Systematic review and meta-analysis to predict mortality in heart failure with preserved ejection fraction: Development and validation of the HF-DANAS score.

AIMS: The morbidity and mortality of heart failure with preserved ejection fraction (HFpEF) continue to increase with the accelerating global aging process. During the past decade, the pathophysiology, diagnostic methods, and prognostic prediction of HFpEF have been revolutionized, resulting in new and effective management strategies. Dynamic prognostic assessment facilitates systematic clinical management of patients, and the aim of this study was to investigate the risk factors for mortality in patients with HFpEF and to develop a risk prediction assessment model. METHODS AND REULTS: Data for the derivation cohort were obtained from three databases, PubMed, Embase, and Cochrane. The validation cohort was obtained from the Chinese Heart Failure Center database. The ß-coefficient was calculated based on the risk ratio (RR) and 95% confidence intervals (CI) corresponding to each risk factor to construct a mortality risk assessment model. A total of 30 studies were included in the meta-analysis: 22 prospective cohort studies and 8 retrospective cohort studies, including 34 196 HFpEF patients. Seven predictors of all-cause mortality in HFpEF patients were derived. Considering the need for feasibility in clinical practice, we performed subgroup and sensitivity analyses and determined the following cutoff values: age > 75 years (RR: 2.07, 95% CI: 1.83-2.35; P < 0.001), male sex (RR: 1.36, 95% CI: 1.17-1.59; P < 0.001), DM (RR: 1.23, 95% CI: 1.11-1.36; P < 0.001), anaemia (RR: 1.53, 95% CI: 1.41-1.67; P < 0.001), albumin concentration < 3.2 g/dL (RR: 1.29, 95% CI: 1.14-1.47; P < 0.001), AF (RR: 1.27, 95% CI: 1.12-1.43; P < 0.001), and NYHA class III/IV (RR: 1.63, 95% CI: 1.43-1.87; P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for this model was 71.3% (95% CI: 0.696-0.736), with an optimal cut-off value of 10.75. The sensitivity and specificity were 0.778 and 0.566, respectively. According to this risk score, we divided patients into three risk classes (low, moderate, and high risk), the numbers of patients who died by the end of the 1-year follow-up were 23 (1.87%), 82 (5.62%), and 382 (15.52%) in these three groups, and the 5-year mortality rates were 9.82%, 20.68%, and 43.28%, respectively. CONCLUSIONS: This study developed an HF-DANAS scoring system for the HFpEF mortality risk containing seven predictors, providing clinicians with a simple assessment tool that can help improve clinical management.

Development and Validation of a Mobile-Centered Digital Health Readiness Scale (mDiHERS): Health Literacy and Equity Scale.

BACKGROUND: There has been a rapid expansion of digital health care services, making the need for measuring and improving digital health readiness a priority. In response, our study team developed the Mobile-Centered Digital Health Readiness: Health Literacy and Equity Scale (mDiHERS) to measure digital health readiness. OBJECTIVE: We aim to develop and validate a scale that assesses digital health readiness, encompassing literacy and equity, and to ensure the effective use of mobile-centered digital health services. METHODS: This study was conducted from October 2021 to October 2022 to develop and validate the mDiHERS. Participants included patients with inflammatory bowel disease, which is a chronic condition requiring continuous management, and experts in medical and nursing informatics. The scale development involved a literature review, focus group interviews, and content validity evaluations. A total of 440 patients with inflammatory bowel disease were recruited for the validation phase, with 403 completing the survey. The scale's validity and reliability were assessed through exploratory factor analysis and Cronbach α. The scale was translated into English by translators and bilingual and native researchers, ensuring its applicability in diverse settings. RESULTS: The mDiHERS consists of 36 items across 6 domains, with a 5-point Likert scale for responses. The validation process confirmed the scale's construct validity, with 4 factors explaining 65.05% of the total variance. The scale's reliability was established with Cronbach α values ranging from 0.84 to 0.91. The scale's development considered the technical proficiency necessary for engaging with health mobile apps and devices, reflecting the importance of subjective confidence and objective skills in digital health literacy. CONCLUSIONS: The mDiHERS is a validated tool for measuring patients' readiness and ability to use digital health services. The mDiHERS assesses user characteristics, digital accessibility, literacy, and equity to contribute to the effective use of digital health services and improve accessibility. The development and validation of the mDiHERS emphasize the importance of confidence and competence in managing health digitally. Continuous improvements are necessary to ensure that all patients can benefit from digital health care.

