Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study

Qualitative assessment of the impact of restricting number of drug treatments for rheumatoid arthritis: Experience of prescribers and patients

INTRODUCTION

In 2020, almost half of all Clinical Commissioning Groups in England were restricting the number of higher cost drugs (HCDs) that could be prescribed for Rheumatoid Arthritis (RA) before an Individual Funding Request was required. We were interested in qualitatively exploring the experiences of prescribers affected by these restrictions and the experiences of patients who required four or more of these drugs.

METHODS

Semi-structured interviews were conducted with five prescribers in restricted areas and six patients from our own service who had received four or more HCDs. The interviews were analysed thematically.

RESULTS

Prescribers reported feeling distressed and frustrated by the unsatisfactory service they were constrained to provide. Some prescribers continued partially effective treatments in order not to run out of options. They did not find Individual Funding Requests or the Blueteq High Cost Drug (HCD) System helpful in the management of these patients. The Blueteq HCD System is an electronic platform that allows health managers to monitor the prescribing of high-cost medicines and manage the complexities associated with their use. Patients expressed severe distress at the prospect of running out of options and anxiety around the process of gaining approval for their next treatment.

CONCLUSIONS

Restricting drugs for RA by the number which can be prescribed results in persistence with partially effective treatments, which is unsatisfactory for prescribers and patients, further it does not save money. Patients need to travel in their journey with RA and be able to try the next drug even though they know that it may not work.

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

Objective

To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.

Design

Federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Setting

10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.

Participants

4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.

Exposure

The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.

Main outcomes measures

The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.

Results

4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.

Conclusions

Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Effect of type of disease-modifying antirheumatic drugs on depression and anxiety of patients with rheumatoid arthritis in Saudi Arabia: a cross-sectional study

Background

Rheumatoid arthritis (RA) can cause depression and anxiety. This study evaluated the factors associated with depression and anxiety in patients with RA and examined the effect of conventional and biologic disease-modifying antirheumatic drugs (DMARDs).

Methods

This cross-sectional study was conducted in a regional hospital in Riyadh between March and November 2022 and included 213 patients with RA. Depression and anxiety were measured using the Hospital Anxiety and Depression Scale (HADS) and data about patients' DMARDs use was obtained from the hospital's medical records.

Results

Based on the HADS scores, 35 (16.4%) and 49 (23%) patients with RA had depression and anxiety, respectively. There was a significant association between the level of depression and anxiety and the use of leflunomide and tocilizumab among patients with RA (p = 0.006 and p = 0.009, respectively). Patients with RA who took leflunomide had significantly higher scores for anxiety (β = 0.158, value of p = 0.037) when compared to patients who did not take leflunomide. Patients with RA who took etanercept showed a significantly lower depression score even after adjusting for confounders, including sociodemographic, clinical, and lifestyle factors (β = -0.189, p = 0.043).

Conclusion

The present study highlighted the prevalence of psychiatric disorders among patients with RA and the level of depression and anxiety may differ between patients with RA depending on the type of DMARDs used. We recommend patients with RA be screened regularly for depression and anxiety to avoid further extra-articular systemic complications associated with RA.

Systematic Review of Psychiatric Adverse Effects Induced by Chloroquine and Hydroxychloroquine: Case Reports and Population Studies

OBJECTIVE

To perform a systematic review on the psychiatric adverse effects of chloroquine (CQ) and hydroxychloroquine (HCQ); to summarize what is known about psychiatric adverse effects of these drugs; to compare clinical trials, populational studies, and case report studies; and to increase awareness of the potential psychiatric adverse effects of these drugs.

DATA SOURCES

A literature search of PubMed, Scopus, and Web of Science was performed to identify manuscripts published between December 1962 and June 2022. Search terms included CQ, HCQ, psychiatry, psychosis, depression, anxiety, bipolar disorder, delirium, and psychotic disorders.

STUDY SELECTION AND DATA EXTRACTION

Relevant studies included reports of adverse effects after CQ or HCQ ingestion.

DATA SYNTHESIS

The current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either CQ or HCQ. However, the populational-level studies presented some limitations regarding the voluntary response in survey data, self-report adverse effects, and placebo group reporting similar symptoms to the case group. Thus, populational-level studies addressing the discussed limitations and the nature and extent of possible psychiatric adverse effects are needed.

RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE

Most of the patients who developed such adverse effects did not report a family history of psychiatric disease. The frequency of psychiatric adverse effects depends on the patient's biological sex, age, and body mass index, but not on the drug dosage.

CONCLUSIONS

Based on clinical trials and case reports, the current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either drug.

COVID-19 and New-Onset Psychosis: A Comprehensive Review

Psychosis is a multifactorial condition that typically involves delusions, hallucinations, and disorganized thought, speech or behavior. The observation of an association between infectious epidemics and acute psychosis dates back to the last century. Recently, concerns have been expressed regarding COVID-19 and the risk for the development of new-onset psychosis. This article reviewed the current evidence of a possible link between SARS-CoV-2 and risk of psychosis as an acute or post-infectious manifestation of COVID-19. We here discuss potential neurobiological and environmental factors as well as a number of challenges in ascribing a causal pathogenic relationship between SARS-CoV-2 infection and new-onset psychosis.

First-episode psychotic disorders in the wake of the COVID-19 pandemic: a descriptive review of casereports

OBJECTIVE

Since the onset of COVID-19 pandemic, many case reports and case series dealt with new-onset psychotic disorders in patients either infected with SARS-CoV-2 or thematically linked to the pandemic, but without an infection. Our aim was to provide a comprehensive collection of these reports to illustrate the nature of these psychoses.

METHODS

We conducted a literature search in MEDLINE, Embase, PsycINFO, using search terms regarding first-episode psychotic disorders in the context of corona.

RESULTS

96 case reports or case series covering 146 patients (62 without and 84 with SARS-CoV-2 infection) were found. Compared to patients without infection, patients with infection showed significantly more often visual hallucinations (28.6% vs 8.1%), confusion (36.9% vs 11.3%), an acute onset of illness (88.5% vs 59.6%) and less often depression (13.1% vs 35.5%) and a delusional content related to the pandemic (29.5% vs 78.3%). Both groups had an equally favourable outcome with a duration of psychosis ≤2 weeks in half and full remission in two-thirds of patients. In patients with infection, signs of inflammation were reported in 78.3% and increased CRP in 58.6%. While reports on patients with infection are continuously published, no report about patients without infection was found after July 2020.

CONCLUSION

Cases without infection were considered reactive and originated all from the first wave of the corona pandemic. In cases with infection, inflammation was considered as the main pathogenetic factor but was not found in all patients. Diagnosis was impeded by the overlap of psychosis with delirium.

Impact of the SARS-CoV-2 Delta Variant on the Psychological States and Health-Related Quality of Life in Patients With Crohn's Disease

Background

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic first reported in Wuhan, China, several research on the psychological impact of the pandemic on patients with Crohn’s disease (CD) have been conducted. However, with the progression of the global pandemic and the emergence of the SARS-CoV-2 B.1.617.2 (Delta) variant, follow-up studies need to be performed to monitor the alterations of psychological status and health-related quality of life (HRQoL) among CD patients.

Aims

We aimed to evaluate the impact of the SARS-CoV-2 Delta variant on the mental health and life quality among the CD population and tried to explore potent risk factors.

Methods

This observational study included 153 CD patients who responded to our pre-designed self-reported questionnaire. Demographic, clinical, and psychological information were collected and analyzed.

Results

Quite a number of CD patients were confronted with different levels of anxiety and depression, with incidence of 28.10 and 31.37% for anxiety and depression, respectively. Compared with non-pandemic circumstances, the life quality of CD patients due to the present situation was more often compromised. Isolation [odds ratio (OR): 4.71, P = 0.007] was verified as a risk factor for anxiety while use of telemedicine could help relieve anxiety (OR: 0.22, P < 0.001). Worsening of symptoms (OR: 4.92, P = 0.006), isolation (OR: 5.75, P = 0.005), and drug withdrawn (OR: 2.66, P = 0.026) were identified to be independent factors for developing depression. Likewise, use of telemedicine (OR: 0.13, P < 0.001) was negatively related to depression. Considering life quality, vaccination (OR: 3.07, P = 0.021) together with no medication (OR: 7.73, P = 0.010) was relevant to better life quality while worsening of symptoms (OR: 0.09, P = 0.034) were an independent risk factor for impaired life quality.

