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Background and Aims: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors., Design: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data., Setting and Cases: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively., Measurements: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts., Findings: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence., Conclusions: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts., (© 2024 The Author(s). Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

The turn of the century brought a resurgence of interest in psychedelics as a treatment for addiction and other psychiatric conditions, accompanied by extensive positive media attention and private equity investment. Government regulatory bodies in Australia, Israel, Canada and the United States now permit use of psychedelics for medical purposes. In the United States, citizen action and corporate financing have led to petitions and ballot initiatives to legalize psilocybin and other psychedelics for medical and recreational use. Given this momentum, policymakers must grapple with important questions that define whether and how psychedelics are made available to the public, as well as how companies produce and promote them. The current push to broaden the production, sale, and use of psychedelics bears many parallels to the movement to legalize cannabis in the United States and other nations-most notably, the use of poorly-evidenced therapeutic claims to create a de facto recreational market via the health care system. Experience with cannabis highlights the value of debating the question of legalization for nonmedical use as such rather than misrepresenting it as a medical issue. The lessons of cannabis policy also suggest a need to challenge hyping of psychedelic research findings; to promote rigorous clinical research on dosing and potency; to minimize the influence of for-profit industry in shaping policies to their economic advantage; and to coordinate federal, state, and local governments to regulate the manufacture, sale and distribution of psychedelic drugs (regardless of whether they are legalized for medical and/or recreational use)., (© 2024 The Author(s). Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Background: Transcranial magnetic stimulation (TMS) offers a promising treatment avenue to modulate brain function in alcohol use disorder (AUD). To the best of our knowledge, this pilot study is the first randomized, double-blind, sham-controlled trial to deliver intermittent theta burst stimulation to the left dorsolateral prefrontal cortex (DLPFC) among US veterans with AUD. We hypothesized that 20 sessions of real TMS are tolerable and feasible. As a secondary line of inquiry, we hypothesized that, relative to sham TMS, individuals receiving real TMS would experience greater reductions in 6-month relapse rates, anhedonia, and alcohol cue-reactivity., Methods: Veterans (n = 17, one woman) were enrolled in a double-blind, sham-controlled trial (2-3 sessions/day; 7-10 days; 600 pulses/session; 20 sessions). Pre- and posttreatment assessments included responses to self-report questionnaires and functional magnetic resonance imaging measures of alcohol cue-reactivity. Alcohol consumption was assessed for 6 months. Linear mixed-effects models were constructed to predict posttreatment craving, mood, and cue-reactivity., Results: Individuals who received active iTBS (n = 8) were less likely to relapse within 3 months after treatment than the sham-treated group (n = 9) (OR = 12.0). Greater reductions in anhedonia were observed following active iTBS (Cohen's d = -0.59), relative to sham (d = -0.25). Alcohol cue-reactivity was reduced following active iTBS and increased following sham within the left insula (d = -0.19 vs. 0.51), left thalamus (d = -0.28 vs. 0.77), right insula (d = 0.18 vs. 0.52), and right thalamus (d = -0.06 vs. 0.62)., Conclusions: Relative to sham, we demonstrate that 20 sessions of real left DLPFC iTBS reduced the likelihood of relapse for at least 3 months. The potential utility of this approach is underscored by observed decreases in anhedonia and alcohol cue-reactivity-strong predictors of relapse among veterans. These initial data offer a valuable set of effect sizes to inform future clinical trials in this patient population., (© 2023 Research Society on Alcohol.)

Objective: Recent research has helped to develop a more detailed understanding of many functional neurologic disorders. The aim of this study was to increase our knowledge of functional visual loss and to compare the findings with those of other functional syndromes., Design: Prospective and retrospective observational cohort study., Methods: This study took place at neuro-ophthalmology clinics at 3 major hospitals in London, United Kingdom, over a 12-month period. The study population consisted of 157 participants, 100 with functional visual loss, 21 pathologic control subjects with organic visual loss, and 36 healthy nonpathologic control subjects. All participants had their diagnosis confirmed with a full neuro-ophthalmic examination, neuroimaging, and visual electrophysiology. A full assessment of all participants' medical history was obtained from their general practitioner, and all participants completed a series of questionnaires assessing relevant associations., Results: Data were obtained on 157 participants, 100 with functional visual loss, 21 pathologic control subjects with organic visual loss, and 36 healthy nonpathologic control subjects. Participants with functional visual loss were typically female (74%) with a mean age at vision loss of 40.0 ± 16 years. Sixty-four percent of participants had bilateral vision loss; the remainder, unilateral loss. Twenty-six percent of the total cohort had organic visual loss with functional overlay. Fifty percent of participants with functional visual loss had a preexisting psychiatric diagnosis, the most common being a depressive disorder. Sixty-two percent of participants had an ocular history, and 87% had a previously diagnosed medical illness, most commonly neurologic (45%). Thirty-five percent of participants self-reported at least 1 additional functional symptom., Conclusions: Our population of functional visual loss subjects shares many similarities with the majority of patients with other functional neurologic disorders. They are generally young and female and have a greater than expected rate of psychiatric, medical, and coexisting ocular conditions. We found increased rates of precipitating stressors, clinical depression, and organic eye problems in subjects with functional visual loss., Competing Interests: Footnotes and Disclosure All the authors contributed equally to this work. The authors have no proprietary or commercial interest in any materials discussed in this article. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors., (Copyright © 2024 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.)

