Reliability and validity of a newly developed PANDAS/PANS questionnaire.

OBJECTIVE: This study aimed to examine the reliability and validity of a newly developed questionnaire for Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal infections (PANDAS) and Pediatric Acute-onset Neuropsychiatric Syndrome (PANS). The aim was to contribute to future standardisation of screening methods for symptoms and comorbidity, as well as the measurement of symptom severity, daily life impairment, and treatment effectiveness in individuals diagnosed with PANDAS/PANS. METHODS: 27 items from the PANDAS/PANS questionnaire concerning symptoms and comorbidities associated with PANDAS/PANS were divided into ten domains. To assess the external validity, 119 PANDAS/PANS questionnaires from a cohort of 65 children with PANDAS/PANS were correlated with three well-known validated questionnaires: the Children's Yale-Brown Obsessive-Compulsive Scale (CY-BOCS), Attention Deficit Hyperactivity Disorder Rating Scale (ADHD-RS), and the Strengths and Difficulties Questionnaire (SDQ). The internal validity of the PANDAS/PANS questionnaire was assessed by correlating the PANDAS/PANS items with the domains. RESULTS: Internal consistency of the PANDAS/PANS questionnaire was high, measuring moderate to very strong correlations. The external correlations for the PANDAS/PANS questionnaire showed a higher correlation with the ADHD-RS and CY-BOCS (rs ≥ 0.60) than with the SDQ (rs < 0.40). CONCLUSION: The validity and clinical feasibility of the PANDAS/PANS questionnaire were confirmed as an effective tool for screening symptoms, assessing symptom severity, and evaluating comorbidity and daily life impairment in individuals with PANDAS/PANS. These findings can potentially enhance the management of PANDAS/PANS patients in both clinical and research settings.

Determination of the effectiveness of the three-minute nutrition screening (3-MinNS) tool in adult patients hospitalized in a cardiology clinic.

OBJECTIVES: To determine the efficacy of the 3-Minute Nutrition Screening (3-MinNS) tool in adults hospitalized for cardiovascular diseases. METHODS: In this descriptive cross-sectional study of 759 cardiovascular disease patients in Erciyes University Cardiology Clinic, anthropometric measurements and some routine biochemical parameters were recorded, and nutrition screening tools were used to determine malnutrition status. The power of 3-MinNS to detect malnutrition in cardiovascular diseases patients was calculated. RESULTS: There was a strong positive correlation between 3-MinNS and Nutrition Risk Screening-2002 (NRS-2002) (r=0.719, p<0.001). A moderate agreement was found between 3-MinNS and NRS-2002 (κ=0.496, p<0.001). The sensitivity, specificity, and AUC of 3-MinNS were 79.1%, 75.0%, and 0.851, respectively, and it was determined to be a moderately effective nutrition screening tool that can be used to identify malnutrition in patients with cardiovascular diseases. CONCLUSION: The 3-MinNS is a moderately effective nutrition screening tool that can be administered within the first 24 hours of hospitalization in patients with cardiovascular diseases.

The Prevalence of Fibromyalgia in Rheumatoid Arthritis Patients Using the Fibromyalgia Assessment Screening Tool (FAST 4) Based on the Multidimensional Health Assessment Questionnaire (MDHAQ).