Conclusion

Many CD patients suffered from symptoms of anxiety and depression and impaired life quality during the COVID-19 pandemic. Those in isolation or with worsening of symptoms and drug withdrawn were more prone to experience psychological stress. Individualized management such as drug delivery and telemedicine should be promoted to maintain control of mental health and life quality during the pandemic.

Data Mining and Meta-Analysis of Psoriasis Based on Association Rules

Psoriasis is a common chronic and recurrent disease in dermatology, which has a great impact on the physical and mental health of patients. Meta-analysis can evaluate the effectiveness and safety of defubao in the treatment of psoriasis vulgaris. This article observes psoriasis skin lesions treated with topical defubao and the changes in blood vessels under dermoscopy. Considering that the Apriori algorithm and the existing improved algorithm have the problems of ignoring the weight and repeatedly scanning the database, this paper proposes a matrix association rule method based on random forest weighting. This method uses the random forest algorithm to assign weights to each item in the data set, and introduces matrix theory to convert the transaction data set into a matrix form and store it, thereby improving operating efficiency. This article included 11 studies, of which 7 studies used the indicator "Researcher's Overall Assessment" (IGA) to evaluate the efficacy, 5 studies used the "Patient Overall Assessment" (PGA) as the efficacy evaluation index, and Loss Area and Severity Index (PASI) was used as an observation index to evaluate the efficacy. Seven studies conducted safety comparisons. In this paper, IGA and PGA were used as evaluation indicators. The treatment effect of the defubao group was better than the calcipotriol group and the betamethasone group. The differences were statistically significant. The effect of the Fubao treatment for 8 weeks is significantly better than that of 4 weeks and 2 weeks, and the differences are statistically different. Using PASI as the evaluation index, a descriptive study was carried out, and it was found that after 4 weeks of treatment for psoriasis vulgaris, the average PASI reduction rate of patients was higher than that of the calcipotriol group and the betamethasone group. The safety evaluation found that after 8 weeks of treatment, the incidence of adverse events in the defubao group was significantly lower than that in the calcipotriol group.

Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System

Introduction: The analysis of pharmacovigilance databases is crucial for the safety profiling of new and repurposed drugs, especially in the COVID-19 era. Traditional pharmacovigilance analyses-based on disproportionality approaches-cannot usually account for the complexity of spontaneous reports often with multiple concomitant drugs and events. We propose a network-based approach on co-reported events to help assessing disproportionalities and to effectively and timely identify disease-, comorbidity- and drug-related syndromes, especially in a rapidly changing low-resources environment such as that of COVID-19. Materials and Methods: Reports on medications administered for COVID-19 were extracted from the FDA Adverse Event Reporting System quarterly data (January-September 2020) and queried for disproportionalities (Reporting Odds Ratio corrected for multiple comparisons). A network (the Adversome) was estimated considering events as nodes and conditional co-reporting as links. Communities of significantly co-reported events were identified. All data and scripts employed are available in a public repository. Results: Among the 7,082 COVID-19 reports extracted, the seven most frequently suspected drugs (remdesivir, hydroxychloroquine, azithromycin, tocilizumab, lopinavir/ritonavir, sarilumab, and ethanol) have shown disproportionalities with 54 events. Of interest, myasthenia gravis with hydroxychloroquine, and cerebrovascular vein thrombosis with azithromycin. Automatic clustering identified 13 communities, including a methanol-related neurotoxicity associated with alcohol-based hand-sanitizers and a long QT/hepatotoxicity cluster associated with azithromycin, hydroxychloroquine and lopinavir-ritonavir interactions. Conclusion: Findings from the Adversome detect plausible new signals and iatrogenic syndromes. Our network approach complements traditional pharmacovigilance analyses, and may represent a more effective signal detection technique to guide clinical recommendations by regulators and specific follow-up confirmatory studies.

