Assessment of Heat Exposure and Health Outcomes in Rural Populations of Western Kenya by Using Wearable Devices: Observational Case Study.

BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time. OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures. METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes. RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness. CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study's application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Outcomes of prognostication in people living with advanced cancer: A qualitative study to inform a Core Outcome Set.

BACKGROUND: Studies of prognostication in advanced cancer use a wide range of outcomes and outcome measures, making it difficult to compare these studies and their findings. Core Outcome Sets facilitate comparability and standardisation between studies and would benefit future prognostic research. This qualitative study, the second step in a wider study developing such a Core Outcome Set, aimed to explore the perceptions and experiences of patients with advanced cancer, informal caregivers, and clinicians regarding the potential outcomes to assess the impact of prognostication. METHODS: We conducted semi-structured interviews with patients living with advanced cancer (n = 8), informal caregivers (n = 10), and clinicians (n = 10) recruited from palliative care services across three sites in London, United Kingdom. Interviews were conducted in-person, via telephone, or video conferencing, and were audio-recorded. Data were analysed using framework analysis. Findings were compared with outcomes identified in a previously published systematic review. RESULTS: We identified 33 outcomes, 16 of which were not previously reported in the literature. We grouped these outcomes into 10 domains, nine from the COMET taxonomy, plus a tenth domain (spiritual/religious/existential functioning/wellbeing) which we added further to the previous systematic review. These findings highlighted discrepancies between the priorities of existing research and those of stakeholders. Novel outcomes highlight the more personal and emotional impacts of prognostication, whilst other outcomes confirm the relevance of survival length, depression, anxiety, pain, hope dynamics, emotional distress, and the quality of patient-clinician relationships for assessing the impact of prognostication. CONCLUSIONS: This study offers valuable insights into outcomes which matter to key stakeholders, particularly patients and informal caregivers, highlights discrepancies between their priorities and those identified in previous studies, and underscores the need for a patient-centred approach in research and clinical practice in prognostication in advanced cancer. This work will contribute to developing a Core Outcome Set for assessing the impact of prognostication in advanced cancer.

Incidence of postoperative complications is underestimated if outcome data are recorded by interns and first year residents in a low volume hospital setting.

The aim of this study is to evaluate the accuracy of outcome reporting after elective visceral surgery in a low volume district hospital. Outcome measurement as well as transparent reporting of surgical complications becomes more and more important. In the future, financial and personal resources may be distributed due to reported quality and thus, it is in the main interest of healthcare providers that outcome data are accurately collected. Between 10/2020 and 09/2021 postoperative complications during the hospitalisation were recorded using the Clavien-Dindo classification (CDC) and comprehensive complication index by residents of a surgical department in a district hospital. After one year of prospective data collection, data were retrospectively analyzed and re-evaluated for accuracy by senior consultant surgeons. In 575 patients undergoing elective general or visceral surgery interns and residents reported an overall rate of patients with complications of 7.3% (n = 42) during the hospitalization phase, whereas a rate of 18.3% (n = 105) was revealed after retrospective analysis by senior consultant surgeons. Thus, residents failed to report patients with postoperative complications in 60% of cases (63/105). In the 42 cases, in which complications were initially reported, the grading of complications was correct only in 33.3% of cases (n = 14). Complication grades that were most missed were CDC grade I and II. Quality of outcome measurement in a district hospital is poor if done by unexperienced residents and significantly underestimates the true complication rate. Outcome measurement must be done or supervised by experienced surgeons to ensure correct and reliable outcome data.

Evaluation of Telemedicine Consultations Using Health Outcomes and User Attitudes and Experiences: Scoping Review.

