir versus no antivirals. Secondary outcomes were comparing the risk of progression to severe disease (ICU admission or death, whichever occurred first) and hospital discharge. In this retrospective matched cohort study using Brazil’s national surveillance database (SIVEP-Gripe), we identified hospitalised obstetric patients with PCR-confirmed COVID-19 between February 2020 and October 2023. Patients first receiving oseltamivir on day zero of admission and admitted within seven days of symptom onset were matched 1:1 using propensity scores to patients receiving no antivirals at all. After matching, 445 oseltamivir recipients and 445 controls were included, of whom 79.5

This study investigates immunity debt in five common cold viruses (adenovirus, parainfluenza virus, human metapneumovirus, rhinovirus, and respiratory syncytial virus) in England using weekly positivity rate data from UKHSA. An interrupted time series analysis assessed post-NPI effects with sensitivity analyses conducted to account for potential structural changes in viral dynamics across years. Results indicate significant immunity debt for adenovirus, parainfluenza virus, and respiratory syncytial virus. These findings highlight the need for public health preparedness post-NPI removal, particularly for vulnerable groups, and emphasize challenges in predicting immunity debt for viruses with high serotype diversity.

Irregular longitudinal data with informative visit times arise when patients' visits are partly driven by concurrent disease outcomes. However, existing methods such as inverse intensity weighting (IIW), often overlook or have not adequately assessed the influence of informative visit times on estimation and inference. Based on novel balancing weights estimators, we propose a new sensitivity analysis approach to addressing informative visit times within the IIW framework. The balancing weights are obtained by balancing observed history variable distributions over time and including a selection function with specified sensitivity parameters to characterize the additional influence of the concurrent outcome on the visit process. A calibration procedure is proposed to anchor the range of the sensitivity parameters to the amount of variation in the visit process that could be additionally explained by the concurrent outcome given the observed history and time. Simulations demonstrate that our balancing weights estimators outperform existing weighted estimators for robustness and efficiency. We provide an R Markdown tutorial of the proposed methods and apply them to analyse data from a clinic-based cohort of psoriatic arthritis.

The DECOVID database contains harmonized pseudonymized electronic health record (EHR) data on all adult (>= 18 years old) patients presenting to two large, digitally mature centers in the United Kingdom between 1 January 2020 and 28 February 2021, with follow-up until at least 28 March 2021. The database was originally developed to support the COVID-19 response but is now available via the PIONEER data hub for researchers to explore a wide range of research questions, including exploratory analyses, risk factor assessment, prediction modeling, and comparative effectiveness studies. Raw data were extracted from local EHRs and transformed into a standardized form (Observational Health Data Sciences and Informatics-Common Data Model version 5.3.1). The database includes 165,420 patients across 256,804 hospital presentations. For these patients, highly granular data are available, including patient demographics, longitudinal vital signs, physiology, treatments, laboratory findings, clinical diagnoses, and outcomes. There are 10,030 patients with COVID-19, of whom 1472 died in hospital.

Endemic coronaviruses (eCoVs) cause the common cold in humans, particularly affecting children, the elderly, and individuals with comorbidities, who are prone to infection-related hospitalization. While vaccination remains the most effective preventative strategy against infections, vaccines against eCoVs are not available. This study investigates the association between SARS-CoV-2 and influenza vaccination and reduced eCoV-related mortality risk. Data from Brazil’s nationwide hospital database included patients PCR-positive for one of four eCoV strains, with known admission and clinical endpoint dates, and either vaccinated against SARS-CoV-2 and/or influenza or unvaccinated. Cox regression assessed the vaccines’ effectiveness in reducing 90-day in-hospital all-cause mortality. Of 4,283,391 registered cases, 2,636 were eCoV infections. Influenza vaccination, primarily inactivated formulations, was associated with a 39% lower mortality hazard. Conversely, SARS-CoV-2 vaccination showed no significant mortality reduction. This disparity may stem from SARS-CoV-2 vaccines targeting the spike protein, which differs markedly from eCoV spike proteins, limiting cross-protection. In contrast, inactivated influenza vaccines may reduce eCoV mortality through trained innate immunity and cross-reactive cellular responses, offering broader protective effects against these viruses.

Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates. However, the backward induction algorithm used in Q-learning can introduce challenges, as nonignorable missing covariates at later stages can result in nonignorable missing pseudo-outcomes at earlier stages, leading to suboptimal DTRs, even if the longitudinal outcome variables are fully observed. To address this unique missing data problem in DTR settings, we propose two weighted Q-learning approaches where inverse probability weights for missingness of the pseudo-outcomes are obtained through estimating equations with valid nonresponse instrumental variables or sensitivity analysis. The asymptotic properties of the weighted Q-learning estimators are derived, and the finite-sample performance of the proposed methods is evaluated and compared with alternative methods through extensive simulation studies. Using EMR data from the Medical Information Mart for Intensive Care database, we apply the proposed methods to investigate the optimal fluid strategy for sepsis patients in intensive care units.

Sequential trial emulation (STE) is an approach to estimating causal treatment effects by emulating a sequence of target trials from observational data. In STE, inverse probability weighting is commonly utilised to address time-varying confounding and/or dependent censoring. Then structural models for potential outcomes are applied to the weighted data to estimate treatment effects. For inference, the simple sandwich variance estimator is popular but conservative, while nonparametric bootstrap is computationally expensive, and a more efficient alternative, linearised estimating function (LEF) bootstrap, has not been adapted to STE. We evaluated the performance of various methods for constructing confidence intervals (CIs) of marginal risk differences in STE with survival outcomes by comparing the coverage of CIs based on nonparametric/LEF bootstrap, jackknife, and the sandwich variance estimator through simulations. LEF bootstrap CIs demonstrated better coverage than nonparametric bootstrap CIs and sandwich-variance-estimator-based CIs with small/moderate sample sizes, low event rates and low treatment prevalence, which were the motivating scenarios for STE. They were less affected by treatment group imbalance and faster to compute than nonparametric bootstrap CIs. With large sample sizes and medium/high event rates, the sandwich-variance-estimator-based CIs had the best coverage and were the fastest to compute. These findings offer guidance in constructing CIs in causal survival analysis using STE.

Estimating optimal dynamic treatment regimes (DTRs) using observational data is often challenged by nonignorable missing covariates arsing from informative monitoring of patients in clinical practice. To address nonignorable missingness of pseudo-outcomes induced by nonignorable missing covariates, a weighted Q-learning approach using parametric Q-function models and a semiparametric missingness propensity model has recently been proposed. However, misspecification of parametric Q-functions at later stages of a DTR can propagate estimation errors to earlier stages via the pseudo-outcomes themselves and indirectly through biased estimation of the missingness propensity of the pseudo-outcomes. This robustness concern motivates us to develop a direct-search-based optimal DTR estimator built on a robust and efficient value estimator, where nonparametric methods are employed for treatment propensity and Q-function estimation, and inverse probability weighting is applied using missingness propensity estimated with the aid of nonresponse instrumental variables. Specifically, in our value estimator, we replace weights estimated by prediction models of treatment propensity with stable weights estimated by balancing covariate functions in a reproducing-kernel Hilbert space (RKHS). Augmented by Q-functions estimated by RKHS-based smoothing splines, our value estimator mitigates the misspecification risk of the weighted Q-learning approach while maintaining the efficiency gain from employing pseudo-outcomes in missing data scenarios. The asymptotic properties of the proposed estimator are derived, and simulations demonstrate its superior performance over weighted Q-learning under model misspecification. We apply the proposed methods to investigate the optimal fluid strategy for sepsis patients using data from the MIMIC database.

Influenza in dogs holds considerable public health significance due to their close companionship with humans, yet several facets of this phenomenon remain largely unexplored. This study undertook a systematic review and meta-analysis of observational studies to gauge the global seroprevalence of influenza in dogs. We also assessed whether pet dogs exhibited a higher seroprevalence of influenza compared to non-pet dogs, explored seasonal variations in seroprevalence, scrutinised the design and reporting standards of existing studies, and elucidated the geographical distribution of canine influenza virus (cIV). A comprehensive analysis of 97 studies spanning 27 countries revealed that seroprevalence of various influenza strains in dogs consistently registered below 10% and exhibited relative stability over the past decade. Significantly, we noted that seroprevalence of human influenza virus was notably higher in pet dogs compared to their non-pet counterparts, whereas seroprevalence of other influenza strains remained relatively uniform among both categories of dogs. Seasonal variations in seroprevalence of cIV were not observed. In summary, our findings indicated the global circulation of cIV strains H3N2 and H3N8, with other strains primarily confined to China. Given the lack of reported cases of the transmission of cIV from dogs to humans, our findings suggest a higher risk of reverse zoonosis than zoonosis. Finally, we strongly advocate for standardised reporting guidelines to underpin future canine influenza research endeavours.

