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Patient-reported outcomes (PROs), such as symptoms, function, and other health-related quality-of-life aspects, are increasingly evaluated in cancer randomised controlled trials (RCTs) to provide information about treatment risks, benefits, and tolerability. However, expert opinion and critical review of the literature showed no consensus on optimal methods of PRO analysis in cancer RCTs, hindering interpretation of results. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data Consortium was formed to establish PRO analysis recommendations. Four issues were prioritised: developing a taxonomy of research objectives that can be matched with appropriate statistical methods, identifying appropriate statistical methods for PRO analysis, standardising statistical terminology related to missing data, and determining appropriate ways to manage missing data. This Policy Review presents recommendations for PRO analysis developed through critical literature reviews and a structured collaborative process with diverse international stakeholders, which provides a foundation for endorsement; ongoing developments of these recommendations are also discussed.

Pre-emptive pharmacogenomic (PGx) testing of a panel of genes may be easier to implement and more cost-effective than reactive pharmacogenomic testing if a sufficient number of medications are covered by a single test and future medication exposure can be anticipated. We analysed the incidence of exposure of individual patients in the United States to multiple drugs for which pharmacogenomic guidelines are available (PGx drugs) within a selected four-year period (2009-2012) in order to identify and quantify the incidence of pharmacotherapy in a nation-wide patient population that could be impacted by pre-emptive PGx testing based on currently available clinical guidelines. In total, 73 024 095 patient records from private insurance, Medicare Supplemental and Medicaid were included. Patients enrolled in Medicare Supplemental age > = 65 or Medicaid age 40-64 had the highest incidence of PGx drug use, with approximately half of the patients receiving at least one PGx drug during the 4 year period and one fourth to one third of patients receiving two or more PGx drugs. These data suggest that exposure to multiple PGx drugs is common and that it may be beneficial to implement wide-scale pre-emptive genomic testing. Future work should therefore concentrate on investigating the cost-effectiveness of multiplexed pre-emptive testing strategies.

To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with [greater than or equal to]1 opioid prescriptions. This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling [greater than or equal to]1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile. The training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age [greater than or equal to]65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000). Machine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.

We took advantage of regional differences in environmental forcing and consumer abundance to examine the relative importance of nutrient availability (bottom-up), grazing pressure (top-down), and storm waves (disturbance) in determining the standing biomass and net primary production (NPP) of the giant kelp Macrocystis pyrifera in central and southern California. Using a nine-year data set collected from 17 sites we show that, despite high densities of sea urchin grazers and prolonged periods of low nutrient availability in southern California, NPP by giant kelp was twice that of central California where nutrient concentrations were consistently high and sea urchins were nearly absent due to predation by sea otters. Waves associated with winter storms were consistently higher in central California, and the loss of kelp biomass to winter wave disturbance was on average twice that of southern California. These observations suggest that the more intense wave disturbance in central California limited NPP by giant kelp under otherwise favorable conditions. Regional patterns of interannual variation in NPP were similar to those of wave disturbance in that year-to-year variation in disturbance and NPP were both greater in southern California. Our findings provide strong evidence that regional differences in wave disturbance overwhelmed those of nutrient supply and grazing intensity to determine NPP by giant kelp. The important role of disturbance in controlling NPP revealed by our study is likely not unique to giant kelp forests, as vegetation dynamics in many systems are dominated by post-disturbance succession with climax communities being relatively uncommon. The effects of disturbance frequency may be easier to detect in giant kelp because it is fast growing and relatively short lived, with cycles of disturbance and recovery occurring on time scales of years. Much longer data sets (decades to centuries) will likely be needed to properly evaluate the role of disturbance relative to other processes in determining patterns of NPP in other systems.

Drug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability. In this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk. We examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters. Among the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration. In summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.

Objectives. We studied housing retention and its predictors in the single-site Housing First model. Methods. Participants (n = 111) were chronically homeless people with severe alcohol problems who lived in a single-site Housing First program and participated in a larger nonrandomized controlled trial (2005–2008) conducted in Seattle, Washington. At baseline, participants responded to self-report questionnaires assessing demographic, illness burden, alcohol and other drug use, and psychiatric variables. Housing status was recorded over 2 years. Results. Participants were interested in housing, although a sizable minority did not believe they would be able to maintain abstinence-based housing. Only 23% of participants returned to homelessness during the 2-year follow-up. Commonly cited risk factors—alcohol and other drug use, illness burden, psychiatric symptoms, and homelessness history—did not predict resumed homelessness. Active drinkers were more likely to stay in this housing project than nondrinkers. Conclusions. We found that single-site Housing First programming fills a gap in housing options for chronically homeless people with severe alcohol problems.

