Long-term seizure diary tracking habits in clinical studies: Evidence from the Human Epilepsy Project.

OBJECTIVE: To characterize seizure tracking patterns of people with focal epilepsy using electronic seizure diary entries, and to assess for risk factors associated with poor tracking. METHODS: We analyzed electronic seizure diary data from 410 participants with newly diagnosed focal epilepsy in the Human Epilepsy Project 1 (HEP1). Each participant was expected to record data each day during the study, regardless of seizure occurrence. The primary outcome of this post-hoc analysis was whether each participant properly tracked a seizure diary entry each day during their study participation. Using finite mixture modeling, we grouped patient tracking trajectories into data-driven clusters. Once defined, we used multinomial modeling to test for independent risk factors of tracking group membership. RESULTS: Using over up to three years of daily seizure diary data per subject, we found four distinct seizure tracking groups: consistent, frequent at study onset, occasional, and rare. Participants in the consistent tracking group tracked a median of 92% (interquartile range, IQR: 82%, 99%) of expected days, compared to 47% (IQR:34%, 60%) in the frequent at study onset group, 37% (IQR: 26%, 49%) in the occasional group, and 9% (IQR: 3%, 15%) in the rare group. In multivariable analysis, consistent trackers had lower rates of seizure days per tracked year during their study participation, compared to other groups. SIGNIFICANCE: Future efforts need to focus on improving seizure diary tracking adherence to improve quality of outcome data, particularly in those with higher seizure burden. In addition, accounting for missing data when using seizure diary data as a primary outcome is important in research trials. If not properly accounted for, total seizure burden may be underestimated and biased, skewing results of clinical trials.

Electrographic screening for infantile epileptic spasms syndrome in a single sleep-wake cycle.

OBJECTIVE: Infantile epileptic spasms syndrome (IESS) is a common and urgent diagnosis with seizure and nonseizure mimics. Evaluation with prolonged video-electroencephalography (EEG) can be time-consuming and costly. This study investigated the use of EEG review of a single sleep-wake cycle to exclude IESS. METHODS: We retrospectively reviewed video-EEG studies to rule out IESS in children between the ages of 2 months and 2 years in the period from January 2019 through June 2020. EEG studies were reviewed from the start of the recording through the first sleep-wake cycle and scored as "normal," "consistent with IESS," or "abnormal but not diagnostic of IESS." Scores were compared to the clinical report created by analysis of the entire video-EEG. RESULTS: Inclusion criteria were met in 238 EEG studies. The mean patient age was 7.6 months. The median duration of the full study was 908 min, compared to 107.5 min for the first sleep-wake cycle only. The median difference in recording time was 801 min, p-value < .01. Scored outcomes were similar. Sixty-eight percent of EEG studies were scored as "normal" on first sleep-wake cycle review as compared to 63% on full study review, 13% scored as "consistent with IESS" compared to 16% and 19% scored as "abnormal but not diagnostic of IESS" compared to 21%. Sensitivity and specificity of the first sleep-wake cycle review for studies "consistent with IESS" was 84% and 100%, respectively. No cases of IESS were scored as normal on first sleep-wake cycle review. SIGNIFICANCE: A single sleep-wake cycle captured on EEG can triage studies when IESS is suspected. A normal first sleep-wake cycle did not miss cases of IESS and could result in reduced EEG recording time. Because most of these cases presented to an emergency department, a normal first sleep-wake cycle may help providers determine the acuity, or necessity, of further testing.

Brown adipose tissue metabolism in women is dependent on ovarian status.