Introduction Fibromyalgia (FM) is characterized by widespread pain and fatigue, accompanied by symptoms such as decreased concentration, autonomic dysfunction, and abdominal pain. It can be either primary or secondary, notably to rheumatoid arthritis (RA). The Fibromyalgia Assessment Screening Tools (FAST 4), derived from the Multidimensional Health Assessment Questionnaire (MDHAQ), is a composite tool allowing for the rapid screening of FM. Our primary objective is to determine the prevalence of FM among RA patients using the FAST 4 index. Secondary objectives include comparing the FAST 4 index with the FiRST score and describing the correlation between FM and RA activity and different factors associated with FM in RA patients. Methods This was an observational cross-sectional study including patients diagnosed with RA according to the ACR/EULAR criteria. The FAST questionnaire comprises four sections assessing pain and fatigue on a visual analog scale, painful joints reported by the patient, and a list of 60 symptoms. A FAST 4 score of ≥ 3/4 indicates a positive screening for FM. Demographics and disease features were compared using descriptive statistics. Univariate and multivariate analyses using logistic regression models were performed to calculate odds ratios (ORs) with 95% CI. The sensitivity and specificity of the FAST 4 index were evaluated, and Fagan's nomograms were used to illustrate post-test probability. Statistically significant results were considered for p-values less than 0.05. Results The study enrolled 97 patients diagnosed with RA. The mean age of the patients was 56 ± 12.7 years, with a predominance of females (90.7%, N=88). The mean duration of RA was 13.5 ± 8.69 years. RA activity measured by DAS 28-ESR showed that 40.2% (N=39) had high disease activity, 38.1% (N=37) had moderate disease activity, 11.3% (N=11) had low disease activity, and 10.3% (N=10) were in remission. The prevalence of comorbid FM, according to the FAST 4 index, was 30.9% (N=30). Based on the Multidimensional Health Assessment Questionnaire (MDHAQ), depression was observed in 66.7% (N=20) patients with FM, while anxiety was reported in 60% (N=18). Moreover, 30.4% of patients screened positive for FM using the FiRST score. The FAST 4 index detected FM patients defined by FiRST with a sensitivity of 78.6% and a specificity of 87.1%. The positive predictive value (PPV) was 73.3%, and the negative predictive value (NPV) was 90%. Univariate analysis revealed that a positive FAST 4 index was associated with the number of painful and swollen joints (p<0.001 and 0.03, respectively). Additionally, patients with a positive FAST 4 index showed higher DAS 28 scores (p=0.002). No significant association was found with CRP levels (p=0.328), ESR (p=0.499), or the use of biological treatments (p=0.146) or corticosteroids (p=0.940). In multivariate analysis, only depression remained a risk factor, increasing the risk sixfold with an OR of 5.917, 95% CI (1.91-18.3), p=0.002. Conclusion Our study suggests a high prevalence of concomitant FM in our population, highlighting the importance of screening for FM, particularly using the FAST 4 index based solely on the MDHAQ questionnaire.

Development of cognitive frailty screening tool among community-dwelling older adults.

Purpose: To develop a brief screening tool consisting of twelve items that can be self-administered for rapid identification of older adults at risk of cognitive frailty (CF), named as Cognitive Frailty Screening Tool (CFST). Patients and methods: A total of 1318 community-dwelling individuals aged 60 years and above were selected and assessed for cognitive frailty using a set of neuropsychology batteries and physical function tests. A binary logistic regression (BLR) was used to identify predictors of CF to be used as items in the screening tool. A suitable cut-off point was developed using receiver operating characteristic analysis. Results: Twelve items were included in the screening tool, comprising of gender, education years, medical history, depressive symptoms and functional status as well as lifestyle activities. The area under the curve (AUC) was 0.817 (95 % CI:0.774-0.861), indicating an excellent discriminating power. The sensitivity and specificity for cut-off 7 were 80.8 % and 79.0 %, with an acceptable range of positive predictive value (PPV) (73.3 %) and negative predictive value (NPV) (85.2 %) for screening tools. Concurrent validity of CFST score with standard cognitive and frailty assessment tools shows a significant association with the total score of CFST with low to moderate correlation (p < 0.05 for all parameters). Conclusion: CFST had good sensitivity and specificity and was valid for community-dwelling older adults. There is a need to evaluate further the cost-effectiveness of implementing CFST as a screening for the risk of CF in the community. Its usage in clinical settings needs further validation.

The Gaming Disorder Identification Test (GADIT) - A screening tool for Gaming Disorder based on ICD-11.

Background: Gaming Disorder was included as an addictive disorder in the latest version of the International Classification of Diseases (ICD-11), published in 2022. The present study aimed to develop a screening tool for Gaming Disorder, the Gaming Disorder Identification Test (GADIT), based on the four ICD-11 diagnostic criteria: impaired control, increasing priority, continued gaming despite harm, and functional impairment. Method: We reviewed 297 questionnaire items from 48 existing gaming addiction scales and selected 68 items based on content validity. Two datasets were collected: 1) an online panel (N = 803) from Australia, United States, United Kingdom and Canada, split into a development set (N = 589) and a validation dataset (N = 214); and 2) a university sample (N = 408) from Australia. Item response theory and confirmatory factor analyses were conducted to select eight items to form the GADIT. Validity was established by regressing the GADIT against known correlates of Gaming Disorder. Results: Confirmatory factor analyses of the GADIT showed good model fit (RMSEA=<0.001-0.108; CFI = 0.98-1.00), and internal consistency was excellent (Cronbach's alphas = 0.77-0.92). GADIT scores were strongly associated with the Internet Gaming Disorder Test (IGDT-10), and significantly associated with gaming intensity, eye fatigue, hand pain, wrist pain, back or neck pain, and excessive in-game purchases, in both the validation and the university sample datasets. Conclusion: The GADIT has strong psychometric properties in two independent samples from four English-speaking countries collected through different channels, and shown validity against existing scales and variables that are associated with Gaming Disorder. A cut-off of 5 is tentatively recommended for screening for Gaming Disorder.