Kelp forests are among the most productive ecosystems on Earth. In combination with their close proximity to the shore, the productivity and biodiversity of these ecosystems generate a wide range of ecosystem services including supporting (e.g., primary production, habitat), regulating (e.g., water flow, coastal erosion), provisioning (e.g., commercial and recreational fisheries), and cultural (e.g., recreational, artisanal) services. For these reasons, kelp forests have long been the target of ecological studies. However, with few exceptions, these studies have been localized and short term (<5 years). In 1999, recognizing the importance of large-scale, long-term studies for understanding the structure, functioning, and dynamics of coastal marine ecosystems, and for informing policy, the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO) designed and initiated a large-scale, long-term monitoring study of kelp forest ecosystems along 1400 km of coast stretching from southern California to southern Oregon, USA. The purpose of the study has been to characterize the spatial and temporal patterns of kelp forest ecosystem structure and evaluate the relative contributions of biological and environmental variables derived from external sources (e.g., sea otter density, Chl-a concentration, sea surface temperature, wave energy) in explaining observed spatial and temporal patterns. For this purpose, the ecological community (i.e., density, percent cover, or biomass of conspicuous fishes, invertebrates, and macroalgae) and geomorphological attributes (bottom depth, substratum type, and vertical relief) of kelp forest ecosystems have been surveyed annually using SCUBA divers trained in both scientific diving and data collection techniques and the identification of kelp forest species. The study region spans distinct ecological and biogeographic provinces, which enables investigations of how variation in environmental drivers and distinctive species compositions influence community structure, and its response to climate-related environmental change across a portion of the California Current Large Marine Ecosystem. These data have been used to inform fisheries management, design and evaluate California’s state-wide network of marine protected areas (MPAs), and assess the ecological consequences of climate change (e.g., marine heatwaves). Over time, the spatial and temporal design of the monitoring program was adapted to fill its role in evaluating the ecological responses to the establishment of MPAs. There are no copyright restrictions; please cite this paper when data are used.

Background Preferences for health states for Duchenne muscular dystrophy (DMD) are necessary to assess costs and benefits of novel therapies. Because DMD progression begins in childhood, the impact of DMD on health-related quality-of-life (HRQoL) affects preferences of both DMD patients and their families. The objective of this review was to synthesize published evidence for health state utility from the DMD patient and caregiver perspectives. Methods A systematic review was performed using MEDLINE and Embase, according to best practices. Data were extracted from studies reporting DMD patient or caregiver utilities; these included study and patient characteristics, health states considered, and utility estimates. Quality appraisal of studies was performed. Results From 888 abstracts, eight publications describing five studies were identified. DMD utility estimates were from preference-based measures presented stratified by ambulatory status, ventilation, and age. Patient (or patient–proxy) utility estimates ranged from 0.75 (early ambulatory DMD) to 0.05 (day-and-night ventilation). Caregiver utilities ranged from 0.87 (for caregivers of adults with DMD) to 0.71 (for caregivers of predominantly childhood patients). Both patient and caregiver utilities trended lower with higher disease severity. Variability in utilities was observed based on instrument, respondent type, and country. Utility estimates for health states within non-ambulatory DMD are under reported; nor were utilities for DMD-related health states such as scoliosis or preserved upper limb function identified. Conclusion Published health state utilities document the substantial HRQoL impacts of DMD, particularly with disease progression. Additional research in patient utilities for additional health states, particularly in non-ambulatory DMD patients, is warranted.

Introduction Tizanidine is primarily metabolized via cytochrome P450 (CYP) 1A2 and therefore medications that inhibit the enzyme will affect the clearance of tizanidine, leading to increased plasma concentrations of tizanidine and potentially serious adverse events. Objectives Our aim was to study the occurrence of adverse events reported in the FDA Adverse Event Reporting System (FAERS) involving the combination of tizanidine and drugs that inhibit the metabolic activity of CYP1A2. Methods A disproportionality analysis of FAERS reports from 2004 quarter 1 through 2020 quarter 3 was conducted to calculate the reporting odds ratio (ROR) of reports mentioning tizanidine in a suspect or interacting role or having any role, a CYP1A2 inhibitor, and the following adverse events: hypotension, bradycardia, syncope, shock, cardiorespiratory arrest, and fall or fracture. Results A total of 89 reports were identified mentioning tizanidine, at least one CYP1A2 inhibitor, and one of the adverse events of interest. More than half of the reports identified tizanidine as having a suspect or interacting role ( n  = 59, 66.3%), and the reports more frequently involved women ( n  = 58, 65.1%). The median age was 56.1 years (standard deviation 17.1). Some of the important safety signals included interactions between tizanidine in a suspect or interacting role and ciprofloxacin (ROR for hypotension 28.1, 95% confidence interval [CI] 19.2–41.2) or fluvoxamine (ROR for hypotension 36.9, 95% CI 13.1–103.4), and also when reported in “any role” with ciprofloxacin (ROR for hypotension 6.3, 95% CI 4.7–8.5), fluvoxamine (ROR for hypotension 11.4, 95% CI 4.5–28.8), and zafirlukast (ROR for falls 16.0, 95% CI 6.1–42.1). Conclusions Reports involving tizanidine and a CYP1A2 inhibitor have higher odds of reporting hypotension. This study suggests that concurrent use of tizanidine with CYP1A2 inhibitors may lead to serious health consequences associated with low blood pressure such as falls and fractures.

Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.