Events Of Interest Research Articles

Factor-augmented transformation models for interval-censored failure time data.

Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.

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From mistakes to mastery: An idiographic microanalytical study comparing processes related to effective and ineffective outcomes in behavioral interventions

The field of psychotherapy research increasingly recognizes the need for idiographic studies linking process of change to outcomes. However, investigations have typically focused on successful outcomes alone, neglecting the analysis of specific behaviors connected to treatment failure. This study aims to link process to both effective and ineffective outcomes. By examining 80 psychotherapy sessions across 13 cases with a mixed-method approach, a turn-by-turn conversational analysis was carried out. After detecting different levels of effectiveness following a multiple baseline design, sequential analysis and intra- and inter-case comparisons were performed. The results indicated that while differential reinforcement of target behavior was found to be related to both successful and unsuccessful outcomes, the defining characteristics of effective sessions were the preciseness of specific strategies and the predominance of appetitive over aversive stimulation throughout the intervention. These findings revealed that splitting units into linear relations to analyze psychotherapeutic interaction might be insufficient. Instead, addressing not only the presence but also the absence and concurrence of covariation associated with the events of interest could be a determining factor in unveiling the processes leading to different outcomes in psychotherapy.

A deep learning approach for overall survival prediction in lung cancer with missing values

Background and Objective:In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. Methods:We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. Results:We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used. Conclusions:The results show that our model not only outperforms the state-of-the-art’s performance but also simplifies the analysis in the presence of missing data, by effectively eliminating the need to identify the most appropriate imputation strategy for predicting OS in NSCLC patients.

Multiple imputation with competing risk outcomes

AbstractIn time-to-event analyses, a competing risk is an event whose occurrence precludes the occurrence of the event of interest. Settings with competing risks occur frequently in clinical research. Missing data, which is a common problem in research, occurs when the value of a variable is recorded for some, but not all, records in the dataset. Multiple Imputation (MI) is a popular method to address the presence of missing data. MI uses an imputation model to generate M (M > 1) values for each variable that is missing, resulting in the creation of M complete datasets. A popular algorithm for imputing missing data is multivariate imputation using chained equations (MICE). We used a complex simulation design with covariates and missing data patterns reflective of patients hospitalized with acute myocardial infarction (AMI) to compare three strategies for imputing missing predictor variables when the analysis model is a cause-specific hazard when there were three different event types. We compared two MICE-based strategies that differed according to which cause-specific cumulative hazard functions were included in the imputation models (the three cause-specific cumulative hazard functions vs. only the cause-specific cumulative hazard function for the primary outcome) with the use of the substantive model compatible fully conditional specification (SMCFCS) algorithm. While no strategy had consistently superior performance compared to the other strategies, SMCFCS may be the preferred strategy. We illustrated the application of the strategies using a case study of patients hospitalized with AMI.

Surveillance of adverse events following immunisation in Australia, COVID-19 vaccines, 2021.