Evaluation of low vagally-mediated heart rate variability as an early marker of depression risk.

BACKGROUND: Both low vagally-mediated heart rate variability (HRV) and depression have been shown to be risk factors for cardiovascular disease (CVD). We recently identified an HRV cutpoint below which persons have an increased risk for several cardiometabolic disorders. However, no cutpoint exists to identify those at risk for depression. METHODS: The association between daytime HRV and diagnostically validated depression cutoffs using the five-item World Health Organization Well-being Index (WHO-5) was examined in adults from the Mannheim Industrial Cohort Study (n = 9973; Mage = 41.9[10.9]; 20 % women [n = 1934]). The aim was to identify HRV cutpoints for individuals who may have clinical depression. RESULTS: Regression adjusting for age, sex, and linear trend showed a significant quadratic association between depression, indexed by WHO-5 scores and HRV, indexed by the root mean square successive differences (RMSSD) in milliseconds (ms) (p < 0.001). Logistic regression models adjusting for age, sex, and heart period (i.e., inter-beat intervals) compared the clinically depressed (WHO-5 ≤ 28) and those with a screening diagnosis of depression (WHO-5 ≤ 50) to the rest of the population. Significant odds ratios suggested two RMSSD values 25 ± 2 ms (OR = 1.39 [1.17, 1.64]) and 35 ± 2 ms (OR = 1.17 [1.02, 1.34]) that may be used to identify those with an elevated risk for depression. LIMITATIONS: The sample was primarily German men. Fitness and anti-depressant use were not available. CONCLUSIONS: As HRV is a brief measure that can be used in clinical settings, our HRV cutpoints have implications for the early detection of those at risk for psychological and cardiometabolic disorders.

Screening Tool for Paroxysmal Atrial Fibrillation Based on a Deep-Learning Algorithm Using Printed 12-Lead Electrocardiographic Records during Sinus Rhythm.

Background: Recent advancements in artificial intelligence (AI) have significantly improved atrial fibrillation (AF) detection using electrocardiography (ECG) data obtained during sinus rhythm (SR). However, the utility of printed ECG (pECG) records for AF detection, particularly in developing countries, remains unexplored. This study aims to assess the efficacy of an AI-based screening tool for paroxysmal AF (PAF) using pECGs during SR. Methods: We analyzed 5688 printed 12-lead SR-ECG records from 2192 patients admitted to Beijing Chaoyang Hospital between May 2011 to August 2022. All patients underwent catheter ablation for PAF (AF group) or other electrophysiological procedures (non-AF group). We developed a deep learning model to detect PAF from these printed SR-ECGs. The 2192 patients were randomly assigned to training (1972, 57.3% with PAF), validation (108, 57.4% with PAF), and test datasets (112, 57.1% with PAF). We developed an applet to digitize the printed ECG data and display the results within a few seconds. Our evaluation focused on sensitivity, specificity, accuracy, F1 score, the area under the receiver-operating characteristic curve (AUROC), and precision-recall curves (PRAUC). Results: The PAF detection algorithm demonstrated strong performance: sensitivity 87.5%, specificity 66.7%, accuracy 78.6%, F1 score 0.824, AUROC 0.871 and PRAUC 0.914. A gradient-weighted class activation map (Grad-CAM) revealed the model's tailored focus on different ECG areas for personalized PAF detection. Conclusions: The deep-learning analysis of printed SR-ECG records shows high accuracy in PAF detection, suggesting its potential as a reliable screening tool in real-world clinical practice.

