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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 ≤7.5mg and SLEDAI ≤4) and ‘Improvement’ (reduction to

Inverse probability of censoring weighting is a popular approach to handling dropout in longitudinal studies. However, inverse probability-of-censoring weighted estimators (IPCWEs) can be inefficient and unstable if the weights are estimated by maximum likelihood. To alleviate these problems, calibrated IPCWEs have been proposed, which use calibrated weights that directly optimize covariate balance in finite samples rather than the weights from maximum likelihood. However, the existing calibrated IPCWEs are all based on the unverifiable assumption of sequential ignorability and sensitivity analysis strategies under non-ignorable dropout are lacking. In this paper, we fill this gap by developing an approach to sensitivity analysis for calibrated IPCWEs under non-ignorable dropout. A simple technique is proposed to speed up the computation of bootstrap and jackknife confidence intervals and thus facilitate sensitivity analyses. We evaluate the finite-sample performance of the proposed methods using simulations and apply our methods to data from an international inception cohort study of systemic lupus erythematosus. An R Markdown tutorial to demonstrate the implementation of the proposed methods is provided.

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.

OBJECTIVES: To identify predictors of overall lupus and lupus nephritis (LN) responses in patients with LN. METHODS: Data from the Aspreva Lupus Management Study (ALMS) trial cohort was used to identify baseline predictors of response at 6 months. Endpoints were major clinical response (MCR), improvement, complete renal response (CRR) and partial renal response (PRR). Univariate and multivariate logistic regressions with least absolute shrinkage and selection operator (LASSO) and cross-validation in randomly split samples were utilised. Predictors were ranked by the percentage of times selected by LASSO and prediction performance was assessed by the area under the receiver operating characteristics (AUROC) curve. RESULTS: We studied 370 patients in the ALMS induction trial. Improvement at 6 months was associated with older age (OR=1.03 (95% CI: 1.01 to 1.05) per year), normal haemoglobin (1.85 (1.16 to 2.95) vs low haemoglobin), active lupus (British Isles Lupus Assessment Group A or B) in haematological and mucocutaneous domains (0.61 (0.39 to 0.97) and 0.50 (0.31 to 0.81)), baseline damage (SDI>1 vs =0) (0.38 (0.16 to 0.91)) and 24-hour urine protein (0.63 (0.50 to 0.80)). LN duration 2-4 years (0.43 (0.19 to 0.97) vs

Aims: Novel oral iron supplements may be associated with a reduced incidence of adverse drug reactions compared to standard treatments of iron deficiency anaemia. The aim was to establish their value‐based price under conditions of uncertainty surrounding their tolerability. Methods: A discrete‐time Markov model was developed to assess the value‐based price of oral iron preparations based on their incremental cost per quality‐adjusted life year (QALY) gained from the perspective of the NHS in the UK. Primary and secondary care resource use and health state occupancy probabilities were estimated from routine electronic health records; and unit costs and health state utilities were derived from published sources. Patients were pre‐menopausal women with iron deficiency anaemia who were prescribed oral iron supplementation between 2000 and 2014. Results: The model reflecting current use of iron salts yielded a mean total cost to the NHS of £779, and 0.84 QALYs over 12 months. If a new iron preparation were to reduce the risk of adverse drug reactions by 30–40%, then its value‐based price, based on a threshold of £20 000 per QALY, would be in the region of £10–£13 per month, or about 7–9 times the average price of basic iron salts. Conclusions: There are no adequate, direct comparisons of new oral iron supplements to ferrous iron salts, and therefore other approaches are needed to assess their value. Our modelling shows that they are potentially cost‐effective at prices that are an order of magnitude higher than existing iron salts.

Marginal structural models (MSMs) with inverse probability weighted estimators (IPWEs) are widely used to estimate causal effects of treatment sequences on longitudinal outcomes in the presence of time-varying confounding and dependent censoring. However, IPWEs for MSMs can be inefficient and unstable if weights are estimated by maximum likelihood. To improve the performance of IPWEs, covariate balancing weight (CBW) methods have been proposed and recently extended to MSMs. However, existing CBW methods for MSMs are inflexible for practical use because they often do not handle dependent censoring, nonbinary treatments, and longitudinal outcomes (instead of eventual outcomes at a study end). In this paper, we propose a joint calibration approach to CBW estimation for MSMs that can accommodate (1) both time-varying confounding and dependent censoring, (2) binary and nonbinary treatments, (3) eventual outcomes and longitudinal outcomes. We develop novel calibration restrictions by jointly eliminating covariate associations with both treatment assignment and censoring processes after weighting the observed data sample (i.e., to optimize covariate balance in finite samples). Two different methods are proposed to implement the calibration. Simulations show that IPWEs with calibrated weights perform better than IPWEs with weights from maximum likelihood and the "Covariate Balancing Propensity Score" method. We apply our method to a natural history study of HIV for estimating the effects of highly active antiretroviral therapy on CD4 cell counts over time.

Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution.

Shared parameter models (SPMs) are a useful approach to addressing bias from informative dropout in longitudinal studies. In SPMs it is typically assumed that the longitudinal outcome process and the dropout time are independent, given random effects and observed covariates. However, this conditional independence assumption is unverifiable. Currently, sensitivity analysis strategies for this unverifiable assumption of SPMs are underdeveloped. In principle, parameters that can and cannot be identified by the observed data should be clearly separated in sensitivity analyses, and sensitivity parameters should not influence the model fit to the observed data. For SPMs this is difficult because it is not clear how to separate the observed data likelihood from the distribution of the missing data given the observed data (i.e., ‘extrapolation distribution’). In this article, we propose a new approach for transparent sensitivity analyses for informative dropout that separates the observed data likelihood and the extrapolation distribution, using a typical SPM as a working model for the complete data generating mechanism. For this model, the default extrapolation distribution is a skew-normal distribution (i.e., it is available in a closed form). We propose anchoring the sensitivity analysis on the default extrapolation distribution under the specified SPM and calibrate the sensitivity parameters using the observed data for subjects who drop out. The proposed approach is used to address informative dropout in the HIV Epidemiology Research Study.

OBJECTIVE: To determine the frequency, associations and outcomes of cerebrovascular events (CerVEs) in a multi-ethnic/racial, prospective, SLE disease inception cohort. METHODS: Patients were assessed annually for 19 neuropsychiatric (NP) events including 5 types of CerVEs: (i) Stroke; (ii) Transient ischemia; (iii) Chronic multifocal ischemia; (iv) Subarachnoid/intracranial hemorrhage; (v) Sinus thrombosis. Global disease activity (SLEDAI-2K), SLICC/ACR damage index (SDI) and SF-36 scores were collected. Time to event, linear and logistic regressions and multi-state models were used as appropriate. RESULTS: Of 1,826 SLE patients, 88.8% were female, 48.8% Caucasian, mean±SD age 35.1±13.3 years, disease duration 5.6±4.2 months and follow-up 6.6±4.1 years. CerVEs were the fourth most frequent NP event: 82/1,826 (4.5%) patients had 109 events, 103/109 (94.5%) were attributed to SLE and 44/109 (40.4%) were identified at enrollment. The predominant events were stroke [60/109 (55.0%)] and transient ischemia [28/109 (25.7%)]. CerVEs were associated with other NP events attributed to SLE (HR (95% CI): (3.16; 1.73-5.75) (p

Background— Chronic thromboembolic pulmonary hypertension results from incomplete resolution of pulmonary emboli. Pulmonary endarterectomy (PEA) is potentially curative, but residual pulmonary hypertension following surgery is common and its impact on long-term outcome is poorly understood. We wanted to identify factors correlated with poor long-term outcome after surgery and specifically define clinically relevant residual pulmonary hypertension post-PEA. Methods and Results— Eight hundred eighty consecutive patients (mean age, 57 years) underwent PEA for chronic thromboembolic pulmonary hypertension. Patients routinely underwent detailed reassessment with right heart catheterization and noninvasive testing at 3 to 6 months and annually thereafter with discharge if they were clinically stable at 3 to 5 years and did not require pulmonary vasodilator therapy. Cox regressions were used for survival (time-to-event) analyses. Overall survival was 86%, 84%, 79%, and 72% at 1, 3, 5, and 10 years for the whole cohort and 91% and 90% at 1 and 3 years for the recent half of the cohort. The majority of patient deaths after the perioperative period were not attributable to right ventricular failure (chronic thromboembolic pulmonary hypertension). At reassessment, a mean pulmonary artery pressure of ≥30 mm Hg correlated with the initiation of pulmonary vasodilator therapy post-PEA. A mean pulmonary artery pressure of ≥38 mm Hg and pulmonary vascular resistance ≥425 dynes·s −1 ·cm −5 at reassessment correlated with worse long-term survival. Conclusions— Our data confirm excellent long-term survival and maintenance of good functional status post-PEA. Hemodynamic assessment 3 to 6 months and 12 months post-PEA allows stratification of patients at higher risk of dying of chronic thromboembolic pulmonary hypertension and identifies a level of residual pulmonary hypertension that may guide the long-term management of patients postsurgery.