Knowledge translation and knowledge mobilization from the FoodBAll project.

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

Evaluation of Machine Learning Approaches for Predicting Warfarin Discharge Dose in Cardiac Surgery Patients: Retrospective Algorithm Development and Validation Study.

BACKGROUND: Warfarin dosing in cardiac surgery patients is complicated by a heightened sensitivity to the drug, predisposing patients to adverse events. Predictive algorithms are therefore needed to guide warfarin dosing in cardiac surgery patients. OBJECTIVE: This study aimed to develop and validate an algorithm for predicting the warfarin dose needed to attain a therapeutic international normalized ratio (INR) at the time of discharge in cardiac surgery patients. METHODS: We abstracted variables influencing warfarin dosage from the records of 1031 encounters initiating warfarin between April 1, 2011, and November 29, 2019, at St Michael's Hospital in Toronto, Ontario, Canada. We compared the performance of penalized linear regression, k-nearest neighbors, random forest regression, gradient boosting, multivariate adaptive regression splines, and an ensemble model combining the predictions of the 5 regression models. We developed and validated separate models for predicting the warfarin dose required for achieving a discharge INR of 2.0-3.0 in patients undergoing all forms of cardiac surgery except mechanical mitral valve replacement and a discharge INR of 2.5-3.5 in patients receiving a mechanical mitral valve replacement. For the former, we selected 80% of encounters (n=780) who had initiated warfarin during their hospital admission and had achieved a target INR of 2.0-3.0 at the time of discharge as the training cohort. Following 10-fold cross-validation, model accuracy was evaluated in a test cohort comprised solely of cardiac surgery patients. For patients requiring a target INR of 2.5-3.5 (n=165), we used leave-p-out cross-validation (p=3 observations) to estimate model performance. For each approach, we determined the mean absolute error (MAE) and the proportion of predictions within 20% of the true warfarin dose. We retrospectively evaluated the best-performing algorithm in clinical practice by comparing the proportion of cardiovascular surgery patients discharged with a therapeutic INR before (April 2011 and July 2019) and following (September 2021 and May 2, 2022) its implementation in routine care. RESULTS: Random forest regression was the best-performing model for patients with a target INR of 2.0-3.0, an MAE of 1.13 mg, and 39.5% of predictions of falling within 20% of the actual therapeutic discharge dose. For patients with a target INR of 2.5-3.5, the ensemble model performed best, with an MAE of 1.11 mg and 43.6% of predictions being within 20% of the actual therapeutic discharge dose. The proportion of cardiovascular surgery patients discharged with a therapeutic INR before and following implementation of these algorithms in clinical practice was 47.5% (305/641) and 61.1% (11/18), respectively. CONCLUSIONS: Machine learning algorithms based on routinely available clinical data can help guide initial warfarin dosing in cardiac surgery patients and optimize the postsurgical anticoagulation of these patients.

Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract.

BACKGROUND: Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints. OBJECTIVE: This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints. METHODS: The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews. RESULTS: A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal. CONCLUSIONS: A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/47119.

A Digital Box and Block Test for Hand Dexterity Measurement: Instrument Validation Study.

