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Background: Sinusoidal obstruction syndrome (SOS)/veno-occlusive disease (VOD) is generally associated with hematopoietic cell transplant (HCT), but little is known about this condition outside the HCT setting. This systematic review examines the burden of illness and current management approaches in non-HCT SOS/VOD. Methods: We searched Embase, Medline, and grey literature sources for non-HCT SOS/VOD studies published 2002–2023. Inclusion criteria were studies of any design reporting incidence, diagnosis, underlying disease and any ongoing treatment at the time of SOS/VOD onset, management of non-HCT SOS/VOD, clinical burden, health-related quality of life, healthcare resource use, costs, and patients’ unmet needs. Studies investigating pulmonary VOD or SOS/VOD related to the ingestion of pyrrolizidine alkaloids were excluded.Two authors independently screened results, extracted data, and assessed the methodological quality of studies using the Motheral scale for retrospective studies, Newcastle-Ottawa scale for prospective studies and case control studies, the Cochrane risk of bias tool for randomized controlled trials, and the Joanna Briggs Institute critical appraisal tool for case series. Results were synthesized narratively. Results: Ninety-two studies were included; 57 % were retrospective cohort studies and 70 % were conducted in the US or Europe. The study populations included hematological and solid tumor cancers, various indications for liver transplant, Wilms’ tumor, and transfusion-dependent beta thalassemia. Non-HCT SOS/VOD occurs most frequently in people with colorectal liver metastases (CRLM), acute lymphoblastic leukemia (ALL), and acute myeloid leukemia (AML). Approximately 35 % of oxaliplatin-treated CRLM patients and 5 % of ALL and AML patients have non-HCT SOS/VOD. Diagnosis varies according to initial disease setting. Defibrotide is the most frequently reported treatment. Most studies did not clearly report their data sources or methods of outcome assessment. Conclusion: Non-HCT SOS/VOD occurs in diverse disease conditions, therefore guidelines on diagnosis and treatment are needed to optimize management in clinical practice.

Disparities in cardiovascular disease (CVD) and associated health and healthcare delivery outcomes have been partially attributed to differential risk factors, and to prevention and treatment inequities within racial and ethnic (including language) minority groups and low socioeconomic status (SES) populations in urban and rural settings. Digital health interventions (DHIs) show promise in promoting equitable access to high-quality care, optimal utilization, and improved outcomes; however, their potential role and impact has not been fully explored. The role of DHIs to mitigate drivers of the health disparities listed above in populations disproportionately affected by atherosclerotic-related CVD was systematically reviewed using published literature (January 2008–July 2020) from multiple databases. Study design, type and description of the technology, health disparities information, type of CVD, outcomes, and notable barriers and innovations associated with the technology utilized were abstracted. Study quality was assessed using the Oxford Levels of Evidence. Included studies described digital health technologies in a disparity population with CVD and reported outcomes. DHIs significantly improved health (eg, clinical, intermediate, and patient-reported) and healthcare delivery (eg, access, quality, and utilization of care) outcomes in populations disproportionately affected by CVD in 24 of 38 included studies identified from 2104 citations. Hypertension control was the most frequently improved clinical outcome. Telemedicine, mobile health, and clinical decision support systems were the most common types of DHIs identified. DHIs improved CVD-related health and healthcare delivery outcomes in racial/ethnic groups and low SES populations in both rural and urban geographies globally.