Summary Weighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions for weight estimation by eliminating the associations between these covariates and treatment assignment characterized in a chosen treatment assignment model after weighting. The moment conditions in covariate balancing weight methods for binary, categorical and continuous treatments in cross-sectional settings are special cases of the conditions in our framework, which extends to longitudinal settings. Simulation shows that our method gives treatment effect estimates with smaller biases and variances than the maximum likelihood approach under treatment assignment model misspecification. We illustrate our method with an application to systemic lupus erythematosus data.

Objective: Little is known about the long-term costs of lupus nephritis (LN). The costs were compared between patients with and without LN using multistate modeling. Methods: Patients from 32 centers in 11 countries were enrolled in the Systemic Lupus International Collaborating Clinics inception cohort within 15 months of diagnosis and provided annual data on renal function, hospitalizations, medications, dialysis, and selected procedures. LN was diagnosed by renal biopsy or the American College of Rheumatology classification criteria. Renal function was assessed annually using the estimated glomerular filtration rate (GFR) or estimated proteinuria. A multistate model was used to predict 10-year cumulative costs by multiplying annual costs associated with each renal state by the expected state duration. Results: A total of 1,545 patients participated; 89.3% were women, the mean ± age at diagnosis was 35.2 ± 13.4 years, 49% were white, and the mean followup duration was 6.3 ± 3.3 years. LN developed in 39.4% of these patients by the end of followup. Ten-year cumulative costs were greater in those with LN and an estimated glomerular filtration rate (GFR) 60 ml/minute) or with LN and estimated proteinuria >3 gm/day ($84,040 versus $20,499 if no LN and estimated proteinuria

Summary Bidirectional changes over time in the estimated glomerular filtration rate and in urine protein content are of interest for the treatment and management of patients with lupus nephritis. Although these processes may be modelled by separate multistate models, the processes are likely to be correlated within patients. Motivated by the lupus nephritis application, we develop a new multistate modelling framework where subject-specific random effects are introduced to account for the correlations both between the processes and within patients over time. Models are fitted by using bespoke code in standard statistical software. A variety of forms for the random effects are introduced and evaluated by using the data from the Systemic Lupus International Collaborating Clinics.

Within the pattern-mixture modeling framework for informative dropout, conditional linear models (CLMs) are a useful approach to deal with dropout that can occur at any point in continuous time (not just at observation times). However, in contrast with selection models, inferences about marginal covariate effects in CLMs are not readily available if nonidentity links are used in the mean structures. In this article, we propose a CLM for long series of longitudinal binary data with marginal covariate effects directly specified. The association between the binary responses and the dropout time is taken into account by modeling the conditional mean of the binary response as well as the dependence between the binary responses given the dropout time. Specifically, parameters in both the conditional mean and dependence models are assumed to be linear or quadratic functions of the dropout time; and the continuous dropout time distribution is left completely unspecified. Inference is fully Bayesian. We illustrate the proposed model using data from a longitudinal study of depression in HIV-infected women, where the strategy of sensitivity analysis based on the extrapolation method is also demonstrated.

Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administrative censoring. Specifically, we assume that the longitudinal outcome process depends on the dropout process through its model parameters. The unconditional distribution of the repeated measures is a mixture over the dropout (administrative censoring) time distribution, and the continuous dropout time distribution with administrative censoring is left completely unspecified. We use Markov chain Monte Carlo to sample from the posterior distribution of the repeated measures given the dropout (administrative censoring) times; Bayesian bootstrapping on the observed dropout (administrative censoring) times is carried out to obtain marginal covariate effects. We illustrate the proposed framework using data from a longitudinal study of depression in HIV-infected women; the strategy for sensitivity analysis on unverifiable assumption is also demonstrated.