BACKGROUND: The Box and Block Test (BBT) measures unilateral gross manual dexterity and is widely used in clinical settings with a wide range of populations, including older people and clients with neurological disorders. OBJECTIVE: In this study, we present a newly developed digitized version of the BBT, called the digital BBT (dBBT). The physical design is similar to the original BBT, but the dBBT contains digital electronics that automate the test procedure, timing, and score measurement. The aim of this study is to investigate the validity and reliability of the dBBT. METHODS: We performed measurements at 2 time points for 29 healthy participants. BBT and dBBT were used at the first measurement time point, and dBBT was used again at the second measurement time point. Concurrent validity was assessed using the correlation between BBT and dBBT, the paired t test, and the Bland-Altman analysis. Test-retest reliability and interrater reliability were examined using the interclass correlation coefficient (ICC) by repeated measures with the dBBT within an interval of 10 days. RESULTS: Our results showed moderate concurrent validity (r=0.48, P=.008), moderate test-retest reliability (ICC 0.72, P<.001), a standard error of measurement of 3.1 blocks, and the smallest detectable change at a 95% CI of 8.5 blocks. Interrater reliability was moderate with an ICC of 0.67 (P=.02). The Bland-Altman analysis showed sufficient accuracy of the dBBT in comparison with the conventional BBT. CONCLUSIONS: The dBBT can contribute to objectifying the measurement of gross hand dexterity without losing its important characteristics and is simple to implement.

A Novel, Expert-Endorsed, Neurocognitive Digital Assessment Tool for Addictive Disorders: Development and Validation Study.

BACKGROUND: Many people with harmful addictive behaviors may not meet formal diagnostic thresholds for a disorder. A dimensional approach, by contrast, including clinical and community samples, is potentially key to early detection, prevention, and intervention. Importantly, while neurocognitive dysfunction underpins addictive behaviors, established assessment tools for neurocognitive assessment are lengthy and unengaging, difficult to administer at scale, and not suited to clinical or community needs. The BrainPark Assessment of Cognition (BrainPAC) Project sought to develop and validate an engaging and user-friendly digital assessment tool purpose-built to comprehensively assess the main consensus-driven constructs underpinning addictive behaviors. OBJECTIVE: The purpose of this study was to psychometrically validate a gamified battery of consensus-based neurocognitive tasks against standard laboratory paradigms, ascertain test-retest reliability, and determine their sensitivity to addictive behaviors (eg, alcohol use) and other risk factors (eg, trait impulsivity). METHODS: Gold standard laboratory paradigms were selected to measure key neurocognitive constructs (Balloon Analogue Risk Task [BART], Stop Signal Task [SST], Delay Discounting Task [DDT], Value-Modulated Attentional Capture [VMAC] Task, and Sequential Decision-Making Task [SDT]), as endorsed by an international panel of addiction experts; namely, response selection and inhibition, reward valuation, action selection, reward learning, expectancy and reward prediction error, habit, and compulsivity. Working with game developers, BrainPAC tasks were developed and validated in 3 successive cohorts (total N=600) and a separate test-retest cohort (N=50) via Mechanical Turk using a cross-sectional design. RESULTS: BrainPAC tasks were significantly correlated with the original laboratory paradigms on most metrics (r=0.18-0.63, P<.05). With the exception of the DDT k function and VMAC total points, all other task metrics across the 5 tasks did not differ between the gamified and nongamified versions (P>.05). Out of 5 tasks, 4 demonstrated adequate to excellent test-retest reliability (intraclass correlation coefficient 0.72-0.91, P<.001; except SDT). Gamified metrics were significantly associated with addictive behaviors on behavioral inventories, though largely independent of trait-based scales known to predict addiction risk. CONCLUSIONS: A purpose-built battery of digitally gamified tasks is sufficiently valid for the scalable assessment of key neurocognitive processes underpinning addictive behaviors. This validation provides evidence that a novel approach, purported to enhance task engagement, in the assessment of addiction-related neurocognition is feasible and empirically defensible. These findings have significant implications for risk detection and the successful deployment of next-generation assessment tools for substance use or misuse and other mental disorders characterized by neurocognitive anomalies related to motivation and self-regulation. Future development and validation of the BrainPAC tool should consider further enhancing convergence with established measures as well as collecting population-representative data to use clinically as normative comparisons.

Burns Depression Scale Today (BDST): A validation study of BDST against the reference standard of PHQ-9.

