Nevin Manimala

JMIR Hum Factors. 2023 Aug 3;10:e48270. doi: 10.2196/48270.

ABSTRACT

BACKGROUND: Mobility is a meaningful aspect of an individual’s health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring.

OBJECTIVE: Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies.

METHODS: We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure.

RESULTS: In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P<.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of >0.75 by 16 days of wear time.

CONCLUSIONS: We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

PMID:37535417 | DOI:10.2196/48270

JMIR Res Protoc. 2023 Aug 3;12:e48128. doi: 10.2196/48128.

ABSTRACT

BACKGROUND: Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients.

OBJECTIVE: The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making.

METHODS: To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile.

RESULTS: The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic.

CONCLUSIONS: This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48128.

PMID:37535416 | DOI:10.2196/48128

JAMA Otolaryngol Head Neck Surg. 2023 Aug 3. doi: 10.1001/jamaoto.2023.1939. Online ahead of print.

ABSTRACT

IMPORTANCE: Current olfactory neuroblastoma (ONB) staging systems inadequately delineate locally advanced tumors, do not incorporate tumor grade, and poorly estimate survival and recurrence.

OBJECTIVE: The primary aims of this study were to (1) examine the clinical covariates associated with survival and recurrence of ONB in a modern-era multicenter cohort and (2) incorporate Hyams tumor grade into existing staging systems to assess its ability to estimate survival and recurrence.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective, multicenter, case-control study included patients with ONB who underwent treatment between January 1, 2005, and December 31, 2021, at 9 North American academic medical centers.

INTERVENTION: Standard-of-care ONB treatment.

MAIN OUTCOME AND MEASURES: The main outcomes were overall survival (OS), disease-free survival (DFS), and disease-specific survival (DSS) as C statistics for model prediction.

RESULTS: A total of 256 patients with ONB (mean [SD] age, 52.0 [15.6] years; 115 female [44.9%]; 141 male [55.1%]) were included. The 5-year rate for OS was 83.5% (95% CI, 78.3%-89.1%); for DFS, 70.8% (95% CI, 64.3%-78.0%); and for DSS, 94.1% (95% CI, 90.5%-97.8%). On multivariable analysis, age, American Joint Committee on Cancer (AJCC) stage, involvement of bilateral maxillary sinuses, and positive margins were associated with OS. Only AJCC stage was associated with DFS. Only N stage was associated with DSS. When assessing the ability of staging systems to estimate OS, the best-performing model was the novel modification of the Dulguerov system (C statistic, 0.66; 95% CI, 0.59-0.76), and the Kadish system performed most poorly (C statistic, 0.57; 95% CI, 0.50-0.63). Regarding estimation of DFS, the modified Kadish system performed most poorly (C statistic, 0.55; 95% CI, 0.51-0.66), while the novel modification of the AJCC system performed the best (C statistic, 0.70; 95% CI, 0.66-0.80). Regarding estimation of DSS, the modified Kadish system was the best-performing model (C statistic, 0.79; 95% CI, 0.70-0.94), and the unmodified Kadish performed the worst (C statistic, 0.56; 95% CI, 0.51-0.68). The ability for novel ONB staging systems to estimate disease progression across stages was also assessed. In the novel Kadish staging system, patients with stage VI disease were approximately 7 times as likely to experience disease progression as patients with stage I disease (hazard ratio [HR], 6.84; 95% CI, 1.60-29.20). Results were similar for the novel modified Kadish system (HR, 8.99; 95% CI, 1.62-49.85) and the novel Dulguerov system (HR, 6.86; 95% CI, 2.74-17.18).

CONCLUSIONS AND RELEVANCE: The study findings indicate that 5-year OS for ONB is favorable and that incorporation of Hyams grade into traditional ONB staging systems is associated with improved estimation of disease progression.