OBJECTIVE: To explore methods for statistical modelling of minimal disease activity (MDA) based on data from intermittent clinic visits. METHODS: The analysis was based on a 2-state model. Comparisons were made between analyses based on "complete case" data from visits at which MDA status was known, and the use of hidden model methodology that incorporated information from visits at which only some MDA defining criteria could be established. Analyses were based on an observational psoriatic arthritis cohort. RESULTS: With data from 856 patients and 7,024 clinic visits, analysis was based on virtually all visits, although only 62.6% provided enough information to determine MDA status. Estimated mean times for an episode of MDA varied from 4.18 years to 3.10 years, with smaller estimates derived from the hidden 2-state model analysis. Over a 10-year period, the estimated expected times spent in MDA episodes of longer than 1 year was 3.90 to 4.22, and the probability of having such an MDA episode was estimated to be 0.85 to 0.91, with longer times and greater probabilities seen with the hidden 2-state model analysis. CONCLUSION: A 2-state model provides a useful framework for the analysis of MDA. Use of data from visits at which MDA status can not be determined provide more precision, and notable differences are seen in estimated quantities related to MDA episodes based on complete case and hidden 2-state model analyses. The possibility of bias, as well as loss of precision, should be recognized when complete case analyses are used.

Summary Both dropout and death can truncate observation of a longitudinal outcome. Since extrapolation beyond death is often not appropriate, it is desirable to obtain the longitudinal outcome profile of a population given being alive. We propose a new likelihood-based approach to accommodating informative dropout and death by jointly modelling the longitudinal outcome and semicompeting event times of dropout and death, with an important feature that the conditional longitudinal profile of being alive can be conveniently obtained in a closed form. We use proposed methods to estimate different longitudinal profiles of CD4 cell count for patients from the ‘HIV Epidemiology Research Study’.

OBJECTIVE: To determine nephritis outcomes in a prospective multi-ethnic/racial SLE inception cohort.METHODS: Patients in the Systemic Lupus International Collaborating Clinics inception cohort (≤15 months of SLE diagnosis) were assessed annually for estimated glomerular filtration rate (eGFR), proteinuria and end-stage renal disease (ESRD). Health-related quality of life was measured by the Short Form (36 questions) health survey questionnaire (SF-36) subscales, mental and physical component summary scores.RESULTS: There were 1827 patients, 89% females, mean (s.d.) age 35.1 (13.3) years. The mean (s.d.) SLE duration at enrolment was 0.5 (0.3) years and follow-up 4.6 (3.4) years. LN occurred in 700 (38.3%) patients: 566/700 (80.9%) at enrolment and 134/700 (19.1%) during follow-up. Patients with nephritis were younger, more frequently men and of African, Asian and Hispanic race/ethnicity. The estimated overall 10-year incidence of ESRD was 4.3% (95% CI: 2.8%, 5.8%), and with nephritis was 10.1% (95% CI: 6.6%, 13.6%). Patients with nephritis had a higher risk of death (HR = 2.98, 95% CI: 1.48, 5.99; P = 0.002) and those with eGFR CONCLUSION: LN occurred in 38.3% of SLE patients, frequently as the initial presentation, in a large multi-ethnic inception cohort. Despite current standard of care, nephritis was associated with ESRD and death, and renal insufficiency was linked to lower health-related quality of life. Further advances are required for the optimal treatment of LN.

We contribute to the empirical literature on household finances by introducing a Bayesian multivariate two-part model, which has been developed to further our understanding of household finances. Our flexible approach allows for the potential interdependence between the holding of assets and liabilities at the household level and also encompasses a two-part process to allow for differences in the influences on asset or liability holding and on the respective amounts held. Furthermore, the framework is dynamic in order to allow for persistence in household finances over time. Our findings endorse the joint modelling approach and provide evidence supporting the importance of dynamics. In addition, we find that certain independent variables exert different influences on the binary and continuous parts of the model thereby highlighting the flexibility of our framework and revealing a detailed picture of the nature of household finances.