Characterization of Psychopathology in Latin American Adolescents Using a Web-Based Screening Tool: Cross-Sectional Study.

BACKGROUND: Mental health problems and suicide ideation are common in adolescents. Early detection of these issues could prevent the escalation of mental health-related symptoms in the long term. Moreover, characterizing different profiles of prevalent symptoms in conjunction with emotional regulation strategies could guide the design of specific interventions. The use of web-based screening (WBS) tools has been regarded as a suitable strategy to timely detect symptomatology while improving the appeal, cost, timeliness, and reach of detection in young populations. However, the evidence regarding the accuracy of these approaches is not fully conclusive. OBJECTIVE: The study aims (1) to examine the capability of a WBS to identify adolescents with psychiatric symptoms and suicidality and (2) to characterize the mental health profiles of a large sample of adolescents using WBS. METHODS: A total of 1599 Latin American Spanish-speaking adolescents (mean age 15.56, SD 1.34 years), consisting of 47.3% (n=753) female, 98.5% Chilean (n=1570), and 1.5% Venezuelan (n=24) participants, responded to a mental health WBS. A randomized subsample of participants also responded to the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID). McNemar χ2 and receiver-operating characteristic curves tested the detection accuracy of WBS contrasted with the MINI-KID. Latent profile analyses explored the symptomatic and emotional regulation profiles of participants. RESULTS: Both measures showed an adequate level of agreement (area under the curve per symptom domain ranging from 0.70 to 0.89); however, WBS yielded a higher prevalence than MINI-KID for all psychiatric symptoms, except suicide ideation and depression. Latent profile analyses yielded 4 profiles-one of them presented elevated psychopathological symptoms, constituting 11% of the sample (n=175). Rumination (odds ratio [OR] 130.15, 95% CI 51.75-439.89; P<.001), entrapment (OR 96.35, 95% CI 29.21-317.79; P<.001), and defeat (OR 156.79, 95% CI 50.45-487.23; P<.001) contributed significantly to the prediction of latent profile memberships, while cognitive reappraisal did not contribute to the prediction of any latent profile memberships, and expressive suppression was only associated to profile-2 membership. CONCLUSIONS: WBS is acceptable for the timely detection of adolescents at risk of mental health conditions. Findings from the symptomatic and emotional regulation profiles highlight the need for comprehensive assessments and differential interventions.

Measuring Social Frailty: A Scoping Review of Available Scales and Tools.

BACKGROUND AND OBJECTIVES: Social frailty is an emerging concept characterized by state of vulnerability due to the lack or absence of social resources that enable health and wellbeing, particularly among older people. However, there is no consensus on how to accurately measure and assess social frailty, given the broad coverage of social dimensions affecting older individuals. This scoping review aimed to identify existing tools and scales used to measure social frailty in older people. RESEARCH DESIGN AND METHODS: A scoping review methodology was employed. Articles published between January 2014 and April 2024 were searched in six electronic databases: PubMed, PsycINFO, ProQuest, Scopus, SocIndex, and CINAHL. The scoping review followed a five-stage process by Arksey and O'Malley and adhered to the guidelines provided by PRISMA-ScR. RESULTS: Nine social frailty tools were identified across the 58 papers included in this review. The individual question item commonly used in the scales were classified into main categories based on their conceptual characteristics and intentions. The most common individual questionnaire constructs used to measure social frailty included financial status, social resources, social behaviours and activities, and sense of purpose. The reviewed tools varied in terms of their robustness and the process of scale development. DISCUSSION AND IMPLICATIONS: Our review suggested the development of a standardized, psychometrically tested, and accurate screening tool to screen social frailty status. An accurate social frailty assessment can inform the development of useful interventions, which also has implications in preventing the development of physical frailty.

Validation of Ten Osteoporosis Screening Tools in Rural Communities of Taiwan.