PMID:37535372 | DOI:10.1001/jamaoto.2023.1939

JAMA Netw Open. 2023 Aug 1;6(8):e2327145. doi: 10.1001/jamanetworkopen.2023.27145.

ABSTRACT

IMPORTANCE: Anti-programmable cell death-1 (anti-PD-1) improves relapse-free survival when used as adjuvant therapy for high-risk resected melanoma. However, it can lead to immune-related adverse events (irAEs), which become chronic in approximately 40% of patients with high-risk melanoma treated with adjuvant anti-PD-1.

OBJECTIVE: To determine the incidence, characteristics, and long-term outcomes of chronic irAEs from adjuvant anti-PD-1 therapy.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective multicenter cohort study analyzed patients treated with adjuvant anti-PD-1 therapy for advanced and metastatic melanoma between 2015 and 2022 from 6 institutions in the US and Australia with at least 18 months of evaluable follow-up after treatment cessation (range, 18.2 to 70.4 months).

MAIN OUTCOMES AND MEASURES: Incidence, spectrum, and ultimate resolution vs persistence of chronic irAEs (defined as those persisting at least 3 months after therapy cessation). Descriptive statistics were used to analyze categorical and continuous variables. Kaplan-Meier curves assessed survival, and Wilson score intervals were used to calculate CIs for proportions.

RESULTS: Among 318 patients, 190 (59.7%) were male (median [IQR] age, 61 [52.3-72.0] years), 270 (84.9%) had a cutaneous primary, and 237 (74.5%) were stage IIIB or IIIC at presentation. Additionally, 226 patients (63.7%) developed acute irAEs arising during treatment, including 44 (13.8%) with grade 3 to 5 irAEs. Chronic irAEs, persisting at least 3 months after therapy cessation, developed in 147 patients (46.2%; 95% CI, 0.41-0.52), of which 74 (50.3%) were grade 2 or more, 6 (4.1%) were grade 3 to 5, and 100 (68.0%) were symptomatic. With long-term follow-up (median [IQR], 1057 [915-1321] days), 54 patients (36.7%) experienced resolution of chronic irAEs (median [IQR] time to resolution of 19.7 [14.4-31.5] months from anti-PD-1 start and 11.2 [8.1-20.7] months from anti-PD-1 cessation). Among patients with persistent irAEs present at last follow-up (93 [29.2%] of original cohort; 95% CI, 0.25-0.34); 55 (59.1%) were grade 2 or more; 41 (44.1%) were symptomatic; 24 (25.8%) were using therapeutic systemic steroids (16 [67%] of whom were on replacement steroids for hypophysitis (8 [50.0%]) and adrenal insufficiency (8 [50.0%]), and 42 (45.2%) were using other management. Among the 54 patients, the most common persistent chronic irAEs were hypothyroid (38 [70.4%]), arthritis (18 [33.3%]), dermatitis (9 [16.7%]), and adrenal insufficiency (8 [14.8%]). Furthermore, 54 [17.0%] patients experienced persistent endocrinopathies, 48 (15.1%) experienced nonendocrinopathies, and 9 (2.8%) experienced both. Of 37 patients with chronic irAEs who received additional immunotherapy, 25 (67.6%) experienced no effect on chronic irAEs whereas 12 (32.4%) experienced a flare in their chronic toxicity. Twenty patients (54.1%) experienced a distinct irAE.

CONCLUSIONS AND RELEVANCE: In this cohort study of 318 patients who received adjuvant anti-PD-1, chronic irAEs were common, affected diverse organ systems, and often persisted with long-term follow-up requiring steroids and additional management. These findings highlight the likelihood of persistent toxic effects when considering adjuvant therapies and need for long-term monitoring and management.

PMID:37535354 | DOI:10.1001/jamanetworkopen.2023.27145

JAMA Netw Open. 2023 Aug 1;6(8):e2327272. doi: 10.1001/jamanetworkopen.2023.27272.

