Analysis Of Clinical Trial Data Research Articles

Association between white blood cell count and coronary artery bypass graft failure: an individual patient data analysis of clinical trials

BackgroundBaseline systemic inflammation is associated with worse long-term outcomes after coronary artery bypass grafting [CABG], but the mechanisms of this association are unclear. This study aims to explore the association between pre-operative white blood cell [WBC] count and CABG graft failure.MethodsWe pooled individual patient data from two randomized clinical trials with systematic CABG graft imaging. The primary analysis was the association between pre-operative WBC count and graft failure, as a continuous variable, at the time of imaging after CABG, using mixed-effects multivariable logistic regression models.ResultsOverall, 910 patients and 2,036 grafts were included in the analysis [1,120 saphenous vein grafts, 828 left internal thoracic arteries, 76 right internal thoracic arteries, and 12 radial arteries]. The median time to imaging was 1.01 [interquartile range (IQR), 0.99;1.03] years and the median pre-operative WBC count was 7.1 [IQR, 6.0;8.4] x 109/L. There was no association between WBC count and graft failure at both the patient and the individual graft level [adjusted odds ratio (aOR) 1.07 (95% confidence interval (CI), 0.98;1.17), p = 0.11 and aOR 1.09 (95% CI, 0.91;1.30), p = 0.37], respectively. When evaluated as a dichotomous variable [≥ 11 vs. < 11 × 109/L] and by quartile, WBC count was not associated with graft failure at the patient and individual graft levels.ConclusionIn this pooled analysis of individual patient data from two randomized clinical trials, WBC count was not associated with graft failure after CABG. The reported association between inflammation and CABG is likely mediated through other mechanisms, such as native coronary artery disease progression.Impact on daily practiceThe lack of a clear association between WBC count and graft failure suggests that pre-operative WBC count should not be routinely used as a predictor of graft failure after CABG.

Statistical analysis of clinical trial data in cancer research

Statistical methods are fundamental to designing and executing cancer clinical trials, which present unique challenges such as patient diversity, incomplete data, and ethical considerations. This article underscores the pivotal role of advanced statistical techniques in ensuring reliable outcomes, with a focus on sample size estimation, randomization, and endpoint selection. Key methodologies discussed include Cox regression, Bayesian modeling, and machine learning applications for predictive analytics and real-time data processing. Practical solutions to challenges like treatment effect assessment, bias reduction, and ethical compliance are highlighted through real-world examples. The paper concludes by envisioning future innovations that enhance accuracy, efficiency, and accessibility in cancer research. By integrating rigorous statistical methods with clinical relevance, this work aims to propel oncology into a new era of precision and patient-centered care.

Gastrointestinal symptoms in patients receiving imeglimin in combination with metformin: A post-hoc analysis of imeglimin clinical trial data.

An increased rate of gastrointestinal (GI) symptoms is reported in patients with type 2 diabetes receiving imeglimin plus metformin vs monotherapy or in combination with other antidiabetic drugs. This post-hoc analysis explored GI symptom incidence, risk factors for their occurrence, and the impact on therapeutic efficacy during imeglimin and metformin combination therapy. Data were derived from the 52-week, open-label, phase 3 TIMES-2 trial in Japanese type 2 diabetes patients. Patients in the imeglimin plus metformin group were divided into two subgroups based on the presence of GI symptoms and diarrhea, with efficacy and safety assessed. Factors associated with their occurrence were explored using multivariate logistic regression analysis. Of 64 patients analyzed, GI symptoms and diarrhea occurred in 40.6% (n = 26) and 17.2% (n = 11) of patients, respectively. Metformin dose and patient age did not significantly affect their incidence. Events occurred more frequently within the first 4 months of treatment. Approximately half resolved within 1 week, and most were mild. Type 2 diabetes duration <5 years was significantly associated with diarrhea (odds ratio = 5.979; P = 0.039). Significant hypoglycemic effects were observed from baseline, irrespective of GI symptoms or diarrhea. However, the degree of HbA1c improvement tended to be greater in patients with GI symptoms and diarrhea. Increased awareness regarding the potential for GI symptoms, including diarrhea, during imeglimin plus metformin combination therapy is warranted. This data will provide clinicians with useful information regarding symptomatic treatment when it occurs and help determine whether to continue treatment administration and is expected to improve patient adherence.