PURPOSE: Patients with osteoporosis are at risk of fractures, which can lead to immobility and reduced quality of life. Early diagnosis and treatment are crucial for preventing fractures, but many patients are not diagnosed until after a fracture has occurred. This study aimed to evaluate the performance of 10 osteoporosis screening tools (OSTs) in rural communities of Taiwan. In this prospective study, a total of 567 senior citizens from rural communities underwent bone mineral density (BMD) measurement using dual-energy X-ray absorptiometry (DXA) and ten OSTs were administered. Discrimination analysis was performed using the area under the receiver operating characteristic curve (AUROC). Primary outcomes included area under curve (AUC) value, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The DXA examination revealed that 63.0% of females and 22.4% of males had osteoporosis. Among females, Osteoporosis Index of Risk (OSIRIS) and Osteoporosis Self-Assessment Tool for Asians (OSTA) presented the best AUC value with 0.71 (0.66-0.76) and 0.70 (0.66-0.75), respectively. Among males, BWC had the best AUC value of 0.77 (0.67-0.86), followed by OSTA, Simple Calculated Osteoporosis Risk Estimation (SCORE), and OSIRIS. OSTA and OSIRIS showed acceptable performance in both genders. The specificity of Fracture Risk Assessment Tool (FRAX-H), SCORE, National Osteoporosis Foundation Score, OSIRIS, Osteoporosis Risk Assessment Instrument, Age, Bulk, One or Never Estrogen (ABONE), and Body weight criteria increased in both genders after applying the optimum cut-off. Considering it high AUC and simplicity of use, OSTA appeared to be the recommended tool for seniors of both genders among the ten OSTs. This study provides a viable reference for future development of OSTs in Taiwan. Further adjustment according to epidemiological data and risk factors is recommended while applying OSTs to different cohorts.

Utilizing Oral Neutrophil Counts as an Indicator of Oral Inflammation Associated With Periodontal Disease: A Blinded Multicentre Study.

BACKGROUND: Periodontal diseases are chronic inflammatory conditions that require early screening for effective long-term management. Oral neutrophil counts (ONCs) correlate with periodontal inflammation. This study investigates a point-of-care test using a neutrophil enzyme activity (NEA) colorimetric strip for measuring periodontal inflammation. METHODS: This prospective study had two phases. Phase 1 validated the relationship between ONCs and periodontal inflammation with 90 participants. Phase 2 examined the test's applicability in a real-world setting through a multicentre clinical trial with 375 participants at four sites. ONCs were quantified in oral rinses using laboratory-based methods, and the NEA strip was used for ONC stratification. Clinical measures included bleeding on probing (BoP), probing depth (PD) and clinical attachment loss (CAL). RESULTS: ONCs were significantly elevated in patients with Grade B periodontitis and deep periodontal pockets (PD ≥ 5 mm, CAL ≥ 5 mm). The NEA strip accurately classified patients into high or low ONC categories, showing 80% sensitivity, 82.5% specificity and an AUC of 0.89. It also assessed the effectiveness of periodontal therapy in reducing ONC and inflammation. The test was user-friendly, with no reported discomfort among patients. CONCLUSION: The NEA strip is a user-friendly and rapid screening tool for detecting high ONCs associated with periodontal inflammation and for evaluating the effectiveness of periodontal therapy.

The Development and Validation of a Disordered Eating Screening Tool for Current and Former Athletes: The Athletic Disordered Eating (ADE) Screening Tool.

BACKGROUND: Current and former athletes are one of the most at-risk population groups for disordered eating (DE), impacting their dietary practices, body composition, performance and health during and following their athletic careers. Few comprehensive DE screening tools exist for this group. To help address this, the current study utilised a mixed-methods approach of Classic Test Theory (CTT) and Item Response Theory (IRT) to develop and validate a DE screening tool suitable for current and former athletes. METHODS: Novel scale development methodologies were used to develop and assess the validity (content, face, cross-cultural, construct), test-retest reliability, internal consistency reliability, factor analysis and Rasch analysis of a new DE scale. RESULTS: A new validated Athletic Disordered Eating (ADE) screening tool was created, with 17 items and four subscales (food control, bingeing, body control, body discontent), with an internal consistency reliability of 0.91, excellent content and construct validity, an Intraclass Correlation Coefficient of 0.97 and excellent Rasch model fit. CONCLUSIONS: The ADE screening tool has been dually developed for research purposes and as a clinically applicable screening tool to detect DE in current and former athletes and is suitable for a global use across sporting categories, diverse genders and levels of competition.

Development and validation of FinTox: A new screening tool to assess cancer-related financial toxicity.

