Baseline Clinical Measures Research Articles

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data

To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. A retrospective cohort study. In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs). We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1+ VF2; (6) V2OC: VF1+ VF2+ Clinical1+ OCT1; (7) V3: VF1+ VF2+ VF3; and (8) V3OC: VF1+ VF2+ VF3+ Clinical1+ OCT1. The AUC of DLMs when forecasting rapidly worsening eyes. Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]). Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy.

Prospective cohort study. Apply a machine learning clustering algorithm to baseline imaging data to identify clinically relevant cervical spondylotic myelopathy (CSM) patient phenotypes. A major shortcoming in improving care for CSM patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques, such as diffusion basis spectrum imaging (DBSI), may help address this limitation by providing detailed evaluations of white matter injury in CSM. Fifty CSM patients underwent comprehensive clinical assessments and diffusion-weighted MRI, followed by DBSI modeling. DBSI metrics included fractional anisotropy, axial and radial diffusivity, fiber fraction, extra-axonal fraction, restricted fraction, and nonrestricted fraction. Neurofunctional status was assessed by the modified Japanese Orthopedic Association, myelopathic disability index, and disabilities of the arm, shoulder, and hand. Quality-of-life was measured by the 36-Item Short Form Survey physical component summary and mental component summary. The neck disability index was used to measure self-reported neck pain. K-means clustering was applied to baseline DBSI measures to identify 3 clinically relevant CSM disease phenotypes. Baseline demographic, clinical, radiographic, and patient-reported outcome measures were compared among clusters using one-way analysis of variance (ANOVA). Twenty-three (55%) mild, 9 (21%) moderate, and 10 (24%) severe myelopathy patients were enrolled. Eight patients were excluded due to MRI data of insufficient quality. Of the remaining 42 patients, 3 groups were generated by k-means clustering. When compared with clusters 1 and 2, cluster 3 performed significantly worse on the modified Japanese Orthopedic Association and all patient-reported outcome measures (P<0.001), except the 36-Item Short Form Survey mental component summary (P>0.05). Cluster 3 also possessed the highest proportion of non-Caucasian patients (43%, P=0.04), the worst hand dynamometer measurements (P<0.05), and significantly higher intra-axonal axial diffusivity and extra-axonal fraction values (P<0.001). Using baseline imaging data, we delineated a clinically meaningful CSM disease phenotype, characterized by worse neurofunctional status, quality-of-life, and pain, and more severe imaging markers of vasogenic edema. II.

Effect of dual trigger with chorionic gonadotropin hormone and follicle-stimulating hormone on endometrial thickness in infertile women who had superovulation with an aromatase inhibitor

Beginning in October 2020 and ending in April 2021, researchers from Al-Nahrain University's High Institute for Infertility Diagnosis and Assisted Reproductive Technologies compared the success rates of two different methods of diagnosing and treating infertility. The major purpose of the research was to assess the impact of a combined trigger (follicle-stimulating hormone [FSH] and human chorionic gonadotropin [hCG]) on endometrial receptivity (endometrial thickness, endometrial pattern, sub-endometrial blood flow). A total of 100 females took part in the study. All patients gave their informed written consent, and the study was approved by the Al-Nahrain University Ethics Committee. Procedure Time To confirm ovulation, measure and analyze the endometrial pattern, and examine the sub-endometrial blood flow, a vaginal ultrasound was done 36 to 48 hours following trigger ovulation. All four hormones (FSH, LH, Progesterone, and E2) were tested in the blood at the same time to determine whether a couple was fertile, a complete medical history, and physical examination whereas performed on each member of the pair. An ultrasound vaginal probe was used to do the transvaginal examination. Patients were placed in the dorsal lithotomy position with an empty bladder for early follicular US (CD 2-3) to assess the number of antral follicles, measure endometrial thickness, and rule out ovarian cysts or other pathology. A second ultrasound was performed during the middle of the cycle (CD9-14) to determine whether a mature follicle had been found. A multiplanar image of the uterus was acquired after an ultrasound scan was swept across the mid-sagittal plane. Endometrial thickness in the median longitudinal plane of the uterus was calculated as the largest distance from one basal endometrial interface via the endometrial canal to the opposite endometrial-myometrial interface of the anterior-posterior uterine wall. A statistically significant difference was found between Group A's average E2 concentration of 69.62 pg/mL and Groups B and C's concentrations of 53.32 and 36.65 pg/mL. (P 0.001). In group C, there was a statistically significant difference in E2 levels on the day of the trigger and the day of the IUI (P = 0.036). On the day of IUI compared to the day of trigger, no statistically significant differences were seen between the study groups for any of the other hormonal indicators (P &gt; 0.05). There were no significant differences (P &gt; 0.05) in any of the baseline clinical measures between the research groups. All clinical indicators were comparable across groups (P &gt; 0.05). There were no statistically significant differences (P &gt; 0.05) between the research groups on any other clinical indicators. When comparing groups, A, B, and C on the decline in RI between the trigger and IUI days, group A significantly outperformed the others (P = 0.003). There was no statistically significant difference (P &gt; 0.05) in any of the other clinical parameters between the IUI and trigger groups on the day of IUI.

