Bayesian Logistic Regression Research Articles

Assessing risk factors of bypass graft surgery through the implementation of Bayesian and non-Bayesian methodologies

The aim of this research is to determine the key risk factors that influence the need for Bypass Graft surgery (BGS). A cohort of one hundred adult patients underwent coronary angiography operations at the Erbil Cardiac Center from January 2016 to December 2020. Multiple logistic regression (MLR) analysis was used to identify the determinants of BGS. Upon detailed statistical analysis, a significant association with BGS was found for several variables, including Age, BMI, Eosinophil, WBC, DBP, HbA1c, MCH, Blood Sugar, PLT, and T3. It was found that the significant variables contributing the most to predicting the likelihood of Bypass Graft surgery through Bayesian logistic regression analysis were Age, WBC, and MCH. These variables were identified as the pivotal risk factors associated with the probability of undergoing BGS. Age, DBP, and Blood Sugar had minimal influence on the likelihood of Bypass surgery. However, for each unit increase in WBC, the odds of surgery reduced by 38 %, and for MCH, the odds reduced by 19 %. DBP and Blood Sugar had little effect on Bypass surgery (Bayesian odds ratio close to 1). This study revealed that Bayesian logistic models offer superiority over classical logistic models. The Bayesian-based models provide more accurate predictions by incorporating informative prior distributions. To improve the accuracy and generalizability of the findings, future research should consider a larger sample size and incorporate informative prior distributions within the Bayesian framework. Also, machine learning algorithms can offer new insights into BGS outcomes by considering various variables and assessing model sensitivity.

Abstract CT184: First-in-human trial of TIGIT inhibitor M6223 as monotherapy or in combination with bintrafusp alfa (BA) in patients (pts) with advanced solid unresectable tumors

Abstract Introduction: M6223 is an intravenous (IV), fully human, antagonistic, anti-TIGIT antibody with an Fc-mediated effector region. In preclinical studies, M6223 combined with BA (a bifunctional fusion protein that simultaneously targets the TGF-β and PD-(L)1 pathways) enhanced antitumor efficacy compared to either agent alone. Methods: This first-in-human, dose escalation study of M6223 as monotherapy (M6223; Part 1A) or in combination with BA (M6223+BA; Part 1B) included pts with advanced solid tumors (aged ≥18 years, ECOG PS ≤1) (NCT04457778). In Part 1A, pts received M6223 at 10 mg, 30 mg, 100 mg, 300 mg, 900 mg, 1600 mg, 2400 mg (all Q2W) or M6223 2400 mg Q3W. In Part 1B, pts received M6223 at 300 mg, 900 mg, or 1600 mg, all in combination with BA 1200 mg (both Q2W, IV). Dose escalation decisions by the Safety Monitoring Committee were assisted by a Bayesian 2-parameter logistic regression model. Primary objectives were safety, tolerability, maximum tolerated dose (MTD), and recommended dose for expansion (RDE). Additional objectives included pharmacokinetics (PK), pharmacodynamics (PD), and clinical activity. Results: At final analysis, 40 pts (21 male, age range 24-79 years) had received M6223 (Q2W: n=32; Q3W: n=8), and 18 pts (7 male, age range 34-80 years) had received M6223+BA. Overall, two dose-limiting toxicities were observed: a grade 3 adrenal insufficiency (M6223, 900 mg) and a grade 3 anemia (M6223+BA, 300 mg; unrelated to M6223). In the M6223 group, grade ≥3 TEAEs were observed in 14 (35.0%) pts and M6223-related grade ≥3 TEAEs in 2 (5.0%) pts. In the M6223+BA group, grade ≥3 TEAEs were observed in 14 (77.8%) pts and M6223-related grade ≥3 TEAEs in 4 (22.2%) pts. No MTD was identified. RDEs were 1600 mg Q2W or 2400 mg Q3W for M6223 monotherapy, and 1600 mg+1200 mg Q2W for M6223+BA. Half-life for the two monotherapy RDEs were 9.0 and 14.6 days, respectively, with moderate accumulation at the selected RDEs. Linear PK was observed for M6223 at 100-2400 mg Q2W and at 2400 mg Q3W; co-administration with BA did not change the PK profile of M6223. PD analyses in blood showed full and sustained TIGIT target occupancy, and depletion of suppressive Tregs at M6223 ≥900 mg. Paired biopsies in the M6223 group (900 mg Q2W, 1600 mg Q2W, 2400 mg Q3W; n=12) showed a trend of decrease in TIGIT+ and increase in CD226+ cells. Median overall survival was 7.6 months (95% CI: 4.9, 12.0) and median progression-free survival was 1.4 months (95% CI: 1.3, 1.8). No pt achieved an objective response (per RECIST v1.1); stable disease as the best response was observed in 13 (32.5%) pts in M6223 and 5 (27.8%) pts in M6223+BA. Conclusion: M6223 monotherapy and in combination with BA had a manageable safety profile, and RDEs for both mono- and combination therapy were defined. Further evaluation of M6223 is ongoing in combination with PD-L1 inhibitor avelumab (JAVELIN Bladder Medley; NCT05327530). Citation Format: Aung Naing, Meredith McKean, Anthony Tolcher, Anja Victor, Ping Hu, Keyvan Tadjalli Mehr, Thomas Kitzing, Daniel Holland, Emilia Richter, Lillian Siu. First-in-human trial of TIGIT inhibitor M6223 as monotherapy or in combination with bintrafusp alfa (BA) in patients (pts) with advanced solid unresectable tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT184.