This report summarises Australia's spontaneous (passive) surveillance data for adverse events following immunisation (AEFI) for coronavirus disease 2019 (COVID-19) vaccines in 2021 reported to the Therapeutic Goods Administration (TGA). The TGA strongly promoted and facilitated adverse event reporting in preparation for, and during, the COVID-19 vaccine rollout as a core component of the most intensive vaccine safety monitoring ever conducted in Australia. There were 111,348 AEFI reports for COVID-19 vaccines administered in 2021, an annual AEFI reporting rate of 271.4 per 100,000 doses of COVID-19 vaccines administered to people aged ≥ 12 years. The annual AEFI reporting rate for non-COVID-19 vaccines in 2021 was 30.6 per 100,000 doses administered to people of all ages. Overall, the most frequently reported symptoms were headache, adverse events classified as 'gastrointestinal nonspecific symptoms and therapeutic procedures', myalgia, pyrexia and fatigue, which were consistent with common expected adverse events following COVID-19 vaccines used in Australia. The most commonly reported adverse events of special interest were myocarditis and/or pericarditis, followed by thrombosis and thromboembolism, and anaphylaxis. Of all COVID-19 vaccine AEFI reports, 762 (0.7%) included a fatal outcome, of which over 80% were in people aged ≥ 60 years. Thirteen deaths reported in 2021 were assessed as likely to be causally linked to vaccination. This report confirms the value of spontaneous post-marketing vaccine pharmacovigilance, especially in the context of new vaccines using novel vaccine technologies and near whole-of-population pandemic vaccination programs. The most frequently reported AEFI for COVID-19 vaccines were common, mild and temporary (lasting 1 or 2 days), and consistent with clinical trial and active surveillance data. Ongoing safety monitoring detected rare, unexpected conditions, such as myocarditis/pericarditis and thrombosis with thrombocytopenia syndrome (TTS), which were investigated and confirmed as safety signals, resulting in changes to vaccine recommendations and product information. The outcomes of TGA monitoring were published in weekly vaccine safety reports. Overall, COVID-19 vaccine safety monitoring provided critical information on the risks of vaccine related adverse events that enabled decisionmakers to undertake informed risk-benefit assessments.

Rare event approximation between subdistribution hazard ratio and cause-specific hazard ratio in survival analysis with competing risks.

BackgroundDespite the fact that competing risks are inevitable in epidemiological and clinical studies, distinctions between the hazard ratio estimated by handling competing risks as censoring and the subditribution hazard ratio are often overlooked.MethodsWe derive quantitative relationships between subdistribution hazard ratio and cause-specific hazard ratio, and derive an approximate calculation method to transform the two into each other. Numerical examinations of hypothetical six scenarios and published information of a randomized clinical trial of cholesterol-lowering therapy and a registry of acute myeloid leukemia were provided.ResultsGeneral and approximate relationships under rare event assumptions between the two types of hazard ratio were given. The approximation formula is based on a survival ratio and has two possible applications. First, one can calculate a subdistribution hazard ratio from published information. Second, this formula allows sample size estimation that takes the presence of competing risks into account.ConclusionsThe distinction between the two types of hazard ratio can be addressed by focusing on two quantities. One is how the event of interest and competing risk is rare, and the other is the survival ratio.

What is the Safety of COVID-19 Vaccines in Immunocompromised Patients? Results from the European "Covid Vaccine Monitor" Active Surveillance Study.

The safety profile of COVID-19 vaccines in immunocompromised patients has not been comprehensively evaluated. To measure the frequency of patient-reported adverse drug reactions (ADRs) related to the first/second/booster dose of COVID-19 vaccine in immunocompromised subject versus matched cohort. As a secondary objective, the time course, evaluated as time to onset (TTO) and time to recovery (TTR), of COVID-19 vaccine-related ADRs was explored. A prospective cohort study, based on electronic questionnaires filled by vaccinees from 11 European countries in the period February 2021 to February 2023 was conducted. All immunocompromised vaccinees who provided informed consent and registered to the project's web-app within 48 h after first/booster vaccine dose administration of any EMA-authorised COVID-19 vaccine were recruited. Participants filled baseline and up to six follow-up questionnaires (FU-Qs) over 6 months from vaccination, collecting information on suspected COVID-19 vaccine-related ADRs. As a control group, non-immunocompromised vaccinees from the same source population were 1:4 matched by sex, age, vaccine dose, and brand. A descriptive analysis of demographic/clinical characteristics of vaccinees was conducted. Heatmaps of the frequency of solicited ADRs, stratified by gender and vaccine brand, were generated. Median TTO/TTR of reported ADRs were visualised using violin/box-plots. A total of 773 immunocompromised vaccines were included in the analyses. Most participants were females (F/M ratio: 2.1 and 1.6) with a median age of 56 (43-74) and 51 (41-60) years, at the first vaccination cycle and booster dose, respectively. Injection-site pain and fatigue were the most frequently reported ADRs in immunocompromised vaccinees with higher frequency than matched control, especially after the first dose (41.2% vs 37.8% and 38.2% vs 32.9%, respectively). For both cohorts, all solicited ADRs were more frequently reported in females than males, and in those who had received a first dose of the Vaxzevria vaccine. Dizziness was the most frequently reported unsolicited ADR after the first dose in both groups (immunocompromised subjects: 2.5% and matched controls: 2.1%). At the booster dose, lymphadenopathy (3.9%) and lymphadenitis (1.8%) were the most reported unsolicited ADRs for immunocompromised subjects and matched controls, respectively. A very low number of subjects reported adverse event of special interest (AESI) (2 immunocompromised, 3 matched controls) and serious ADRs (5 immunocompromised, 5 matched controls). A statistically significant difference among study cohorts was observed for median TTO after the booster dose, and for median TTR after the first vaccination cycle and booster dose (p < 0.001). The overall safety profile of COVID-19 vaccines in immunocompromised people was favourable, with minor differences as compared to non-immunocompromised vaccinees. Participants mostly experienced mild ADRs, mainly reported after the first dose of Vaxzevria and Jcovden vaccines. Serious ADRs and AESI were rare.