DEVELOPMENT OF AN OPTIMAL MACHINE LEARNING MODEL USING TREATMENT NAÏVE DIAGNOSTIC PATHOLOGY IMAGES TO PREDICT STEROID-FREE CLINICAL REMISSION AT ONE YEAR IN PEDIATRIC ULCERATIVE COLITIS

Abstract BACKGROUND The majority of children with ulcerative colitis present with extensive colitis at diagnosis, but the response to therapy is heterogenous. Identifying the optimal window for biologic treatment and risk stratification of patients remains an unmet need. AIM To develop a pathology based histomic model to predict corticosteroid free clinical remission (CSF) with mesalamine alone at one year. METHODS 292 hematoxylin and eosin diagnostic treatment naïve rectal mucosal biopsies from the multi-center PROTECT study were digitized. Whole slide images (WSIs) underwent two-step pre-processing a)stain normalization and b)informative patch selection (size 512x512). We trained machine learning (ML) models using 250 histomic features (texture, color, histogram and nuclei features) with 5-fold cross-validation, for patch-level classification. Feature importance was determined by the Gini index. We re-trained the classifier using the top features. Slide-level prediction was defined by threshold voting. Performance metrics at the patch and WSI level was evaluated. A total of 161 patients underwent high-throughput RNA sequencing to define rectal gene expression. We undertook unsupervised weighted gene co-expression network analysis (WGCNA) to discover networks of co-expressed genes with shared biologic functions correlated with histomic features, histological traits, and outcomes. RESULTS 187571 informative patches from 292 patients (Male:55%; Age:12.7y (IQR:11-15); CSF remission:41%) were trained on 23 ML classifiers. The best model trained on 250 features at the patch-level was random forest (RF). At a remission ratio threshold of 0.48, WSI area under the receiver operator curve (AUROC) was 0.90 (95%CI:0.70, 1.00), accuracy 90.4%, precision 90.9% and recall 84.7%. 18 top features were identified and trained, and the corresponding WSI AUROC was 0.87 (95%CI:0.72, 1.00), accuracy 90.1%, precision 89.4%, and recall 83.9% (Fig. 1). We re-trained the 18 features on an independent real-world dataset of 131 UC patients and the model WSI AUROC was 0.85 (95%CI:0.74, 1.00) and an accuracy of 88.5%. Of the 13 modules identified by WGCNA analysis, six modules significantly correlated with clinical and/or histomic features (Fig. 2). Two of the gene co-expression modules were negatively associated with baseline clinical, endoscopic, and histologic measures of severity, and positively associated with nuclei features (Otsu area, perimeter, and equivalent diameter) and the outcome measure of CSF remission. Intersection of genes with adult single cell RNA seq data demonstrated enrichment for enterocytes (SLC26A3) and extracellular matrix (IHH) CONCLUSION We developed a predictive model for UC disease course using histomic features from standard of care pre-treatment pathology images. Characterization of the underlying molecular basis of the histomic features is ongoing.