In rodents, loss of estradiol (E2) reduces brown adipose tissue (BAT) metabolic activity. Whether E2 impacts BAT activity in women is not known. BAT oxidative metabolism was measured in premenopausal (n = 27; 35 ± 9 yr; body mass index = 26.0 ± 5.3 kg/m2) and postmenopausal (n = 25; 51 ± 8 yr; body mass index = 28.0 ± 5.0 kg/m2) women at room temperature and during acute cold exposure using [11C]acetate with positron emission tomography coupled with computed tomograph. BAT glucose uptake was also measured during acute cold exposure using 2-deoxy-2-[18F]fluoro-d-glucose. To isolate the effects of ovarian hormones from biological aging, measurements were repeated in a subset of premenopausal women (n = 8; 40 ± 4 yr; BMI = 28.0 ± 7.2 kg/m2) after 6 mo of gonadotropin-releasing hormone agonist therapy to suppress ovarian hormones. At room temperature, there was no difference in BAT oxidative metabolism between premenopausal (0.56 ± 0.31 min-1) and postmenopausal women (0.63 ± 0.28 min-1). During cold exposure, BAT oxidative metabolism (1.28 ± 0.85 vs. 0.91 ± 0.63 min-1, P = 0.03) and net BAT glucose uptake (84.4 ± 82.5 vs. 29.7 ± 31.4 nmol·g-1·min-1, P < 0.01) were higher in premenopausal than postmenopausal women. In premenopausal women who underwent gonadotropin-releasing hormone agonist, cold-stimulated BAT oxidative metabolism was reduced to a similar level (from 1.36 ± 0.66 min-1 to 0.91 ± 0.41 min-1) to that observed in postmenopausal women (0.91 ± 0.63 min-1). These results provide the first evidence in humans that reproductive hormones are associated with BAT oxidative metabolism and suggest that BAT may be a target to attenuate age-related reduction in energy expenditure and maintain metabolic health in postmenopausal women.NEW & NOTEWORTHY In rodents, loss of estrogen reduces brown adipose tissue (BAT) activity. Whether this is true in humans is not known. We found that BAT oxidative metabolism and glucose uptake were lower in postmenopausal compared to premenopausal women. In premenopausal women who underwent ovarian suppression to reduce circulating estrogen, BAT oxidative metabolism was reduced to postmenopausal levels. Thus the loss of ovarian function in women leads to a reduction in BAT metabolic activity independent of age.

Walking Exercise Sustainability Through Telehealth for Veterans With Lower-Limb Amputation: A Study Protocol.

OBJECTIVE: This randomized controlled superiority trial will determine if an 18-month telehealth walking exercise self-management program produces clinically meaningful changes in walking exercise sustainability compared to attention-control education for veterans living with lower-limb amputation. METHODS: Seventy-eight participants with lower-limb amputation (traumatic or nontraumatic) aged 50 to 89 years will be enrolled. Two groups will complete 6 one-on-one intervention sessions, and 6 group sessions over an 18-month intervention period. The experimental arm will receive a self-management program focusing on increasing walking exercise and the control group will receive attention-control education specific to healthy aging. Daily walking step count (primary outcome) will be continuously monitored using an accelerometer over the 18-month study period. Secondary outcomes are designed to assess potential translation of the walking exercise intervention into conventional amputation care across the Veteran Affairs Amputation System of Care. These secondary outcomes include measures of intervention reach, efficacy, likelihood of clinical adoption, potential for clinical implementation, and ability of participants to maintain long-term exercise behavior. IMPACT: The unique rehabilitation paradigm used in this study addresses the problem of chronic sedentary lifestyles following lower-limb amputation through a telehealth home-based walking exercise self-management model. The approach includes 18 months of exercise support from clinicians and peers. Trial results will provide rehabilitation knowledge necessary for implementing clinical translation of self-management interventions to sustain walking exercise for veterans living with lower-limb amputation, resulting in a healthier lifestyle.

Maximal daily stepping cadence partially explains functional capacity of individuals with end-stage knee osteoarthritis.