Background: Case finding for low mood is essential in primary care, but it is time-consuming using current depression inventories. The Burns Depression Scale Today (BDST) is a short, simple inventory which assesses mood for today, and we aimed to validate it in this study. Materials and Methods: Consecutive patients with emotional distress seen in a single primary care clinic by one of the authors over 22 months were eligible for this retrospective audit (N = 160). Multiple visits (N = 421) from the same patient were included in the study. The index test was BDST, which assesses the patient's mood for today. The reference standard was the 9-item Patient Health Questionnaire (PHQ-9), which assesses mood over the past 2 weeks. PHQ-9 had a cut-off point of ≥10 and BDST had a cut-off point of ≥6 for a significant mood issue. Results: The median age of patients was 35 years, and 63% of the cohort were women. The median BDST score was 8, indicative of moderately low mood, and the median PHQ-9 score was 15, indicative of moderately severe depression. For patients with a BDST score ≥6, the likelihood ratio of a positive test was 2.67. The sensitivity was 85% (95% confidence interval [CI]: 89%-96%) and the specificity was 68% (95% CI: 60%-76%). The area under the curve was 84% (95% CI: 80%-87%). Conclusion: This audit validates BDST against PHQ-9 and finds it an excellent case-finding tool compared to PHQ-9. This is the first validation study of BDST.

A numerical simulation of thermally-enhanced soil vapor extraction with a validation of an actual contaminated site.

Thermally-enhanced soil vapor extraction (T-SVE) remediation technology is widely used in organic-contaminated sites due to its high efficiency, short remediation period and controllable secondary contamination. However, the remediation efficiency is affected by the complex site factors, which leads to the uncertainty of the remediation process and energy waste. Thus, it is necessary to optimize T-SVE systems to accurately remediate the sites. In this work, a pilot site of reagent factory in Tianjin was taken as the research object to validate the model, and the T-SVE process parameters of a VOCs-contaminated sites were predicted by this simulation method. The simulation results showed that the Nash efficiency coefficient E of the measured and simulated temperature rise data in the study area was 0.885, and the linear correlation coefficient R of the measured and simulated concentrations of cis-1,2-dichloroethylene after remediation was 0.877, indicating that this simulation method is highly reliable. Based on this numerical simulation method, some parameters of the T-SVE process at the VOCs-contaminated site of an insulation plant in Harbin were simulated and optimized. Included a heating well spacing of 3.0 m, extraction pressure of 40 Kpa, extraction well influence radius of 4.35 m, extraction flow rate of 2.97 × 10-4 m3/s, and a theoretical number of 25 extraction wells (adjusted to 29 wells in practice), and the corresponding extraction well layout has been designed. The results can provide a technical reference for the future application of T-SVE in the remediation of organic-contaminated sites.

A New Questionnaire to Assess Respiratory Symptoms (The Respiratory Symptom Experience Scale): Quantitative Psychometric Assessment and Validation Study.