Interplay between on-demand treatment trials for hereditary angioedema and treatment guidelines.

Over the past two decades, guidelines for the on-demand treatment of hereditary angioedema (HAE) attacks have undergone significant evolution. Early treatment guidelines, such as the Canadian 2003 International Consensus Algorithm, often gated on-demand treatment by attack location and/or severity. Pivotal trials for on-demand injectable treatments (plasma-derived C1 esterase inhibitor [C1INH], icatibant, ecallantide [US only], recombinant C1INH), which were approved in the US and EU between 2008-2014, were designed accordingly. Subsequent post hoc analyses of clinical trial data alongside real-world evidence led to a paradigm shift. In 2013, the US HAE Association guidelines recommended that all attacks, irrespective of location or severity, be considered for treatment as early as possible after onset to minimize morbidity and mortality. This approach remains the cornerstone of current treatment guidelines and has shaped the design of recent clinical trials, such as those for the investigational agents, oral plasma kallikrein inhibitor sebetralstat and oral bradykinin B2 receptor antagonist deucrictibant. This narrative review discusses the evolution of on-demand treatment guidelines, the clinical trial and real-world data that prompted significant revisions, and the subsequent changes to trial designs introduced to facilitate guideline compliance.

Cardio-Renal Syndrome: Latest Developments in Device-Based Therapy.

Background: Cardio-renal syndrome (CRS) is a complex condition involving bidirectional dysfunction of the heart and kidneys, in which the failure of one organ exacerbates failure in the other. Traditional pharmacologic treatments are often insufficient to manage the hemodynamic and neurohormonal abnormalities underlying CRS, especially in cases resistant to standard therapies. Device-based therapies have emerged as a promising adjunct or alternative approach, offering targeted intervention to relieve congestion, improve renal perfusion, and modulate hemodynamics. This study aimed to evaluate the efficacy and safety of various device-based therapies in CRS management, utilizing DRI2P2S classification to categorize interventions as dilators, reducers, interstitial modulators, pullers, pushers, and shifters. Methods: A comprehensive analysis of clinical trial data and observational studies involving device-based therapies in patients with CRS was conducted, with a focus on hemodynamic endpoints, renal and cardiac function, symptom relief, and adverse events. Devices included in the analysis were splanchnic denervation systems (dilators), devices for central and pulmonary pressure reduction (reducers), and systems targeting interstitial fluid (fluid shifters), among others. A systematic literature review from 2004 to 2024 was performed using databases including PubMed, Embase, and ClinicalTrials.gov, following PRISMA guidelines for study selection. Data were extracted on patient demographics, device type, trial design, outcomes, and follow-up duration. Results: Device-based therapies demonstrated varying levels of efficacy in CRS, with significant improvements observed in specific parameters. Notable results were a reduction in central venous pressure and improved diuretic responsiveness in acute CRS cases, while also stabilizing or improving renal function. Other relevant endpoints were fewer heart failure hospitalizations and a reduction in renal adverse events, reduced tissue congestion and improved quality of life scores. However, some devices presented challenges, including procedure-related complications and a learning curve for optimal device implantation. Conclusions: Device-based therapies offer a valuable addition to the CRS treatment paradigm, particularly in cases unresponsive to conventional diuretics and other pharmacologic measures. Each of them addresses specific pathophysiological components of CRS and shows promise in improving clinical outcomes. Nevertheless, further large-scale, long-term trials with comprehensive endpoints are needed to establish these therapies' roles in standard care and to optimize patient selection criteria. Enhanced understanding of device mechanisms and refinement of trial endpoints will be key to maximizing the impact of these therapies on quality of life and clinical outcomes for CRS patients.