BACKGROUND: Individuals with end-stage knee osteoarthritis (OA) walk at a lower intensity (ie, slower step cadence) contributing to worse physical function. Previous literature reports daily step counts and sedentary time, with little information regarding stepping bouts or cadence. Determining relationships between daily higher stepping cadence duration and clinical outcomes can move the field toward optimal daily stepping prescription. OBJECTIVE: To quantify daily physical activity patterns of individuals with end-stage knee OA and determine the contribution of high stepping cadence to explain functional capacity variability. DESIGN: Cross-sectional analysis. SETTING: Veterans Administration medical center. PARTICIPANTS: U.S. military veterans (n = 104; age: 67.1 years [7.2]; mean [SD]; male [89.3%]) with end-stage knee OA were enrolled. INTERVENTION: Not applicable. MAIN OUTCOME MEASURE: Functional capacity (6-Minute Walk Test [6MWT]). Physical activity (activPAL wearable sensor; cadence and time sitting, standing, and stepping), pain (Western Ontario and McMaster Universities Osteoarthritis Index-pain subscale) sociodemographic variables, and comorbidities (body mass index and Functional Comorbidity Index) are the main explanatory variables. RESULTS: Participants' wake time was mainly sitting (11.0 h/day) in ≥60-minute bouts (29.7% ± 12.7 of sitting time). Standing (3.4 hours/day) and stepping (1.4 h/day) primarily occurred in 0-5 minute bouts (standing: 87.7% ± 14.4 of standing time, stepping: 98.7% ± 12.7 of stepping time) and stepping cadence was predominantly incidental (1-19 spm; 52.9% ± 9.6 of total stepping time). Backward elimination model results indicated shorter medium-to-brisk cadence bout duration, older age, and higher pain significantly explained shorter 6MWT distance (Adj R2 =0.24, p < .01). CONCLUSIONS: Individuals with knee OA spend most of their waking hours sitting, while standing and stepping occurs in short bouts at very low stepping cadence. Decreased time in high stepping cadence is associated with lower functional capacity. Future studies should explore if increasing the daily time spent in higher step cadence can improve functional capacity in this population.

Stepped-care to improve mental health outcomes among underserved patients with lung and head and neck cancer.

BACKGROUND: The comparative effectiveness study (ClinicalTrials.gov, NCT03016403) assessed the effects of a stepped-care intervention versus usual care on mental health outcomes, including anxiety, depression, coping self-efficacy, emotional distress (anxiety and depression combined), health-related quality of life (HRQoL), and perceived stress among underserved patients (i.e., low-income, uninsured, underinsured) with lung cancer (LC) and head-and-neck cancer (HNC). METHODS: In a randomized controlled trial, we investigated if 147 patients who received the stepped-care intervention had better mental health outcomes compared to 139 patients who received usual care. Using an intent-to-treat approach, we analyzed outcomes with linear mixed models. RESULTS: For the primary outcomes estimated mean differences (denoted by "Δ"), depression (Δ = 1.75, 95% CI = 0.52, 2.98, p = 0.01) and coping self-efficacy (Δ = -15.24, 95% CI = -26.12, -4.36, p = 0.01) were better for patients who received the intervention compared to patients who received usual care, but anxiety outcomes were not different. For secondary outcomes, emotional distress (Δ = 1.97, 95% CI: 0.68, 3.54, p =< 0.01) and HRQoL (Δ = -4.16 95% CI: -7.45, -0.87, p = 0.01) were better for patients who received the intervention compared to usual care patients, while perceived stress was not different across groups. CONCLUSIONS: The stepped-care intervention influenced depression and coping self-efficacy, important outcomes for patients with acute illnesses like LC and HNC. Although differences in emotional distress met the minimally important differences (MID) previously reported, depression and HRQoL were not above the MID threshold. Our study is among a few to report differences in mental health outcomes for underserved LC and HNC patients after receiving a psychological intervention. GOV IDENTIFIER: NCT03016403.

Transportability Methods for Time-to-Event Outcomes: Application in Adjuvant Colon Cancer Trials.

PURPOSE: Differences in the benefits of treatment on 5-year overall survival have been observed in 12 randomized phase III colon cancer adjuvant clinical trials from the ACCENT group. We investigated the reasons for these differences by incorporating the distribution of the observed covariates from each trial. MATERIALS AND METHODS: We applied state-of-the-art transportability methods on the basis of causal inference, and compared them with a conventional meta-analysis approach to predict the treatment effect for the target population. Prediction errors were defined to evaluate whether the identifiability conditions necessary for causal inference were satisfied among the 12 trials, and to measure the performance of each method. RESULTS: In the one-trial-at-a-time transportability analysis, the ranks of prediction errors for the target population were mostly consistent with the discrepancy in treatment effects among the 12 trials across the three models. The overall prediction errors between the leave-one-trial-out transportability method and the conventional individual participant data meta-analysis approach were very similar, and more than 40% lower than the overall prediction errors from the one-trial-at-a-time transportability method. CONCLUSION: The discrepancy in treatment effects among the 12 trials is unlikely to arise from the choice of model specification or distribution of observed covariates but from the distribution of unobserved covariates or study-level features. The ability to quantify heterogeneity among the 12 trials was greatly reduced in both the leave-one-trial-out transportability method and the conventional meta-analysis approach compared with the one-trial-at-a-time transportability method.