BACKGROUND: Smokers often experience respiratory symptoms (eg, morning cough), and those who stop smoking, including those who do so by switching completely to electronic nicotine delivery systems (ENDS), may experience reductions in symptoms. Existing respiratory symptom questionnaires may not be suitable for studying these changes, as they are intended for patient populations, such as those with chronic obstructive pulmonary disease (COPD). OBJECTIVE: This study aimed to develop a respiratory symptom questionnaire appropriate for current smokers and for assessing changes when smokers stop smoking. METHODS: The Respiratory Symptom Experience Scale (RSES) was derived from existing instruments and subject matter expert input and refined through cognitive debriefing interviews (n=49). Next, for purposes of the quantitative psychometric evaluation, the RSES was administered to smokers (n=202), former smokers (no tobacco use in >6 months; n=200), and switchers (n=208, smokers who switched to ENDS for >6 months), all of whom had smoked for at least 10 years (mean age 33 years). Participants, who averaged 62 (SD 12) years of age, included 28% (173/610) with respiratory allergy symptoms and 17% (104/610) with COPD. Test-retest reliability was assessed by repeat assessment after 1 week in 128 participants. RESULTS: A generalized partial credit model confirmed that the response options were ordered, and a parallel analysis using principal components confirmed that the scale was unidimensional. With allowance for 2 sets of correlated errors between pairs of items, a 1-factor graded response model fit the data. Discrimination parameters were approximately 1 or greater for all items. Scale reliability was 0.80 or higher across a broad range of severity (standardized scores -0.40 to 3.00). Test-retest reliability (absolute intraclass correlation) was good, at 0.89. RSES convergent validity was supported by substantial differences (Cohen d=0.74) between those with and without a diagnosis of respiratory disease (averaging 0.57 points, indicating that differences of this size or smaller represent meaningful differences). RSES scores also strongly differentiated those with and without COPD (d=1.52). Smokers' RSES scores were significantly higher than former smokers' scores (P<.001). Switchers' RSES scores were significantly lower than smokers' scores (P<.001) and no different from former smokers' scores (P=.34). CONCLUSIONS: The RSES fills an important gap in the existing toolkit of respiratory symptom questionnaires; it is a reliable and valid tool to assess respiratory symptoms in adult current and former smokers, including those who have switched to noncombusted nicotine products. This suggests that the scale is sensitive to respiratory symptoms that develop in smokers and to their remission when smokers quit or switch to noncombusted nicotine products intended to reduce the harm of smoking. The findings also suggest that switching from cigarettes to ENDS may improve respiratory health.

Assessing bystander first aid: development and validation of a First Aid Quality Assessment (FAQA) tool.

BACKGROUND: Injuries are one of the leading causes of death worldwide. Bystanders at the scene can perform first aid measures before the arrival of health services. The quality of first aid measures likely affects patient outcome. However, scientific evidence on its effect on patient outcome is limited. To properly assess bystander first aid quality, measure effect, and facilitate improvement, validated assessment tools are needed. The purpose of this study was to develop and validate a First Aid Quality Assessment (FAQA) tool. The FAQA tool focuses on first aid measures for injured patients based on the ABC-principle, as assessed by ambulance personnel arriving on scene. METHODS: In phase 1, we drafted an initial version of the FAQA tool for assessment of airway management, control of external bleeding, recovery position and hypothermia prevention. A group of ambulance personnel aided presentation and wording of the tool. In phase 2 we made eight virtual reality (VR) films, each presenting an injury scenario where bystander performed first aid. In phase 3, an expert group discussed until consensus on how the FAQA tool should rate each scenario. Followingly, 19 respondents, all ambulance personnel, rated the eight films with the FAQA tool. We assessed concurrent validity and inter-rater agreement by visual inspection and Kendall's coefficient of concordance. RESULTS: FAQA-scores by the expert group concurred with ± 1 of the median of the respondents on all first aid measures for all eight films except one case, where a deviation of 2 was seen. The inter-rater agreement was "very good" for three first aid measures, "good" for one, and "moderate" for the scoring of overall quality on first aid measures. CONCLUSION: Our findings show that it is feasible and acceptable for ambulance personnel to collect information on bystander first aid with the FAQA tool and will be of importance for future research on bystander first aid for injured patients.

A culturally appropriate method for validating self-reported drug administration among indigenous people who use injection drugs.

Compared with other racial/ethnic groups in the United States (US), American Indians/Alaska Natives have one of the fastest climbing rates of drug overdose deaths involving stimulants. Validating the substances self-reported by Indigenous people who use injection drugs (IPWIDs) can present logistical and cultural challenges. While the collection of biospecimens (e.g., urine, blood, hair follicle) can be one way to cross-validate the substances self-reported by IPWIDs, the collection of biospecimens has been historically problematic when conducting substance use research with Indigenous North Americans. In our National Institutes of Health (NIH)-supported pilot research conducted with IPWIDs, we have documented low willingness to provide a biospecimen to a research team. This article demonstrates an alternative method for validating self-reported substances injected by IPWIDs that does not require the extraction of biospecimens from Indigenous bodies and spaces. The method described includes:•Collecting used, unwashed syringes from IPWIDs at the time of behavioral assessment,•Sampling the used syringe by washing the syringe needle/barrel with methanol,•Analyzing the samples with gas chromatography mass spectrometry (GC-MS) and liquid chromatography coupled to triple-quadrupole mass spectrometry (LC-QQQ-MS). This method offers a more culturally appropriate alternative to validate substances self-reported by IPWIDs during behavioral assessments.