In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive definite. Moreover, in certain settings, careful modeling of the covariance structure for irregular longitudinal data can be crucial in order to ensure no bias arises in the mean structure. Two common settings where this occurs are studies with ‘outcome-dependent follow-up’ and studies with ‘ignorable missing data’. ‘Outcome-dependent follow-up’ occurs when individuals with a history of poor health outcomes had more follow-up measurements, and the intervals between the repeated measurements were shorter. When the follow-up time process only depends on previous outcomes, likelihood-based methods can still provide consistent estimates of the regression parameters, given that both the mean and covariance structures of the irregular longitudinal data are correctly specified and no model for the follow-up time process is required. For ‘ignorable missing data’, the missing data mechanism does not need to be specified, but valid likelihood-based inference requires correct specification of the covariance structure. In both cases, flexible modeling approaches for the covariance structure are essential. In this paper, we develop a flexible approach to modeling the covariance structure for irregular continuous longitudinal data using the partial autocorrelation function and the variance function. In particular, we propose semiparametric non-stationary partial autocorrelation function models, which do not suffer from complex positive definiteness restrictions like the autocorrelation function. We describe a Bayesian approach, discuss computational issues, and apply the proposed methods to CD4 count data from a pediatric AIDS clinical trial. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Objective The aim of this study was to describe the outcomes and predictors for development of damage in a large inception cohort of SLE patients. Methods This was a prospective longitudinal study of a cohort of SLE patients. SLE patients were included if they were recruited within 3 years of achieving the fourth ACR criterion for SLE. Data were collected on disease activity, damage and treatment. Information on death was provided by the Office for National Statistics. The censoring date for analysis was 31 December 2010. A standardized mortality ratio was calculated. Poisson regression was used to determine the incidence rate for damage accrual. Multistate Markov modelling was used to determine predictors for development of damage. Results There were 382 patients (92.4% females, 51.6% Caucasian, 22% South Asian, 20.7% Afro-Caribbean) with 12 072 assessments and total follow-up of 2958 patient-years. There were 300 items of damage (in 143 patients) and 37 deaths. The overall standardized mortality ratio was 2.0 (95% CI 1.5, 2.8) and the most common causes of death were infection (37.8%), cardiovascular (27%) and malignancy (13.5%). The predictors for damage accrual were higher prior damage, older age at diagnosis, active disease, systemic corticosteroid exposure and CYC exposure. Patients were more likely to develop new damage earlier in their disease than later. Ethnicity was not predictive of damage accrual or death in this cohort. Conclusion SLE patients have premature mortality. Active disease, corticosteroid exposure and CYC exposure were independently associated with the development of damage. Damage accrual is more likely to occur in early disease.

Joint models for longitudinal and time-to-event data are particularly relevant to many clinical studies where longitudinal biomarkers could be highly associated with a time-to-event outcome. A cutting-edge research direction in this area is dynamic predictions of patient prognosis (e.g., survival probabilities) given all available biomarker information, recently boosted by the stratified/personalized medicine initiative. As these dynamic predictions are individualized, flexible models are desirable in order to appropriately characterize each individual longitudinal trajectory. In this paper, we propose a new joint model using individual-level penalized splines (P-splines) to flexibly characterize the coevolution of the longitudinal and time-to-event processes. An important feature of our approach is that dynamic predictions of the survival probabilities are straightforward as the posterior distribution of the random P-spline coefficients given the observed data is a multivariate skew-normal distribution. The proposed methods are illustrated with data from the HIV Epidemiology Research Study. Our simulation results demonstrate that our model has better dynamic prediction performance than other existing approaches.

For semi-continuous data which are a mixture of true zeros and continuously distributed positive values, the use of two-part mixed models provides a convenient modelling framework. However, deriving population-averaged (marginal) effects from such models is not always straightforward. Su et al. presented a model that provided convenient estimation of marginal effects for the logistic component of the two-part model but the specification of marginal effects for the continuous part of the model presented in that paper was based on an incorrect formulation. We present a corrected formulation and additionally explore the use of the two-part model for inferences on the overall marginal mean, which may be of more practical relevance in our application and more generally.

Time-to-event and similar analyses can be problematic if the event of interest is operationally defined by some condition being true for a prolonged period of time. A particular example of this, remission in psoriatic arthritis, is considered in detail for illustration. A 3-state model is proposed for characterizing the transition rates into and out of remission. Remission is linked to an initial and subsequent state for the purpose of introducing the condition that remission must be of some duration to be clinically meaningful. The model is compared with alternative approaches that have been used in such situations. These involve 2-state models where the duration of remission is allowed for through different definitions for the time of entry into remission. Both definitions are linked to prolonged observation of a particular clinical state.

Semicontinuous data in the form of a mixture of zeros and continuously distributed positive values frequently arise in biomedical research. Two-part mixed models with correlated random effects are an attractive approach to characterize the complex structure of longitudinal semicontinuous data. In practice, however, an independence assumption about random effects in these models may often be made for convenience and computational feasibility. In this article, we show that bias can be induced for regression coefficients when random effects are truly correlated but misspecified as independent in a 2-part mixed model. Paralleling work on bias under nonignorable missingness within a shared parameter model, we derive and investigate the asymptotic bias in selected settings for misspecified 2-part mixed models. The performance of these models in practice is further evaluated using Monte Carlo simulations. Additionally, the potential bias is investigated when artificial zeros, due to left censoring from some detection or measuring limit, are incorporated. To illustrate, we fit different 2-part mixed models to the data from the University of Toronto Psoriatic Arthritis Clinic, the aim being to examine whether there are differential effects of disease activity and damage on physical functioning as measured by the health assessment questionnaire scores over the course of psoriatic arthritis. Some practical issues on variance component estimation revealed through this data analysis are considered.