Introduction: To combat high-risk alcohol consumption, we introduced a 30-day alcohol abstinence challenge targeted at heavy drinkers with and without chronic pain. Our study aimed to assess the challenge's feasibility and safety and to explore its perceived benefits. Our exploratory aim was to identify participants' coping strategies during the challenge., Methods: Our single-arm study recruited heavy drinkers from a pain clinic and a university setting (n = 34, 64.7% chronic pain). Participants underwent a modified community-based 30-day challenge, which included motivational interviewing, an individualized start date, and weekly phone check-ins., Results: We found the 30-day challenge was feasible and safe; 72.3% of eligible heavy drinkers participated in the challenge with no serious adverse events. Most challengers (94.1%) reported some benefit from the challenge, which included improvements in alcohol withdrawal symptoms, sleep, and alcohol abstinence self-efficacy, but not in pain. We identified 25 perceived benefits and 21 coping strategies., Conclusion: Our study confirms that a 30-day alcohol abstinence challenge is a feasible and safe intervention for heavy drinkers with and without chronic pain, yielding notable health benefits. The challenge also facilitated the development of effective coping strategies. Future studies should explore the long-term benefits of such interventions in broader outpatient settings., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The perception of taking a generic, relative to brand, medication has been demonstrated to exacerbate the nocebo effect. Conversely, positive attribute framing has been shown to attenuate the nocebo effect. However, little is known about the longevity of positive attribute framing nor how it interacts with generic versus brand treatment cues. Healthy participants (N = 205) were randomised to receive either sham-modafinil capsules with a brand or generic appearance, in conjunction with standard negative side effect framing (brand-negative: N = 42; generic-negative: N = 41) or positive side effect framing (brand-positive: N = 40; generic-positive: N = 40). The remainder were randomised to a no-treatment control (N = 42). Participants were informed that modafinil could enhance alertness and cognitive performance and reduce fatigue. Critically, modafinil was described as having several potential side effects. Treatment-related side effects, alertness, fatigue and cognitive performance were measured at baseline, 30-min post-treatment and 24 h later. Nocebo and placebo effects were observed across modafinil-treated participants relative to control. Positive framing significantly reduced warned side effects for 24 h. Perceived side effect likelihood, severity, and worry mediated the nocebo, but not framing, effect. Results have important implications for the presentation of side effect information, providing a potential route to reduce unwanted negative effects of generic medication., (© 2024 The Author(s). Applied Psychology: Health and Well‐Being published by John Wiley & Sons Ltd on behalf of International Association of Applied Psychology.)

Background: Collegiate Recovery Programs (CRPs) play a vital and expanding role in supporting students in recovery from substance use disorders and behavioral addictions at higher education institutions. Despite their importance, there is a lack of comprehensive research characterizing CRPs, including their program directors and the nature and influence of their funding streams., Methods: A survey was administered to 70 CRP directors across the US and Canada. Directors reported on their CRPs across a variety of site physical features, policy and program offerings, and funding sources, with "sustainable" funding defined as two or more sources., Results: CRP directors were predominantly non-Hispanic, White, and female, with representation from gender and sexual minorities. The highest concentration of directors responding were in the US states of North Carolina, California, and Texas. CRPs with more than one funding source served twice as many students and had significantly more space, drop-in facilities, and relapse management policies. CRP directors had positive perceptions of harm reduction principles. Additionally, results highlighted the robust availability of All Recovery meetings and the wide diversity of mutual-help group meetings offered within CRPs and directors' positive perceptions of these meetings., Conclusion: This research lays a foundation for enhancing CRPs within higher education settings, emphasizing the significance of sustained funding and an inclusive support framework for the program directors that run CRPs. Future studies should further explore the effectiveness of CRPs and their impacts on the schools and students they serve.

Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have used platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened these platforms' usability in research. We evaluate the potential of a broad set of platforms to detect emerging trends in the opioid epidemic. From these, we created a shortlist of 11 platforms, for which we documented official policies regulating drug-related discussion, data accessibility, geolocatability, and prior use in opioid-related studies. We quantified their volumes of opioid discussion, capturing informal language by including slang generated using a large language model. Beyond the most commonly used Reddit and X, the platforms with high potential for use in opioid-related surveillance are TikTok, YouTube, and Facebook. Leveraging many different social platforms, instead of a single platform, safeguards against sudden changes to data access and may better capture all populations that use opioids than any single platform.