PURPOSE: This study aimed to develop and validate FinTox, a concise tool for screening and managing financial toxicity in oncology settings. METHODS: Development involved qualitative interviews with healthcare providers and patients, and feedback from a 7-member expert panel resulting in a 5-item measure that evaluates financial strain, psychological responses, and care modifications. Psychometric evaluations examined factor structure, internal consistency, test-retest reliability, and concurrent and convergent validity. Associations between FinTox scores and sociodemographic/medical factors were also analyzed using univariate and multivariable regression models. RESULTS: Twelve healthcare providers and 20 patients were interviewed, and 268 patients (69.8% female, 47.4% non-Hispanic White) completed surveys including FinTox, the Comprehensive Score for Financial Toxicity (COST), health-related quality of life (HRQOL) measures, and sociodemographic questions. FinTox demonstrated excellent internal consistency (Cronbach's alpha = 0.90) and test-retest reliability (ICC = 0.95). Significant correlations with the COST (r = -0.62, p < 0.001) and HRQOL measures corroborated content and convergent validity. Diagnostic accuracy was evidenced by a sensitivity of 72.3%, specificity of 85.2%, positive predictive value of 83.2%, and negative predictive value of 70.3%. Higher FinTox scores were also associated with receiving care at a safety-net hospital, Black race, household income <600% of the federal poverty level, and Stage 4 cancer. CONCLUSION: FinTox's robust psychometric properties and diagnostic accuracy position it as a reliable tool for detecting financial toxicity. Future research should evaluate its responsiveness to changes over time and integration into clinical workflows.

Screening a Suspected Malingerer Using the Symptom Checklist-90-Revised and Visual Analogue Pain Scale Questionnaires.

Malingering in healthcare leads to a significant financial burden, so identifying patients who may be more likely to malinger is a critical step in minimizing the ever-growing cost of healthcare in the United States. Malingering is a clinical diagnosis with no well-established diagnostic tests. General guiding principles exist to determine whether or not a patient is malingering, but there is no well-established set of guidelines that can be used in common to identify malingering. Our team cared for a 51-year-old black, female patient who presented to an outpatient clinic due to generalized pain following a motor vehicle accident (MVA). The patient's symptomatology, clinical progression, and imaging results were discordant with one another, which prompted clinical suspicion of malingering. After careful deliberation, the care team suspected that the patient was malingering. Therefore, the clinical management was limited to a conservative pain management regimen and minimal clinical follow-up to avoid unnecessary healthcare expenditures. This article aims to discuss general principles and specific strategies for how a clinician can approach a case of suspected malingering.

Development and validation of the BMQ-AIR©: a screening tool for assessing patients' treatment beliefs about switching to anti-inflammatory reliever (AIR) therapy.

Introduction: Despite anti-inflammatory reliever (AIR) therapy now being the preferred treatment choice across all severities of asthma, many patients are still "attached" to their short-acting beta2-agonist (SABA) reliever, believing this to be the best way to control their asthma. To encourage individuals to switch to AIR, it is important to first identify the beliefs that patients hold about AIR. Objective: The aim of this paper was to describe the initial development and validation of the BMQ-AIR©, a six-item screening tool which assesses and identifies patients' treatment beliefs about switching to AIR therapy. Methods: Statements were identified from the primary literature that assessed patients' perceptions of AIR therapy and adapted from the Beliefs about Medicines Questionnaire (BMQ). Internal reliability was examined using Cronbach's alpha coefficient. Construct validity was evaluated by comparing scores on BMQ-AIR© with a validated measure of medication adherence and SABA beliefs. Results: A total of 446 participants completed the online survey. The BMQ-AIR© contained two subscales with three items each. Both the Necessity and Concerns subscales demonstrated good internal reliability, with Cronbach's α-values of 0.70 and 0.69, respectively. Both subscales were negatively correlated with self-report inhaled corticosteroid adherence (Necessity: r = -0.28, p < 0.0001; Concerns: r = -0.28, p < 0.0001) and positively correlated with SRQ scores (Necessity: r = 0.51, p < 0.0001; Concerns: r = 0.44, p < 0.0001). Conclusion: Preliminary findings indicate that BMQ-AIR© demonstrates satisfactory reliability and validity. BMQ-AIR© is a promising tool that may help tailor interventions to an individual's specific beliefs and barriers to switching to better support individuals in stopping SABA and initiating AIR therapy.