Abstract CT067: The anti-BCMA antibody-drug conjugate HDP-101 with a novel amanitin payload shows promising initial first in human results in relapsed multiple myeloma

Abstract Background: HDP-101 is a new antibody drug conjugatetargeting B-cell maturation antigen (BCMA) that carries a synthetic amanitin payload with a new mode of action. Amanitin inhibits RNA polymerase II, effectively stopping transcription and inducing apoptosis in tumor cells, regardless of their proliferation status. It's shown cytotoxicity in vitro against BCMA-positive myeloma cell lines and non-proliferating primary CD138+ cells from refractory myeloma patients withefficacy even in cells with low BCMA density. Clinical Study: HDP-101-01 is a first-in-human, open label, non-randomized, multicenter, phase 1/2a trial in patients with multiple myeloma whose disease has progressed or is refractory, aiming to determine the Maximum Tolerated Dose and/or the Recommended phase 2 Dose in Phase 1. Dose escalation is guided by an adaptive Bayesian logistic regression model with overdose control. The primary objective in phase 2 is to assess anti-tumor activity. Study Progress: As of November 2023, 18 patients (7 females, 11 males) were enrolled in 5 consecutive dose cohorts of 20, 30, 60, 80, and 100µg/kg. Patients had a median age of 70 years (48-82), were heavily pre-treated and multidrug-resistant, and had a median of 6,5 prior treatments (2-15). Study Results: Preliminary data shows that pharmacokinetics of HDP-101 aligns with expectations, and exposure to HDP-101 is dose proportional. Free payload was not detected in serum at a limit of detection of 30 ng/mL, and no anti-drug antibodiesor immunogenic reactions were noted. 17 of 18 patients were evaluable for dose limiting toxicities (DLT) in the 5 treatment cohorts. Initial cohorts were well tolerated, without any DLTs, including no signs of hepatic and renal toxicities, infusions reactions, or ocular disorders. All patients in Cohort 5 had Grade 1-4 transient thrombocytopeniawith platelet reductions starting on Cycle 1/Day 2 (C1/D2), a nadir on C1D5, and full recovery by C1D15 at the latest without any clinical sequelae or interventions. Notably, subsequent dosing with HDP-101 did not produce similarly deep episodes of thrombocytopenia, supporting the possibility that this event is not due to a direct cytotoxic effect against megakaryocytes. After Cohort 5, mitigation strategies are being considered and further dose and schedule optimization is planned with fractionated dosing and/or with premedication. Efficacy: Cohort3 has a patient with stable disease (SD) after 15 cycles. Cohort 5 has 4 ongoing patients with promising results after 3-4 cycles: 2 patients achieved PR, and 2 have SD. Updated data will be presented at the AACR2024 meeting. Citation Format: Jonathan Kaufman, Marc Raab, Shambavi Richard, Sebastian Grosicki, István Takács, András Strassz, Andreas Pahl, Michael Kulke, Thorsten Michael, Anette Last, Hajnalka Szabóki, Garrit Jentsch, Oliver Schönborn-Kellenberger, Robert Orlowski. The anti-BCMA antibody-drug conjugate HDP-101 with a novel amanitin payload shows promising initial first in human results in relapsed multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT067.