Aims: To compare healthcare costs and use between United States (US) Veterans Health Administration (VHA) patients with opioid use disorder (OUD) who experienced an opioid overdose (OD cohort) and patients with OUD who did not experience an opioid overdose (non-OD cohort)., Design: This is a retrospective cohort study of administrative and clinical data., Setting: The largest integrated national health-care system is the US Veterans Health Administration's healthcare systems., Participants: We included VHA patients diagnosed with OUD from October 1, 2017 through September 30, 2018. We identified the index date of overdose for patients who had an overdose. Our control group, which included patients with OUD who did not have an overdose, was randomly assigned an index date. A total of 66 513 patients with OUD were included for analysis (OD cohort: n = 1413; non-OD cohort: n = 65 100)., Measurements: Monthly adjusted healthcare-related costs and use in the year before and after the index date. We used generalized estimating equation models to compare patients with an opioid overdose and controls in a difference-in-differences framework., Findings: Compared with the non-OD cohort, an opioid overdose was associated with an increase of $16 890 [95% confidence interval (CI) = $15 611-18 169; P < 0.001] in healthcare costs for an estimated $23.9 million in direct costs to VHA (95% CI = $22.1 million, $25.7 million) within the 30 days following overdose after adjusting for baseline characteristics. Inpatient costs ($13 515; 95% CI = $12 378-14 652; P < 0.001) reflected most of this increase. Inpatient days (+6.15 days; 95% CI, = 5.33-6.97; P < 0.001), inpatient admissions (+1.01 admissions; 95% CI = 0.93-1.10; P < 0.001) and outpatient visits (+1.59 visits; 95% CI = 1.34-1.84; P < 0.001) also increased in the month after opioid overdose. Within the overdose cohort, healthcare costs and use remained higher in the year after overdose compared with pre-overdose trends., Conclusions: The US Veterans Health Administration patients with opioid use disorder (OUD) who have experienced an opioid overdose have increased healthcare costs and use that remain significantly higher in the month and continuing through the year after overdose than OUD patients who have not experienced an overdose., (© 2023 The Authors. Addiction published by Society for the Study of Addiction on behalf of Society for the Study of Addiction. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Objective: Pay-for-performance (P4P) initiatives hold promise for improving health care delivery but are rarely applied to behavioral health or tested in randomized controlled trials (RCTs). This RCT examined the effectiveness of a P4P initiative to reduce total cost of 24-hour care among patients with high needs for psychiatric care in a large county in California., Methods: From August 2016 to March 2022, a total of 652 adult residents of Santa Clara County, California, were enrolled in a P4P initiative (mean±SD age=46.7±13.3 years, 61% male, 51% White, and 60% diagnosed as having a bipolar or psychotic disorder). Participants were randomly assigned to usual full-service partnerships from the county (N=327) or a comparable level of care from a contractor who agreed to a schedule of financial penalties and rewards based on whether enrollees (N=325) used more or less care than a historical cohort of similar county patients. The primary outcome was total cost of 24-hour psychiatric services. Secondary outcomes were costs of each of the 24-hour care services., Results: The proportion of the total sample that used 24-hour psychiatric services decreased over the 36-month study period. Intent-to-treat analyses revealed no differences between the two study conditions in total care costs during the follow-up period. No significant care utilization differences were observed between the two conditions in most of the individual 24-hour services., Conclusions: A P4P initiative for high-need patients was no more effective than usual care for reducing costs of 24-hour psychiatric care., Competing Interests: The authors report no financial relationships with commercial interests.

Therapeutic claims about many psychedelic drugs have not been evaluated in any studies of even modest rigor. The science of psychedelic drugs is strengthening however, making it easier to differentiate some promising findings amid the hype that suffuses this research area. Ketamine has risks of adverse side effects (e.g., addiction and cystitis), but multiple studies suggest it can benefit individuals with treatment-resistant depression. Other therapeutic signals from psychedelic drug research that merit rigorous replication studies include 3,4-Methylenedioxymethamphetamine (MDMA) for post-traumatic stress disorder (PTSD) and psilocybin for depression, end of life dysphoria, and alcohol use disorder. The precise mechanisms through which psychedelic drugs can produce benefit and harm are not fully understood. Rigorous research is the best path forward for evaluating the therapeutic potential and mechanisms of psychedelic drugs. Policies governing the clinical use of these drugs should be informed by evidence and prioritize the protection of public health over the profit motive.

Gastric malabsorptive conditions may prevent patients from deriving benefit from orally administered medications intended for enteric absorption. While malabsorption is an increasingly common issue, current data on alternative oral options for agitation in these patients are very sparse. Sublingual (SL) asenapine is absorbed transmucosally, bypassing gut absorption, making it a viable consideration. We report on three patients, one with short bowel syndrome, one with viral gastritis, and one with aortic dissection who were trialed on SL asenapine for agitation after failing alternative antipsychotics. Two of these patients had an extensive history of psychiatric admissions for bipolar disorder and substance-induced psychosis. All three patients had significant reductions in agitation within 1-5 days, with no reported adverse effects. However, benefit of SL asenapine was hindered in two of these patients as they began inappropriately swallowing the medication, reducing bioavailability to nil. Clinicians should consider the use of SL asenapine for medically complex agitated patients where gastric absorption is questionable. There is an urgent need for guidelines on this matter, as well as more, alternative dosage forms for various medications that may help with agitation in this population., Competing Interests: The authors declare that there is no conflict of interest., (© The Author(s), 2024.)