Using deep-learning predictions of inter-residue distances for model validation.

Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org).

The Persian Version of the Mobile Application Rating Scale (MARS-Fa): Translation and Validation Study.

BACKGROUND: Approximately 110 million Farsi speakers worldwide have access to a growing mobile app market. Despite restrictions and international sanctions, Iran's internal mobile health app market is growing, especially for Android-based apps. However, there is a need for guidelines for developing health apps that meet international quality standards. There are also no tools in Farsi that assess health app quality. Developers and researchers who operate in Farsi could benefit from such quality assessment tools to improve their outputs. OBJECTIVE: This study aims to translate and culturally adapt the Mobile Application Rating Scale in Farsi (MARS-Fa). This study also evaluates the validity and reliability of the newly developed MARS-Fa tool. METHODS: We used a well-established method to translate and back translate the MARS-Fa tool with a group of Iranian and international experts in Health Information Technology and Psychology. The final translated version of the tool was tested on a sample of 92 apps addressing smartphone addiction. Two trained reviewers completed an independent assessment of each app in Farsi and English. We reported reliability and construct validity estimates for the objective scales (engagement, functionality, aesthetics, and information quality). Reliability was based on the evaluation of intraclass correlation coefficients, Cronbach α and Spearman-Brown split-half reliability indicators (for internal consistency), as well as Pearson correlations for test-retest reliability. Construct validity included convergent and discriminant validity (through item-total correlations within the objective scales) and concurrent validity using Pearson correlations between the objective and subjective scores. RESULTS: After completing the translation and cultural adaptation, the MARS-Fa tool was used to assess the selected apps for smartphone addiction. The MARS-Fa total scale showed good interrater reliability (intraclass correlation coefficient=0.83, 95% CI 0.74-0.89) and good internal consistency (Cronbach α=.84); Spearman-Brown split-half reliability for both raters was 0.79 to 0.93. The instrument showed excellent test-retest reliability (r=0.94). The correlations among the MARS-Fa subdomains and the total score were all significant and above r=0.40, suggesting good convergent and discriminant validity. The MARS-Fa was positively and significantly correlated with subjective quality (r=0.90, P<.001), and so were the objective subdomains of engagement (r=0.85, P<.001), information quality (r=0.80, P<.001), aesthetics (r=0.79, P<.001), and functionality (r=0.57, P<.001), indicating concurrent validity. CONCLUSIONS: The MARS-Fa is a reliable and valid instrument to assess mobile health apps. This instrument could be adopted by Farsi-speaking researchers and developers who want to evaluate the quality of mobile apps. While we tested the tool with a sample of apps addressing smartphone addiction, the MARS-Fa could assess other domains or issues since the Mobile App Rating Scale has been used to rate apps in different contexts and languages.

Construction and validation of a prognostic model for predicting overall survival of primary adrenal malignant tumor patients: A population-based study with 1,080 patients.