Purpose This study investigated the topological structural characteristics of systemic lupus erythematosus (SLE) with and without neuropsychiatric symptoms (NPSLE and non-NPSLE), and explore their clinical implications.Methods We prospectively recruited 50 patients with SLE (21 non-NPSLE and 29 NPSLE) and 32 age-matched healthy controls (HCs), using MRI diffusion tensor imaging. Individual structural networks were constructed using fibre numbers between brain areas as edge weights. Global metrics (eg, small-worldness, global efficiency) and local network properties (eg, degree centrality, nodal efficiency) were computed. Group comparisons of network characteristics were conducted. Clinical correlations were assessed using partial correlation, and differentiation between non-NPSLE and NPSLE was performed using support vector classification.Results Patients with oth non-NPSLE and NPSLE exhibited significant global and local topological alterations compared with HCs. These changes were more pronounced in NPSLE, particularly affecting the default mode and sensorimotor networks. Topological changes in patients with SLE correlated with lesion burdens and clinical parameters such as disease duration and the systemic lupus international collaborating clinics damage index. The identified topological features enabled accurate differentiation between non-NPSLE and NPSLE with 87% accuracy.Conclusion Structural networks in patients SLE may be altered at both global and local levels, with more pronounced changes observed in NPSLE, notably affecting the default mode and sensorimotor networks. These alterations show promise as biomarkers for clinical diagnosis.

Randomised controlled trials (RCTs) are regarded as the gold standard for estimating causal treatment effects on health outcomes. However, RCTs are not always feasible, because of time, budget or ethical constraints. Observational data such as those from electronic health records (EHRs) offer an alternative way to estimate the causal effects of treatments. Recently, the `target trial emulation' framework was proposed by Hernan and Robins (2016) to provide a formal structure for estimating causal treatment effects from observational data. To promote more widespread implementation of target trial emulation in practice, we develop the R package TrialEmulation to emulate a sequence of target trials using observational time-to-event data, where individuals who start to receive treatment and those who have not been on the treatment at the baseline of the emulated trials are compared in terms of their risks of an outcome event. Specifically, TrialEmulation provides (1) data preparation for emulating a sequence of target trials, (2) calculation of the inverse probability of treatment and censoring weights to handle treatment switching and dependent censoring, (3) fitting of marginal structural models for the time-to-event outcome given baseline covariates, (4) estimation and inference of marginal intention to treat and per-protocol effects of the treatment in terms of marginal risk differences between treated and untreated for a user-specified target trial population. In particular, TrialEmulation can accommodate large data sets (e.g., from EHRs) within memory constraints of R by processing data in chunks and applying case-control sampling. We demonstrate the functionality of TrialEmulation using a simulated data set that mimics typical observational time-to-event data in practice.

BACKGROUND:Oseltamivir is a low-cost antiviral agent that could support or complement treatment of COVID-19. This study assessed whether oseltamivir is effective in reducing COVID-19-related mortality. METHODS:This retrospective cohort study evaluated real-world data from a nationwide database of hospitalisation due to severe acute respiratory syndrome in Brazil. Propensity score matching was used to mimic a randomised controlled trial with 'oseltamivir' and 'no antivirals at all' as the intervention and control groups, respectively. RESULTS:A total of 21 480 and 268 486 patients admitted between February 2020 and January 2023 were included in the intervention and control groups, respectively. After matching, the odds ratio (OR) for death was 0.901 (95% confidence interval [CI] 0.873-0.930). The OR (95% CI) for death in patients who were admitted to the ICU, and on non-invasive or invasive ventilation was 0.868 (0.821-0.917), 0.935 (0.893-0.980), and 0.883 (0.814-0.958), respectively. CONCLUSIONS:Overall, the use of oseltamivir was associated with an attributable risk reduction of 2.50% (95% CI 1.77-3.29). Similar results were observed in patients who were admitted to the ICU, and on non-invasive or invasive ventilation. Oseltamivir is a low-cost potential antiviral treatment for COVID-19.