A Bayesian cure rate regression model using Hamiltonian Monte Carlo methods

In this paper, we propose a flexible cure rate survival model by assuming the number of competing causes, related to the event of interest, to follow generalized Poisson distribution. The generalized Poisson distribution has several important properties, e.g. it can effectively model both underdispersed (mean > variance) and overdispersed (mean < variance) mechanisms present in the data. Further, it includes the promotion time cure model as a special case. In this work, for a non-cure subject, we model the time to event of interest with the piecewise exponential distribution. We emphasize that the piecewise exponential distribution has a greater flexibility to model failure times, and can be considered as a distribution-free alternative to the parametric distributions based assumptions. Inferences for the model parameters are considered via full Bayesian analysis. Further, a detailed simulation study is performed with different parameter settings, sample sizes and censoring percentages to study the behavior of the estimators. Finally, a data set from the medical area is analyzed for further illustration.

Applicability of selected Brighton Collaboration case definitions in low-resource settings: A prospective hospital-based active surveillance in Addis Ababa, Ethiopia

IntroductionStandardizing case definitions for priority vaccine safety conditions facilitates uniform evaluation and consolidation of data obtained from different settings. The Brighton Collaboration case definitions (BCCD) were created to support this harmonization and enable classification from level 1 (most certain) to level 5 (not a case) of certainty. Assessing the performance of BCCD in practice is critical, particularly in resource-limited settings, where many new vaccines may be introduced without prior monitoring in high-income countries. We assessed the performance of BCCD in Addis Ababa, Ethiopia, as applicable to COVID-19 and other vaccines. MethodsActive surveillance was conducted at Tikur Anbessa Specialized Hospital, the largest referral hospital in Ethiopia. During June 1, 2022–May 31, 2023, three trained physicians prospectively identified patients eligible for COVID-19 vaccination (regardless of vaccine receipt) who presented with one or more of eleven pre-specified adverse events of special interest (AESI) from the emergency department and inpatient wards. Standardized data collection forms were used to capture patient information and assign level of certainty (LOC), regardless of vaccination status for COVID-19. We conducted descriptive analysis to characterize cases and the LOCs reached for each AESI. ResultsWe detected 203 AESI cases. The most detected conditions were thrombosis and thromboembolism (n = 100, 49 %) and generalized convulsions (n = 38, 19 %). Ninety-six percent of the cases were confirmed at levels 1–3 (n = 187) or level 5 (n = 9) LOC. Non-classifiable (level 4) cases were observed for pericarditis (n = 2), encephalitis (n = 2), myelitis (n = 2), and generalized convulsion (n = 1). ConclusionThe BCCD were successfully applied in > 95 % of cases in a large referral hospital in Ethiopia, with generalized convulsion, pericarditis, and encephalomyelitis as the exceptions. We recommend further evaluation in other low-resource settings, particularly in rural or non-referral hospitals, to gain additional insights into performance of these definitions for revision or adaptation, as needed.

