Management of early pregnancy loss with mifepristone and misoprostol: clinical predictors of treatment success from a randomized trial.

BackgroundEarly pregnancy loss is a common event in the first trimester, occurring in 15%-20% of confirmed pregnancies. A common evidence-based medical regimen for early pregnancy loss uses misoprostol, a prostaglandin E1 analog, with a dosage of 800 μg, self-administered vaginally. The clinical utility of this regimen is limited by suboptimal effectiveness in patients with a closed cervical os, with 29% of patients experiencing early pregnancy loss requiring a second dose after 3 days and 16% of patients eventually requiring a uterine aspiration procedure.ObjectiveThis study aimed to evaluate clinical predictors associated with treatment success in patients receiving medical management with mifepristone-misoprostol or misoprostol alone for early pregnancy loss.Study designWe performed a planned secondary analysis of a randomized trial comparing mifepristone-misoprostol with misoprostol alone for management of early pregnancy loss. The published prediction model for treatment success of single-dose misoprostol administered vaginally included the following variables: active bleeding, type of early pregnancy loss (anembryonic pregnancy or embryonic and/or fetal demise), parity, gestational age, and treatment site; previous significant predictors were vaginal bleeding within the past 24 hours and parity of 0 or 1 vs >1. To determine if these characteristics predicted differential proportions of patients with treatment success or failure, we performed bivariate analyses; given the small proportion of treatment failures in the combined treatment arm, both arms were combined for analysis. Thereafter, we performed a logistic regression analysis to assess the effect of these predictors collectively in each of the 2 treatment groups separately as well as in the full cohort as a proxy for the combined treatment arm. Finally, by using receiver operating characteristic curves, we tested the ability of these predictors in association with misoprostol treatment success to discriminate between treatment success and treatment failure. To quantify the ability of the score to discriminate between treatment success and treatment failure in each treatment arm as well as in the entire cohort, we calculated the area under the curve. Using multivariable logistic regression, we then assessed our study population for other predictors of treatment success in both treatment groups, with and without mifepristone pretreatment.ResultsOverall, 297 evaluable participants were included in the primary study, with 148 in the mifepristone-misoprostol combined treatment group and 149 in the misoprostol-alone treatment group. Among patients who had vaginal bleeding at the time of treatment, 15 of 17 (88%) in the mifepristone-misoprostol combined treatment group and 12 of 17 (71%) in the misoprostol-alone treatment group experienced expulsion of pregnancy tissue. Among patients with a parity of 0 or 1, 94 of 108 (87%) in the mifepristone-misoprostol treatment group and 66 of 95 (69%) in the misoprostol-alone treatment group experienced expulsion of pregnancy tissue. These clinical characteristics did not predict treatment success in the combined cohort alone (area under the curve=0.56; 95% confidence interval, 0.48-0.64). No other baseline clinical factors predicted treatment success in the misoprostol-alone treatment arm or mifepristone pretreatment arm. In the full cohort, the significant predictors of treatment success were pretreatment with mifepristone (adjusted odds ratio=2.51; 95% confidence interval, 1.43-4.43) and smoking (adjusted odds ratio=2.15; 95% confidence interval, 1.03-4.49).ConclusionNo baseline clinical factors predicted treatment success in women receiving medical management with misoprostol for early pregnancy loss. Adding mifepristone to the medical management regimen of early pregnancy loss improved treatment success; thus, mifepristone treatment should be considered for management of early pregnancy loss regardless of baseline clinical factors

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value OBFC1indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes

Oral abstracts 3: RA Treatment and outcomesO13. Validation of jadas in all subtypes of juvenile idiopathic arthritis in a clinical setting