Mother's Milk Messaging™: trial evaluation of app and texting for breastfeeding support.

BACKGROUND: New mothers experience BF challenges but have limited evidence-based technology-enabled support. OBJECTIVES: 1) Determine if using the Mother's Milk Messaging™ app improved aspects of breastfeeding and breastfeeding rates and 2) Describe engagement as well as themes from the qualitative feedback on the app. METHOD: Randomized Controlled Trial National sample of primiparous, singleton mothers recruited online and then randomized using stratification by language into three arms: 1) BF text messages plus app; 2) BF text messages, app and physician-moderated private Facebook (FB) group; 3) Attention control group who received injury prevention texts. Exclusive breastfeeding rates as primary outcome and knowledge/attitude, confidence, and social support as secondary outcomes. We determined engagement through analysis of app usage metrics. We conducted and content-coded interviews with participants to learn more about app usage and BF experience. Due to the nature of the intervention participants could not be blinded. RESULTS: There were a total of 346 participants in the trial, with 227 in the Intervention (n = 154 group 1 and n = 156 group 2) and 119 in the control group. Because of minimal Facebook activity, the two intervention groups 1 and 2 were combined. There were no differences in breastfeeding exclusivity and duration. (NS). Women in the intervention arm reported significantly higher confidence with breastfeeding and perceived social support to the control group (p < .05). Greater than 80% registered the app and those that engaged with the app had higher scores with time. Mothers appreciated receiving text messages and videos with reliable information. No harm was reported in this study. CONCLUSION: MMM increased confidence with breastfeeding and with gathering social supports. Exclusively BF was high in all participants. Mothers perceived it as useful and dependable especially the texting.

Comparing "people-like-me" and linear mixed model predictions of functional recovery following knee arthroplasty.

OBJECTIVE: Prediction models can be useful tools for monitoring patient status and personalizing treatment in health care. The goal of this study was to compare the relative strengths and weaknesses of 2 different approaches for predicting functional recovery after knee arthroplasty: a neighbors-based "people-like-me" (PLM) approach and a linear mixed model (LMM) approach. MATERIALS AND METHODS: We used 2 distinct datasets to train and then test PLM and LMM prediction approaches for functional recovery following knee arthroplasty. We used the Timed Up and Go (TUG)-a common test of mobility-to operationalize physical function. Both approaches used patient characteristics and baseline postoperative TUG values to predict TUG recovery from days 1-425 following surgery. We then compared the accuracy and precision of PLM and LMM predictions. RESULTS: A total of 317 patient records with 1379 TUG observations were used to train PLM and LMM approaches, and 456 patient records with 1244 TUG observations were used to test the predictions. The approaches performed similarly in terms of mean squared error and bias, but the PLM approach provided more accurate and precise estimates of prediction uncertainty. DISCUSSION AND CONCLUSION: Overall, the PLM approach more accurately and precisely predicted TUG recovery following knee arthroplasty. These results suggest PLM predictions may be more clinically useful for monitoring recovery and personalizing care following knee arthroplasty. However, clinicians and organizations seeking to use predictions in practice should consider additional factors (eg, resource requirements) when selecting a prediction approach.

Application of gap time analysis with flexible hazards to pulmonary exacerbations in the EPIC observational study.

Cystic fibrosis and other chronic lung disease clinical trials often use time to first pulmonary exacerbation (PEx) or total PEx count as endpoints. The use of these outcomes may fail to capture patterns or timing of multiple exacerbations and how covariates influence the risk of future exacerbations. Analysis of gap times between PEx provides a useful framework to understand risks of subsequent events, particularly to assess if there is a temporary increase in a hazard of a subsequent PEx following the occurrence of a PEx. This may be useful for estimating the amount of time needed to follow patients after a PEx and predicting which patients are more likely to have multiple PEx. We propose a smoothed hazard for gap times to account for elevated hazards after exacerbations. A simulation study was conducted to explore model performance and was able to appropriately estimate parameters in all situations with an underlying change point with independent or correlated recurrent events. Models with different change-point structures and trends are compared using Early Pseudomonas Infection Control (EPIC) observational study data, using a quasi-likelihood modification of the Akaike information criterion; a model with a change-point provided a better fit than a model without one. The analysis suggests that the change point may be 1.8 years (SE 0.09) after the end of a PEx. Models including covariates in the hazard function revealed that having one or two copies of the Δ$\Delta$ F508 mutation, female sex, and higher numbers of previous PEx were significantly associated with increased risk of another PEx.