Comparative effectiveness and safety of BNT162b2 and CoronaVac in Hong Kong: A target trial emulation

ObjectivesTo evaluate the difference between BNT162b2 and CoronaVac in vaccine effectiveness and safety. MethodsThis target trial emulation study included individuals aged ≥12 during 2022. Propensity score matching was applied to ensure group balance. The Cox proportional hazard model was used to compare the effectiveness outcomes including COVID-19 infection, severity, 28-day hospitalization, and 28-day mortality after infection. Poisson regression was used for safety outcomes including 32 adverse events of special interests between groups. ResultsA total of 639,818 and 1804,388 individuals were identified for the 2-dose and 3-dose comparison, respectively. In 2-dose and 3-dose comparison, the hazard ratios (95% confidence intervals [CI]) were 0.844 [0.833-0.856] and 0.749 [0.743-0.755] for COVID-19 infection, 0.692 [0.656-0.731] and 0.582 [0.559-0.605] for hospitalization, 0.566 [0.417-0.769] and 0.590 [0.458-0.76] for severe COVID-19, and 0.563 [0.456-0.697] and 0.457 [0.372-0.561] for mortality for BNT162b2 recipients versus CoronaVac recipients, respectively. Regarding safety, 2-dose BNT162b2 recipients had a significantly higher incidence of myocarditis (incidence rate ratio [IRR] [95% CI]: 8.999 [1.14-71.017]) versus CoronaVac recipients, but the difference was insignificant in 3-dose comparison (IRR [95% CI]: 2.000 [0.500-7.996]). ConclusionBNT162b2 has higher effectiveness among individuals aged ≥12 against COVID-19-related outcomes for SARS-CoV-2 omicron compared to CoronaVac, with almost 50% lower mortality risk.

Fuzzy-defined entities: A key concept to strengthen forensic science foundations?

According to the Sydney Declaration, “Forensic science is [… an] endeavour to study traces […] through their detection, recognition, recovery, examination and interpretation to understand anomalous events of public interest (e.g., crimes, security incidents).” This science is focused on establishing the nature and relationships among entities related to events having a potential legal impact. Entities can be (groups of) persons, objects, activities and their corresponding sources, events and traces. Although uniqueness of an entity has been traditionally accepted as a principle of forensic science, this paper argues and illustrates that such uniqueness is illusory: Not only can an entity evolve spatially and temporally, but at any specific instant, it differs from itself according to the level of precision at which it is considered. Its characteristics vary based on when, how and by whom it is perceived. We introduce the concept of fuzzy entities - defined to formally include some essential uncertainty or imprecision. The essential impreciseness and subjectivity of an entity gives a new perspective that allows us to revisit Kirk's principle of individuality and to propose to replace it with a new principle of fuzzy unicity. We believe that this new perspective has the potential to strengthen forensic science foundations and bring closer its disciplines, which is an important step towards a harmonized forensic science.

Effectiveness and safety of modified fully oral 9-month treatment regimens for rifampicin-resistant tuberculosis: a prospective cohort study