Conflicting results have appeared in the literature on whether the amount of non-dense, adipose tissue in the breast is a risk factor or a protective factor for breast cancer, and biological hypotheses supporting both have been proposed. We suggest here that limitations in study design and statistical methodology could potentially explain the inconsistent results. Specifically, we exploit recent advances in methodology and software developed for the joint analysis of multiple longitudinal outcomes and time-to-event data to jointly analyze dense and non-dense tissue trajectories and the risk of breast cancer in a large, Swedish, screening cohort. We also perform extensive sensitivity analyses by mimicking analyses/designs of previously published studies, e.g. ignoring available longitudinal data. Overall, we did not find strong evidence supporting an association between non-dense tissue and the risk of incident breast cancer. We hypothesize that (1) previous studies have not been able to isolate the effect of non-dense tissue from dense tissue or adipose tissue elsewhere in the body, that (2) estimates of the effect of non-dense tissue on risk are strongly sensitive to modeling assumptions, or that (3) the effect size of non-dense tissue on breast cancer risk is likely to be small/not clinically relevant., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Mammography screening programs are aimed at reducing mortality due to breast cancer by detecting tumors at an early stage. There is currently interest in moving away from the age-based screening programs, and toward personalized screening based on individual risk factors. To accomplish this, risk prediction models for breast cancer are needed to determine who should be screened, and when. We develop a novel approach using a (random effects) continuous growth model, which we apply to a large population-based, Swedish screening cohort. Unlike existing breast cancer prediction models, this approach explicitly incorporates each woman's individual screening visits in the prediction. It jointly models invasive breast cancer tumor onset, tumor growth rate, symptomatic detection rate, and screening sensitivity. In addition to predicting the overall risk of invasive breast cancer, this model can make separate predictions regarding specific tumor sizes, and the mode of detection (eg, detected at screening, or through symptoms between screenings). It can also predict how these risks change depending on whether or not a woman will attend her next screening. In our study, we predict, given a future diagnosis, that the probability of having a tumor less than (as opposed to greater than) 10-mm diameter, at detection, will be, on average, 2.6 times higher if a woman in the cohort attends their next screening. This indicates that the model can be used to evaluate the short-term benefit of screening attendance, at an individual level., (© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Postsurgical falls have significant patient and societal implications but remain challenging to identify and track. Detecting postsurgical falls is crucial to improve patient care for older adults and reduce healthcare costs. Large language models (LLMs) offer a promising solution for reliable and automated fall detection using unstructured data in clinical notes. We tested several LLM prompting approaches to postsurgical fall detection in two different healthcare systems with three open-source LLMs. The Mixtral-8×7B zero-shot had the best performance at Stanford Health Care (PPV = 0.81, recall = 0.67) and the Veterans Health Administration (PPV = 0.93, recall = 0.94). These results demonstrate that LLMs can detect falls with little to no guidance and lay groundwork for applications of LLMs in fall prediction and prevention across many different settings., Competing Interests: Competing Interests None declared.

Introduction: Lifetime and past-year alcohol use disorder (AUD) prevalence is significantly higher in US Armed Services Veterans than in non-veterans across adulthood. This study examined the associations of perceived transformational leadership styles (TLS) experienced during military service and anhedonic depression and self-efficacy related to confidence to abstain or reduce alcohol consumption in Veterans seeking treatment for AUD. The ramifications of perceived leadership styles on multiple aspects of follower psychiatric functioning, including depressive and PTSD symptomatology, during and after military service, may be substantial and enduring. Higher anhedonic depression and lower abstinence self-efficacy are related to increased risk of relapse after treatment. We predicted Veterans, in treatment for AUD, who reported higher perceived levels of transformational leadership during military service, demonstrate lower anhedonic depressive symptoms and higher alcohol abstinence self-efficacy., Materials and Methods: Veterans with AUD (n = 60; 50 ± 14 years of age) were recruited from residential treatment at the VA Palo Alto Health Care System. All procedures were approved by the VA Palo Alto Health Care System and Stanford University institutional review boards. A series of mediation analyses were completed with The Multifactor Leadership Questionnaire measures of TLS (average across leadership measures [transformational leadership average; TLS average]) as predictor and the Alcohol Abstinence Self-Efficacy Scale, Mood and Anxiety Symptom Questionnaire, anhedonic depression subscale, as dependent measures. PTSD Checklist for DSM-5 score was tested as a mediator variable., Results: Higher reported perceived TLS average during military service was significantly related to lower anhedonic depressive symptoms. Higher TLS average was related to higher self-efficacy to resist alcohol use in contexts involving experience of physical issues and withdrawal/cravings and urges. These relationships were not mediated by PTSD symptomatology or duration of military service, age, education, time since military service, military branch, combat exposure, or current psychiatric diagnosis., Conclusions: The significant associations of perceived TLS during military service with anhedonic depression and alcohol use self-efficacy are clinically relevant because these measures are associated with relapse risk after AUD treatment. Further study of the implications of perceived TLS during military service for AUD and other substance use disorder treatment outcome is warranted in Veterans., (Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2023. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Competing Interests: The authors report no financial relationships with commercial interests.

Background: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability., Methods: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts., Results: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05)., Conclusions: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aim: To assess the effectiveness and cost-effectiveness of buprenorphine and methadone treatment in the U.S. if exemptions expanding coverage for substance use disorder services via telehealth and allowing opioid treatment programs to supply a greater number of take-home doses of medications for opioid use disorder (OUD) continue (Notice of Proposed Rule Making, NPRM)., Design Setting and Participants: Model-based analysis of buprenorphine and methadone treatment for a cohort of 100,000 individuals with OUD, varying treatment retention and overdose risk among individuals receiving and not receiving methadone treatment compared to the status quo (no NPRM)., Intervention: Buprenorphine and methadone treatment under NPRM., Measurements: Fatal and nonfatal overdoses and deaths over five years, discounted lifetime per person QALYs and costs., Findings: For buprenorphine treatment under the status quo, 1.21 QALYs are gained at a cost of $19,200/QALY gained compared to no treatment; with 20% higher treatment retention, 1.28 QALYs are gained at a cost of $17,900/QALY gained compared to no treatment, and the strategy dominates the status quo. For methadone treatment under the status quo, 1.11 QALYs are gained at a cost of $17,900/QALY gained compared to no treatment. In all scenarios, methadone provision cost less than $20,000/QALY gained compared to no treatment, and less than $50,000/QALY gained compared to status quo methadone treatment., Conclusions: Buprenorphine and methadone OUD treatment under NPRM are likely to be effective and cost-effective. Increases in overdose risk with take-home methadone would reduce health benefits. Clinical and technological strategies could mitigate this risk., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes., (©2023 AMIA - All rights reserved.)