BACKGROUND: Despite a recent rise in adoption, telemedicine consultations retention remains challenging, and aspects around the associated experiences and outcomes remain unclear. The need to further investigate these aspects was a motivating factor for conducting this scoping review. OBJECTIVE: With a focus on synchronous telemedicine consultations between patients with nonmalignant chronic illnesses and health care professionals (HCPs), this scoping review aimed to gain insights into (1) the available evidence on telemedicine consultations to improve health outcomes for patients, (2) the associated behaviors and attitudes of patients and HCPs, and (3) how supplemental technology can assist in remote consultations. METHODS: PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guided the scoping review process. Inclusion criteria were (1) involving adults with nonmalignant, noncommunicable chronic conditions as the study population; (2) focusing on health outcomes and experiences of and attitudes toward synchronous telemedicine consultations between patients and HCPs; and (3) conducting empirical research. A search strategy was applied to PubMed (including MEDLINE), CINAHL Complete, APA PsycNet, Web of Science, IEEE, and ACM Digital. Screening of articles and data extraction from included articles were performed in parallel and independently by 2 researchers, who corroborated their findings and resolved any conflicts. RESULTS: Overall, 4167 unique articles were identified from the databases searched. Following multilayer filtration, 19 (0.46%) studies fulfilled the inclusion criteria for data extraction. They investigated 6 nonmalignant chronic conditions, namely chronic obstructive pulmonary disease, diabetes, chronic kidney disease, ulcerative colitis, hypertension, and congestive heart failure, and the telemedicine consultation modality varied in each case. Most observed positive health outcomes for patients with chronic conditions using telemedicine consultations. Patients generally favored the modality's convenience, but concerns were highlighted around cost, practical logistics, and thoroughness of clinical examinations. The majority of HCPs were also in favor of the technology, but a minority experienced reduced job satisfaction. Supplemental technological assistance was identified in relation to technical considerations, improved remote workflow, and training in remote care use. CONCLUSIONS: For patients with noncommunicable chronic conditions, telemedicine consultations are generally associated with positive health outcomes that are either directly or indirectly related to their ailment, but sustained improvements remain unclear. These modalities also indicate the potential to empower such patients to better manage their condition. HCPs and patients tend to be satisfied with remote care experience, and most are receptive to the modality as an option. Assistance from supplemental technologies mostly resides in addressing technical issues, and additional modules could be integrated to address challenges relevant to patients and HCPs. However, positive outcomes and attitudes toward the modality might not apply to all cases, indicating that telemedicine consultations are more appropriate as options rather than replacements of in-person visits.

Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods.

BACKGROUND: In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual's risk of experiencing an outcome, then measuring treatment effect by subgroup. Given the limited availability of externally validated outcome risk prediction models, internal models (created using the same dataset in which heterogeneity of treatment analyses also will be performed) are commonly developed for subgroup identification. We aim to compare different methods for generating internally developed outcome risk prediction models for subject partitioning in HTE analysis. METHODS: Three approaches were selected for generating subgroups for the 2,441 participants from the United States enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial. An extant proportional hazards-based outcomes predictive risk model developed on the overall ASPREE cohort of 19,114 participants was identified and was used to partition United States' participants by risk of experiencing a composite outcome of death, dementia, or persistent physical disability. Next, two supervised non-parametric machine learning outcome classifiers, decision trees and random forests, were used to develop multivariable risk prediction models and partition participants into subgroups with varied risks of experiencing the composite outcome. Then, we assessed how the partitioning from the proportional hazard model compared to those generated by the machine learning models in an HTE analysis of the 5-year absolute risk reduction (ARR) and hazard ratio for aspirin vs. placebo in each subgroup. Cochran's Q test was used to detect if ARR varied significantly by subgroup. RESULTS: The proportional hazard model was used to generate 5 subgroups using the quintiles of the estimated risk scores; the decision tree model was used to generate 6 subgroups (6 automatically determined tree leaves); and the random forest model was used to generate 5 subgroups using the quintiles of the prediction probability as risk scores. Using the semi-parametric proportional hazards model, the ARR at 5 years was 15.1% (95% CI 4.0-26.3%) for participants with the highest 20% of predicted risk. Using the random forest model, the ARR at 5 years was 13.7% (95% CI 3.1-24.4%) for participants with the highest 20% of predicted risk. The highest outcome risk group in the decision tree model also exhibited a risk reduction, but the confidence interval was wider (5-year ARR = 17.0%, 95% CI= -5.4-39.4%). Cochran's Q test indicated ARR varied significantly only by subgroups created using the proportional hazards model. The hazard ratio for aspirin vs. placebo therapy did not significantly vary by subgroup in any of the models. The highest risk groups for the proportional hazards model and random forest model contained 230 participants each, while the highest risk group in the decision tree model contained 41 participants. CONCLUSIONS: The choice of technique for internally developed models for outcome risk subgroups influences HTE analyses. The rationale for the use of a particular subgroup determination model in HTE analyses needs to be explicitly defined based on desired levels of explainability (with features importance), uncertainty of prediction, chances of overfitting, and assumptions regarding the underlying data structure. Replication of these analyses using data from other mid-size clinical trials may help to establish guidance for selecting an outcomes risk prediction modelling technique for HTE analyses.