COVID-19 continues to impact different parts of the world. In countries where the “living with COVID” strategy is adopted and endemic is likely to be declared, radical public health measures, such as lockdowns, have been and will be removed. However, the transmission of SARS-CoV-2 persists and waves continue to emerge, putting pressures on the healthcare system. Because the majority of the population have been immunised, the number of the vaccinated and recovered may not be informative measures to study transmission dynamics. Instead, it is of great public health importance to understand how outbreaks in different settings contribute to the transmission of the virus differently. This is particularly true for Japan since, rather than eradicating COVID-19, reducing the number of outbreaks and maintaining the medical system while normalizing social interactions are the main goals of the country's policy on COVID-19.1Song P. Mitsuya H. Kokudo N. COVID-19 in Japan: an update on national policy, research, clinical practice, and vaccination campaign.Glob Health Med. 2022; 4: 64-66https://doi.org/10.35772/ghm.2022.01036Crossref PubMed Google Scholar We demonstrated that outbreaks in certain settings contributed significantly to more confirmed cases than other settings did, using the publicly available nationwide data published by the Ministry of Health, Labour and Welfare2Ministry of Health, Labour and WelfareVisualizing the data: information on COVID-19 infections [Internet]. Ministry of Health, Labour and Welfare, Government of Japan, Tokyo2022https://covid19.mhlw.go.jp/en/Google Scholar and the Cabinet Secretariat3Cabinet Secretariat Measures against novel coronavirus infection [新型コロナウイルス感染症対策] [Internet]. Office for COVID-19 and Other Emerging Infectious Disease Control, Cabinet Secretariat, Government of Japan, Tokyo2022https://corona.go.jp/dashboard/Google Scholar of the Japanese Government. We collected the weekly data of the number of confirmed cases, the number of outbreaks in different settings, including medical institutions, care homes (for elderly, children, and individuals with disabilities), restaurants, sports facilities, schools and companies, and the ratio between the number of people at outdoor major points/crowds in Japan relative to that before the pandemic (to account for the impact of public health measures). Using negative binomial regression, we regressed the number of confirmed cases on the number of outbreaks in each type of settings (exposure variables), adjusting for the flow of people in public places and the number of confirmed cases in previous weeks. Details of data collection procedures, statistical analysis, and model diagnostics can be found in the Supplementary Materials. The data were downloaded from the web pages on 10th November 2022. Because of data availability, we included the weekly data dated between 1st January 2021 and 30th October 2022, totalling 95 observations that described 38,516 outbreaks and 27,156,511 confirmed cases. 12,515 (32.5%) outbreaks occurred in elderly homes and 8247 (21.4%) in schools. Other settings accounted for less than 13% of all outbreaks. Results of the multivariable negative binomial regression model are shown in Table 1. The outbreaks at medical institutions (p < 0.001), sports facilities (p < 0.001), care homes for disabilities (p = 0.035), and companies (p < 0.001) were found to be significantly associated (at 5% level) with the number of confirmed cases. An additional outbreak in a medical institution, sports facility, care homes for disabilities, and a company was associated with 2.0%, 8.0%, 2.2%, and 1.3% increase in the number of confirmed cases, respectively.Table 1Results of negative binomial regression.Dependent variable: Number of confirmed cases (weekly)SettingIncidence rate ratio (95% confidence interval)pMedical institutions1.020 (1.010,1.030)<0.001Care home (Disabilities)1.022 (1.002,1.044)0.035Sports facilities1.080 (1.042,1.119)<0.001Companies1.013 (1.006,1.021)<0.001Flow of people at 8am1.027 (1.003,1.051)0.030Lag 1−2.434 × 10−6 (−5.458 × 10−6, −5.902 × 10−7)aLog of incidence rate ratio.0.115Lag 2−2.977 × 10−6 (−7.217 × 10−6, −1.263 × 10−6)aLog of incidence rate ratio.0.169Lag 35.086 × 10−6 (−2.624 × 10−6, −7.459 × 10−6)aLog of incidence rate ratio.<0.001a Log of incidence rate ratio. Open table in a new tab Surprisingly, while elderly homes accounted for most of the outbreaks, they did not make significant contribution to the number of confirmed cases, suggesting effective measures in reducing the transmission of the virus. Instead, outbreaks in other settings, sports facilities in particular, contributed significantly to the transmission. Although no spectators were allowed in the Summer Olympics in Tokyo, other sports events were permitted to have at most 5000 spectators, or 50% of the venue capacity. Our analysis can inform public health policies on sporadic and more targeted measures. First, the difference in the impact on the number of confirmed cases between settings, suggesting that public health policies should aim beyond reducing the number of outbreaks. That is, more effective measures should be implemented to isolate these facilities from infection and to curb the transmission within these facilities. Second, although the model did not show that outbreaks in elderly homes made significant contribution to the number of confirmed cases, effective infection control should remain in elderly homes. Moreover, it might be due to confounding. It is possible that cases originated in elderly homes led to hospital outbreaks upon hospitalisation. In Japan, infected residents of elderly homes are admitted to long-term care wards with different management and infection control. Third, the difference in the impact on disease dynamics between settings might be attributed to heterogeneous compliance among lockdowns. Unlike the large scale lockdowns in other countries, sporadic lockdowns have been used by the Japanese Government for infection control.4Kodama S. Campbell M. Tanaka M. Inoue Y. Understanding Japan's response to the COVID-19 pandemic.J Med Ethics. 2022; 48: 173https://doi.org/10.1136/medethics-2022-108189Crossref PubMed Scopus (7) Google Scholar However, the effectiveness of each lockdown largely depends on the voluntary compliance of the public. Prefectural governors were given authority to request quarantine from the public but without penalties for non-compliance. One exception was the financial punishment imposed on businesses that fail to comply with requests to suspend operations.4Kodama S. Campbell M. Tanaka M. Inoue Y. Understanding Japan's response to the COVID-19 pandemic.J Med Ethics. 2022; 48: 173https://doi.org/10.1136/medethics-2022-108189Crossref PubMed Scopus (7) Google Scholar However, it is rarely enforced. In summary, we demonstrated that outbreaks in different settings contributed differently to the transmission of SARS-CoV-2. Certain settings with a lower number of outbreaks were found to make more significant contribution to the number of confirmed cases. CL: study design, literature review, data collection, data analysis, manuscript drafting; LS: data analysis, interpretation of data; MM: interpretation of data. Data available upon reasonable request. Not required in the UK and Japan. Publicly available data were used. None. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. This study was supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and Leicester NIHR Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Funding: None. Download .docx (.39 MB) Help with docx files Supplementary Materials