ABSTRACT

IMPORTANCE: Deviations from international resuscitation guidelines during the management of pediatric cardiac arrest are frequent and affect clinical outcomes. An interactive tablet application (app), PediAppRREST, was developed to reduce guideline deviations during pediatric cardiac arrest.

OBJECTIVE: To assess the effectiveness of PediAppRREST in improving the management of simulated in-hospital pediatric cardiac arrest.

DESIGN, SETTING, AND PARTICIPANTS: This multicenter 3-group simulation-based randomized clinical trial was conducted from September 2020 to December 2021 at 4 Italian university hospitals (Padua, Florence, Rome, Novara). Participants included residents in pediatrics, emergency medicine, and anesthesiology. Analyses were conducted as intention-to-treat. Data were analyzed from January to June 2022.

INTERVENTIONS: Teams were randomized to 1 of 3 study groups: an intervention group that used the PediAppRREST app; a control group that used a paper-based cognitive aid, the Pediatric Advanced Life Support (PALS) pocket card; and a control group that used no cognitive aids. All the teams managed the same standardized simulated scenario of nonshockable pediatric cardiac arrest.

MAIN OUTCOMES AND MEASURES: The primary outcome was the number of deviations from guidelines, measured by a 15-item checklist based on guideline recommendations. The main secondary outcomes were quality of chest compressions, team clinical performance (measured by the Clinical Performance Tool), and perceived team leader’s workload. Study outcomes were assessed via video reviews of the scenarios.

RESULTS: Overall 100 teams of 300 participants (mean [SD] age, 29.0 [2.2] years; 195 [65%] female) were analyzed by intention-to-treat, including 32 teams randomized to the PediAppRREST group, 35 teams randomized to the PALS control group, and 33 teams randomized to the null control group. Participant characteristics (210 pediatric residents [70%]; 48 anesthesiology residents [16%]; 42 emergency medicine residents [14%]) were not statistically different among the study groups. The number of deviations from guidelines was significantly lower in the PediAppRREST group than in the control groups (mean difference vs PALS control, -3.0; 95% CI, -4.0 to -1.9; P < .001; mean difference vs null control, -2.6; 95% CI, -3.6 to -1.5; P < .001). Clinical Performance Tool scores were significantly higher in the PediAppRREST group than control groups (mean difference vs PALS control, 1.4; 95% CI, 0.4 to 2.3; P = .002; mean difference vs null control, 1.1; 95% CI, 0.2 to 2.1; P = .01). The other secondary outcomes did not significantly differ among the study groups.

CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, the use of the PediAppRREST app resulted in fewer deviations from guidelines and a better team clinical performance during the management of pediatric cardiac arrest.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04619498.

PMID:37535352 | DOI:10.1001/jamanetworkopen.2023.27272

J Sports Med Phys Fitness. 2023 Aug 3. doi: 10.23736/S0022-4707.23.15056-0. Online ahead of print.

ABSTRACT

BACKGROUND: Poor information is available regarding real field data on the different factors that could have an influence on curve sprint and its association with anthropometric and strength parameters.

METHODS: We designed a crossover pilot-study that enrolled 14 track and field athletes of 200 and 400 m (8/14 men, age: 20.5±2.3 years, height: 1.73±0.06 m; body mass: 60.5±6.2 kg) that performed randomly in two different days assessment of anthropometric parameters, jump test by squat jump (SJ) and triple hop distance (THD), performance during a 20-m curve sprint (day 1), and assessment of 1RM for right and left limb on Bulgarian split squat (BSS) (day 2). The unpaired t test and Pearson’s correlation were used for data analysis.

RESULTS: No statistical differences for anthropometric and strength parametric parameters between right and left lower limbs were observed. Twenty-meter curve sprints were negatively associated with body mass (P=0.0059, R=-0.7) and Body Mass Index (BMI; P=0.032, R=0.6). Moreover, a negative association was observed with SJ height (P=0.0025, R=-0.7), speed (P=0.0028; R=-0.7), strength (P=0.009, R=-0.7) and power (P=0.009, R=-0.7). Finally, 20-m curve sprint negatively correlated with right (P=0.0021, R=-0.7) and left (P<0.0001, R=-0.9) THD and 1 RM right (P=0.025, R=-0.6;) and left (P=0.0049, R=-0.7) BSS, respectively.