OBJECTIVE: To examine the frequency, characteristics, and outcome of mood disorders, as well as clinical and autoantibody associations, in a multiethnic/racial, prospective inception cohort of patients with systemic lupus erythematosus (SLE). METHODS: Patients were assessed annually for mood disorders (4 types, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) and 18 other neuropsychiatric events. Global disease activity scores (SLE Disease Activity Index 2000 [SLEDAI-2K]), damage scores (Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index [SDI]), and Short Form 36 subscales, mental and physical component summary scores were collected. Time to event, linear and ordinal regressions, and multi-state models were used as appropriate. RESULTS: Among the 1,827 patients with SLE, 88.9% were female, and 48.9% were Caucasian. The mean ± SD age of the patients was 35.1 ± 13.3 years, disease duration was 5.6 ± 4.8 months, and the length of followup was 4.7 ± 3.5 years. During the course of the study, 863 (47.2%) of the 1,827 patients had 1,627 neuropsychiatric events. Mood disorders occurred in 232 (12.7%) of 1,827 patients, and 98 (38.3%) of 256 mood disorder events were attributed to SLE. The estimated cumulative incidence of any mood disorder after 10 years was 17.7% (95% confidence interval 15.1, 20.2%). A greater risk of mood disorder was associated with concurrent neuropsychiatric events (P ≤ 0.01), and a lower risk was associated with Asian race/ethnicity (P = 0.01) and treatment with immunosuppressive drugs (P = 0.003). Mood disorders were associated with lower mental health and mental component summary scores but not with the SLEDAI-2K, SDI, or lupus autoantibodies. Among the 232 patients with depression, 168 (72.4%) were treated with antidepressants. One hundred twenty-six (49.2%) of 256 mood disorders resolved in 117 (50.4%) of 232 patients. CONCLUSION: Mood disorders, the second most frequent neuropsychiatric event in patients with SLE, have a negative impact on health-related quality of life and improve over time. The lack of association with global SLE disease activity, cumulative organ damage, and lupus autoantibodies emphasizes the multifactorial etiology of mood disorders and a role for non-lupus-specific therapies.

Pulmonary vascular resistance (PVR) is traditionally used to describe pulmonary hemodynamic characteristics. However, it does not take into account pulmonary artery compliance (Ca) or pulsatile flow. The product of PVR and Ca is known as RC time. Previous studies assert that the PVR-Ca relationship is fixed and RC time is constant between health and disease states. We hypothesized that RC time was not constant in health and pulmonary vascular disease. Right heart catheterizations performed in Papworth Hospital over a 6 year period were analyzed. Subjects were divided into those with normal pulmonary hemodynamics (NPH group; n = 156) and pulmonary arterial hypertension (PAH group; n = 717). RC time and the right ventricle (RV) oscillatory power fraction were calculated. RC time for the NPH group (0.47 ± 0.13 sec) is significantly lower than the PAH group (0.56 ± 0.16 sec; P < 0.0001). The RV oscillatory power fraction is lower in the NPH group (P < 0.0001). RC time correlates inversely with the RV oscillatory power fraction in each group. We conclude, there is an inverse relationship between PVR and Ca, however, this relationship is not always fixed. Consequently, RC time is significantly lower in health compared to disease with elevated pulmonary artery pressures. PAH leads to a decrease in cardiac efficiency.

Down syndrome (DS) is the most common chromosomal disorder. It is not clear if it is a risk factor for COVID-19 mortality and morbidity in pediatric patients, those in low-to-middle income countries in particular. Data of participants aged below 18 years with SARS-CoV-2 infection were gathered from a national registry in Brazil. A total of 14,684 hospitalized patients were included, including 261 patients with DS. After adjusted for sociodemographic and medical factors, participants with DS had 1.8 times higher odds of dying from COVID-19 (OR 1.82, 95% CI 1.22-2.68) and had 27% longer time to recovery (HR 0.73, 95% CI 0.61-0.86). Down syndrome was found to be associated with increased risk for mortality and morbidity. The increased risk of death among people with DS may be due to social factors and biological factors such as immune responses.