Abstract CT090: DAREON™-7: A phase 1, open-label, dose escalation and expansion cohort trial of the delta-like ligand (DLL3)-targeting T-cell engager BI 764532, plus first-line platinum-based chemotherapy in patients with DLL3-positive (+) neuroendocrine carcinomas

Abstract Background Neuroendocrine carcinomas (NECs) have limited treatment options. DLL3 is highly expressed in NECs and is a promising treatment target. In an ongoing phase 1 trial (NCT04429087), BI 764532, a DLL3/CD3 IgG-like T-cell engager, was tolerable with clinical activity in patients with DLL3-positive (+) tumors, including those with small cell lung cancer and other NECs. Purpose DAREON™-7 (NCT06132113), a phase 1, open-label, dose-escalation (Part A) and dose-expansion (Part B) trial, aims to determine the maximum tolerated dose (MTD), recommended dose for expansion (RDE)/recommended phase 2 dose (RP2D), and the safety and efficacy of BI 764532 + platinum and etoposide in patients with DLL3+ NEC. Experimental design Part A: ~25 patients will receive intravenous BI 764532 (target dose after step-in dosing) plus carboplatin/etoposide (CE) until intolerable toxicity, or progression per Response Evaluation Criteria in Solid Tumors (RECIST) v1.1, or for a maximum of 36 months. BI 764532 dose escalation will be guided by a Bayesian logistic regression model with overdose control. Part B: 2 cohorts (~15 patients each) will receive BI 764532 at the RDE/RP2D + CE or cisplatin/etoposide (carboplatin/etoposide or cisplatin/etoposide are the most-used standard of care [SOC] regimens). Key inclusion criteria: patients must have no prior systemic treatment for DLL3+ locally advanced or metastatic NEC (extrapulmonary or unknown primary) or large cell NEC of the lung except for 1 cycle of standard platinum/etoposide regimen as first-line treatment prior to entering the treatment period of the trial; patients with localized NEC or resectable disease who received prior adjuvant therapy can participate in the trial if they experienced a treatment-free interval of >6 months prior to the diagnosis of metastatic disease; and patients must have at least 1 measurable lesion as defined per RECIST v1.1. Patients must be adequate candidates to receive platinum/etoposide as the SOC treatment. Primary endpoints: occurrence of dose-limiting toxicities (DLTs) in the MTD evaluation (Part A) and on-treatment (Part B) periods. Secondary endpoints: occurrence of DLTs and adverse events during the on-treatment period (Part B), and efficacy measured by objective response and duration of response (Part B). Enrollment is ongoing. Citation Format: Jaume Capdevila, Timon Vandamme, Patricia Niccoli, Saori Mishima, Ken Kato, Yiyuan Ma, Ping Sun, Lijiang Geng, Ulrich M. Lauer. DAREON™-7: A phase 1, open-label, dose escalation and expansion cohort trial of the delta-like ligand (DLL3)-targeting T-cell engager BI 764532, plus first-line platinum-based chemotherapy in patients with DLL3-positive (+) neuroendocrine carcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT090.

NSCs from groups to clusters: A catalogue of dwarf galaxies in the Shapley Supercluster and the role of environment in galaxy nucleation

Abstract Nuclear star clusters (NSCs) are dense star clusters located at the centre of galaxies spanning a wide range of masses and morphologies. Analysing NSC occupation statistics in different environments provides an invaluable window into investigating early conditions of high-density star formation and mass assembly in clusters and group galaxies. We use HST/ACS deep imaging to obtain a catalogue of dwarf galaxies in two galaxy clusters in the Shapley Supercluster: the central cluster Abell 3558 and the northern Abell 1736a. The Shapley region is an ideal laboratory to study nucleation as it stands as the highest mass concentration in the nearby Universe. We investigate the NSC occurrence in quiescent dwarf galaxies as faint as MI = −10 mag and compare it with all other environments where nucleation data is available. We use galaxy cluster/group halo mass as a proxy for the environment and employ a Bayesian logistic regression framework to model the nucleation fraction (fn) as a function of galaxy luminosity and environment. We find a notably high fn in Abell 3558: at MI ≈ −13.1 mag, half the galaxies in the cluster host NSCs. This is higher than in the Virgo and Fornax clusters but comparable to the Coma Cluster. On the other hand, the fn in Abell 1736a is relatively lower, comparable to groups in the Local Volume. We find that the probability of nucleation varies with galaxy luminosity remarkably similarly in galaxy clusters. These results reinforce previous findings of the important role of the environment in NSC formation/growth.

Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity

The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.