CONCLUSIONS: This pilot study demonstrated that 20-m curve sprint was negatively associated with body mass, BMI, vertical jump performance, THD and 1RM BSS. This information could be useful to coaches and sport scientists as a reference value to improve athlete performance for 200- and 400-m athletes.

PMID:37535342 | DOI:10.23736/S0022-4707.23.15056-0

Med Sci Sports Exerc. 2023 Aug 2. doi: 10.1249/MSS.0000000000003271. Online ahead of print.

ABSTRACT

PURPOSE: To compare the effects of free-weight and machine-based resistance training on strength, hypertrophy, and joint discomfort.

METHODS: Thirty-eight resistance-trained men participated in an 8-week resistance program allocated into free-weight (n = 19) or machine-based (n = 19) groups. Training variables were identical for both modalities, so they only differed in the use of barbells or machines to execute the full squat, bench press, prone bench pull, and shoulder press exercises. The velocity-based method was implemented to accurately adjust the intensity throughout the program. Strength changes were evaluated using 8 velocity-monitored loading tests (4 exercises x 2 modalities) and included the relative one-repetition maximum (1RMRel), as well as the mean propulsive velocity against low (MPVLow) and high (MPVHigh) loads. Ultrasound-derived cross-sectional area (CSA) of quadriceps (proximal and distal regions), pectoralis major, and rectus abdominis was measured to examine hypertrophy. Complementarily, WOMAC and DASH questionnaires were administrated to assess changes in lower- and upper-limb joint discomfort. Outcomes were compared using ANCOVA and percentage of change (∆) statistics.

RESULTS: Each group significantly (p < 0.001) increased 1RMRel, MPVLow, and MPVHigh for both modalities tested, but especially in the one they trained. When considering together the 8 exercises tested, strength changes for both modalities were similar (∆ differences ≤1.8%, p ≥ 0.216). Likewise, the CSA of all the muscles evaluated was significantly increased by both modalities, with no significant differences between them (∆ difference ≤ 2.0%, p ≥ 0.208). No between-group differences (p ≥ 0.144) were found for changes in stiffness, pain, and functional disability levels, which were reduced by both modalities.

CONCLUSIONS: Free-weight and machine-based modalities are similarly effective to promote strength and hypertrophy without increasing joint discomfort.

PMID:37535335 | DOI:10.1249/MSS.0000000000003271

Med Sci Sports Exerc. 2023 Jul 28. doi: 10.1249/MSS.0000000000003260. Online ahead of print.

ABSTRACT

INTRODUCTION: Objectively measured physical activity (PA) data were collected in the accelerometry sub-study of the UK Biobank. UK Biobank also contains information about MS diagnosis at the time of and after PA collection. This study aims to: 1) Quantify the difference in PA between prevalent MS cases and matched healthy controls; 2) Evaluate the predictive performance of objective PA measures for incident MS cases.

METHODS: The first analysis compared eight accelerometer-derived PA summaries between MS patients (N = 316) and matched controls (30 controls for each MS case). The second analysis focused on predicting time to MS diagnosis among participants who were not diagnosed with MS. A total of 19 predictors including eight measures of objective PA were compared using Cox proportional hazards models (number of events = 47; 585,900 person-years of follow-up).

RESULTS: In the prevalent MS study, the difference between MS cases and matched controls was statistically significant for all PA summaries (p < 0.001). In the incident MS study, the most predictive variable of progression to MS in univariate Cox regression models was lower age (C = 0.604) and the most predictive PA variable was lower relative amplitude (RA, C = 0.594). A two-stage forward selection using Cox regression resulted in a model with concordance C = 0.693 and four predictors: age (p = 0.015), stroke (p = 0.009), Townsend deprivation index (p = 0.874), and RA (p = 0.004). A model including age, stroke, and RA had a concordance of C = 0.691.