Objective: Primary adrenal malignant tumor is rare. The factors affecting the prognosis remain poorly defined. This study targeted to construct and corroborate a model for predicting the overall survival of adrenal malignant tumor patients. Methods: We investigated the SEER database for patients with primary adrenal malignant tumor. 1,080 patients were divided into a construction cohort (n = 756) and a validation cohort (n = 324), randomly. The prognostic factors for overall survival were evaluated using univariate and multivariate Cox analyses. The nomogram was constructed and then validated with C-index, calibration curve, time-dependent ROC curve, and decision curve analysis in both cohorts. Then we divided the patients into 3 different risk groups according to the total points of the nomogram and analyzed their survival status by Kaplan-Meier curve with log-rank test. Results: The baseline characteristics of these two cohorts were not statistically different (P > 0.05). Using univariate and multivariate Cox analyses, 5 variables, including age, tumor size, histological type, tumor stage, and surgery of primary site, were distinguished as prognostic factors (P < 0.05). Based on these variables, we constructed a nomogram to predict the 3- year, 5- year, and 10-year overall survival. The C-indexes were 0.780 (0.760-0.800) in the construction cohort and 0.780 (0.751-0.809) in the validation cohort. In both cohorts, the AUC reached a fairly high level at all time points. The internal and external calibration curves and ROC analysis showed outstanding accuracy and discrimination. The decision curves indicated excellent clinical usefulness. The best cut-off values for the total points of the nomogram were 165.4 and 243.1, and the prognosis was significantly different for the three different risk groups (P < 0.001). Conclusion: We successfully constructed a model to predict the overall survival of primary adrenal malignant tumor patients. This model was validated to perform brilliantly internally and externally, which can assist us in individualized clinical management.

Using QR Codes as a Form of eHealth to Promote Health Among Women in a Pandemic: Cross-sectional Study.

BACKGROUND: QR codes have played an integral role during the pandemic in many sectors, but their use has been limited in the health care sector, especially by patients. Although some authors have stated that developing specific content for women on how to cope with health problems could be an effective way to prevent problems, especially during pandemics, there is little research regarding the use of QR codes to promote health during a pandemic, and even fewer studies are focused on women. Moreover, although the importance of assessing these interventions from the users' perspective has been stated, research carried out from this point of view is still scarce. OBJECTIVE: This study aimed to assess the usefulness of using QR codes with information to promote women's health in the context of a pandemic. We also sought to design and validate a questionnaire to assess this. METHODS: A cross-sectional study was conducted among women in the gynecology waiting rooms of a reference hospital. Exploratory factorial analysis with the split-half method and Cronbach α values was performed for questionnaire validation. Univariant and bivariant analyses were performed to analyze the data obtained. RESULTS: In total, 186 women took part in the study. Exploratory factor analysis identified 2 domains: usability and applicability in medical practice. The Cronbach α value was .81. Overall, 83.7% of the answers to the first domain and 56.4% of those to the second were favorable. Women with university education or those who had used QR codes before scored better in the usability domain, while no differences were observed in the applicability scores. CONCLUSIONS: Using QR codes in the gynecology clinics' waiting rooms can help promote women's health during a pandemic, regardless of their education level or whether they have used QR codes before. The questionnaire developed herein is a helpful tool to assess this. These findings are important for clinical practice. This research can be performed in other ambits, specialties, or countries.

Validation of an Electronic Visual Analog Scale App for Pain Evaluation in Children and Adolescents With Symptomatic Hypermobility: Cross-sectional Study.

BACKGROUND: Rapid advances in mobile apps for clinical data collection for pain evaluation have resulted in more efficient data handling and analysis than traditional paper-based approaches. As paper-based visual analogue scale (p-VAS) scores are commonly used to assess pain levels, new emerging apps need to be validated prior to clinical application with symptomatic children and adolescents. OBJECTIVE: This study aimed to assess the validity and reliability of an electronic visual analogue scale (e-VAS) method via a mobile health (mHealth) App in children and adolescents diagnosed with hypermobility spectrum disorder/hypermobile Ehlers-Danlos syndrome (HSD/HEDS) in comparison with the traditional p-VAS. METHODS: Children diagnosed with HSD/HEDS aged 5-18 years were recruited from a sports medicine center in Sydney (New South Wales, Australia). Consenting participants assigned in random order to the e-VAS and p-VAS platforms were asked to indicate their current lower limb pain level and completed pain assessment e-VAS or p-VAS at one time point. Instrument agreement between the 2 methods was determined from the intraclass correlation coefficient (ICC) and through Bland-Altman analysis. RESULTS: In total, 43 children with HSD/HEDS aged 11 (SD 3.8) years were recruited and completed this study. The difference between the 2 VAS platforms of median values was 0.20. Bland-Altman analysis revealed a difference of 0.19 (SD 0.95) with limits of agreement ranging -1.67 to 2.04. An ICC of 0.87 (95% CI 0.78-0.93) indicated good reliability. CONCLUSIONS: These findings suggest that the e-VAS mHealth App is a validated tool and a feasible method of collecting pain recording scores when compared with the traditional paper format in children and adolescents with HSD/HEDS. The e-VAS App can be reliably used for pediatric pain evaluation, and it could potentially be introduced into daily clinical practice to improve real-time symptom monitoring. Further research is warranted to investigate the usage of the app for remote support in real clinical settings.