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.

The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant. The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits. The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use. The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

Abstract C004: Importance of statistical considerations in multi-center, transatlantic cancer disparity studies: Lessons learned from the CaPTC prostate cancer familial cohort study

Abstract Background: The CaPTC prostate cancer (CaP) Familial Cohort study (CaPFCS) was launched in 2017 to explore the CaP risk factors of African Black men (ABM). The study design is a prospective study that observes and follows healthy ABM and ABM diagnosed with CaP. Aim: The specific aim is to provide recommendations on statistical considerations for cohort studies focused on cancer disparities, based on lessons learned from the CaPTC CaPFCS. Methodology: Study participants were ABM in Cameroon, Nigeria, and the US. Using validated measures, data were collected mostly by self-administered or research assistant administered survey using paper survey (especially in Cameroon and Nigeria) with the responses transferred to REDCap by research coordinators. The statistical analysis for the CaPTC CaPFCS focused on the baseline epidemiological, behavioral, clinical and anthropometric measures of Black men. A total of 819 participants were included in the analyses. Statistical analyses included descriptive statistics (sample median, range, frequency and percentages) and unadjusted logistic regression models. Results: Data collection for the CaPTC CaPFCS were by multiple investigators/research coordinators at multiple sites in Cameroon, Nigeria, and the US. In preparation for the data analyses, we noted data entry errors and/or misinterpretation of data entry, which required extensive cleaning of the database prior to the analyses. The biostatisticians spent significant effort cleaning and ensuring data validity prior to the analyses. Confounding variables is another statistical consideration for cohort studies, such as the CaPTC CaPFCS. There was variability observed across age, education levels, employment status, household income and CaP diagnosis based on country of residence. CaP diagnosis was not reported by any of the ABM in the US, which may be due to the fact that they were all recruited within the community and no recruitment took place in the clinic. Finally, the CaPTC CaPFCS has several variables. Performing a lot of statistical tests may result in false-positive findings at P&amp;lt;0.05. Conclusion: The advantage of the CaPFCS is that it allows the CaPTC investigators to study epidemiological, behavioral, clinical and anthropometric variables associated with CaP over time. Being a longitudinal cohort study, investigators can track the changes in exposure and outcomes over time.However, statistical considerations need to be at the fore-front of multi-center, transatlantic cancer disparity studies such as the CaPTC CaPFCS, from data collection to data entry. The use of web-based survey with the ability to enter data directly into REDCaP will significantly reduce (if not totally eliminate) the need for database cleaning. There are multiple strategies that can be used to reduce confounder effect, including statistical adjustment and multivariate analysis to adjust for the confounders. The use of propensity score methods have especially been found to be effective in controlling baseline confounding factors. Citation Format: Daniel Lee, Michael Heckman, Zhongwei Peng, Emeka Iweala, Ademola Popoola, Paul Jibrin, Mohammed Faruk, Anthonia Sowumi, Omolara Fatiregun, Nkegoum Blaise, Catherine Oladoyinbo, Ifeoma Okoye, Abdulkadir Ayo Salako, Abidemi Omonisi, Iya Eze Bassey, Kayode Adeniji, Nggada Haruna Asura, Ernest Kaninjing, Oluwole Kukoyi, Fathi Parisa, Ruth Enuka, Oluwaseyi Toye, Jennifer Crook, CaPTC Investigators, Folakemi Odedina. Importance of statistical considerations in multi-center, transatlantic cancer disparity studies: Lessons learned from the CaPTC prostate cancer familial cohort study [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C004.

Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

BackgroundTo address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes. MethodsPretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD17) and response/remission rates by treatment group. ResultsThree subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline. LimitationsDue to the exploratory nature of these analyses, there were no adjustments for multiple comparisons. ConclusionsBaseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.

Prognostic value of the combination of neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio and platelet-to-lymphocyte ratio on mortality in patients on maintenance hemodialysis.