Background: Juvenile Arthritis Disease Activity Score (JADAS) is a 4 variable composite disease activity (DA) score for JIA (including active 10, 27 or 71 joint count (AJC), physician global (PGA), parent/child global (PGE) and ESR). The validity of JADAS for all ILAR subtypes in the routine clinical setting is unknown. We investigated the construct validity of JADAS in the clinical setting in all subtypes of JIA through application to a prospective inception cohort of UK children presenting with new onset inflammatory arthritis. Methods: JADAS 10, 27 and 71 were determined for all children in the Childhood Arthritis Prospective Study (CAPS) with complete data available at baseline. Correlation of JADAS 10, 27 and 71 with single DA markers was determined for all subtypes. All correlations were calculated using Spearman's rank statistic. Results: 262/1238 visits had sufficient data for calculation of JADAS (1028 (83%) AJC, 744 (60%) PGA, 843 (68%) PGE and 459 (37%) ESR). Median age at disease onset was 6.0 years (IQR 2.6-10.4) and 64% were female. Correlation between JADAS 10, 27 and 71 approached 1 for all subtypes. Median JADAS 71 was 5.3 (IQR 2.2-10.1) with a significant difference between median JADAS scores between subtypes (p < 0.01). Correlation of JADAS 71 with each single marker of DA was moderate to high in the total cohort (see Table 1). Overall, correlation with AJC, PGA and PGE was moderate to high and correlation with ESR, limited JC, parental pain and CHAQ was low to moderate in the individual subtypes. Correlation coefficients in the extended oligoarticular, rheumatoid factor negative and enthesitis related subtypes were interpreted with caution in view of low numbers. Conclusions: This study adds to the body of evidence supporting the construct validity of JADAS. JADAS correlates with other measures of DA in all ILAR subtypes in the routine clinical setting. Given the high frequency of missing ESR data, it would be useful to assess the validity of JADAS without inclusion of the ESR. Disclosure statement: All authors have declared no conflicts of interest. Table 1Spearman's correlation between JADAS 71 and single markers DA by ILAR subtype ILAR Subtype Systemic onset JIA Persistent oligo JIA Extended oligo JIA Rheumatoid factor neg JIA Rheumatoid factor pos JIA Enthesitis related JIA Psoriatic JIA Undifferentiated JIA Unknown subtype Total cohort Number of children 23 111 12 57 7 9 19 7 17 262 AJC 0.54 0.67 0.53 0.75 0.53 0.34 0.59 0.81 0.37 0.59 PGA 0.63 0.69 0.25 0.73 0.14 0.05 0.50 0.83 0.56 0.64 PGE 0.51 0.68 0.83 0.61 0.41 0.69 0.71 0.9 0.48 0.61 ESR 0.28 0.31 0.35 0.4 0.6 0.85 0.43 0.7 0.5 0.53 Limited 71 JC 0.29 0.51 0.23 0.37 0.14 -0.12 0.4 0.81 0.45 0.41 Parental pain 0.23 0.62 0.03 0.57 0.41 0.69 0.7 0.79 0.42 0.53 Childhood health assessment questionnaire 0.25 0.57 -0.07 0.36 -0.47 0.84 0.37 0.8 0.66 0.4

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Management of early pregnancy loss with mifepristone and misoprostol: clinical predictors of treatment success from a randomized trial.

BackgroundEarly pregnancy loss is a common event in the first trimester, occurring in 15%-20% of confirmed pregnancies. A common evidence-based medical regimen for early pregnancy loss uses misoprostol, a prostaglandin E1 analog, with a dosage of 800 μg, self-administered vaginally. The clinical utility of this regimen is limited by suboptimal effectiveness in patients with a closed cervical os, with 29% of patients experiencing early pregnancy loss requiring a second dose after 3 days and 16% of patients eventually requiring a uterine aspiration procedure.ObjectiveThis study aimed to evaluate clinical predictors associated with treatment success in patients receiving medical management with mifepristone-misoprostol or misoprostol alone for early pregnancy loss.Study designWe performed a planned secondary analysis of a randomized trial comparing mifepristone-misoprostol with misoprostol alone for management of early pregnancy loss. The published prediction model for treatment success of single-dose misoprostol administered vaginally included the following variables: active bleeding, type of early pregnancy loss (anembryonic pregnancy or embryonic and/or fetal demise), parity, gestational age, and treatment site; previous significant predictors were vaginal bleeding within the past 24 hours and parity of 0 or 1 vs &gt;1. To determine if these characteristics predicted differential proportions of patients with treatment success or failure, we performed bivariate analyses; given the small proportion of treatment failures in the combined treatment arm, both arms were combined for analysis. Thereafter, we performed a logistic regression analysis to assess the effect of these predictors collectively in each of the 2 treatment groups separately as well as in the full cohort as a proxy for the combined treatment arm. Finally, by using receiver operating characteristic curves, we tested the ability of these predictors in association with misoprostol treatment success to discriminate between treatment success and treatment failure. To quantify the ability of the score to discriminate between treatment success and treatment failure in each treatment arm as well as in the entire cohort, we calculated the area under the curve. Using multivariable logistic regression, we then assessed our study population for other predictors of treatment success in both treatment groups, with and without mifepristone pretreatment.ResultsOverall, 297 evaluable participants were included in the primary study, with 148 in the mifepristone-misoprostol combined treatment group and 149 in the misoprostol-alone treatment group. Among patients who had vaginal bleeding at the time of treatment, 15 of 17 (88%) in the mifepristone-misoprostol combined treatment group and 12 of 17 (71%) in the misoprostol-alone treatment group experienced expulsion of pregnancy tissue. Among patients with a parity of 0 or 1, 94 of 108 (87%) in the mifepristone-misoprostol treatment group and 66 of 95 (69%) in the misoprostol-alone treatment group experienced expulsion of pregnancy tissue. These clinical characteristics did not predict treatment success in the combined cohort alone (area under the curve=0.56; 95% confidence interval, 0.48-0.64). No other baseline clinical factors predicted treatment success in the misoprostol-alone treatment arm or mifepristone pretreatment arm. In the full cohort, the significant predictors of treatment success were pretreatment with mifepristone (adjusted odds ratio=2.51; 95% confidence interval, 1.43-4.43) and smoking (adjusted odds ratio=2.15; 95% confidence interval, 1.03-4.49).ConclusionNo baseline clinical factors predicted treatment success in women receiving medical management with misoprostol for early pregnancy loss. Adding mifepristone to the medical management regimen of early pregnancy loss improved treatment success; thus, mifepristone treatment should be considered for management of early pregnancy loss regardless of baseline clinical factors