Development and internal validation of prognostic models for event-free survival in newly diagnosed Ewing sarcoma patients based on routinely collected clinical characteristics from the European randomised controlled trial, EE2012

IntroductionEwing sarcoma is a rare paediatric cancer. Currently, there is no way of accurately predicting these patients’ survival at diagnosis. Disease type (ie, localised disease, lung/pleuropulmonary metastases and other metastases)...

Insomnia treatment effects on negative emotionality among veterans in treatment for alcohol use disorder.

Insomnia symptoms are pervasive and persistent in alcohol use disorder (AUD), though little is known about the mechanisms that underlie this association. We previously found that cognitive behavioral therapy for insomnia (CBT-I) reduced alcohol-related problems among veterans by improving insomnia severity (NCT03806491). In this planned secondary analysis of the same clinical trial data, we tested negative emotionality as one potential mechanism to explain this effect. Specifically, we tested the change in negative emotionality as a mediator of the association between change in insomnia symptoms and alcohol-related outcomes (craving, heavy drinking frequency, and alcohol-related problems). Participants were 67 veterans in treatment for AUD who also met the criteria for insomnia disorder (91% male, 84% White, average age = 46.3 years). Participants were randomized to five sessions of CBT-I or a single-session sleep hygiene control. Assessments occurred at baseline, immediately posttreatment (~6 weeks after baseline), and at 6-week follow-up. Measures included the Insomnia Severity Index, Penn Alcohol Craving Scale, Timeline Followback, and Short Inventory of Problems. We created a latent negative emotionality indicator based on five validated and reliable measures of negative emotionality. Contrary to hypotheses, CBT-I did not improve negative emotionality relative to sleep hygiene control. However, across both treatment conditions, decreases in insomnia symptoms from baseline to posttreatment were associated with concurrent decreases in negative emotionality, which in turn predicted reductions in alcohol craving and heavy drinking. Negative emotionality may help explain links between insomnia symptoms and alcohol-related outcomes.

Improvement in coping skills from culturally-adapted digital CBT for Spanish-speaking Hispanics with substance use disorder: Secondary analysis of a randomized clinical trial

IntroductionDeveloping adaptive coping skills for avoiding substance use is a proposed treatment mechanism of cognitive behavioral therapy (CBT) for substance use disorder (SUD). However, the generalizability of research on treatment mechanisms of CBT for SUD is limited by the underrepresentation of racial/ethnic minorities in clinical trials. In a secondary analysis of clinical trial data, we tested whether a culturally-adapted digital CBT program for Hispanics (“Spanish CBT4CBT”) improved the quality of coping skills for avoiding substance use. We also tested whether coping skills' quality was associated with reductions in primary substance use. MethodsParticipants were Spanish-speaking Hispanic adults seeking outpatient treatment for SUD (n = 85; 68 % male; primary substance type: 36 % cannabis, 33 % alcohol, 26 % cocaine, 5 % other). They were randomized to 8 weeks of outpatient treatment as usual (TAU) or TAU + Spanish CBT4CBT and assessed for 6 months after treatment. The study conducted separate analyses for the full sample (n = 85) and for those who engaged in at least 5 treatment sessions (“treatment exposed”; n = 64). Daily substance use and coping skills' quality were assessed repeatedly during the treatment and follow-up periods. Bayesian mixed models for repeated measures tested hypotheses. ResultsAmong treatment-exposed participants, those receiving TAU + Spanish CBT4CBT improved the quality of coping skills more than TAU alone during the treatment period (b = 0.77; 95 % CI[0.08, 1.47]), but this difference was not detected during the follow-up period. In the full sample and treatment exposed subsample, participants with higher quality coping skills during the study reported less primary substance use (b = −0.67; 95 % CI[−1.08, −0.26]). Among treatment-exposed participants only, within-person increases in the quality of coping skills were associated with reductions in future primary substance use (b = −0.18; 95 % CI[−0.36, −0.01]). ConclusionsSpanish-speaking Hispanics with SUD may improve the quality of their coping skills more when they are sufficiently exposed to a culturally-adapted digital CBT program during outpatient treatment. Coping skills' quality may be a mechanism of CBT for SUD among Hispanic populations. Spanish-speaking Hispanics' access to treatments that target mechanisms of behavior change may be expanded by digital therapeutics.