Hemodialysis (HD) is the most important renal replacement therapy for patients with end-stage kidney disease (ESKD). Systemic inflammation is a risk factor of mortality in HD patients. Neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) are new inflammatory markers. However, previous studies have inconsistent conclusions about the predictive value of NLR, MLR and PLR on mortality of HD patients. The aim of this study was to establish an inflammation scoring system by including NLR, MLR and PLR, and evaluate the association between the inflammation score and all-cause and cardiovascular mortality in HD patients. In this single center retrospective cohort study, 213 incident HD patients from January 1, 2015 to December 31, 2020 were included. Baseline demographic and clinical data and laboratory measurements were collected. According to the optimal cut-off values, NLR, MLR and PLR were assigned 0 or 1 point, respectively. Then, the inflammation score was obtained by adding the NLR, MLR and PLR scores. All patients were followed until July 31, 2021. The associations of the inflammation score with all-cause and cardiovascular mortality were assessed by multivariable-adjusted Cox models. Of 213 patients, the mean (± SD) age was 56.8 ± 14.4 years, 66.2% were men, and 32.9% with diabetes. The primary cause of ESKD was mainly chronic glomerulonephritis (46.5%) and diabetic nephropathy (28.6%). The median inflammation score was 2 (interquartile range = 1-3). During a median 30 months (interquartile range = 17-50 months) follow-up period, 53 patients had died, of which 33 deaths were caused by cardiovascular disease. After adjusting for demographics, primary diseases and other confounders in multivariable model, the inflammation score = 3 was associated with a hazard ratio for all-cause mortality of 4.562 (95% confidence interval, 1.342-15.504, P = 0.015) and a hazard ratio for cardiovascular mortality of 4.027 (95% confidence interval, 0.882-18.384, P = 0.072). In conclusion, an inflammation scoring system was established by including NLR, MLR and PLR, and the higher inflammation score was independently associated with all-cause mortality in HD patients.

MRI biomarkers of freezing of gait development in Parkinson’s disease

This study investigated longitudinal clinical, structural and functional brain alterations in Parkinson’s disease patients with freezing of gait (PD-FoG) and in those developing (PD-FoG-converters) and not developing FoG (PD-non-converters) over two years. Moreover, this study explored if any clinical and/or MRI metric predicts FoG development. Thirty PD-FoG, 11 PD-FoG-converters and 11 PD-non-converters were followed for two years. Thirty healthy controls were included at baseline. Participants underwent clinical and MRI visits. Cortical thickness, basal ganglia volumes and functional network graph metrics were evaluated at baseline and over time. In PD groups, correlations between baseline MRI and clinical worsening were tested. A ROC curve analysis investigated if baseline clinical and MRI measures, selected using a stepwise model procedure, could differentiate PD-FoG-converters from PD-non-converters. At baseline, PD-FoG patients had widespread cortical/subcortical atrophy, while PD-FoG-converters and non-converters showed atrophy in sensorimotor areas and basal ganglia relative to controls. Over time, PD-non-converters accumulated cortical thinning of left temporal pole and pallidum without significant clinical changes. PD-FoG-converters showed worsening of disease severity, executive functions, and mood together with an accumulation of occipital atrophy, similarly to PD-FoG. At baseline, PD-FoG-converters relative to controls and PD-FoG showed higher global and parietal clustering coefficient and global local efficiency. Over time, PD-FoG-converters showed reduced parietal clustering coefficient and sensorimotor local efficiency, PD-non-converters showed increased sensorimotor path length, while PD-FoG patients showed stable graph metrics. Stepwise prediction model including dyskinesia, postural instability and gait disorders scores and parietal clustering coefficient was the best predictor of FoG conversion. Combining clinical and MRI data, ROC curves provided the highest classification power to predict the conversion (AUC = 0.95, 95%CI: 0.86–1). Structural MRI is a useful tool to monitor PD progression, while functional MRI together with clinical features may be helpful to identify FoG conversion early.

Thrombus radiomics in patients with anterior circulation acute ischemic stroke undergoing endovascular treatment

BackgroundThrombus radiomics (TR) describe complex shape and textural thrombus imaging features. We aimed to study the relationship of TR extracted from non-contrast CT with procedural and functional outcome in endovascular-treated...