Comparison of Cosyntropin, Vigabatrin, and Combination Therapy in New-Onset Infantile Spasms in a Prospective Randomized Trial.

Objective: In a randomized trial, we aimed to evaluate the efficacy of cosyntropin injectable suspension, 1 mg/mL, compared to vigabatrin for infantile spasms syndrome. An additional arm was included to assess the efficacy of combination therapy (cosyntropin and vigabatrin) compared with cosyntropin monotherapy. Methods: Children (2 months to 2 years) with new-onset infantile spasms syndrome and hypsarhythmia were randomized into 3 arms: cosyntropin, vigabatrin, and cosyntropin and vigabatrin combined. Daily seizures and adverse events were recorded, and EEG was repeated at day 14 to assess for resolution of hypsarhythmia. The primary outcome measure was the composite of resolution of hypsarhythmia and absence of clinical spasms at day 14. Fisher exact test was used to compare outcomes. Results: 37 children were enrolled and 34 were included in the final efficacy analysis (1 withdrew prior to treatment and 2 did not return seizure diaries). Resolution of both hypsarhythmia and clinical spasms was achieved in in 9 of 12 participants (75%) treated with cosyntropin, 1/9 (11%) vigabatrin, and 5/13 (38%) cosyntropin and vigabatrin combined. The primary comparison of cosyntropin versus vigabatrin was significant (64% [95% confidence interval 21, 82], P < .01). Adverse events were reported in all 3 treatment arms: 31 (86%) had an adverse event, 7 (19%) had a serious adverse event, and 15 (42%) had an adverse event of special interest with no difference between treatment arms. Significance: This randomized trial was underpowered because of incomplete enrollment, yet it demonstrated that cosyntropin was more effective for short-term outcomes than vigabatrin as initial treatment for infantile spasms.

Delivery of home-based post-partum contraception in rural Guatemalan women: feasibility, recruitment and retention in a cluster-randomized trial.

BACKGROUND: Few cluster-randomized trials have been performed in rural Guatemala. Our objective was to describe the feasibility, recruitment and retention in our cluster-randomized trial. METHODS: In our cluster-randomized trial, a range of contraceptives were brought to mothers' homes in rural Guatemala. RESULTS: Of 173 women approached, 33 were excluded. Of the 140 eligible women, 127 (91%) consented to participate. Of the 87 women who should have been assessed for the primary outcome, three were lost to follow-up, which represents a retention rate of 97%. CONCLUSIONS: Nurses who are both clinical providers and study staff can feasibly conduct research, which leads to high enrollment and retention rates.

Development of reference charts for monitoring quadriceps strength with handheld dynamometry after total knee arthroplasty.

Purpose: To develop reference charts that describe normative quadriceps strength recovery after total knee arthroplasty (TKA) as measured by handheld dynamometry (HHD).Materials and Methods: We conducted a retrospective analysis of post-TKA quadriceps strength recovery in a longitudinal dataset consisting of both clinical and research HHD data. We created sex-specific models for recovery using Generalized Additive Models for Location, Scale, and Shape. We created reference charts from the models to display the recovery of population centiles over the first six postoperative months.Results: A total of 588 patient records with 1176 observations were analyzed. Reference charts for both sexes demonstrated a rapid increase in quadriceps strength over the first 60 postoperative days followed by a more gradual increase over the next 120 days. Males appeared to demonstrate faster recovery and greater strength on average compared to females. The quadriceps strength recovery of three female patient records was plotted on the reference chart to illustrate the charts' potential clinical utility.Conclusions: These reference charts provide normative data for quadriceps strength recovery after TKA as assessed by HHD. The reference charts may augment clinicians' ability to monitor and intervene upon quadriceps weakness-a pronounced and debilitating post-TKA impairment-throughout rehabilitation.Implications for RehabilitationHandheld dynamometry (HHD) is an objective and clinically feasible method for assessing muscle strength, but normative HHD values are lacking for quadriceps strength recovery after total knee arthroplasty (TKA).We created sex-specific reference charts which provide normative quadriceps strength HHD values for the first 180 days after TKA.These reference charts may improve clinicians' ability to monitor and intervene upon post-TKA quadriceps strength deficits.