Streamlining Clinical Trial Data from Raw to Regulatory Submission with R Shiny and Pharmaverse

In recent years, the pharmaceutical and biotech industries have increasingly adopted open-source tools to enhance the efficiency, transparency, and reproducibility of clinical trial data analysis. R Shiny, a web application framework for R, has become a key component in creating interactive dashboards and visualizations that support real-time data exploration and decision-making. As part of the Pharmaverse ecosystem, R Shiny plays a vital role in regulatory submissions, data analysis, and reporting, offering dynamic, user-friendly interfaces for clinical trial monitoring, patient safety reporting, and efficacy analysis. This abstract explores the application of R Shiny within the Pharmaverse, emphasizing its seamless integration with tools such as Tplyr and admiral for generating CDISC-compliant SDTM and ADaM datasets. Metadata management and validation are facilitated through OAK, ensuring consistency and traceability. The automated creation of Define.xml using tools like defineR further enhances transparency and regulatory compliance. Through case studies and practical examples, this abstract highlights how R Shiny and related tools improve data transparency, accelerate decision-making, and support regulatory compliance in clinical trials. By leveraging these open-source technologies, clinical trial teams can streamline data workflows, enhance collaboration, and improve outcomes, ultimately driving innovation within the pharmaceutical industry. The integration of these tools into a cohesive framework significantly enhances the submission process to regulatory bodies like the FDA and EMA, ensuring that clinical data meets both industry and regulatory standards.

A multiparametric heart failure score at baseline is associated with long-term outcome in patients with remotely monitored implantable cardioverter-defibrillators: A pooled analysis of 9 clinical trials

BackgroundTo predict worsening heart failure hospitalizations (WHFHs), the HeartInsight multiparametric algorithm calculates a heart failure (HF) Score based on temporal trends of physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators (ICDs). ObjectiveWe studied the association of the baseline HF Score, determined at algorithm activation, with long-term patient outcomes. MethodsData from 9 clinical trials were pooled, including 1841 ICD patients with a preimplantation ejection fraction ≤35%, New York Heart Association class II/III, and no long-standing atrial fibrillation. The primary end point was a composite of death or WHFH. ResultsAfter a median follow-up of 631 days (interquartile range, 385–865 days), there were 243 WHFHs in 173 patients (9.4%) and 122 deaths (6.6%), 52 of which (42.6%) were cardiovascular. The primary end point occurred in 265 patients (14.4%). A multivariable time-to-first-event analysis showed that a high baseline HF Score (>23, as determined by a time-dependent receiver operating characteristics curve analysis) was significantly associated with the occurrence of the primary end point (adjusted hazard ratio [HR], 2.05; 95% confidence interval [CI], 1.54–2.71; P < .0001), all-cause death (HR, 2.37; CI, 1.56–3.58; P < .0001), cardiovascular death (HR, 2.19; CI, 1.14–4.22; P = .019), and WHFH (HR, 1.91; CI, 1.35–2.71; P = .0003). In a hierarchical event analysis of all-cause death as the outcome with highest priority and WHFHs as repeated event outcomes, the win ratio was 2.47 (CI, 1.89–3.24; P < .0001). ConclusionBased on a retrospective analysis of clinical trial data with adjudicated events, baseline HF Score derived from device-monitored variables was able to stratify patients at higher long-term risk of death or WHFH.