Navigating the complexities of student understanding: Exploring the coherency of students' conceptions about the greenhouse effect

AbstractThe greenhouse effect is a complex scientific phenomenon that plays a crucial role in understanding climate change. Grasping students' understanding of this phenomenon on the content‐specific level but also how students' conceptions are organized is vital for effective climate change education. This study addresses both levels and delves into the relationship between students' frameworks and knowledge pieces of the greenhouse effect through the analysis of multiple‐choice questions, employing Bayesian correlations and multiple logistic regression. We thereby focus on specific types of conceptualizations of the greenhouse effect that have been identified in previous research and furthermore investigate the coherency of them. To do so, we analyzed answers of N = 604 grade 11 students in Austria and Germany and interpreted them from different theoretical perspectives. The findings showed that students hold various ideas about the greenhouse effect that are only seldom coherent, in particular when it comes to adequate ideas about the greenhouse effect. However, especially for a reflection‐based framework of the greenhouse effect, our results demonstrate that students' conceptions show some form of coherency. We argue that our results can inform the development of effective teaching strategies that address students' existing knowledge and alternative conceptions. In terms of practical implications, the findings suggest that teaching strategies should provide opportunities for students to integrate their knowledge pieces into a more coherent understanding of the greenhouse effect. The study highlights the need for further investigation into the relationship between knowledge pieces and frameworks not only for the greenhouse effect, but for science education in general.

56 Impact of ACTIV-6 treatment on PROMIS-29 at 7, 14, 28, and 90 days

OBJECTIVES/GOALS: As mortality and morbidity from acute COVID-19 decline, the impact of COVID-19 on short- and long-term quality of life (QoL) becomes critical to address. We assessed the impact of re-purposed COVID-19 therapies on QoL as a secondary outcome measure in ACTIV-6, a decentralized platform trial. METHODS/STUDY POPULATION: Adults aged ≥30 with mild-to-moderate COVID-19 enroll in ACTIV-6 online or through a study site. Patients are randomized to a medication of interest or placebo. Medications are mailed and symptoms are tracked using electronic diaries. QoL is measured#_msocom_1 using the PROMIS-29 questionnaire. Adjusted Bayesian logistic regression models are used to measure effects of treatment on the seven PROMIS-29 QoL domains at days 7, 14, 28#_msocom_2 and 90. Covariates are treatment, age, gender, symptom duration and severity, vaccination status, geographic region, call center#_msocom_3#_msocom_4, and calendar time. Treatment effects are described using ORs, 95% credible intervals, and posterior probabilities of efficacy, P(eff). RESULTS/ANTICIPATED RESULTS: There are 5,362 patients included, representing four of the study arms in ACTIV-6. We report results where P(eff)<0.025 and P(eff)>0.975 in the table below. Table 1. Scale Day: OR* (95% credible interval, P(eff)) Therapy Physical Anxiety Depression Fatigue Sleep Social Pain Ivermectin 400 — Ivermectin 600 D7: 0.77 (0.61-0.96, 0.01) D14: 0.65 (0.49-0.85, <0.01) D28: 0.69 (0.52-0.92, 0.01) — D7: 0.79 (0.64-0.97, 0.01) — D14 0.78 (0.60-1.00, 0.02) D28: 0.66 (0.50-0.87, <0.01) Fluticasone - D14: 0.77 (0.60-0.99, 0.02) — D7: 0.76 (0.62-0.93, <0.01) D90: 0.79 (0.64-0.98, 0.01) — D7: 0.74 (0.59-0.93, 0.01) Fluvoxamine D7: 0.66 (0.51-0.84, 0.01) — D28: 1.38 (1.02, 1.85, 0.98) D7: 0.78 (0.63-0.97, 0.01) D7: 0.77 (0.62-0.95, 0.01) — *OR > 1 favors active intervention DISCUSSION/SIGNIFICANCE: Results suggest fluvoxamine may improve depression scores by day 28, while placebo is favored in several other scales across treatments. Differences between treatment and placebo are not seen at most other timepoints. This trial is ongoing and future work will include results from additional ACTIV-6 study arms.

Bayesian estimation of the measurement of interactions in epidemiological studies.

Interaction identification is important in epidemiological studies and can be detected by including a product term in the model. However, as Rothman noted, a product term in exponential models may be regarded as multiplicative rather than additive to better reflect biological interactions. Currently, the additive interaction is largely measured by the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (S), and confidence intervals are developed via frequentist approaches. However, few studies have focused on the same issue from a Bayesian perspective. The present study aims to provide a Bayesian view of the estimation and credible intervals of the additive interaction measures. Bayesian logistic regression was employed, and estimates and credible intervals were calculated from posterior samples of the RERI, AP and S. Since Bayesian inference depends only on posterior samples, it is very easy to apply this method to preventive factors. The validity of the proposed method was verified by comparing the Bayesian method with the delta and bootstrap approaches in simulation studies with example data. In all the simulation studies, the Bayesian estimates were very close to the corresponding true values. Due to the skewness of the interaction measures, compared with the confidence intervals of the delta method, the credible intervals of the Bayesian approach were more balanced and matched the nominal 95% level. Compared with the bootstrap method, the Bayesian method appeared to be a competitive alternative and fared better when small sample sizes were used. The proposed Bayesian method is a competitive alternative to other methods. This approach can assist epidemiologists in detecting additive-scale interactions.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model

BackgroundMultidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). MethodsWe developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FindingsWe found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. InterpretationOur results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option.