Importance: False-positive mammography results are common. However, long-term outcomes after a false-positive result remain unclear., Objectives: To examine long-term outcomes after a false-positive mammography result and to investigate whether the association of a false-positive mammography result with cancer differs by baseline characteristics, tumor characteristics, and time since the false-positive result., Design, Setting, and Participants: This population-based, matched cohort study was conducted in Sweden from January 1, 1991, to March 31, 2020. It included 45 213 women who received a first false-positive mammography result between 1991 and 2017 and 452 130 controls matched on age, calendar year of mammography, and screening history (no previous false-positive result). The study also included 1113 women with a false-positive result and 11 130 matched controls with information on mammographic breast density from the Karolinska Mammography Project for Risk Prediction of Breast Cancer study. Statistical analysis was performed from April 2022 to February 2023., Exposure: A false-positive mammography result., Main Outcomes and Measures: Breast cancer incidence and mortality., Results: The study cohort included 497 343 women (median age, 52 years [IQR, 42-59 years]). The 20-year cumulative incidence of breast cancer was 11.3% (95% CI, 10.7%-11.9%) among women with a false-positive result vs 7.3% (95% CI, 7.2%-7.5%) among those without, with an adjusted hazard ratio (HR) of 1.61 (95% CI, 1.54-1.68). The corresponding HRs were higher among women aged 60 to 75 years at the examination (HR, 2.02; 95% CI, 1.80-2.26) and those with lower mammographic breast density (HR, 4.65; 95% CI, 2.61-8.29). In addition, breast cancer risk was higher for women who underwent a biopsy at the recall (HR, 1.77; 95% CI, 1.63-1.92) than for those without a biopsy (HR, 1.51; 95% CI, 1.43-1.60). Cancers after a false-positive result were more likely to be detected on the ipsilateral side of the false-positive result (HR, 1.92; 95% CI, 1.81-2.04) and were more common during the first 4 years of follow-up (HR, 2.57; 95% CI, 2.33-2.85 during the first 2 years; HR, 1.93; 95% CI, 1.76-2.12 at >2 to 4 years). No statistical difference was found for different tumor characteristics (except for larger tumor size). Furthermore, associated with the increased risk of breast cancer, women with a false-positive result had an 84% higher rate of breast cancer death than those without (HR, 1.84; 95% CI, 1.57-2.15)., Conclusions and Relevance: This study suggests that the risk of developing breast cancer after a false-positive mammography result differs by individual characteristics and follow-up. These findings can be used to develop individualized risk-based breast cancer screening after a false-positive result.

After years of minimal innovation in pharmacotherapeutics, impressive outcomes in the treatment of opioid use disorder are being obtained from a new way of delivering an old medication; long-acting injectable formulations of buprenorphine appear to produce compelling reductions in relapse to illicit opioid use not only during use but also following depot discontinuation. This commentary discusses potential mechanisms behind this observation, asks if the removal of the need for daily oral opioid agonist dosing furthers our understanding of addiction treatment and whether we should therefore consider expanding access to depot formulations., (© 2023 The Authors. Drug and Alcohol Review published by John Wiley & Sons Australia, Ltd on behalf of Australasian Professional Society on Alcohol and other Drugs.)

Background: The UK and USA currently report their highest number of drug-related deaths since records began, with higher rates among individuals experiencing homelessness., Aims: Given that overdose prevention in homeless populations may require unique strategies, we evaluated whether substances implicated in death differed between (a) housed decedents and those experiencing homelessness and (b) between US and UK homeless populations., Method: We conducted an internationally comparative retrospective cohort study utilising multilevel multinomial regression modelling of coronial/medical examiner-verified drug-related deaths from 1 January 2012 to 31 December 2021. UK data were available for England, Wales and Northern Ireland; US data were collated from eight county jurisdictions. Data were available on decedent age, sex, ethnicity, housing status and substances implicated in death., Results: Homeless individuals accounted for 16.3% of US decedents versus 3.4% in the UK. Opioids were implicated in 66.3 and 50.4% of all studied drug-related deaths in the UK and the USA respectively. UK homeless decedents had a significantly increased risk of having only opioids implicated in death compared with only non-opioids implicated (relative risk ratio RRR = 1.87, 95% CI 1.76-1.98, P < 0.001); conversely, US homeless decedents had a significantly decreased risk (RRR = 0.37, 95% CI 0.29-0.48, P < 0.001). Methamphetamine was implicated in two-thirds (66.7%) of deaths among US homeless decedents compared with 0.4% in the UK., Conclusions: Both the rate and type of drug-related deaths differ significantly between homeless and housed populations in the UK and USA. The two countries also differ in drugs implicated in death. Targeted programmes for country-specific implicated drug profiles appear warranted.