In 2020, WHO guidelines prioritised the use of a standard fully oral short treatment regimen (STR) consisting of bedaquiline, levofloxacin or moxifloxacin, ethionamide, ethambutol, high-dose isoniazid, pyrazinamide, and clofazimine for the management of rifampicin-resistant tuberculosis. A high prevalence of resistance to constituent drugs precluded its widespread use by countries in the WHO European region. We evaluated three 9-month fully oral modified STRs (mSTRs) in which ethionamide, ethambutol, isoniazid, and pyrazinamide were replaced by linezolid, cycloserine, or delamanid (or a combination). This multicountry, prospective, single-arm, cohort study examined the effectiveness and safety of mSTRs for fluoroquinolone-susceptible, rifampicin-resistant pulmonary tuberculosis in 13 countries in the WHO European region during 2020-23. We enrolled adults and children of all ages with bacteriologically confirmed rifampicin-resistant, fluoroquinolone-susceptible pulmonary tuberculosis, and children (aged 0-18 years) with clinically diagnosed disease and a confirmed contact with rifampicin-resistant, fluoroquinolone-susceptible tuberculosis. Participants aged 6 years or older received one of two regimens: bedaquiline, linezolid, levofloxacin, clofazimine, and cycloserine; or bedaquiline, linezolid, levofloxacin, clofazimine, and delamanid. Children younger than 6 years received delamanid, linezolid, levofloxacin, and clofazimine. Participants were followed up for 12 months after successful treatment completion to detect recurrence and death. The primary outcome was the cumulative probability of not having an unsuccessful study outcome (defined as treatment failure, on-treatment loss to follow-up, death, or recurrence) before 22 months of study follow-up. The primary safety outcome was the incidence of each adverse event of interest (peripheral neuropathy, optic neuritis, myelosuppression, hepatitis, prolonged QT interval, hypokalaemia, and acute kidney injury) of grade 3 or higher severity during the treatment course. Between Aug 28, 2020 and May 26, 2021, 7272 patients were screened and 2636 were included in the treatment cohort. 1966 (74·6%) were male, 670 (25·4%) were female, and median age was 43 years (IQR 33-53). Treatment success was recorded for 2181 (82·7%) participants. The cumulative probability of not having an unsuccessful study outcome 22 months after treatment initiation was 79% (95% CI 78-81). Increasing age (adjusted hazard ratio 2·61 [95% CI 1·70-4·04] for people aged >64 years vs 35-44 years), HIV-positive status (1·53 [1·16-2·01]), presence of bilateral cavities (1·68 [1·29-2·19]), smoking history (1·34 [1·05-1·71]), baseline anaemia (1·46 [1·15-1·86]), unemployment (1·37 [1·04-1·80]), elevated baseline liver enzymes (1·40 [1·13-1·73]), and excessive alcohol use (1·47 [1·14-1·89]) were positively associated with unsuccessful study outcomes. In the safety cohort of 2813 participants who received at least one dose, 301 adverse events of interest were recorded in 252 (9·0%) participants with the most frequent being myelosuppression (139 [4·9%] participants, 157 [52·2%] events). The high treatment success and good safety results indicate considerable potential for the use of mSTRs in programmatic conditions, especially for individuals not eligible for the current WHO-recommended 6-month regimen and in settings with a need for alternative options. The Global Fund to Fight AIDS, Tuberculosis and Malaria; United States Agency for International Development; Government of Germany; and WHO. For the Russian translation of the abstract see Supplementary Materials section.

A machine learning case study to predict rare clinical event of interest: imbalanced data, interpretability, and practical considerations

ABSTRACT Accurate prediction of a rare and clinically important event following study treatment has been crucial in drug development. For instance, the rarity of an adverse event is often commensurate with the seriousness of medical consequences, and delayed detection of the rare adverse event can pose significant or even life-threatening health risks to patients. In this machine learning case study, we demonstrate with an example originated from a real clinical trial setting how to define and solve the rare clinical event prediction problem using machine learning in pharmaceutical industry. The unique contributions of this work include the proposal of a six-step investigation framework that facilitates the communication with non-technical stakeholders and the interpretation of the model performance in terms of practical consequences in the context of patient screenings for conducting a future clinical trial. In terms of machine learning methodology, for data splitting into the training and test sets, we adapt the rare-event stratified split approach (from scikit-learn) to further account for group splitting for multiple records of a patient simultaneously. To handle imbalanced data due to rare events in model training, the cost-sensitive learning approach is employed to give more weights to the minor class and the metrics precision together with recall are used to capture prediction performance instead of the raw accuracy rate. Finally, we demonstrate how to apply the state-of-the-art SHAP values to identify important risk factors to improve model interpretability.