Predicting Peri-Operative Cardiorespiratory Adverse Events in Children with Idiopathic Pulmonary Arterial Hypertension Undergoing Cardiac Catheterization Using Echocardiography: A Cohort Study.

General anesthesia in children with idiopathic pulmonary arterial hypertension (PAH) carries an increased risk of peri-operative cardiorespiratory complications though risk stratifying individual children pre-operatively remains difficult. We report the incidence and echocardiographic risk factors for adverse events in children with PAH undergoing general anesthesia for cardiac catheterization. Echocardiographic, hemodynamic, and adverse event data from consecutive PAH patients are reported. A multivariable predictive model was developed from echocardiographic variables identified by Bayesian univariable logistic regression. Model performance was reported by area under the curve for receiver operating characteristics (AUCroc) and precision/recall (AUCpr) and a pre-operative scoring system derived (0-100). Ninety-three children underwent 158 cardiac catheterizations with mean age 8.8 ± 4.6years. Adverse events (n = 42) occurred in 15 patients (16%) during 16 catheterizations (10%) including cardiopulmonary resuscitation (n = 5, 3%), electrocardiographic changes (n = 3, 2%), significant hypotension (n = 2, 1%), stridor (n = 1, 1%), and death (n = 2, 1%). A multivariable model (age, right ventricular dysfunction, and dilatation, pulmonary and tricuspid regurgitation severity, and maximal velocity) was highly predictive of adverse events (AUCroc 0.86, 95% CI 0.75 to 1.00; AUCpr 0.68, 95% CI 0.50 to 0.91; baseline AUCpr 0.10). Pre-operative risk scores were higher in those who had a subsequent adverse event (median 47, IQR 43 to 53) than in those who did not (median 23, IQR 15 to 33). Pre-operative echocardiography informs the risk of peri-operative adverse events and may therefore be useful both for consent and multi-disciplinary care planning.

Gender-specific prolactin thresholds to determine prolactinoma size: a novel Bayesian approach and its clinical utility.

In clinical practice, the size of adenomas is crucial for guiding prolactinoma patients towards the most suitable initial treatment. Consequently, establishing guidelines for serum prolactin level thresholds to assess prolactinoma size is essential. However, the potential impact of gender differences in prolactin levels on estimating adenoma size (micro- vs. macroadenoma) is not yet fully comprehended. To introduce a novel statistical method for deriving gender-specific prolactin thresholds to discriminate between micro- and macroadenomas and to assess their clinical utility. We present a novel, multilevel Bayesian logistic regression approach to compute observationally constrained gender-specific prolactin thresholds in a large cohort of prolactinoma patients (N = 133) with respect to dichotomized adenoma size. The robustness of the approach is examined with an ensemble machine learning approach (a so-called super learner), where the observed differences in prolactin and adenoma size between female and male patients are preserved and the initial sample size is artificially increased tenfold. The framework results in a global prolactin threshold of 239.4 μg/L (95% credible interval: 44.0-451.2 μg/L) to discriminate between micro- and macroadenomas. We find evidence of gender-specific prolactin thresholds of 211.6 μg/L (95% credible interval: 29.0-426.2 μg/L) for women and 1,046.1 μg/L (95% credible interval: 582.2-2,325.9 μg/L) for men. Global (that is, gender-independent) thresholds result in a high sensitivity (0.97) and low specificity (0.57) when evaluated among men as most prolactin values are above the global threshold. Applying male-specific thresholds results in a slightly different scenario, with a high specificity (0.99) and moderate sensitivity (0.74). The male-dependent prolactin threshold shows large uncertainty and features some dependency on the choice of priors, in particular for small sample sizes. The augmented datasets demonstrate that future, larger cohorts are likely able to reduce the uncertainty range of the prolactin thresholds. The proposed framework represents a significant advancement in patient-centered care for treating prolactinoma patients by introducing gender-specific thresholds. These thresholds enable tailored treatment strategies by distinguishing between micro- and macroadenomas based on gender. Specifically, in men, a negative diagnosis using a universal prolactin threshold can effectively rule out a macroadenoma, while a positive diagnosis using a male-specific prolactin threshold can indicate its presence. However, the clinical utility of a female-specific prolactin threshold in our cohort is limited. This framework can be easily adapted to various biomedical settings with two subgroups having imbalanced average biomarkers and outcomes of interest. Using machine learning techniques to expand the dataset while preserving significant observed imbalances presents a valuable method for assessing the reliability of gender-specific threshold estimates. However, external cohorts are necessary to thoroughly validate our thresholds.