Myocardial extracellular volume assessed by cardiovascular magnetic resonance may predict adverse left ventricular remodeling in rheumatic heart disease after valvular surgery.

Only a few studies to date have focused on the application of cardiovascular magnetic resonance (CMR) in rheumatic heart disease (RHD); in particular, research on the application of T1-mapping CMR sequences is limited. This study aimed to investigate whether diffuse myocardial fibrosis evaluated using preoperative T1 mapping and extracellular volume (ECV) fraction measurement could predict the progression of adverse left ventricular remodeling (LVR) after surgery. A total of 32 adult patients with RHD and 30 healthy controls were recruited. Baseline clinical characteristics, CMR findings, and T1 mapping measurements were compared between the two groups. Transthoracic echocardiography measurements were collected before and after surgery. Patients with an increase in left ventricular end-diastolic volume of >15% or a decrease in left ventricular ejection fraction of >10% were classified into the adverse remodeling group; otherwise, patients were categorized into the non-adverse remodeling group. Compared with the healthy controls, patients with RHD had impaired biventricular function, enlarged ventricular volume, and increased native T1 and ECV values. Patients in the adverse remodeling group had higher ECV values than those in the non-adverse remodeling group (33.25%±3.67% vs. 28.45%±4.46%, P=0.002). Binary logistic regression analysis showed that the ECV value was associated with adverse LVR (odds ratio: 1.273, P=0.045). ECV was found to be a sensitive biomarker for predicting adverse LVR (area under the curve: 0.78; sensitivity: 75.0%; specificity: 77.3%). ECV has potential value for predicting the progression of adverse LVR and for identifying non-responders among patients with RHD undergoing surgery.

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study.

BackgroundRemote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools.ObjectiveBy applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month’s BG levels.MethodsIn this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association.ResultsAnalysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=–2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=–3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=–6.42, P<.001) and diastolic (t=–4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP.ConclusionsThe results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

Prostate Surgery for Men with Lower Urinary Tract Symptoms: Do We Need Urodynamics to Find the Right Candidates? Exploratory Findings from the UPSTREAM Trial

BackgroundIdentifying men whose lower urinary tract symptoms (LUTS) may benefit from surgery is challenging. ObjectiveTo identify routine diagnostic and urodynamic measures associated with treatment decision-making, and outcome, in exploratory analyses of the UPSTREAM trial. Design, setting, and participantsA randomised controlled trial was conducted including 820 men, considering surgery for LUTS, across 26 hospitals in England (ISCTRN56164274). InterventionMen were randomised to a routine care (RC) diagnostic pathway (n = 393) or a pathway that included urodynamics (UDS) in addition to RC (n = 427). Outcome measurements and statistical analysisMen underwent uroflowmetry and completed symptom questionnaires, at baseline and 18 mo after randomisation. Regression models identified baseline clinical and symptom measures that predicted recommendation for surgery and/or surgical outcome (measured by the International Prostate Symptom Score [IPSS]). We explored the association between UDS and surgical outcome in subgroups defined by routine measures. Results and limitationsThe recommendation for surgery could be predicted successfully in the RC and UDS groups (area under the receiver operating characteristic curve 0.78), with maximum flow rate (Qmax) and age predictors in both groups. Surgery was more beneficial in those with higher symptom scores (eg, IPSS >16), age <74 yr, Qmax <9.8 ml/s, bladder outlet obstruction index >47.6, and bladder contractility index >123.0. In the UDS group, urodynamic measures were more strongly predictive of surgical outcome for those with Qmax >15, although patient-reported outcomes were also more predictive in this subgroup. ConclusionsTreatment decisions were informed with UDS, when available, but without evidence of change in the decisions reached. Despite the small group sizes, exploratory analyses suggest that selective use of UDS could detect obstructive pathology, missed by routine measures, in certain subgroups. Patient summaryBaseline clinical and symptom measurements were able to predict treatment decisions. The addition of urodynamic test results, while useful, did not generally lead to better surgical decisions and outcomes over routine tests alone.