Effectiveness and Safety of the MVA-BN Vaccine against Mpox in At-Risk Individuals in the United States (USMVAc).

The mpox 2022 outbreak was declared a public health emergency in July 2022. In August 2022, the MVA-BN vaccine received emergency use authorization in the United States (US) to target at-risk groups. This study (EUPAS104386) used HealthVerity's administrative US healthcare data to generate real-world evidence for MVA-BN vaccine effectiveness and safety to prevent mpox disease in men who have sex with men (MSM) and transgender women, the most affected population during the 2022 mpox outbreak. Fully vaccinated subjects (two doses ≥ 28 days apart) were initially matched with five unvaccinated subjects on calendar date, age, US region, and insurance type. Subjects were followed from index date (14 days after the second dose) until death or data end to ascertain mpox occurrence. After propensity score adjustment, the MVA-BN vaccine effectiveness against mpox disease was 89% (95% CI: 12%, 99%) among those fully vaccinated; attenuated to 64% (95% CI: 40%, 78%) among those with any dose and 70% (95% CI: 44%, 84%) for those with only a single dose. One pericarditis adverse event of special interest was observed when the risk window was extended to 28 days. These results contribute to the totality of evidence supporting the favorable benefit/risk profile of the MVA-BN vaccine.

Event prediction by estimating continuously the completion of a single temporal pattern’s instances

Objective:Develop a new method for continuous prediction that utilizes a single temporal pattern ending with an event of interest and its multiple instances detected in the temporal data. Methods:Use temporal abstraction to transform time series, instantaneous events, and time intervals into a uniform representation using symbolic time intervals (STIs). Introduce a new approach to event prediction using a single time intervals-related pattern (TIRP), which can learn models to predict whether and when an event of interest will occur, based on multiple instances of a pattern that end with the event. Results:The proposed methods achieved an average improvement of 5% AUROC over LSTM-FCN, the best-performed baseline model, out of the evaluated baseline models (RawXGB, Resnet, LSTM-FCN, and ROCKET) that were applied to real-life datasets. Conclusion:The proposed methods for predicting events continuously have the potential to be used in a wide range of real-world and real-time applications in diverse domains with heterogeneous multivariate temporal data. For example, it could be used to predict panic attacks early using wearable devices or to predict complications early in intensive care unit patients.

Artificial Intelligence and Virtual Reality in Headache Disorder Diagnosis, Classification, and Management.

This review provides an overview of the current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders. Through machine learning and natural language processing approaches, AI offers unprecedented opportunities to identify patterns within complex and voluminous datasets, including brain imaging data. This technology has demonstrated promise in optimizing diagnostic approaches to headache disorders and automating their classification, an attribute particularly beneficial for non-specialist providers. Furthermore, AI can enhance headache disorder management by enabling the forecasting of acute events of interest, such as migraine headaches or medication overuse, and by guiding treatment selection based on insights from predictive modeling. Additionally, AI may facilitate the streamlining of treatment efficacy monitoring and enable the automation of real-time treatment parameter adjustments. VR technology, on the other hand, offers controllable and immersive experiences, thus providing a unique avenue for the investigation of the sensory-perceptual symptomatology associated with certain headache disorders. Moreover, recent studies suggest that VR, combined with biofeedback, may serve as a viable adjunct to conventional treatment. Addressing challenges to the widespread adoption of AI and VR in headache medicine, including reimbursement policies and data privacy concerns, mandates collaborative efforts from stakeholders to enable the equitable, safe, and effective utilization of these technologies in advancing headache disorder care. This review highlights the potential of AI and VR to support precise diagnostics, automate classification, and enhance management strategies for headache disorders.

Safety and effectiveness of three novel all-oral shortened regimens for rifampicin- or multidrug-resistant tuberculosis in Kazakhstan.