Palliative Non-Operative Management in Geriatric Hip Fracture Patients: When Would Surgeons Abstain from Surgery?

Background: For hip fracture patients with a limited life expectancy, operative and palliative non-operative management (P-NOM) can yield similar quality of life outcomes. However, evidence on when to abstain from surgery is lacking. The aim of this study was to quantify the influence of patient characteristics on surgeons' decisions to recommend P-NOM. Methods: Dutch surgical residents and orthopaedic trauma surgeons were enrolled in a conjoint analysis and structured expert judgement (SEJ). The participants assessed 16 patient cases comprising 10 clinically relevant characteristics. For each case, they recommended either surgery or P-NOM and estimated the 30-day postoperative mortality risk. Treatment recommendations were analysed using Bayesian logistic regression, and perceived risks were pooled with equal and performance-based weights using Cooke's Classical Model. Results: The conjoint analysis and SEJ were completed by 14 and 9 participants, respectively. Participants were more likely to recommend P-NOM to patients with metastatic carcinomas (OR: 4.42, CrI: 2.14-8.95), severe heart failure (OR: 4.05, CrI: 1.89-8.29), end-stage renal failure (OR: 3.54, CrI: 1.76-7.35) and dementia (OR: 3.35, CrI: 1.70-7.06). The patient receiving the most P-NOM recommendations (12/14) had a pooled perceived risk of 30-day mortality between 50.8 and 62.7%. Conclusions: Overall, comorbidities had the strongest influence on participants' decisions to recommend P-NOM. Nevertheless, practice variation and heterogeneity in risk perceptions were substantial. Hence, more decision support for considering P-NOM is needed.

Molecular detection of SARS-CoV-2 and other respiratory viruses in saliva and classroom air: a two winters tale

ObjectivesTo compare the prevalence of SARS-CoV-2 and other respiratory viruses in saliva and bioaerosols between two winters and to model the probability of virus detection in classroom air for different viruses. MethodsWe analysed saliva, air, and air cleaner filter samples from studies conducted in two Swiss secondary schools (students aged 14–17 years) over 7 weeks during the winters of 2021/22 and 2022/23. Two bioaerosol sampling devices and high efficiency particulate air (HEPA) filters from air cleaners were used to collect airborne virus particles in four classrooms. Daily bioaerosol samples were pooled for each sampling device before PCR analysis of a panel of 19 respiratory viruses and viral subtypes. The probability of detection of airborne viruses was modelled using an adjusted Bayesian logistic regression model. ResultsThree classes (58 students) participated in 2021/22, and two classes (38 students) in 2022/23. During winter 2021/22, SARS-CoV-2 dominated in saliva (19 of 21 positive samples) and bioaerosols (9 of 10). One year later, there were 50 positive saliva samples, mostly influenza B, rhinovirus, and adenovirus, and two positive bioaerosol samples, one rhinovirus and one adenovirus. The weekly probability of airborne detection was 34% (95% credible interval [CrI] 22–47%) for SARS-CoV-2 and 10% (95% CrI 5–16%) for other respiratory viruses. DiscussionThere was a distinct shift in the distribution of respiratory viruses from SARS-CoV-2 during the omicron wave to other respiratory viruses one year later. SARS-CoV-2 is more likely to be detected in the air than other endemic respiratory viruses, possibly reflecting differences in viral characteristics and the composition of virus-carrying particles that facilitate airborne long-range transmission.

Morning administration enhances humoral response to SARS-CoV-2 vaccination in kidney transplant recipients

Although severe acute respiratory syndrome coronavirus 2 messenger ribonucleic acid (SARS-CoV-2 mRNA) vaccines are effective in kidney transplant recipients (KTRs), their immune response to vaccination is blunted by immunosuppression. Other tools enhancing vaccination response are therefore needed. Interestingly, aligning vaccine administration with circadian rhythms (chronovaccination) has been shown to boost immune response. However, its applicability in KTRs, whose circadian rhythms are likely disrupted by immunosuppressants, remains unclear. To assess the impact of vaccination timing on seroconversion in the KTRs population, we analyzed data from 553 virus-naïve KTRs who received 2 doses of messenger ribonucleic acid (mRNA) vaccine. Bayesian logistic regression was employed, adjusting for previously identified predictors of seroconversion, including allograft function, maintenance immunosuppressants, or time since transplantation. SARS-CoV-2 immunoglobulin G (IgG) levels were measured with a median of 47 days after the second dose. The results did not reveal a reliable effect of timing of the first dose but did indicate that earlier timing for the second dose brings a notable benefit—every 1-hour delay in the application was associated with a 16% reduction in the odds of seroconversion (OR 0.84, 95% CI 0.71, 0.998). Similar results were obtained from quantile regression modeling IgG levels. In conclusion, morning vaccination is emerging as a promising and easily implementable strategy to enhance vaccine response in KTRs.