Abstract Background Adverse events (AEs) in clinical trials may be reported in multiple sources. Different methods for reporting adverse events across trials or across sources for a single trial may produce inconsistent information about the adverse events associated with interventions. Methods We compared the methods authors use to decide which AEs to include in a particular source (i.e., “selection criteria”), including the number of different types of AEs reported (i.e., rather than the number of events). We compared sources (e.g., journal articles, clinical study reports (CSRs)) of trials for two drug-indications—gabapentin for neuropathic pain and quetiapine for bipolar depression. Electronic searches were completed in 2015. We identified selection criteria and assessed how criteria affected AE reporting. Results We identified 21 gabapentin and 7 quetiapine trials. We found 6 gabapentin CSRs and 2 quetiapine CSRs, all written by drug manufacturers. All CSRs reported all AEs without applying selection criteria; by comparison, no other source reported all AEs, and 15/68 (22%) gabapentin sources and 19/48 (40%) quetiapine sources reported using selection criteria. Selection criteria greatly affected the number of AEs reported. For example, 67/316 (21%) AEs in one quetiapine trial met the criterion “occurring in ≥2% of participants in any treatment group,” while only 5/316 (2%) AEs met the criterion “occurring in ≥10% of quetiapine-treated patients and twice as frequent in the quetiapine group as the placebo group.” Conclusions Selection criteria for reporting AEs vary across trials and across sources for individual trials. If investigators do not pre-specify selection criteria, they might “cherry-pick” AEs based on results. Even if investigators pre-specify selection criteria, selective reporting will produce biased meta-analyses and clinical practice guidelines. Data about all AEs identified in clinical trials should be publicly available; however, sharing data will not solve all the problems identified in this study.

Abstract Clinical trials and systematic reviews of clinical trials inform healthcare decisions. There is growing concern, however, about results from clinical trials that cannot be reproduced. Reasons for nonreproducibility include that outcomes are defined in multiple ways, results can be obtained using multiple methods of analysis, and trial findings are reported in multiple sources (“multiplicity”). Multiplicity combined with selective reporting can influence dissemination of trial findings and decision-making. In particular, users of evidence might be misled by exposure to selected sources and overly optimistic representations of intervention effects. In this commentary, drawing from our experience in the Multiple Data Sources in Systematic Reviews (MUDS) study and evidence from previous research, we offer practical recommendations to enhance the reproducibility of clinical trials and systematic reviews.

Abstract Background We used various methods for identifying and prioritizing patient-centered outcomes (PCOs) for comparative effectiveness research (CER). Methods We considered potential PCOs (“benefits” and “harms”) related to (1) gabapentin for neuropathic pain and (2) quetiapine for bipolar depression. Part 1 (April 2014 to March 2015): we searched for PCO research and core outcome sets (COSs). We conducted electronic searches of bibliographic databases and key websites and examined FDA prescribing information and reports of clinical trials and systematic reviews. We asked patient and clinician co-investigators to identify PCOs. Part 2 (not part of our original study protocol): in 2015, we surveyed members of The TMJ Association, Ltd., a patient group associated with temporomandibular disorders (4130 invitations sent). Participants prioritized (1) the importance of six potential benefits and (2) 21 potential harms selected by the investigators in part 1, using stated preference methods. We calculated descriptive statistics. Results In part 1, we identified a COS for pain, the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) recommendations. The COS identified several important benefits, but it lacked specific recommendations about which potential harms to include in CER. We did not identify a COS for bipolar depression. Research reports, prescribing information, and patient co-investigators helped identify but not prioritize outcomes. We abandoned our electronic search for PCO research because we found it would be resource-intensive and yield few relevant reports. In part 2, surveying patients was useful for prioritizing PCOs. Members of The TMJ Association, Ltd., completed the survey (N = 746) and successfully prioritized both benefits and harms. Participants did not identify many benefits other than those we identified in part 1; several participants identified additional harms. Conclusions These exploratory results could inform future research about identifying and prioritizing PCOs. We found that stakeholder co-investigators and research reports contributed to identifying PCOs; surveying a patient group contributed to prioritizing PCOs. Prioritizing potential harms was particularly challenging because there are many more potential harms than potential benefits. Methods for identifying and prioritizing potential benefits for CER might not be appropriate for harms. Further research is needed to determine the generalizability of these results.

Abstract The correct title of the article [1] should be “Integrating multiple data sources (MUDS) for meta-analysis to improve patient-centered outcomes research: a protocol”.