Targeted intervention for patellofemoral pain (TIPPS): psychosocial characteristics of clinical subgroups

Background: Patellofemoral pain (PFP) is a specific musculoskeletal disorder that causes significant pain and dysfunction around the knee cap and knee joint leading to long term limitation in societal participation and physical activity. PFP is a condition commonly referred for physiotherapy intervention, however long term outcomes are poor. We have undertaken a study to explore the presence of clinical subgroups within the PFP population based on the scores of 6 tests that could be used within routine clinical practice. Our research study identified 3 clinical subgroups: Strong subgroup; Weak and Tight subgroup; Pronated feet subgroup. Purpose: There is mounting evidence that factors such as gender and activity levels influence development and outcome in PFP. There is also interest in whether psychosocial factors and different pain mechanisms play a role in determining potential subgroups. Therefore the main objective of this study was to explore interactions between demographic, clinical and psychosocial characteristics and clinical subgroups. Methods: Observational study, at one time point (commencement of physiotherapy). Demographic (Age; Body Mass Index (BMI)), clinical (Time since onset; Patellar skin fold; Tibialis anterior/patella temperature index; Cold Knees questions), and psychosocial characteristics (numeric pain rating scale (NPRS); International physical activity questionnaire (IPAQ); Modified functional index questionnaire (MFIQ); Short Leeds assessment neuropathic sign and symptoms (SLANSS); EQ-5D-5L; Hopkins symptoms check list (HSCL); Movement specific reinvestment scale (MSRS)) were assessed. Differences between the three clinical subgroups were explored using ANOVA, with post hoc tests, or chi-squared tests. Results: Of 127 participants, 66% were female and the mean age was 26 years (SD 5.7), BMI 25.5 (SD 5.8), time since onset 45 months (SD 55) and MFIQ was 34 (SD 17). The strong subgroup had significant higher function, quality of life and there were proportionally more males in this subgroup. The weak and tight subgroup significantly had the highest BMI, and there was a trend towards the lowest levels of activity. The pronated feet subgroup was significantly younger at time of first assessment and there was a trend for higher mean scores for neuropathic pain (SLANSS). There was no difference in mean scores of other characteristics across the subgroups. Conclusion(s): This study, the largest on PFP subgrouping, provides data on other characteristics of PFP patients and suggests significant differences in non-clinical factors between clinical subgroups. The latter provides support for the existence of clinical subgroups and possible insight into potential intervention strategies. For example, higher function and quality of life in the strong subgroup may suggest a focus on movement control is required whereas the higher BMI in the weak and tight subgroup may suggest adjunct weight management strategies may be needed. The neuropathic pain in the pronated feet subgroup may warrant desensitisation interventions. Implications: Demographic and psychosocial characteristics may be important factors to consider in addition to clinical factors in PFP patients. Such factors are increasingly important in managing patients with other musculoskeletal problems. The next step is to understand the relationship between these factors and treatment outcome. Keywords: Patellofemoral pain; Psychosocial; Subgroupin

Targeted intervention for patellofemoral pain (TIPPS): identifying potential clinical subgroups