Aripiprazole Once-Monthly for the Treatment of Adult Patients in the Earlier Stages of Bipolar I Disorder: A Post Hoc Analysis of Clinical Trial Data

Aripiprazole Once-Monthly for the Treatment of Adult Patients in the Earlier Stages of Bipolar I Disorder: A Post Hoc Analysis of Clinical Trial Data

An Eye on Extracellular Vesicles: Trends and Clinical Translations in Vision Research

PurposeTo perform a review of research, funding, and clinical translation efforts for extracellular vesicles (EVs) within vision science. DesignRetrospective analysis of publication, funding, and clinical trials data. MethodsA pre-trained large language model (Jina2) was used to create semantic embeddings for 41,282 abstracts from articles related to extracellular vesicles archived on EMBASE and published between January 1966 and January 2024. The articles were projected and clustered according to semantic embedding similarity, and research subdomains for extracellular vesicles were determined through inspection of tf-idf weighted word clouds. Mann-Kendall trend analysis was performed to identify current areas of growth within extracellular vesicle research. Additionally, NIH funding data from RePORTER and clinical trials data from ClinicalTrials.gov were analyzed to correlate publication trends with funding support and clinical translation efforts. ResultsUnsupervised clustering and Mann-Kendall trend analysis identified wound healing/regeneration (p=0.030) and neurodegenerative disease (p=0.049) as significantly accelerating in growth of publication over time. Ophthalmology-restricted subset analysis identified that publications in age-related macular degeneration (p=0.191) and clinical applications (p=0.086) are no longer growing at a significant rate. Analysis of funding data identified that the NCI was the top funding institution overall, but that the NIA is rapidly advancing in terms of funding extracellular vesicle research and trials. Analysis of ClinicalTrials.gov data highlights a dearth of clinical trials within ophthalmology despite a growing number of studies in other medical subfields. ConclusionsExtracellular vesicles (EVs) remain a promising substrate for both the identification and treatment of vision-threatening diseases. A better understanding of the current landscape of research and funding trends should help to inform future funding and translational efforts.

Estimand Framework and Statistical Considerations for Integrated Analysis of Clinical Trial Safety Data.

Safety analyses play a pivotal role in drug development, ensuring the protection of patients while advancing innovative pharmaceuticals to market. A single study generally does not have sufficient sample size to evaluate all important safety events with reasonable precision and may not cover the full target population for the investigational treatment. Integrated analyses (pooled or meta-analysis) over several studies may be helpful in that regard. But without a structured conscious workflow accompanied with appropriate statistical methods for the integrated analysis, this can easily take a route compromising the interpretation. In this article we apply the ICH estimand framework to clinical trial integration and summarize respective critical statistical assumptions to ensure the integrated analyses are interpretable. The estimand framework is valuable for developing principles for a deeper understanding of the critical statistical aspects of planning an integrated safety analysis. Our principles address the clinical question of interest, estimand and estimation. Special focus was given to the criteria for inclusion and exclusion of the component studies in the integrated analysis, and to integration of estimates pertaining to signal detection. Performing an integrated analysis and its preparatory steps calls for a good understanding of the clinical question of interest and its estimand, care and sound practice, to enable interpretation and avoid introducing unnecessary bias. It is valuable to use the estimand framework not only for efficacy evaluations, but also for safety evaluations in clinical trials and for integrated safety analyses.

Mental health related adverse events of cytisine and varenicline in smokers with and without mental health disorders: Secondary analysis of a randomized controlled trial

IntroductionLittle is known about the adverse events (AEs) of cytisine versus varenicline among individuals with mental health disorders (MHDs), highlighting the necessity for further exploration to inform clinical practice. This secondary analysis of clinical trial data aimed to investigate the effect of varenicline vs. cytisine regarding mental-health-related AEs (MH-related AEs) on smokers with and without MHDs. MethodsAustralian daily smokers interested in quitting were randomised to varenicline (84 days) or cytisine (25 days) and categorised by self-reported MHD diagnosis or treatment in the past year (MHD or non-MHD groups). Treatment adherence was assessed by self-reported number of doses taken during the active treatment phase via two check-in calls (at one month), while AEs were evaluated through four phone interviews: two check-in calls (one month) and follow-up calls at four and seven months. Logistic regression analysis compared MH-related AEs between groups, including only participants taking at least one dose. ResultsOf 1452 smokers 246 reported MHDs, 725 received cytisine and 727 received varenicline. Median number of doses taken was comparable between MHD (34 cytisine and 12 varenicline) and non-MHD (33 cytisine and 13 varenicline) groups. MH-related AEs were: 14.1 % (n = 30) in MHD (12.5 % in cytisine and 15.4 % in varenicline), and 11.8 % (n = 126) in non-MHD group (10.9 % in cytisine and 13.7 % in varenicline). No significant difference in MH-related AE occurrence was identified between medication groups (aOR=0.96, 95 % CI 0.4 to 2.2, p-value = 0.94). ConclusionComparable MH-related AEs were observed between smokers with and without MHDs, suggesting that cytisine, like varenicline, may be well-tolerated by those with MHDs. However, larger clinical trials focused on MH-related AEs are needed for more conclusive evidence.

Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data.

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.

Broad versus narrow research questions in evidence synthesis: A parallel to (and plea for) estimands.

There has been a transition from broad to more specific research questions in the practice of network meta-analysis (NMA). Such convergence is also taking place in the context of individual registrational trials, following the recent introduction of the estimand framework, which is impacting the design, data collection strategy, analysis and interpretation of clinical trials. The language of estimands has much to offer to NMA, particularly given the "narrow" perspective of treatments and target populations taken in health technology assessment.

Longitudinal invariance of the Patient Health Questionnaire-9 among patients receiving pharmacotherapy for major depressive disorder: A secondary analysis of clinical trial data.

Comparing self-reported symptom scores across time requires longitudinal measurement invariance (LMI), a psychometric property that means the measure is functioning identically across all time points. Despite its prominence as a measure of depression symptom severity in both research and health care, LMI has yet to be firmly established for the Patient Health Questionnaire-9 depression module (PHQ-9), particularly over the course of antidepressant pharmacotherapy. Accordingly, the objective of this study was to assess for LMI of the PHQ-9 during pharmacotherapy for major depressive disorder. This was a secondary analysis of data collected during a randomized controlled trial. A total of 1,944 veterans began antidepressant monotherapy and completed the PHQ-9 six times over 24 weeks of treatment. LMI was assessed using a series of four confirmatory factor analysis models that included all six time points, with estimated parameters increasingly constrained across models to test for different aspects of invariance. Root-mean-square error of approximation of the chi-square difference test values below 0.06 indicated the presence of LMI. Exploratory LMI analyses were also performed for separate sex, age, and race subgroups. Root-mean-square error of approximation of the chi-square difference test showed minimal change in model fits during invariance testing (≤ 0.06 for all steps), supporting full LMI for the PHQ-9. LMI was also supported for all tested veteran subgroups. As such, PHQ-9 sum scores can be compared across extended pharmacotherapy treatment durations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Differences in imeglimin response in subgroups of patients with type 2 diabetes stratified by data-driven cluster analysis: A post-hoc analysis of imeglimin clinical trial data.

To explore differences in imeglimin response among type 2 diabetes (T2D) patient clusters using data-driven cluster analysis. Data-driven cluster analysis (non-hierarchical k-means clustering) was performed on randomized, double-blind, imeglimin monotherapy and adjunctive (to insulin) therapy trials based on four baseline variables: (1) disease duration; (2) body mass index (BMI); (3) HbA1c; and (4a) homeostatic model assessment of β-cell function (HOMA-β) (monotherapy trials) or (4b) insulin total daily dose (adjunctive trial). Four clusters were identified with distinct clinical characteristics in both monotherapy (1-4) and adjunctive therapy (I-IV) trials; clusters 1 and I had lower values across all four indices versus the overall population, clusters 2 and II had a longer diabetes duration, cluster 3 had higher baseline BMI and HOMA-β, and cluster III had higher baseline BMI and insulin total daily dose, while clusters 4 and IV had higher baseline HbA1c. Between-group differences in HbA1c change (95% confidence interval) and effect size (ES) at week 24 varied considerably by cluster (cluster 1: -0.82 [-1.00, -0.63], ES = 1.47; cluster 2: -0.64 [-0.89, -0.39], ES = 1.18; cluster 3: -0.86 [-1.38, -0.33], ES = 0.84; cluster 4: -1.27 [-1.73, -0.82], ES = 1.44). For imeglimin adjunctive therapy, HbA1c improvements were significant versus placebo at week 16, excluding cluster III (cluster I: -0.63 [-0.95, -0.31], ES = 0.88; cluster II: -0.66 [-1.02, -0.30], ES = 1.13; cluster III: -0.31 [-0.73, 0.11], ES = 0.46; cluster IV: -0.82 [-1.29, -0.35], ES = 0.99). Differences in imeglimin response were observed among T2D patient clusters. Patient stratification may help with selection of those most probable to respond to imeglimin.