Bariatric Surgery Leads to a Reduction in Antibodies to Apolipoprotein A-1: a Prospective Cohort Study

PurposeAutoantibodies against apolipoprotein A-1 have been associated with cardiovascular disease, poorer CV outcomes and all-cause mortality in obese individuals. The impact of bariatric surgery (BS) on the presence of circulating anti-apoA-1 IgG antibodies is unknown. This study aimed to determine the effect of bariatric surgery on auto-antibodies titres against Apolipoprotein A-1 (anti-apoA-1 IgG), looking for changes associated with lipid parameters, insulin resistance, inflammatory profile and percentage of excess body mass index loss (%EBMIL).Materials and MethodsWe assessed 55 patients (40 women) before, 6 and 12 months post-operatively. Baseline and post-operative clinical history and measurements of body mass index (BMI), serum cholesterol, triglycerides, high- and low-density lipoprotein cholesterol (HDL-C and LDL-C), apoA-1, highly sensitive C-reactive protein (hsCRP), fasting glucose (FG), glycated haemoglobin (HbA1c) and HOMA-IR were taken at each point. Human anti-apoA-1 IgG were measured by ELISA.ResultsThe mean age of participants was 50 years. BS significantly improved BMI, %EBMIL triglycerides, HDL-C, apoA-1, hsCRP, HBA1c, FG and HOMA-IR. Baseline anti-apoA-1 IgG seropositivity was 25% and was associated with lower apoA-1 and higher hsCRP levels. One year after BS, anti-apoA-1 IgG seropositivity decreased to 15% (p = 0.007) and median anti-apoA-1 IgG values decreased from 0.70 (0.56–0.84) to 0.47 (0.37–0.61) AU (p < 0.001). Post-operative anti-apoA-1 IgG levels were significantly associated with a decreased post-surgical %EBMIL at 1 year.ConclusionBariatric surgery results in significant reduction in anti-apoA-1 IgG levels, which may adversely influence weight loss. The exact mechanisms underpinning these results are elusive and require further study before defining any clinical recommendations.Graphical abstract

Does adjunctive digital CBT for insomnia improve clinical outcomes in an improving access to psychological therapies service?

BackgroundInsomnia has a bidirectional relationship with broader mental health functioning, including anxiety and depression. Yet, poor sleep has historically been neglected as a specific treatment target in mental health programmes (Freeman, Sheaves, Waite, Harvey, & Harrison, 2020). MethodAll patients over a 12-month period entering the Improving Access to Psychological Therapies (IAPT) service endorsing a ‘poor sleep’ questionnaire item at assessment, were offered a self-guided digital sleep intervention, Sleepio, in addition to routine care. Sleepio is based on the principles of Cognitive Behavioural Therapy for Insomnia (CBT-I). Propensity score matching established a non-Sleepio control group matched on demographic and baseline clinical measures. ResultsPatients who signed up to Sleepio (n = 510) achieved significantly better outcomes on core clinical metrics (PHQ-9, GAD-7, WSAS) than controls. IAPT recovery rates11IAPT recovery rate is percentage of those above clinical cutoff on either PHQ-9 or GAD-7 at baseline who no longer meet this criterion at discharge. (on PHQ-9 and GAD-7) were 64.7%, versus 58% in the control group. Duration of clinical contact time was marginally elevated overall in the Sleepio group but by less than 1 h ConclusionsSignificant clinical benefit was associated with the introduction of an evidence-based digital sleep intervention alongside other mental health interventions for depression and anxiety. Widespread deployment was achieved with immediate availability, minimal additional clinical time or staff training. This approach provides a feasible and highly scalable model for improving mental health outcomes in clinical services.