Background: Risk assessment is important for breast cancer prevention and early detection. We aimed to examine whether common risk factors, mammographic features, and breast cancer risk prediction scores of a woman were associated with breast cancer risk for her sisters., Methods: We included 53 051 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Established risk factors were derived using self-reported questionnaires, mammograms, and single nucleotide polymorphism genotyping. Using the Swedish Multi-Generation Register, we identified 32 198 sisters of the KARMA women (including 5352 KARMA participants and 26 846 nonparticipants). Cox models were used to estimate the hazard ratios of breast cancer for both women and their sisters, respectively., Results: A higher breast cancer polygenic risk score, a history of benign breast disease, and higher breast density in women were associated with an increased risk of breast cancer for both women and their sisters. No statistically significant association was observed between breast microcalcifications and masses in women and breast cancer risk for their sisters. Furthermore, higher breast cancer risk scores in women were associated with an increased risk of breast cancer for their sisters. Specifically, the hazard ratios for breast cancer per 1 standard deviation increase in age-adjusted KARMA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), and Tyrer-Cuzick risk scores were 1.16 (95% confidence interval [CI] = 1.07 to 1.27), 1.23 (95% CI = 1.12 to 1.35), and 1.21 (95% CI = 1.11 to 1.32), respectively., Conclusion: A woman's breast cancer risk factors are associated with her sister's breast cancer risk. However, the clinical utility of these findings requires further investigation., (© The Author(s) 2023. Published by Oxford University Press.)

Despite available treatment options, many patients with phenylketonuria (PKU) cannot achieve target plasma phenylalanine (Phe) levels 1 . We previously modified Escherichia coli Nissle 1917 to metabolize Phe in the gut after oral administration (SYNB1618) and designed a second strain (SYNB1934) with enhanced activity of phenylalanine ammonia lyase 2,3 . In a 14-day open-label dose-escalation study (Synpheny-1, NCT04534842 ), we test a primary endpoint of change from baseline in labeled Phe (D5-Phe AUC 0-24 ; D5-Phe area under the curve (AUC) over 24 hours after D5-Phe administration) in plasma after D5-Phe challenge in adult participants with screening Phe of greater than 600 µM. Secondary endpoints were the change from baseline in fasting plasma Phe and the incidence of treatment-emergent adverse events. A total of 20 participants (ten male and ten female) were enrolled and 15 completed the study treatment. Here, we show that both strains lower Phe levels in participants with PKU: D5-Phe AUC 0-24 was reduced by 43% from baseline with SYNB1934 and by 34% from baseline with SYNB1618. SYNB1934 led to a decrease in fasting plasma Phe of 40% (95% CI, -52, -24). There were no serious adverse events or infections. Four participants discontinued because of adverse events, and one withdrew during the baseline period. We show that synthetic biotics can metabolize Phe in the gut, lower post-prandial plasma Phe levels and lower fasting plasma Phe in patients with PKU., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Drug consumption in prisons is a concern for the safety of incarcerated people and staff. Typically, drug use prevalence in prisons is estimated through urinalysis and intelligence operations, which can be intrusive and stressful. An alternative approach, wastewater-based epidemiology (WBE), was used in this study to estimate the consumption of licit and illicit drugs for the entire population of a prison in Australia. Wastewater samples were collected from March to December 2020, covering periods of no restrictions and periods when prison access was restricted to prevent the transmission of COVID-19. Target biomarkers were analysed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The average consumption of common illicit drugs (MDMA, methamphetamine and cocaine) over the sampling period in the prison (0.5 - 4.5 mg/1000 people/day) was two to three orders of magnitude lower than in the community population (254 - 1000 mg/1000 people/day). Comparison of WBE estimates against pharmacy dispensing data suggested potential illicit buprenorphine consumption at the prison. Methamphetamine and buprenorphine use decreased when no visitors were allowed (18% - 72% decrease for methamphetamine; about half decrease for buprenorphine) and increased once these restrictions were eased (22% - 39% increase for methamphetamine; 44% - 67% increase for buprenorphine). The changes in drug use may be attributed in part to a reduction of drug trafficking into the prison from visitors or non-essential staffs and in part to the reduced contribution of urine from staff who used toilets within the prison. This study provided useful information on the scale of illicit drug use and extra-medical use of licit drugs in prison, and its changes under different security conditions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

We recently described a joint model of breast cancer tumor size and number of affected lymph nodes, which conditions on screening history, mammographic density, and mode of detection, and can be used to infer growth rates, time to symptomatic detection, screening sensitivity, and rates of lymph node spread. The model of lymph node spread can be estimated in isolation from measurements of tumor volume and number of affected lymph nodes, giving inference identical to the joint model. Here, we extend our model to include covariate effects. We also derive theoretical results in order to study the role of screening on lymph node metastases at diagnosis. We analyze the association between hormone replacement therapy (HRT) and breast cancer lymph node spread, using data from a case-control study designed specifically to study the effects of HRT on breast cancer. Using our method, we estimate that women using HRT at time of diagnosis have a 36% lower rate of lymph node spread than nonusers (95% confidence interval [CI] =(8%,58%)). This can be contrasted with the effect of HRT on the tumor growth rate, estimated here to be 15% slower in HRT users (95% CI = (-34%,+7%)). For screen-detected cancers, we illustrate how lead time can relate to lymph node spread; and using symptomatic cancers, we illustrate the potential consequences of false negative screens in terms of lymph node spread., (© 2021 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.)