Intervening With Smoking Parents of Inpatients to Reduce Exposure: The INSPIRE Randomized Controlled Trial.

BACKGROUND: Hospitalized children have high rates of tobacco smoke exposure; parents who smoke may be receptive to interventions during their child's hospitalization. OBJECTIVE: We tested the efficacy of a smoking cessation intervention for parents of hospitalized children. METHODS: We conducted a randomized, single-blind clinical trial from 12/14-5/18 at the Children's Hospital Colorado. Hospitalized children who had a parent who smoked tobacco were eligible. INTERVENTION: Intervention participants received motivational interviewing sessions, 2 weeks of nicotine replacement therapy; both groups received referral to the Quitline Consenting parents completed a questionnaire; urine was collected from the child for measurement of cotinine. Our primary outcome was: 1) increase in reporting "no one is allowed to smoke anywhere" in the home (smoke-free home rule). Additional outcomes included: 2) change in child's cotinine from baseline to 1 year, and 3) parental quitting at 1 year. Data were analyzed using Chi-square and t tests for bivariable data, and multivariable logistic and linear regression. RESULTS: Of 1641 eligible families approached, 252 were randomized (15%); 149 families had follow-up data at 12 months (59%). In the adjusted analysis, there was no difference between the groups in smoke free home rules, or child cotinine level; in an intention-to-treat analysis, 15% in the intervention group versus 8% of controls reported quit (p=0.07). CONCLUSIONS: A smoking cessation intervention can be delivered to parents of hospitalized children. While hospitalization provides an opportunity to help parents quit smoking, more efficient and effective engagement strategies are needed to optimize tobacco control success.

Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods.

BACKGROUND: Coronavirus disease (COVID-19) presents an unprecedented threat to global health worldwide. Accurately predicting the mortality risk among the infected individuals is crucial for prioritizing medical care and mitigating the healthcare system's burden. The present study aimed to assess the predictive accuracy of machine learning methods to predict the COVID-19 mortality risk. METHODS: We compared the performance of classification tree, random forest (RF), extreme gradient boosting (XGBoost), logistic regression, generalized additive model (GAM) and linear discriminant analysis (LDA) to predict the mortality risk among 49,216 COVID-19 positive cases in Toronto, Canada, reported from March 1 to December 10, 2020. We used repeated split-sample validation and k-steps-ahead forecasting validation. Predictive models were estimated using training samples, and predictive accuracy of the methods for the testing samples was assessed using the area under the receiver operating characteristic curve, Brier's score, calibration intercept and calibration slope. RESULTS: We found XGBoost is highly discriminative, with an AUC of 0.9669 and has superior performance over conventional tree-based methods, i.e., classification tree or RF methods for predicting COVID-19 mortality risk. Regression-based methods (logistic, GAM and LASSO) had comparable performance to the XGBoost with slightly lower AUCs and higher Brier's scores. CONCLUSIONS: XGBoost offers superior performance over conventional tree-based methods and minor improvement over regression-based methods for predicting COVID-19 mortality risk in the study population.

Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.

BACKGROUND: Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient's biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance. METHODS: We propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions. RESULTS: In simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling. CONCLUSIONS: RSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.

Cerebral Visual Impairment in CDKL5 Deficiency Disorder Correlates With Developmental Achievement.

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder is a rare neurodevelopmental disorder characterized by infantile-onset refractory epilepsy, profound developmental delays, and cerebral visual impairment. Although there is evidence that the presence of cerebral visual impairment in CDKL5 deficiency disorder is common, the potential impact of cerebral visual impairment severity on developmental attainment has not been explored directly. Focusing on a cohort of 46 children with CDKL5 deficiency disorder, examination features indicative of cerebral visual impairment were quantified and compared to developmental achievement. The derived cerebral visual impairment severity score was inversely correlated with developmental attainment, bolstering the supposition that cerebral visual impairment severity may provide a useful early biomarker of disease severity and prognosis. This study demonstrates the utility of a cerebral visual impairment score to better capture the range of cerebral visual impairment severity in the CDKL5 deficiency disorder population and further elucidates the interaction between cerebral visual impairment and developmental outcomes.