Guideline for reporting systematic reviews of outcome measurement instruments (OMIs): PRISMA-COSMIN for OMIs 2024.

PURPOSE: Although comprehensive and widespread guidelines on how to conduct systematic reviews of outcome measurement instruments (OMIs) exist, for example from the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative, key information is often missing in published reports. This article describes the development of an extension of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline: PRISMA-COSMIN for OMIs 2024. METHODS: The development process followed the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) guidelines and included a literature search, expert consultations, a Delphi study, a hybrid workgroup meeting, pilot testing, and an end-of-project meeting, with integrated patient/public involvement. RESULTS: From the literature and expert consultation, 49 potentially relevant reporting items were identified. Round 1 of the Delphi study was completed by 103 panelists, whereas round 2 and 3 were completed by 78 panelists. After 3 rounds, agreement (≥67%) on inclusion and wording was reached for 44 items. Eleven items without consensus for inclusion and/or wording were discussed at a workgroup meeting attended by 24 participants. Agreement was reached for the inclusion and wording of 10 items, and the deletion of 1 item. Pilot testing with 65 authors of OMI systematic reviews further improved the guideline through minor changes in wording and structure, finalized during the end-of-project meeting. The final checklist to facilitate the reporting of full systematic review reports contains 54 (sub)items addressing the review's title, abstract, plain language summary, open science, introduction, methods, results, and discussion. Thirteen items pertaining to the title and abstract are also included in a separate abstract checklist, guiding authors in reporting for example conference abstracts. CONCLUSION: PRISMA-COSMIN for OMIs 2024 consists of two checklists (full reports; abstracts), their corresponding explanation and elaboration documents detailing the rationale and examples for each item, and a data flow diagram. PRISMA-COSMIN for OMIs 2024 can improve the reporting of systematic reviews of OMIs, fostering their reproducibility and allowing end-users to appraise the quality of OMIs and select the most appropriate OMI for a specific application. NOTE: In order to encourage its wide dissemination this article is freely accessible on the web sites of the journals: Health and Quality of Life Outcomes; Journal of Clinical Epidemiology; Journal of Patient-Reported Outcomes; Quality of Life Research.

Outcome measurements in epidermal necrolysis: a systematic review.

Steven Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN), grouped together under the terminology of epidermal necrolysis (EN), are a spectrum of life-threatening dermatologic conditions. A lack of standardization and validation for existing endpoints has been identified as a key barrier to the comparison of these therapies and development of evidenced-based treatment. Following PRISMA guidelines, we conducted a systematic review of prospective studies involving systemic or topical treatments for EN, including dressing and ocular treatments. Outcomes were separated into mortality assessment, cutaneous outcomes, non-cutaneous clinical outcomes, and mucosal outcomes. The COSMIN Risk of Bias tool was used to assess the quality of studies on reliability and measurement error of outcome measurement instruments. Outcomes across studies assessing treatment in the acute phase of EN were varied. Most data came from prospective case reports and cohort studies representing the lack of available randomized clinical trial data available in EN. Our search did not reveal any EN-specific validated measures or scoring tools used to assess disease progression and outcomes. Less than half of included studies were considered "adequate" for COSMIN risk of bias in reliability and measurement error of outcome measurement instruments. With little consensus about management and treatment of EN, consistency and validation of measured outcomes is of the upmost importance for future studies to compare outcomes across treatments and identify the most effective means of combating the disease with the highest mortality managed by dermatologists.

Patient-centered outcomes and outcome measurements for people aged 65 years and older-a scoping review.