A comparison of Bayesian and frequentist approaches to incorporating clinical and biological information for the prediction of response to standardized pediatric colitis therapy.

The prospective cohort study PROTECT is the largest study in pediatric ulcerative colitis (UC) with standardized treatments, providing valuable data for predicting clinical outcomes. PROTECT and previous studies have identified characteristics associated with clinical outcomes. In this study, we aimed to compare predictive modeling between Bayesian analysis including machine learning and frequentist analysis. The key outcomes for this analysis were week 4, 12 and 52 corticosteroid (CS)-free remission following standardized treatment from diagnosis. We developed predictive modeling with multivariable Bayesian logistic regression (BLR), Bayesian additive regression trees (BART) and frequentist logistic regression (FLR). The effect estimate of each risk factor was estimated and compared between the BLR and FLR models. The predictive performance of the models was assessed including area under curve (AUC) of the receiver operating characteristic (ROC) curve. Ten-fold cross-validation was performed for internal validation of the models. The estimation contained 95% credible (or confidence) interval (CI). The statistically significant associations between the risk factors and early or late outcomes were consistent between all BLR and FLR models. The model performance was similar while BLR and BART models had narrower credible intervals of AUCs. To predict week 4 CS-free remission, the BLR model had AUC of 0.69 (95% CI 0.67-0.70), the BART model had AUC of 0.70 (0.67-0.72), and the FLR had AUC of 0.70 (0.65-0.76). To predict week 12 CS-free remission, the BLR model had AUC of 0.78 (0.77-0.79), the BART model had AUC of 0.78 (0.77-0.79), and the FLR model had AUC of 0.79 (0.74-0.83). To predict week 52 CS-free remission, the BLR model had AUC of 0.69 (0.68-0.70), the BART model had AUC of 0.69 (0.67-0.70), and the FLR model had AUC of 0.69 (0.64-0.74). The BART model identified nonlinear associations. BLR and BART models had intuitive interpretation on interval estimation, better precision in estimating the AUC and can be alternatives for predicting clinical outcomes in pediatric patients with UC. BART model can estimate nonlinear nonparametric association.

Outcomes for people with TB by disease severity at presentation.

<sec id="st1"><title>BACKGROUND</title>There is substantial heterogeneity in disease presentation for individuals with TB disease, which may correlate with disease outcomes. We estimated disease outcomes by disease severity at presentation among individuals with TB during the pre-chemotherapy era.</sec><sec id="st2"><title>METHODS</title>We extracted data on people with TB enrolled between 1917 and 1948 in the USA, stratified by three disease severity categories at presentation using the U.S. National Tuberculosis Association diagnostic criteria. These criteria were based largely on radiographic findings ("minimal", "moderately advanced", and "far advanced"). We used Bayesian parametric survival analysis to model the survival distribution overall, and by disease severity and Bayesian logistic regression to estimate the severity-level specific natural recovery odds within 3 years.</sec><sec id="st3"><title>RESULTS</title>People with minimal TB at presentation had a 2% (95% CrI 0-11%) probability of TB death within 5 years vs. 40% (95% CrI 15-68) for those with far advanced disease. Individuals with minimal disease had 13.62 times the odds (95% CrI 9.87-19.10) of natural recovery within 3 years vs. those with far advanced disease.</sec><sec id="st4"><title>CONCLUSION</title>Mortality and natural recovery vary by disease severity at presentation. This supports continued work to evaluate individualized (e.g., shortened or longer) regimens based on disease severity at presentation, identified using radiography.</sec>.

First-in-Human Study of the Ataxia Telangiectasia and Rad3-Related (ATR) Inhibitor Tuvusertib (M1774) as Monotherapy in Patients with Solid Tumors.