Background: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer., Methods: Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262)., Results: We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10 -2 ). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm)., Conclusion: We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer., Significance: Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: J.H. has obtained speaker’s honoraria or advisory board remunerations from Roche, Novartis, AstraZeneca, Eli Lilly and MSD. J.H. has received institutional research grants from Cepheid, Roche and Novartis. M.R. and J.H. are shareholders of Stratipath AB. All remaining authors have declared no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Understanding the detectability of breast cancer using mammography is important when considering nation-wide screening programmes. Although the role of imaging settings on image quality has been studied extensively, their role in detectability of cancer at a population level is less well studied. We wish to quantify the association between mammographic screening sensitivity and various imaging parameters. Using a novel approach applied to a population-based breast cancer screening cohort, we specifically focus on sensitivity as defined in the classical diagnostic testing literature, as opposed to the screen-detected cancer rate, which is often used as a measure of sensitivity for monitoring and evaluating breast cancer screening. We use a natural history approach to model the presence and size of latent tumors at risk of detection at mammography screening, and the screening sensitivity is modeled as a logistic function of tumor size. With this approach we study the influence of compressed breast thickness, x-ray exposure, and compression pressure, in addition to (percent) breast density, on the screening test sensitivity. When adjusting for all screening parameters in addition to latent tumor size, we find that percent breast density and compressed breast thickness are statistically significant factors for the detectability of breast cancer. A change in breast density from 6.6 to 33.5% (the inter-quartile range) reduced the odds of detection by 61% (95% CI 48-71). Similarly, a change in compressed breast thickness from 46 to 66 mm reduced the odds by 42% (95% CI 21-57). The true sensitivity of mammography, defined as the probability that an examination leads to a positive result if a tumour is present in the breast, is associated with compressed breast thickness after accounting for mammographic density and tumour size. This can be used to guide studies of setups aimed at improving lesion detection. Compressed breast thickness-in addition to breast density-should be considered when assigning complementary screening modalities and personalized screening intervals., (© 2023. Springer Nature Limited.)

Background: Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states., Methods: Five machine learning models were developed and validated across seven US states (90-10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression., Results: We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination., Conclusions: A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 El Hajouji et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Within the addiction field, some advocates support a suite of de-regulatory policies that aim to reduce harm by providing people who use drugs with a "safe supply" of pharmaceutical-grade medications. Such initiatives have commenced without the evidence standards normally used to label medication provision as "safe." This perspective suggests that continued debate and research in this area acknowledge the potential toxicity of any provided safe supply medications and highlights that these initiatives could result in an unhelpful reduction in interactions between people who use drugs and health care professionals.

Objectives: Because buprenorphine treatment of opioid use disorder reduces opioid overdose deaths (OODs), expanding access to care is an important policy and clinical care goal. Policymakers must choose within capacity limitations whether to expand the number of people with opioid use disorder who are treated or extend duration for existing patients. This inherent tradeoff could be made less acute with expanded buprenorphine treatment capacity., Methods: To inform such decisions, we used a validated simulation model to project the effects of increasing buprenorphine treatment-seeking, average episode duration, and capacity (patients per provider) on OODs in the United States from 2023 to 2033, varying the start time to assess the effects of implementation delays., Results: Results show that increasing treatment duration alone could cost lives in the short term by reducing capacity for new admissions yet save more lives in the long term than accomplished by only increasing treatment seeking. Increasing provider capacity had negligible effects. The most effective 2-policy combination was increasing capacity and duration simultaneously, which would reduce OODs up to 18.6% over a decade. By 2033, the greatest reduction in OODs (≥20%) was achieved when capacity was doubled and average duration reached 2 years, but only if the policy changes started in 2023. Delaying even a year diminishes the benefits. Treatment-seeking increases were equally beneficial whether they began in 2023 or 2025 but of only marginal benefit beyond what capacity and duration achieved., Conclusions: If policymakers only target 2 policies to reduce OODs, they should be to increase capacity and duration, enacted quickly and aggressively., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 American Society of Addiction Medicine.)