INTRODUCTION: The aging population is a challenge for the healthcare system that must identify strategies that meet their needs. Practicing patient-centered care has been shown beneficial for this patient-group. The effect of patient-centered care is called patient-centered outcomes and can be appraised using outcomes measurements. OBJECTIVES: The main aim was to review and map existing knowledge related to patient-centered outcomes and patient-centered outcomes measurements for older people, as well as identify key-concepts and knowledge-gaps. The research questions were: How can patient-centered outcomes for older people be measured, and which patient-centered outcomes matters the most for the older people? STUDY DESIGN: Scoping review. METHODS: Search for relevant publications in electronical databases, grey literature databases and websites from year 2000 to 2021. Two reviewers independently screened titles and abstracts, followed by full text review and extraction of data using a data extraction framework. RESULTS: Eighteen studies were included, of which six with involvement of patients and/or experts in the process on determine the outcomes. Outcomes that matter the most to older people was interpreted as: access to- and experience of care, autonomy and control, cognition, daily living, emotional health, falls, general health, medications, overall survival, pain, participation in decision making, physical function, physical health, place of death, social role function, symptom burden, and time spent in hospital. The most frequently mentioned/used outcomes measurements tools were the Adult Social Care Outcomes Toolkit (ASCOT), EQ-5D, Gait Speed, Katz- ADL index, Patient Health Questionnaire (PHQ9), SF/RAND-36 and 4-Item Screening Zarit Burden Interview. CONCLUSIONS: Few studies have investigated the older people's opinion of what matters the most to them, which forms a knowledge-gap in the field. Future research should focus on providing older people a stronger voice in what they think matters the most to them.

Study of machine learning techniques for outcome assessment of leptospirosis patients.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.

Multimorbidity prevalence and health outcome prediction: assessing the impact of lookback periods, disease count, and definition criteria in health administrative data at the population-based level.

BACKGROUND: Health administrative databases play a crucial role in population-level multimorbidity surveillance. Determining the appropriate retrospective or lookback period (LP) for observing prevalent and newly diagnosed diseases in administrative data presents challenge in estimating multimorbidity prevalence and predicting health outcome. The aim of this population-based study was to assess the impact of LP on multimorbidity prevalence and health outcomes prediction across three multimorbidity definitions, three lists of diseases used for multimorbidity assessment, and six health outcomes. METHODS: We conducted a population-based study including all individuals ages > 65 years on April 1st, 2019, in Québec, Canada. We considered three lists of diseases labeled according to the number of chronic conditions it considered: (1) L60 included 60 chronic conditions from the International Classification of Diseases (ICD); (2) L20 included a core of 20 chronic conditions; and (3) L31 included 31 chronic conditions from the Charlson and Elixhauser indices. For each list, we: (1) measured multimorbidity prevalence for three multimorbidity definitions (at least two [MM2+], three [MM3+] or four (MM4+) chronic conditions); and (2) evaluated capacity (c-statistic) to predict 1-year outcomes (mortality, hospitalisation, polypharmacy, and general practitioner, specialist, or emergency department visits) using LPs ranging from 1 to 20 years. RESULTS: Increase in multimorbidity prevalence decelerated after 5-10 years (e.g., MM2+, L31: LP = 1y: 14%, LP = 10y: 58%, LP = 20y: 69%). Within the 5-10 years LP range, predictive performance was better for L20 than L60 (e.g., LP = 7y, mortality, MM3+: L20 [0.798;95%CI:0.797-0.800] vs. L60 [0.779; 95%CI:0.777-0.781]) and typically better for MM3 + and MM4 + definitions (e.g., LP = 7y, mortality, L60: MM4+ [0.788;95%CI:0.786-0.790] vs. MM2+ [0.768;95%CI:0.766-0.770]). CONCLUSIONS: In our databases, ten years of data was required for stable estimation of multimorbidity prevalence. Within that range, the L20 and multimorbidity definitions MM3 + or MM4 + reached maximal predictive performance.

Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research.

OBJECTIVE: Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening. METHODS: This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms. RESULTS AND CONCLUSIONS: The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.

Composite outcome measures in high-impact critical care randomised controlled trials: a systematic review.

BACKGROUND: The use of composite outcome measures (COM) in clinical trials is increasing. Whilst their use is associated with benefits, several limitations have been highlighted and there is limited literature exploring their use within critical care. The primary aim of this study was to evaluate the use of COM in high-impact critical care trials, and compare study parameters (including sample size, statistical significance, and consistency of effect estimates) in trials using composite versus non-composite outcomes. METHODS: A systematic review of 16 high-impact journals was conducted. Randomised controlled trials published between 2012 and 2022 reporting a patient important outcome and involving critical care patients, were included. RESULTS: 8271 trials were screened, and 194 included. 39.1% of all trials used a COM and this increased over time. Of those using a COM, only 52.6% explicitly described the outcome as composite. The median number of components was 2 (IQR 2-3). Trials using a COM recruited fewer participants (409 (198.8-851.5) vs 584 (300-1566, p = 0.004), and their use was not associated with increased rates of statistical significance (19.7% vs 17.8%, p = 0.380). Predicted effect sizes were overestimated in all but 6 trials. For studies using a COM the effect estimates were consistent across all components in 43.4% of trials. 93% of COM included components that were not patient important. CONCLUSIONS: COM are increasingly used in critical care trials; however effect estimates are frequently inconsistent across COM components confounding outcome interpretations. The use of COM was associated with smaller sample sizes, and no increased likelihood of statistically significant results. Many of the limitations inherent to the use of COM are relevant to critical care research.