CONCLUSIONS: Objective PA summaries were significantly different and consistent with lower activity among study participants who had MS at the time of the accelerometry study. Among individuals who did not have MS, younger age, stroke history, and lower RA were significantly associated with higher risk of a future MS diagnosis.

PMID:37535318 | DOI:10.1249/MSS.0000000000003260

Aging Clin Exp Res. 2023 Aug 3. doi: 10.1007/s40520-023-02516-6. Online ahead of print.

ABSTRACT

BACKGROUND: Recently developed absolute and body size normalized handgrip strength (HGS) cut-points could be used individually and collectively to predict mobility problems and falls.

AIMS: We examined the associations of (1) each absolute and normalized weakness cut-point, (2) collective weakness categories, and (3) changes in weakness status on future falls in older Americans.

METHODS: The analytic sample included 11,675 participants from the 2006-2018 waves of the Health and Retirement Study. Falls were self-reported. Men were classified as weak if their HGS was < 35.5-kg (absolute), < 0.45 kg/kg (body mass normalized), or < 1.05 kg/kg/m² (body mass index normalized). While, women were considered weak if their HGS was < 20.0-kg, < 0.337 kg/kg, or < 0.79 kg/kg/m². Collective weakness categorized those below 1, 2, or all 3 cut-points. The collective weakness categories were also used to observe changes in weakness status over time.

RESULTS: Older Americans below each absolute and normalized cut-point had greater odds for future falls: 1.23 (95% confidence interval (CI): 1.15-1.32) for absolute weakness, 1.20 (CI 1.11-1.29) for body mass index normalized weakness, and 1.26 (CI 1.17-1.34) for body mass normalized weakness. Persons below 1, 2, or all 3 weakness cut-points had 1.17 (CI 1.07-1.27), 1.29 (CI 1.18-1.40), and 1.36 (CI 1.24-1.48) greater odds for future falls, respectively. Those in some changing weakness categories had greater odds for future falls: 1.26 (CI 1.08-1.48) for persistent and 1.31 (CI 1.11-1.55) for progressive.

DISCUSSION: Collectively using these weakness cut-points may improve their predictive value.

CONCLUSION: We recommend HGS be evaluated in mobility and fall risk assessments.

PMID:37535311 | DOI:10.1007/s40520-023-02516-6

Med Biol Eng Comput. 2023 Aug 3. doi: 10.1007/s11517-023-02894-z. Online ahead of print.

ABSTRACT

Humeral motion can be challenging to measure and analyze. Typically, Euler/Cardan sequences are used for humeral angle decomposition, but choice of rotation sequence has substantial effects on outcomes. A new method called True axial rotation calculation may be more precise. The objective of this study is to compare humeral axial rotation measured from two systems (optical motion capture and inertial measurement units (IMUs)) and calculated with two methods (Euler angles and True axial). Motion of torso and dominant humerus of thirty participants free from any upper limb impairments was tracked using both systems. Each participant performed a functional tasks protocol. Humeral axial rotation was calculated with Euler decomposition and the True axial method. Waveforms were compared with two-way ANOVA statistical parametric mapping. A consistent pattern emerged: axial rotation was not different between motion capture systems when using the True axial method (p > .05), but motion capture systems showed relatively large magnitude differences (~ 20-30°) when using Euler angle calculation. Between-calculation method differences were large for both motion capture systems. Findings suggest that the True axial rotation method may result in more consistent findings that will allow for precise measurements and comparison between motion capture systems. Two methods for calculating humeral axial rotation measured from optical motion capture and inertial measurement units were compared.

PMID:37535299 | DOI:10.1007/s11517-023-02894-z