Background: Patellofemoral pain (PFP) is a specific musculoskeletal disorder that causes significant pain and dysfunction around the knee cap and knee joint, which can lead to long term limitation in societal participation and physical activity. There is a consensus that conservative intervention should be the first treatment option so PFP is a condition that is commonly referred for physiotherapy. Unfortunately, however, current multi modal approaches to intervention in PFP are failing in the long term. Purpose: Given the poor outcome of current treatment regimens, identification of patellofemoral subgroups is an international priority. Large individual variation in outcome results is consistently reported in response to conservative management, therefore understanding whether subgroups have different outcomes has become imperative. However it is unknown whether subgrouping is possible in PFP. The main purpose of this study was to describe the distribution of PFP patients into different subgroups using six routine clinical assessment test criteria. Methods: Design: Cross-sectional with single point assessment prior to commencement of physiotherapy. Participants with PFP underwent clinical assessment of muscle strength (quadriceps and hip abductor), muscle length (quadriceps, hamstrings, gastrocnemius), patellar mobility and foot posture. The presence of subgroups was explored using two classification techniques: Hierarchical Clustering and Latent Profile Analysis. Results: One hundred and twenty seven of 130 recruited participants had complete assessment data: 66% were female, mean (SD) age was 26 (5.7) years, BMI 25.5 (5.8), and Modified Functional Index Questionnaire (MFIQ) was 34 (17). A three subgroup solution appeared optimal from a modelling and clinical perspective. The three suggested clinical subgroups were characterised as: Strong subgroup (n = 44), weak and tight subgroup (n = 58), pronated feet subgroup (n = 25). An ANOVA and post hoc tests confirmed significant differences between subgroups. Strong subgroup: quadriceps strength 1.5 Nm/kg (0.51), hip abductor strength 1.5 Nm/kg (0.51), lowest patellar mobility of the three groups 9.6 mm (3.43). Weak and tight subgroup: hip abductor strength 0.7 Nm/kg (0.27), shortest quadriceps length 118° (17.9). Pronated feet subgroup: highest FPI 7 (3.0), highest patellar mobility 17.6 mm (4.75). Conclusion(s): This study is the largest conducted to date on subgrouping of PFP. The results lend support to the idea that clinical subgroups exist within the patellofemoral population, with three potential subgroups emerging. The key clinical tests for identifying the three subgroups are strength measurement of the quadriceps and hip abductors, and foot posture index. Patellar mobility and quadriceps flexibility contributed to subgroup profiles, so also appear important to consider when identifying PFP subgroups. Implications: These results fit well with other findings in musculoskeletal research where subgrouping has been successful in informing targeted intervention in low back pain patients and is being increasingly adopted as a strategy for managing patients with shoulder problems. Our next step is explore variation in outcome across the identified PFP subgroups and whether subgroup targeted intervention improves overall patient outcomes

Normal Tissue Integral Dose as a Result of Prostate Radiation Therapy: A Quantitative Comparison Between High-Dose-Rate Brachytherapy and Modern External Beam Radiation Therapy Techniques

Purpose: Quantification of integral radiation dose delivered during treatment for prostate cancer is lacking. We performed a comparative quantification of dose to nontarget body tissues delivered via 4 common radiation techniques: conventional volumetric modulated arc therapy, stereotactic body radiation therapy, pencil-beam scanning proton therapy, and high-dose-rate brachytherapy. Methods and Materials: Plans for each radiation technique were generated for 10 patients with typical anatomy. For brachytherapy plans, virtual needles were placed to achieve standard dosimetry. Standard planning target volume margins or robustness margins were applied as appropriate. A “normal tissue” structure (entire computed tomography simulation volume minus planning target volume) was generated for integral dose computation. Dose-volume histogram parameters for targets and normal structures were tabulated. Normal tissue integral dose was calculated by multiplying normal tissue volume by mean dose. Results: Normal tissue integral dose was lowest for brachytherapy. Pencil-beam scanning protons, stereotactic body radiation therapy, and brachytherapy resulted in 17%, 57%, and 91% absolute reductions compared with standard volumetric modulated arc therapy, respectively. Mean nontarget tissues receiving 25%, 50%, and 75% of the prescription dose were reduced by 85%, 76%, and 83% for brachytherapy relative to volumetric modulated arc therapy, by 79%, 64%, and 74% relative to stereotactic body radiation therapy, and 73%, 60%, and 81% relative to proton therapy. All reductions observed using brachytherapy were statistically significant. Conclusions: High-dose-rate brachytherapy is an effective technique for reducing dose to nontarget body tissues relative to volumetric modulated arc therapy, stereotactic body radiation therapy, and pencil-beam scanning proton therapy