We investigate the association between rate of breast cancer lymph node spread and grade, estrogen receptor (ER) status, progesteron receptor status, decision tree derived PAM50 molecular subtype and a polygenic risk score (PRS), using data on 10 950 women included from two different data sources. Lymph node spread was analyzed using a novel continuous tumor progression model that adjusts for tumor volume in a biologically motivated way and that incorporates covariates of interest. Grades 2 and 3 tumors, respectively, were associated with 1.63 and 2.17 times faster rates of lymph node spread than Grade 1 tumors (P < 10 -16 ). ER/PR negative breast cancer was associated with a 1.25/1.19 times faster spread than ER/PR positive breast cancer, respectively (P = .0011 and .0012). Among the molecular subtypes luminal A, luminal B, Her2-enriched and basal-like, Her2-enriched breast cancer was associated with 1.53 times faster spread than luminal A cancer (P = .00072). PRS was not associated with the rate of lymph node spread. Continuous growth models are useful for quantifying associations between lymph node spread and tumor characteristics. These may be useful for building realistic progression models for microsimulation studies used to design individualized screening programs., (© 2021 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Background: Innovation ecosystems are an important driver of regional economic growth and development. STEM assets connected to universities may play an important role in such ecosystems., Objective: To systematically review the literature relating to the effect of university STEM assets on regional economies and innovation ecosystems, providing a better understanding of how the impact is generated and constrained, as well as identifying any gaps in knowledge., Methods: Keyword and text word searches using the Web of Science Core Collection (Clarivate), Econlit (EBSCO) and ERIC (EBSCO) were performed in July 2021 and February 2023. Papers were double screened on abstract and title, and were included if there was consensus that they fulfilled the inclusion criteria of: (i) relating to an OECD country; (ii) having been published between 1 January 2010 and 28 February 2023; and (iii) concerning the impact of STEM assets. Data extraction was undertaken for each article by a single reviewer and checked by a second reviewer. Due to the heterogeneity of the study designs and outcome measures used, it was not possible to perform a quantitative synthesis of results. A narrative synthesis was subsequently undertaken., Results: Of the 162 articles identified for detailed review, 34 were accepted as being sufficiently relevant to the study to be included for final analysis. Three important features identified were that the literature: i) is predominately concerned with supporting new businesses; ii) describes a high level of involvement with a university in providing that support; and iii studies economic impacts at local, regional and national levels., Discussion: The evidence points to a gap in the literature relating to looking at the broader impact of STEM assets and any corresponding transformational, system-level effects that go beyond narrowly defined, short to medium-term outcomes. The main limitation of this review is that information on STEM assets in the non-academic literature is not captured., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Billing et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

FANCM germline protein truncating variants (PTVs) are moderate-risk factors for ER-negative breast cancer. We previously described the spectrum of FANCM PTVs in 114 European breast cancer cases. In the present, larger cohort, we report the spectrum and frequency of four common and 62 rare FANCM PTVs found in 274 carriers detected among 44,803 breast cancer cases. We confirmed that p.Gln1701* was the most common PTV in Northern Europe with lower frequencies in Southern Europe. In contrast, p.Gly1906Alafs*12 was the most common PTV in Southern Europe with decreasing frequencies in Central and Northern Europe. We verified that p.Arg658* was prevalent in Central Europe and had highest frequencies in Eastern Europe. We also confirmed that the fourth most common PTV, p.Gln498Thrfs*7, might be a founder variant from Lithuania. Based on the frequency distribution of the carriers of rare PTVs, we showed that the FANCM PTVs spectra in Southwestern and Central Europe were much more heterogeneous than those from Northeastern Europe. These findings will inform the development of more efficient FANCM genetic testing strategies for breast cancer cases from specific European populations.

Background: The COVID-19 pandemic has amplified the need for web-based behavioral interventions to support individuals who are diagnosed with chronic conditions and their informal caregivers. However, most interventions focus on patient outcomes. Dyadic technology-enabled interventions that simultaneously improve outcomes for patients and caregivers are needed., Objective: This study aimed to describe the methodology used to adapt a telephone-based, facilitated, and dyadic self-management program called Self-care Using Collaborative Coping Enhancement in Diseases (SUCCEED) into a self-guided, web-based version (web-SUCCEED) and to conduct usability testing for web-SUCCEED., Methods: We developed web-SUCCEED in 6 steps: ideation-determine the intervention content areas; prototyping-develop the wireframes, illustrating the look and feel of the website; prototype refinement via feedback from focus groups; finalizing the module content; programming web-SUCCEED; and usability testing. A diverse team of stakeholders including content experts, web designers, patients, and caregivers provided input at various stages of development. Costs, including full-time equivalent employee, were summarized., Results: At the ideation stage, we determined the content of web-SUCCEED based on feedback from the program's original pilot study. At the prototyping stage, the principal investigator and web designers iteratively developed prototypes that included inclusive design elements (eg, large font size). Feedback about these prototypes was elicited through 2 focus groups of veterans with chronic conditions (n=13). Rapid thematic analysis identified two themes: (1) web-based interventions can be useful for many but should include ways to connect with other users and (2) prototypes were sufficient to elicit feedback about the esthetics, but a live website allowing for continual feedback and updating would be better. Focus group feedback was incorporated into building a functional website. In parallel, the content experts worked in small groups to adapt SUCCEED's content, so that it could be delivered in a didactic, self-guided format. Usability testing was completed by veterans (8/16, 50%) and caregivers (8/16, 50%). Veterans and caregivers gave web-SUCCEED high usability scores, noting that it was easy to understand, easy to use, and not overly burdensome. Notable negative feedback included "slightly agreeing" that the site was confusing and awkward. All veterans (8/8, 100%) agreed that they would choose this type of program in the future to access an intervention that aims to improve their health. Developing and maintaining the software and hosting together cost approximately US $100,000, excluding salary and fringe benefits for project personnel (steps 1-3: US $25,000; steps 4-6: US $75,000)., Conclusions: Adapting an existing, facilitated self-management support program for delivery via the web is feasible, and such programs can remotely deliver content. Input from a multidisciplinary team of experts and stakeholders can ensure the program's success. Those interested in adapting programs should have a realistic estimate of the budget and staffing requirements., (©Ranak Trivedi, Sierra Kawena Hirayama, Rashmi Risbud, Madhuvanthi Suresh, Marika Blair Humber, Kevin Butler, Alex Razze, Christine Timko, Karin Nelson, Donna M Zulman, Steven M Asch, Keith Humphreys, John D Piette. Originally published in JMIR Formative Research (https://formative.jmir.org), 16.06.2023.)