Error-Manipulation Gait Training for Veterans With Nontraumatic Lower Limb Amputation: A Randomized Controlled Trial Protocol.

OBJECTIVE: he purpose of this study will be to determine the efficacy of error-manipulation gait training (error-augmentation or error-correction) to improve step length symmetry and secondary health outcomes in veterans with unilateral nontraumatic transtibial amputation (TTA). METHODS: This will be a 3-arm, parallel, assessor-blinded, randomized controlled trial, with baseline, postintervention, and 3-month follow-up assessments in a VA Geriatric Research Education and Clinical Center. Participants are 54 US military veterans ranging in age from 50 to 85 years with a unilateral transtibial nontraumatic amputation. Participants will be randomized into 1 of 3 groups: error-augmentation training (EAT), error-correction training (ECT), or supervised walking (CTL). Each group will complete 8 split-belt treadmill walking training sessions of 25 minutes each over 4 weeks. The EAT group will walk with belts moving at asymmetrical speeds. The ECT group will walk to the cadence of a metronome and the split belts moving at symmetrical speeds. The CTL group will walk with symmetrical belt speeds and without feedback. Step length symmetry is the primary outcome and will be assessed using an asymmetry index during overground walking. Secondary outcomes include lower extremity joint work during treadmill walking, 6-Minute Walk Test performance, daily step count, participant-perceived disability, and residual-limb integumentary health. Outcomes will be assessed at preintervention, 1-week postintervention (primary endpoint), and 13 weeks postintervention. IMPACT: This study protocol focuses on an understudied area of rehabilitation for patients with nontraumatic unilateral TTA who have high levels of spatiotemporal gait asymmetry and mobility impairment. The results of this study will inform future implementation of clinical gait training interventions to improve spatiotemporal gait symmetry and long-term health and disability outcomes in patients with nontraumatic unilateral TTA.

Body Composition and Markers of Cardiometabolic Health in Transgender Youth on Gonadotropin-Releasing Hormone Agonists.

Purpose: Up to 1.8% of youth identify as transgender; many will be treated with a gonadotropin-releasing hormone agonist (GnRHa). The impact of GnRHa on insulin sensitivity and body composition in transgender youth is understudied. We aimed to evaluate differences in insulin sensitivity and body composition in transgender youth on GnRHa therapy compared with cisgender youth. Methods: Transgender participants were matched to cisgender participants on age, body mass index, and sex assigned at birth. Transgender males (n=9, ages 10.1-16.0 years) on GnRHa (mean±standard deviation duration of exposure: 20.9±19.8 months) were compared with cisgender females (n=14, ages 10.6-16.2). Transgender females (n=8, ages 12.6-16.1) on GnRHa (11.3±7 months) were compared with cisgender males (n=17, ages 12.5-15.5). Differences in insulin sensitivity (1/[fasting insulin], homeostatic model of insulin resistance [HOMA-IR]), glycemia (hemoglobin A1C [HbA1c], fasting glucose), and body composition (dual-energy X-ray absorptiometry) were evaluated using a mixed linear regression model. Results: Transgender males had lower 1/fasting insulin and higher HOMA-IR (p=0.031, p=0.01, respectively), fasting glucose (89±4 vs. 79±13 mg/dL, p=0.012), HbA1c (5.4±0.2 vs. 5.2±0.2%, p=0.039), and percent body fat (36±7 vs. 32±5%, p=0.042) than matched cisgender females. Transgender females had lower 1/fasting insulin and higher HOMA-IR (p=0.028, p=0.035), HbA1c (5.4±0.1% vs. 5.1±0.2%, p=0.007), percent body fat (31±9 vs. 24±10%, p=0.002), and lower percent lean mass (66±8 vs. 74±10%, p<0.001) than matched cisgender males. Conclusion: Transgender youth on a GnRHa have lower estimated insulin sensitivity and higher glycemic markers and body fat than cisgender controls with similar characteristics. Longitudinal studies are needed to understand the significance of these changes. Clinical Trial.gov ID: NCT02550431.