The Accuracy of Height Prediction Equations in Greek Patients: A Cross-Sectional Study.

In clinical settings, standing height measurement is often difficult to perform due to patients' inability to stand upright. Height prediction equations derived from measurements of the length of other body segments have been published; however, they are not readily applicable to all populations since ethnic differences affect the relationship between standing height and body segment length. This cross-sectional study aimed to examine the accuracy of height prediction using the Malnutrition Universal Screening Tool (MUST) height predictive equations among Greek patients and to develop new, nationally representative equations. The study population consisted of 1198 Greek adult outpatients able to stand upright without assistance and without medical conditions that affected their height. Standing height, ulna length, knee height and demi-span measurements were obtained from 599 males and 599 females. Patients were stratified into age groups of <55 and ≥55 years, <60 and ≥60 years and <65 and ≥65 years according to the categories indicated by the MUST for height prediction from alternative measurements. There were positive correlations between standing height and ulna length and knee height and demi-span length (p < 0.001) in both sexes and all age categories. A strong correlation was observed between the measured and predicted standing height using ulna length (rho = 0.870, p < 0.001), knee height (rho = 0.923, p < 0.001) and demi-span length (rho = 0.906, p < 0.001). The average difference between the MUST indicative equations' height predictions from alternative measurements and actual height was -3.04 (-3.32, -2.76), -1.21 (-1.43, -0.988) and 2.16 (1.92, 2.41), respectively. New height prediction equations for Greek patients were identified, with the predicted values closer to the measured standing heights than those predicted with the MUST indicative equations for height prediction from alternative measurements.

Barriers and facilitators to effective electronic health record-based sepsis screening in the pediatric intensive care unit.

Introduction: The Pediatric Surviving Sepsis Campaign supports the implementation of automated tools for early sepsis recognition. In 2019 the C.S. Mott Children's Hospital Pediatric Intensive Care Unit deployed an electronic medical record (EMR)-based screening for early recognition and treatment of sepsis. Materials and Methods: We analyzed all automated primary sepsis alerts, secondary screens, and bedside huddles from November 2019 to January 2020 (Cohort 1) and from November 2020 to January 2021 (Cohort 2) to identify barriers and facilitators for the use of this tool. We distributed surveys to frontline providers to gather feedback on end-user experience. Results: In Cohort 1, 895 primary alerts were triggered, yielding 503 completed secondary screens and 40 bedside huddles. In Cohort 2, 925 primary alerts were triggered, yielding 532 completed secondary screens and 12 bedside huddles. Surveys assessing end-user experience identified the following facilitators: (1) 73% of nurses endorsed the bedside huddle as value added; (2) 74% of medical providers agreed the bedside huddle increased the likelihood of interventions. The greatest barriers to successful implementation included the (1) overall large number of primary alerts from the automated tool and (2) rate of false alerts, many due to routine respiratory therapy interventions. Discussion: Our data suggests that the successful implementation of EMR-based sepsis screening tools requires countermeasures focusing on 3 key drivers for change: education, technology, and patient safety. Conclusion: While both medical providers and bedside nurses found merit in our EMR-based sepsis early recognition system, continued refinement is necessary to avoid sepsis alert fatigue.

A Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor.

Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18-65 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. Our findings show that the model correctly classified glycemic status 93.37% of the time as high, normal, or low.

Artificial Intelligence in Malnutrition: A Systematic Literature Review.

Malnutrition among the population of the world is a frequent yet underdiagnosed problem in both children and adults. Development of malnutrition screening and diagnostic tools for early detection of malnutrition is necessary to prevent long-term complications to patients' health and well-being. Most of these tools are based on predefined questionnaires and consensus guidelines. The use of artificial intelligence (AI) allows for automated tools to detect malnutrition in an earlier stage to prevent long-term consequences. In this study, a systematic literature review was carried out with the goal of providing detailed information on what patient groups, screening tools, machine learning algorithms, data types, and variables are being used, as well as the current limitations and implementation stage of these AI-based tools. The results showed that a staggering majority exceeding 90% of all AI models go unused in day-to-day clinical practice. Furthermore, supervised learning models seemed to be the most popular type of learning. Alongside this, disease-related malnutrition was the most common category of malnutrition found in the analysis of all primary studies. This research provides a resource for researchers to identify directions for their research on the use of AI in malnutrition.