Transcultural Adaptation of and Theoretical Validation Models for the Spanish Version of the Nurses' Global Assessment of Suicide Risk Scale: Protocol for a Multicenter Cross-sectional Study.

BACKGROUND: The use of validated instruments means providing health professionals with reliable and valid tools. The Nurses' Global Assessment of Suicide Risk (NGASR) scale has proven to be valid and reliable in supporting the nursing evaluation of suicide risk in different languages and cultural environments. OBJECTIVE: The aims of our study are to translate and adapt the NGASR scale for the Spanish population and evaluate its psychometric properties in patients with suicide risk factors. METHODS: The translation, adaptation, and modeling of the tool will be performed. The sample will include 165 participants. The psychometric analysis will include reliability and validity tests of the tool's internal structure. The tool's reliability will be assessed by exploring internal consistency and calculating the Cronbach α coefficient; significance values of .70 or higher will be accepted as indicators of good internal consistency. The underlying factor structure of the Spanish version of the NGASR scale will be assessed by performing an exploratory factor analysis. The Kaiser-Meyer-Olkin measure of sample adequacy and the Bartlett sphericity statistic will be calculated beforehand. For the latter, if P is <.05 for the null hypothesis of sphericity, the null hypothesis will be rejected. RESULTS: Participants will be recruited between April 2022 and December 2022. Our study is expected to conclude in the first quarter of 2023. CONCLUSIONS: We hope to find the same firmness that colleagues have found in other countries in order to consolidate and promote the use of the NGASR tool in the Spanish population. The prevention and treatment of suicidal behavior require holistic, multidisciplinary, and comprehensive management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39482.

The Curtis Hand Injury Matrix Score: Determining the Need for Specialized Upper Extremity Care.

PURPOSE: Given the limited impact of transfer guidelines and the lack of comparative metrics for upper extremity trauma, we introduced the Curtis Hand Injury Matrix (CHIM) score to evaluate upper extremity injury acuity from the specialist perspective. Our goal was to evaluate the CHIM score as an indicator of complexity and specialist need by correlating the score with arrival mode, length of stay (LOS), discharge disposition, and procedure location. METHODS: We identified all hand and upper extremity emergency room visits at our institution in 2018 and 2019. On initial evaluation, our institution's hand surgery team assigned each patient an alphanumeric score with a number (1-5) and letter (A-H) corresponding to injury severity and pathology, respectively. Patients were divided into 5 groups (1-5) with lower scores indicating greater severity. We compared age, LOS, discharge disposition, procedure location, transfer status, and arrival mode between groups and assessed the relationships between matrix scores and discharge disposition, procedure performed, and LOS. RESULTS: There were 3,822 patients that accounted for 4,026 upper extremity evaluations. There were significant differences in LOS, discharge dispositions, procedure locations, transfer status, and arrival modes between groups. Patients with more severe scores had higher rates of admission and more operating room procedures. Higher percentages of patients who arrived via helicopter, ambulance, or transfer had more severe scores. Patients with more severe scores were significantly more likely to have a procedure, hospital admission, and longer hospital stay. CONCLUSIONS: The CHIM score provides a framework to catalog the care and resources required when covering specialized hand and upper extremity calls and accepting transfers. This clinical validation supports considering broader use. TYPE OF STUDY/LEVEL OF EVIDENCE: Prognostic IV.