Tuvusertib (M1774) is a potent, selective, orally administered ataxia telangiectasia and Rad3-related (ATR) protein kinase inhibitor. This first-in-human study (NCT04170153) evaluated safety, tolerability, maximum tolerated dose (MTD), recommended dose for expansion (RDE), pharmacokinetics (PK), pharmacodynamics (PD), and preliminary efficacy of tuvusertib monotherapy. Ascending tuvusertib doses were evaluated in 55 patients with metastatic or locally advanced unresectable solid tumors. A safety monitoring committee determined dose escalation based on PK, PD, and safety data guided by a Bayesian 2-parameter logistic regression model. Molecular responses (MR) were assessed in circulating tumor DNA samples. Most common grade ≥3 treatment-emergent adverse events were anemia (36%), neutropenia, and lymphopenia (both 7%). Eleven patients experienced dose-limiting toxicities, most commonly grade 2 (n = 2) or 3 (n = 8) anemia. No persistent effects on blood immune cell populations were observed. The RDE was 180 mg tuvusertib QD (once daily), 2 weeks on/1 week off treatment, which was better tolerated than the MTD (180 mg QD continuously). Tuvusertib median time to peak plasma concentration ranged from 0.5 to 3.5 hours and mean elimination half-life from 1.2 to 5.6 hours. Exposure-related PD analysis suggested maximum target engagement at ≥130 mg tuvusertib QD. Tuvusertib induced frequent MRs in the predicted efficacious dose range; MRs were enriched in patients with radiological disease stabilization, and complete MRs were detected for mutations in ARID1A, ATRX, and DAXX. One patient with platinum- and PARP inhibitor-resistant BRCA wild-type ovarian cancer achieved an unconfirmed RECIST v1.1 partial response. Tuvusertib demonstrated manageable safety and exposure-related target engagement. Further clinical evaluation of tuvusertib is ongoing.

Association Between Past and Present Operative Mortality in Congenital Heart Surgery: An Analysis of The Society of Thoracic Surgeons Congenital Heart Surgery Database

BackgroundMortality after congenital heart surgery is an important metric across benchmarking, quality, and reporting initiatives. All rely on estimates from prior years, and how well these reflect current outcomes is unclear. MethodsIndex operations from The Society of Thoracic Surgeons Congenital Database (2014-2019) were included. Adjusted operative mortality in “past” 4-year and 1-year intervals vs the most recent year (“present”) was evaluated using Bayesian hierarchical logistic regression with results presented as odds ratios and 95% credible intervals (CrI). ResultsAmong 115,699 operations (106 hospitals), overall present observed mortality was 2.5%. Regression to the mean was evident, and individual hospital’s present vs past adjusted mortality were only weakly correlated (−0.11 vs past 1-year data, 0.22 vs past 4-year data). A significant relationship was found between past and present mortality only for the group of hospitals in the highest mortality quartile, most prominent for past 4-year data (adjusted odds ratio vs lowest mortality quartile, 2.04; 95% CrI, 1.44-2.80). The proportion of present hospital mortality variation explained by past mortality quartile was 52% (95% CrI, 20%-90%) using past 4-year data and only 27% (95% CrI, 92%-83%) using past 1-year data. Overall 66% of hospitals changed mortality quartiles from past to present (30% by ≥2 quartiles). ConclusionsPast mortality relates to present primarily for groups of hospitals at the extremes, with past 4-year data more informative than past 1-year data. For individual hospitals, past may differ from present, regression to the mean is common, and many change quartiles. Past mortality should be used thoughtfully and not as the sole factor informing present decision making.

Lower versus higher blood pressure targets in comatose patients resuscitated from out-of-hospital cardiac arrest-Protocol for a secondary Bayesian analysis of the box trial.

The management of blood pressure targets during intensive care after out-of-hospital cardiac arrest (OHCA) remains a topic of debate. The blood Pressure and Oxygenation Targets After OHCA (BOX) trial explored the efficacy of two different blood pressure targets in 789 patients during intensive care after OHCA. In the primary frequentist analysis, no statistically significant differences were found for neurological outcome after 90 days. This protocol outlines secondary Bayesian analyses of 365-day all-cause mortality and two secondary outcomes: neurological outcome after 365 days, and plasma neuron-specific enolase, a biomarker of brain injury, after 48 h. We will employ adjusted Bayesian logistic and linear regressions, presenting results as relative and absolute differences with 95% confidence intervals. We will use weakly informative priors for the primary analyses, and skeptical and evidence-based priors (where available) in sensitivity analyses. Exact probabilities for any benefit/harm will be presented for all outcomes, along with probabilities of clinically important benefit/harm (risk differences larger than 2%-points absolute) and no clinically important differences for the binary outcomes. We will assess whether heterogeneity of treatment effects on mortality is present according to lactate at admission, time to return of spontaneous circulation, primary shockable rhythm, age, hypertension, and presence of ST-elevation myocardial infarction. This secondary analysis of the BOX trial aim to complement the primary frequentist analysis by quantifying the probabilities of beneficial or harmful effects of different blood pressure targets. This approach seeks to provide clearer insights for researchers and clinicians into the effectiveness of these blood pressure management strategies in acute medical conditions, particularly focusing on mortality, neurological outcomes, and neuron-specific enolase.