Background We aimed to identify factors associated with a significant reduction in SLE disease activity over 12 months assessed by the BILAG Index. Methods In an international SLE cohort, we studied patients from their ‘inception enrolment’ visit. We also defined an ‘active disease’ cohort of patients who had active disease similar to that needed for enrolment into clinical trials. Outcomes at 12 months were; Major Clinical Response (MCR: reduction to classic BILAG C in all domains, steroid dose of ≤7.5 mg and SLEDAI ≤ 4) and ‘Improvement’ (reduction to ≤1B score in previously active organs; no new BILAG A/B; stable or reduced steroid dose; no increase in SLEDAI). Univariate and multivariate logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) and cross-validation in randomly split samples were used to build prediction models. Results ‘Inception enrolment’ ( n = 1492) and ‘active disease’ ( n = 924) patients were studied. Models for MCR performed well (ROC AUC = .777 and .732 in the inception enrolment and active disease cohorts, respectively). Models for Improvement performed poorly (ROC AUC = .574 in the active disease cohort). MCR in both cohorts was associated with anti-malarial use and inversely associated with active disease at baseline (BILAG or SLEDAI) scores, BILAG haematological A/B scores, higher steroid dose and immunosuppressive use. Conclusion Baseline predictors of response in SLE can help identify patients in clinic who are less likely to respond to standard therapy. They are also important as stratification factors when designing clinical trials in order to better standardize overall usual care response rates.

Down syndrome is the most common human chromosomal disorder. Whether Down syndrome is a risk factor for severe COVID-19 outcomes in pediatric patients remains unclear, especially in low-to-middle income countries. We gathered data on patients <18 years of age with SARS-CoV-2 infection from a national registry in Brazil to assess the risk for severe outcomes among patients with Down syndrome. We included data from 14,684 hospitalized patients, 261 of whom had Down syndrome. After adjustments for sociodemographic and medical factors, patients with Down syndrome had 1.8 times higher odds of dying from COVID-19 (odds ratio 1.82, 95% CI 1.22–2.68) and 27% longer recovery times (hazard ratio 0.73, 95% CI 0.61–0.86) than patients without Down syndrome. We found Down syndrome was associated with increased risk for severe illness and death among COVID-19 patients. Guidelines for managing COVID-19 among pediatric patients with Down syndrome could improve outcomes for this population.