473 Effectiveness of tailored peer-based sleep health education and social support in increasing home-based OSA screening among blacks

Abstract Introduction Our Sleep Disparity Workgroup has investigated several approaches to reducing sleep health disparities. In the METSO trial, we observed phone-delivered sleep education addressing impediments to OSA care among blacks was successful in increasing OSA evaluation. We found in the TASHE trial web-based sleep education significantly increased OSA self-efficacy among blacks. In the present RCT, we examined the role of congruent peer sleep educators and social support (PEERS-ED) in navigating blacks seeking OSA care. Methods In the two study arms RCT, we ascertained PEERS-ED’s effectiveness in increasing OSA screening among 317 blacks at OSA risk (intervention=159 and control=158); the average age was 47±12.9 years, 41% were male. OSA risk was assessed with the ARES questionnaire, administered in barbershops, and places of worship. Data also included assessment of beliefs and attitude (DBAS), apnea knowledge (AKT), apnea beliefs (ABS), readiness to change, anxiety, depression, and social support. Participants in the intervention arm received quality-controlled, culturally and linguistically tailored OSA education by trained PEERS during a 6-month period. The present analysis focused on the PEERS-ED effectiveness in increasing physician-recommended home-based OSA screening. Analysis also considered the role of psychosocial factors in adherence to OSA screening. Analyses were performed using the R-studio software. Results Results showed no significant differences in baseline demographic and clinical measures contrasting patients in the arms. The adherence rates for OSA screening between the intervention and control arms were 45.9% and 45.6%, respectively. The average DBAS and ABS scores were significantly greater among blacks who had home-based screening (DBAS: 6.0±1.8 vs. 4.9±2.2; p=0.024 and ABS: 77.0±7.1 vs 73.2±7.4; p=0.041). Other measures did not show significant differences between patients who had OSA screening versus those who did not. We observed those who screened were likely to experience greater level of social support (8.23±2.36 vs 7.31±2.35; p=0.063). Conclusion Our previous METSO trial demonstrated tailored OSA education is critical to increase adherence to recommended OSA care. While delivery of health information is generally associated with enhanced adherence to medical care, results of the present RCT favored an important role of peer-based social support leading to behavioral change towards receipt of OSA care. Support (if any) K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

Trajectories in early rheumatoid arthritis related fatigue over 10 years: results from the ESPOIR cohort.

In a cohort of early rheumatoid arthritis (RA) patients, we aimed to determine and characterise fatigue trajectories over 10 years of follow-up and identify predictors of trajectory membership. We selected patients fulfilling the 2010 ACR/EULAR criteria for RA included in the ESPOIR cohort. We used a cluster analysis to obtain fatigue (assessed by fatigue visual analogue scale) trajectories over the course of 10 years from enrolment. Chi-square tests or ANOVA were performed to evaluate differences of baseline variables between fatigue trajectories. Using a multinomial logistic regression we were able to identify predictors of trajectory membership. We analysed 598 patients with mean disease duration at enrolment of 26.2±40.9 days. Cluster analysis revealed 3 trajectories: high (18%), moderate (52%) and low fatigue (30%). Compared to patients with moderate or low fatigue trajectory, patients with high fatigue trajectory were predominantly women and reported significantly higher duration and intensity of morning stiffness, HAQ score, tender joints count, levels of pain, number of awakenings due to arthritis, frequency of fibromyalgic RA, levels of physician and patient global assessment, more frequent sleep problems, and increased psychological distress. Female patients with pain, psychological distress and presence of sicca symptoms had a higher risk of being in the high trajectory group. These findings suggest that levels of fatigue are rather stable over time in each trajectory. Baseline clinical measures and baseline patient-reported measures of functional status better distinguished the three fatigue trajectories. We did not find any differences between trajectories in baseline laboratory measures. Inflammatory activity was not a predictor of being in the high trajectory fatigue group.

Using Treatment Fidelity Measures to Understand Walking Recovery: A Secondary Analysis From the Community Ambulation Project.