Treatment of SARS-CoV-2 (COVID-19): A safety perspective

The goal of this review is to report a balanced perspective of current evidence for efficacy of treatments for coronavirus disease 2019 (COVID-19) against the historical safety of these treatments as of May 2021. We preselected therapies of interest for COVID-19 based on national guidelines and modified over time. We searched PubMed and Medline for these specific COVID-19 treatments and data related to their efficacy. We also searched for prior randomized controlled trials of each therapy to assess adverse effects, and we obtained the Food and Drug Administration Approval label for this information. Several drugs have been approved for the treatment of COVID-19, and many more are under study. This includes dexamethasone, remdesivir, hydroxychloroquine/chloroquine, lopinvir/ritonavir, interferon or interleukin inhibitors, convalescent plasma and several vitamins and minerals. The strongest evidence for benefit is mortality benefit with dexamethasone in patients with COVID-19 and hypoxemia, although there is a signal of harm if this is started too early. There are several other promising therapies, like interleukin inhibitors and ivermectin. Hydroxychloroquine/chloroquine, lopinvir/ritonavir, and convalescent plasma do not have enough evidence of benefit to outweigh the known risks of these drugs.

Chloroquine/Hydroxychloroquine Use and Suicide Risk: Hypotheses for Confluent Etiopathogenetic Mechanisms?

Chloroquine (CQ) and hydroxychloroquine (HCQ) are classical anti-malarial and anti-inflammatory treatments, which were used as first-line therapy at the beginning of the 2019 coronavirus disease (COVID-19) pandemic. Besides the emerging data on their lack of efficacy against COVID-19 infection, such treatments have been associated with some severe health concerns, including those of neuropsychiatric nature, such as a possible increase in suicide risk. Here we report a case of a patient with no history of psychiatric illnesses, who abruptly developed depression with melancholic features, severe suicidal ideation (SI), and attempted suicide (SA) shortly after receiving HCQ for his COVID-19 infection. The case was followed by a mini-review of the heterogeneous scientific literature on the hypothetical association between neuropsychiatric symptoms, with a focus on SI and suicidal behavior (SB, including SA and death by suicide), when CQ and HCQ are used in COVID-19, rheumatologic diseases, and malaria settings. Considering the anti-inflammatory properties of CQ and HCQ and the implications for neuroinflammation in suicide pathogenesis, the possible increase in suicide risk caused by these medications appears paradoxical and suggests that other underlying pathological trajectories might account for this eventuality. In this regard, some of these latter mechanistic postulates were proposed. Certainly the role and contribution of psycho-social factors that a COVID-19 patient had to face can neither be minimized nor excluded in the attempt to understand his suffering until the development of SI/SB. However, while this case report represents a rare scenario in clinical practice and no consensus exists in the literature on this topic, a psychiatric screening for suicide risk in patients using of CQ and HCQ could be carefully considered.

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

BACKGROUND AND OBJECTIVE

As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code).

METHODS

We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA.

RESULTS

Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated.

CONCLUSION

Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.

Attenuating the Effects of Novel COVID-19 (SARS-CoV-2) Infection-Induced Cytokine Storm and the Implications

The COVID-19 pandemic constitutes an arduous global health challenge, and the increasing number of fatalities calls for the speedy pursuit of a remedy. This review emphasizes the changing aspects of the COVID-19 disease, featuring the cytokine storm's pathological processes. Furthermore, we briefly reviewed potential therapeutic agents that may modulate and alleviate cytokine storms. The literature exploration was made using PubMed, Embase, MEDLINE, Google scholar, and China National Knowledge Infrastructure databases to retrieve the most recent literature on the etiology, diagnostic markers, and the possible prophylactic and therapeutic options for the management of cytokine storm in patients hospitalized with COVID-19 disease. The causative agent, severe acute respiratory coronavirus-2 (SARS-CoV-2), continually threatens the efficiency of the immune system of the infected individuals. As the first responder, the innate immune system provides primary protection against COVID-19, affecting the disease's progression, clinical outcome, and prognosis. Evidence suggests that the fatalities associated with COVID-19 are primarily due to hyper-inflammation and an aberrant immune function. Accordingly, the magnitude of the release of pro-inflammatory cytokines such as interleukin (IL)-1, (IL-6), and tumor necrosis alpha (TNF-α) significantly differentiate between mild and severe cases of COVID-19. The early prediction of a cytokine storm is made possible by several serum chemistry and hematological markers. The prompt use of these markers for diagnosis and the aggressive prevention and management of a cytokine release syndrome is critical in determining the level of morbidity and fatality associated with COVID-19. The prophylaxis and the rapid treatment of cytokine storm by clinicians will significantly enhance the fight against the dreaded COVID-19 disease.