Outcome Measures in Adult Vulvar Lichen Sclerosus: A Systematic Review.

OBJECTIVES: Core outcome domains (CODs) for treatment of adult vulvar lichen sclerosus (VLS) have recently been established through a Delphi study. A number of measuring tools are available for evaluating VLS. The aim of this study is to identify available standardized measurement tools for the major CODs for VLS that have recently been defined, namely, physical findings and quality of life (QoL) specific to VLS. MATERIALS AND METHODS: A systematic search through September 8, 2023, for measuring tools applicable to VLS regarding physical findings and QoL including sexual function or sexual well-being and self-image was performed. RESULTS: Thirty-five studies were included in the systematic review describing 26 tools covering the following 6 outcome domains: QoL-general health, QoL-lichen sclerosus specific, symptoms, clinical signs, emotional impact, and sexual functioning. CONCLUSIONS: In current research, there is no uniformity in use of measurement tools for evaluating VLS. The established CODs to evaluate treatment of VLS are applicable for evaluating disease course as well. A comprehensive study to reach consensus regarding measurement of physical findings, QoL-lichen sclerosus specific, sexuality, and self-image taking the predetermined CODs and other factors such as age into account is needed.

Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Adults with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations.

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. Therefore, the International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy and their representatives to develop minimum sets of standardized outcomes and outcomes measurement methods for clinical practice that support patient-clinician decision-making and quality improvement. Consensus methods identified 20 core outcomes. Measurement tools were recommended based on their evidence of strong clinical measurement properties, feasibility, and cross-cultural applicability. The essential outcomes included many non-seizure outcomes: anxiety, depression, suicidality, memory and attention, sleep quality, functional status, and the social impact of epilepsy. The proposed set will facilitate the implementation of the use of patient-centered outcomes in daily practice, ensuring holistic care. They also encourage harmonization of outcome measurement, and if widely implemented should reduce the heterogeneity of outcome measurement, accelerate comparative research, and facilitate quality improvement efforts.

Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Infants, Children, and Adolescents with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations.

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. The International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy, and their representatives to develop minimum sets of standardized outcomes and outcome measurement methods for clinical practice. Using modified Delphi consensus methods with consecutive rounds of online voting over 12 months, a core set of outcomes and corresponding measurement tool packages to capture the outcomes were identified for infants, children, and adolescents with epilepsy. Consensus methods identified 20 core outcomes. In addition to the outcomes identified for the ICHOM Epilepsy adult standard set, behavioral, motor, and cognitive/language development outcomes were voted as essential for all infants and children with epilepsy. The proposed set of outcomes and measurement methods will facilitate the implementation of the use of patient-centered outcomes in daily practice.

Methods for Neuroscience Drug Development: Guidance on Standardization of the Process for Defining Clinical Outcome Strategies in Clinical Trials.

Neurosciences clinical trials continue to have notoriously high failure rates. Appropriate outcomes selection in early clinical trials is key to maximizing the likelihood of identifying new treatments in psychiatry and neurology. The field lacks good standards for designing outcome strategies, therefore The Outcomes Research Group was formed to develop and promote good practices in outcome selection. This article describes the first published guidance on the standardization of the process for clinical outcomes in neuroscience. A minimal step process is defined starting as early as possible, covering key activities for evidence generation in support of content validity, patient-centricity, validity requirements and considerations for regulatory acceptance. Feedback from expert members is provided, regarding the risks of shortening the process and examples supporting the recommended process are summarized. This methodology is now available to researchers in industry, academia or clinics aiming to implement consensus-based standard practices for clinical outcome selection, contributing to maximizing the efficiency of clinical research.