Objectives Ischemic cerebrovascular disease (ICVD) is one of the most common and severe complications in systemic lupus erythematosus (SLE). We aim to explore the risk factors for ICVD in SLE and to assess their associated clinical characteristics. Methods In this study, 44 lupus patients with ICVD (ICVD-SLE) and 80 age- and sex-matched lupus patients without ICVD (non-ICVD-SLE) who were hospitalized in our center between 2014 and 2021 were enrolled. A comprehensive set of clinical and socio-demographic data was recorded. In the ICVD-SLE group, the modified Rankin score (mRS) at 90 days after the occurrence of ICVD, the brain MRI, and arterial ultrasonography findings were collected. Group comparisons were made with continuous variables using an independent t-test or the Mann–Whitney test, and with categorical variables using the chi-square test or Fisher exact test. Multivariate logistic regression analysis was performed to identify the risk factors for ICVD in SLE. Patients with ICVD-SLE were divided into three subgroups according to the gradations of intracranial arterial stenosis (ICAS). The subgroup comparisons were performed by one-way ANOVA test or Kruskal–Wallis test. Results Of the 44 patients with ICVD, 45% had a large-vessel ischemic stroke, 50% had a symptomatic lacunar stroke, and 9% had a transient ischemic attack. 2 (4.5%) had both large-vessel ischemic stroke and symptomatic lacunar stroke. Multivariate logistic regression analysis showed that cutaneous vasculitis (OR=7.36, 95% CI=2.11–25.65), anticardiolipin antibody (aCL) (OR=4.38, 95% CI=1.435–13.350), and lupus anticoagulant (LA) (OR=7.543,95% CI=1.789–31.808) were the risk factors, and hydroxychloroquine (HCQ) therapy (OR=0.198, 95% CI=0.078–0.502) was the protective factor, after controlling for confounders. During the analysis of the subgroups, no significant difference was observed between the patients in the group without internal carotid arterial occlusion (ICAS) and those with severe ICAS except for diagnostic delay. However, patients in the moderate ICAS group were older when SLE occurred (P<0.01), had a longer diagnostic delay (P<0.01), a lower percentage of hypocomplementemia (P=0.05) and steroids and HCQ therapy (P=0.01, P=0.05, respectively), a trend toward lower mRS score, but a higher incidence of carotid atherosclerotic plaque (P<0.01), when compared with the other two subgroups. Conclusion Cutaneous vasculitis and antiphospholipid antibodies (aPLs) are associated with an increased risk of ICVD, while HCQ therapy may provide protection against ICVD in SLE. The ICVD in younger lupus patients is associated with complement-mediated inflammation and poorer outcome, and require immunosuppressive therapy, whereas the ICVD in elderly patients are characterized by moderate ICAS and carotid atherosclerotic plaques.

Objective COVID-19 in post-partum women is commonly overlooked. The present study assessed whether puerperium is an independent risk factor of COVID-19 related in-hospital maternal death and whether fatality is preventable in the Brazilian context. Methods We retrospectively studied the clinical data of post-partum/pregnant patients hospitalized with COVID-19 gathered from a national database that registered severe acute respiratory syndromes (SIVEP-Gripe) in Brazil. Logistic regressions were used to examine the associations of in-hospital mortality with obstetric status and with the type of public healthcare provider, adjusting for socio-demographic, epidemiologic, clinical and healthcare-related measures. Results As of 30 November 2021, 1943 (21%) post-partum and 7446 (79%) pregnant patients of age between 15 and 45 years with COVID-19 that had reached the clinical endpoint (death or discharge) were eligible for inclusion. Case-fatality rates for the two groups were 19.8% and 9.2%, respectively. After the adjustment for covariates, post-partum patients had almost twice the odds of in-hospital mortality compared with pregnant patients. Patients admitted to private (not-for-profit) hospitals, those that had an obstetric centre or those located in metropolitan areas were less likely to succumb to SARS-CoV-2 infection. Those admitted to the Emergency Care Unit had similar mortality risk to those admitted to other public healthcare providers. Conclusion We demonstrated that puerperium was associated with an increased odds of COVID-19-related in-hospital mortality. Only part of the risk can be reduced by quality healthcare such as non-profit private hospitals, those that have an obstetric centre or those located in urban areas.