Dynamic Prediction of Overall Survival for Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data.

Current prediction models for patients with ostosarcoma are restricted to predictions from a single, static point in time, such as diagnosis or surgery. These approaches discard information which becomes available during follow-up and may have an impact on patient's prognosis. This study aims at developing a dynamic prediction model providing 5-year overall survival (OS) predictions from different time points during follow-up. The developed model considers relevant baseline prognostic factors, accounting for where appropriate time-varying effects and time-varying intermediate events such as local recurrence (LR) and new metastatic disease (NM). A landmarking approach is applied to 1965 patients with high-grade resectable osteosarcoma from the EURAMOS-1 trial (NCT00143030). Results show that LR and NM negatively affected 5-year OS (HRs: 2.634, 95% CI 1.845-3.761; 8.558, 95% CI 7.367-9.942, respectively). Baseline factors with strong prognostic value (HRs > 2) included poor histological response (≥10% viable tumor), axial tumor location, and the presence of lung metastases. The effect of poor versus good histological response changed over time, becoming non-significant from 3.25 years post-surgery onwards. This time-varying effect, as well as the strong impact of disease-related time-varying variables, show the importance of including updated information collected during follow-up in the model to provide more accurate survival predictions.

Development of normal reference intervals for renal function in pregnancy: a secondary analysis of clinical trial data

Background Due to its potential nephrotoxicity, screening for pre-existing renal function disorders has become a routine clinical assessment for initiating Tenofovir diphosphate fumarate (TDF)-containing antiretroviral treatment (ART) or pre-exposure prophylaxis (PrEP) in pregnant and non-pregnant adults. We aimed to establish reference values for commonly used markers of renal function in healthy pregnant women of African origin. Methods Pregnant women ≥18 years, not living with HIV, and at 14–28 weeks gestation were enrolled in a PrEP clinical trial in Durban, South Africa between September 2017 and December 2019. Women were monitored 4-weekly during pregnancy until six months postpartum. We measured maternal weight and serum creatinine (sCr) at each visit and calculated creatinine clearance (CrCl) rates using the Cockcroft–Gault (CG) and Modification of Diet in Renal Disease (MDRD) formulae. Reference ranges for sCr and CrCl by CG and MDRD calculations were derived from the mean ± 2SD of values for pregnancy and postdelivery. Results Between 14-–and 40 weeks gestation, 249 African women not exposed to TDF-PrEP contributed a total of 1193 renal function values. Postdelivery, 207 of these women contributed to 800 renal function values. The normal reference range for sCr was 30–57 and 32–60 umol/l in the 2nd and 3rd trimesters of pregnancy. Normal reference ranges for CrCl using the MDRD calculation were 129–282 and 119–267 ml/min/1.73m2 for the 2nd and 3rd trimesters, respectively. Using the CG method of calculation, normal reference ranges for CrCl were 120—304 and 123–309 ml/min/1.73m2 for the 2nd and 3rd trimesters respectively. In comparison, the normal reference range for sCr, CrCl by MDRD and CG calculations postpartum was 40–77 umol/l, 92–201, and 90–238 ml/min/1.73m2, respectively. Conclusions In African women, the Upper Limit of Normal (ULN) for sCr in pregnancy is approximately 20% lower than 6 months postnatally. Inversely, the Lower Limit of Normal (LLN) for CrCl using either MDRD or CG equation is approximately 35% higher than 6 months postnatally. We provide normal reference ranges for sCr and CrCl for both methods of calculation and appropriate for the 2nd and 3rd trimesters of pregnancy in African women.