Physical therapist intervention studies can be deemed ineffective when, in fact, they may not have been delivered as intended. Measurement of treatment fidelity (TF) can address this issue. The purpose of this study was to describe TF of a home-based intervention, identify factors associated with TF, and examine whether components of TF were associated with the outcome of change in 6-minute walk distance (∆6MWD). This is a secondary analysis of community-dwelling hip fracture participants who completed standard therapy and were randomly assigned to the active intervention (Push). Push was 16 weeks of lower extremity strengthening, function, and endurance training. TF was defined as delivery (attendance rate, exercise duration) and receipt (progression in training load, heart rate reserve [HRR] during endurance training, and exercise position [exercise on floor]). The outcome was ∆6MWD. Independent variables included baseline (demographic and clinical) measures. Descriptive statistics were calculated; linear and logistic regressions were performed. Eighty-nine participants were included in this analysis; 59 (66%) had attendance of 75% or greater. Participants walked for 20minutes or more for 78% of sessions. The average training load increased by 22%; the mean HRR was 35%; and 61 (69%) participants exercised on the floor for at least 75% of sessions. Regression analyses showed that a higher body mass index and greater baseline 6MWD were related to components of TF; 4 out of 5 components of TF were significantly related to ∆6MWD. The strongest TF relationship showed that those who exercised on the floor improved by 62 m (95% CI = 31-93 m) more than those who did not get on the floor. Measures of TF should extend beyond attendance rate. This analysis demonstrates how measures of TF, including program attendance, progression in training load, endurance duration, and exercising on the floor were significantly related to improvement in 6MWD in participants post hip fracture. This careful analysis of treatment fidelity assured that the intervention was delivered and received as intended. Analysis of data from a large trial with participants after hip fracture showed that regular attendance, frequent endurance training for 20minutes, increases in lower extremity training loads, and exercising on the floor were associated with improvements in the outcome of 6-minute-walk distance. The strongest association with improvement was exercising on the floor.

Clinical and inflammatory characteristics of patients with asthma in the Spanish MEGA project cohort.

IntroductionThe MEGA (MEchanism underlying the Genesis and evolution of Asthma) project is a multicenter cohort study carried out in eight Spanish hospitals, gathering clinical, physiological, and molecular data from patients with asthma and multimorbidities in order to gain insight into the different physiopathological mechanisms involved in this disorder.Material and MethodsWe report the baseline clinical and physiological characteristics and biomarker measures of adult participants in the project with the aim of better understanding the natural history and underlying mechanisms of asthma as well as the associated multimorbidities across different levels of severity. We carried out a detailed clinical examination, pulmonary function testing, measurement of fractional exhaled nitric oxide (FeNO), blood counts, induced sputum, skin prick tests, chest computed tomography scan, asthma questionnaires, and multimorbidity assessment in 512 asthmatic patients.ResultsWhen compared to patients with milder disease, severe asthmatic patients showed greater presence of symptoms, more exacerbations, lower asthma control, increased airflow obstruction, and higher frequency of chronic rhinosinusitis with nasal polyps, severe rhinitis, anxiety and depression, gastroesophageal reflux, and bronchiectasis.ConclusionThe MEGA project succeeded in recruiting a high number of asthma patients, especially those with severe disease, who showed lower control and higher frequency of multimorbidities.

Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study.

CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Subtyping CKD with multidimensional patient data holds the key to precision medicine. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes. We used unsupervised consensus clustering on 72 baseline characteristics among 2696 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) study to identify novel CKD subgroups that best represent the data pattern. Calculation of the standardized difference of each parameter used the cutoff of ±0.3 to show subgroup features. CKD subgroup associations were examined with the clinical end points of kidney failure, the composite outcome of cardiovascular diseases, and death. The algorithm revealed three unique CKD subgroups that best represented patients' baseline characteristics. Patients with relatively favorable levels of bone density and cardiac and kidney function markers, with lower prevalence of diabetes and obesity, and who used fewer medications formed cluster 1 (n=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (n=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (n=395). These three subgroups, when linked with future clinical end points, were associated with different risks of CKD progression, cardiovascular disease, and death. Furthermore, patient heterogeneity among predefined subgroups with similar baseline kidney function emerged. Consensus clustering synthesized the patterns of baseline clinical and laboratory measures and revealed distinct CKD subgroups, which were associated with markedly different risks of important clinical outcomes. Further examination of patient subgroups and associated biomarkers may provide next steps toward precision medicine.