In 2019, WHO called for operational research on all-oral shortened regimens for multidrug- and rifampicin-resistant tuberculosis (MDR/RR-TB). We report safety and effectiveness of three nine-month all-oral regimens containing bedaquiline (Bdq), linezolid (Lzd), and levofloxacin (Lfx) and reinforced with cycloserine (Cs) and clofazimine (Cfz), delamanid (Dlm) and pyrazinamide (Z), or Dlm and Cfz. We conducted a prospective cohort study of patients initiating treatment for pulmonary MDR/RR-TB under operational research conditions at public health facilities in Kazakhstan. Participants were screened monthly for adverse events. Participants with baseline resistance were excluded from the study and treated with a longer regimen. We analyzed clinically relevant adverse events of special interest in all participants and sputum culture conversion and end-of-treatment outcomes among individuals who were not excluded. Of 510 participants, 41% were women, median age was 37 years (interquartile range: 28-49), 18% had a body mass index <18·5 kg/m2, and 51% had cavitary disease. Three hundred and ninety-nine (78%) initiated Bdq-Lzd-Lfx-Cs-Cfz, 83 (16%) started Bdq-Lzd-Lfx-Dlm-Z, and 28 (5%) initiated Bdq-Lzd-Lfx-Dlm-Cfz. Fifty-eight individuals (11%) were excluded from the study, most commonly due to identification of baseline drug resistance (n = 52; 90%). Among the remaining 452 participants, treatment success frequencies were 92% (95% confidence interval [CI]: 89 to 95), 89% (95%CI: 80 to 94), and 100% (95%CI: 86 to 100) for regimens with Cs/Cfz, Dlm/Z, and Dlm/Cfz respectively. Clinically-relevant adverse events of special interest were uncommon. All regimens demonstrated excellent safety and effectiveness, expanding the potential treatment options for patients, providers, and programs.

Long-Term Safety and Effectiveness of Rituximab Biosimilar RTXM83: A Retrospective Extension Study in Brazilian Patients with Diffuse Large B-Cell Lymphoma.

RTXM83, a biosimilar of rituximab, was approved after physicochemical, functional, non-clinical,and clinical studies demonstrated their similarity; these studies included RTXM83-AC-01-11, a multicentric double-blind international prospective pivotal study. Long-term data on biosimilars can potentially elucidate their clinical robustness and facilitate their broader adoption. In this retrospective observational study, we analyzed a dataset from a Brazilian cohort previously randomized in the RTXM83-AC-01-11 study followed by the assessment of long-term outcomes in an observational extension phase from randomization in the RTXM83-AC-01-11 study to the last recorded evaluation. Patients with diffuse large B cell lymphoma (DLBCL) received either reference rituximab (R) or RTXM83 plus cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) as adjuvant treatment. The median follow-up period was 77.0months. Patients with initial DLBCL stages III and IV comprised 50% of the R-CHOP group and 40% of the biosimilar group. Five (18.5%) patients, including two RTXM83-CHOP-treated and three R-CHOP-treated individuals, experienced late adverse events (AEs) of interest. No new safety signs were established. At the final assessment, the progression-free survival (PFS) rates were 93.3% and 50.0% in the RTXM83-CHOP and R-CHOP groups, respectively. Median PFS was not achieved in the RTXM83-CHOP group, which was 40.5months in the R-CHOP group. The overall survival (OS) rates were 100% and 66.7% in the RTXM83-CHOP and R-CHOP groups, respectively. The median OS was not reached in any group. This study demonstrated the long-term safety and effectiveness of RTXM83 in treating DLBCL; outcomes comparable to those of the reference product and potentially improved access to treatment have been indicated. However, further research with more diverse patient groups can validate these findings and advocate the broader adoption of biosimilars in cancer care. ClinicalTrials.gov Identifier:NCT04928573. June 16, 2021, "retrospectively registered".

SOMA: Observability, monitoring, and in situ analytics for exascale applications

SummaryWith the rise of exascale systems and large, data‐centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service‐based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application‐specific diagnostic data and performance data addresses these needs. We present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi‐GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring.