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Background Latina women are less likely to start prenatal care in the first trimester and to attend the recommended amount of prenatal visits compared to their non-Latina white counterparts. Objectives This study aimed to assess challenges and facilitators to first-trimester prenatal care (FTPNC) and prenatal care utilization (PNCU) in a Midwestern urban area with a growing immigrant Latino community. Methods This study used a mixed-method approach based on the Theoretical Domains Framework. Nine semi-structured interviews were conducted with healthcare professionals that worked in birth centers, clinics, or hospitals that provided prenatal care (PNC) services for Latina women. Eight focus groups and quantitative surveys were conducted with Latina women and their supporters in Kansas City metropolitan area. Results FTPNC was challenged by women's immigrant status, lack of healthcare coverage due to immigrant status, and complexity of Medicaid application. PNCU was challenged by the cost of PNC when diagnosed with gestational diabetes, lack of healthcare coverage, PNC literacy, late access to gynecologists/obstetricians, inadequate interpretation services, transportation, and mental health distress. Meanwhile, FTPNC was facilitated by social support and connectedness. PNCU was facilitated by Spanish-proficient providers and interpreters, effective nonverbal communication and education techniques, and pregnancy prevention access and education. Conclusions for Practice Results from this study highlight important targets to improve PNC for Latina women. Participants called for various types of support to address identified challenges, ranging from information on social media about PNC services to broader efforts such as building trust from the community toward PNC providers and making PNC services affordable for women with gestational diabetes., Author(s): Juliana Teruel Camargo [sup.1] [sup.2] , Romina L. Barral [sup.3] , Elizabeth H. Kerling [sup.1] , Lillian Saavedra [sup.4] , Susan E. Carlson [sup.1] , Byron J. Gajewski [sup.5] [...]

Anticoagulant-associated traumatic intracranial hemorrhage (tICrH) is a devastating injury with high morbidity and mortality. For survivors, treating clinicians face the dilemma of restarting oral anticoagulation with scarce evidence to guide them. Thromboembolic risk is high from the bleeding event, patients' high baseline risks, that is, the pre-existing indication for anticoagulation, and the risk of immobility after the bleeding episode. This must be balanced with potentially devastating hematoma expansion or new hemorrhagic lesions. Retrospective evidence and expert opinion support restarting oral anticoagulants in most patients with tICrH, but timing is uncertain. Researchers have failed to make clear distinctions between tICrH and spontaneous intracranial hemorrhage (sICrH), which have differing natural histories. While both appear to benefit from restarting, sICrH has a higher rebleeding risk and similar or lower thrombotic risk. Clinical equipoise on restarting is also divergent. In sICrH, equipoise is centered on whether to restart. In tICrH, it is centered on when. Several prospective randomized clinical trials are ongoing or about to start to examine the risk-benefit of restarting. Most of them are restricted to patients with sICrH, with antiplatelet control groups. Most are also restricted to direct oral anticoagulants (DOACs), as they are associated with a lower overall risk of ICrH. There is some overlap with tICrH via subdural hematoma, and one trial is specific to restart timing with DOACs in only traumatic cases. This is a narrative review of the current evidence for restarting anticoagulation and restart timing after tICrH along with a summary of the ongoing and planned clinical trials.

Docosahexaenoic acid (DHA) supplementation has proven beneficial in reducing preterm births. However, the challenge lies in addressing nonadherence to prescribed supplementation regimens—a hurdle that significantly impacts clinical trial outcomes. Conventional methods of adherence estimation, such as pill counts and questionnaires, usually fall short when estimating adherence within a specific dosage group. Thus, we propose a Bayesian finite mixture model to estimate adherence among women with low baseline red blood cell phospholipid DHA levels (<6%) receiving higher DHA doses. In our model, adherence is defined as the proportion of participants classified into one of the two distinct components in a normal mixture distribution. Subsequently, based on the estimands from the adherence model, we introduce a novel Bayesian adaptive trial design. Unlike conventional adaptive trials that employ regularly spaced interim schedules, the novelty of our proposed trial design lies in its adaptability to adherence percentages across the treatment arm through irregular interims. The irregular interims in the proposed trial are based on the effect size estimation informed by the finite mixture model. In summary, this study presents innovative methods for leveraging the capabilities of Bayesian finite mixture models in adherence analysis and the design of adaptive clinical trials. [ABSTRACT FROM AUTHOR]

Background and Purpose: Cerebral infarction remains an important cause of death or disability in patients with aneurysmal subarachnoid hemorrhage (SAH). The prevalence, trends, and outcomes of cerebral infarction in patients with aneurysmal SAH at a national level are not known. Methods: We identified the proportion of patients who develop cerebral infarction (ascertained using validated methodology) among patients with aneurysmal SAH and annual trends using the Nationwide Inpatient Sample (NIS) from 2016 to 2021. We analyzed the effect of cerebral infarction on in‐hospital mortality, routine discharge without palliative care (based on discharge disposition), poor outcome defined by the NIS SAH outcome measure, and length and costs of hospitalization after adjusting for potential confounders. Results: A total of 35,305 (53.6%) patients developed cerebral infarction among 65,840 patients with aneurysmal SAH over a 6‐year period. There was a trend toward an increase in the proportion of patients who developed cerebral infarction from 51.5% in 2016 to 56.1% in 2021 (p trend p<.001). Routine discharge was significantly lower (30.5% vs. 37.8%, odds ratio [OR] 0.82, 95% confidence interval [CI] 0.75‐0.89, p<.001), and poor outcome defined by NIS‐SAH outcome measure was significantly higher among patients with cerebral infarction compared with those without cerebral infarction (67.4% vs. 59.3%, OR 1.29, 95% CI 1.18‐1.40, p<.001). There was no difference in in‐hospital mortality (13.0% vs. 13.6%, OR 0.94, 95% CI 0.85‐1.05, p =.30). The length of stay (median 18 days [interquartile range [IQR] 13‐25] vs. 14 days [IQR 9‐20]), coefficient 3.04, 95% CI 2.44‐3.52 and hospitalization cost (median $96,823 vs. $71,311, coefficient 22,320, 95% CI 20,053‐24,587) were significantly higher among patients who developed cerebral infarction compared with those who did not develop cerebral infarction. Conclusions: Cerebral infarction was seen in 54% of the patients with a trend toward an increase in the affected proportion of patients with aneurysmal SAH. Patients with cerebral infarction had higher rates of adverse outcomes and required higher resources during hospitalization. [ABSTRACT FROM AUTHOR]

Emergency medical diseases (EMDs) are the leading cause of death worldwide. A time‐to‐death analysis is needed to accurately identify the risks and describe the pattern of an EMD because the mortality rate can peak early and then decline. Dose‐ranging Phase II clinical trials are essential for developing new therapies for EMDs. However, most dose‐finding trials do not analyze mortality as a time‐to‐event endpoint. We propose three Bayesian dose‐response time‐to‐event models for a secondary mortality analysis of a clinical trial: a two‐group (active treatment vs control) model, a three‐parameter sigmoid EMAX model, and a hierarchical EMAX model. The study also incorporates one specific active treatment as an active comparator in constructing three new models. We evaluated the performance of these six models and a very popular independent model using simulated data motivated by a randomized Phase II clinical trial focused on identifying the most effective hyperbaric oxygen dose to achieve favorable functional outcomes in patients with severe traumatic brain injury. The results show that the three‐group, EMAX, and EMAX model with an active comparator produce the smallest averaged mean squared errors and smallest mean absolute biases. We provide a new approach for time‐to‐event analysis in early‐phase dose‐ranging clinical trials for EMDs. The EMAX model with an active comparator can provide valuable insights into the mortality analysis of new EMDs or other conditions that have changing risks over time. The restricted mean survival time, a function of the model's hazards, is recommended for displaying treatment effects for EMD research. [ABSTRACT FROM AUTHOR]

Clinical trials powered to detect subgroup effects provide the most reliable data on heterogeneity of treatment effect among different subpopulations. However, pre-specified subgroup analysis is not always practical and post hoc analysis results should be examined cautiously. Bayesian hierarchical modelling provides grounds for defining a controlled post hoc analysis plan that is developed after seeing outcome data for the population but before unblinding the outcome by subgroup. Using simulation based on the results from a tobacco cessation clinical trial conducted among the general population, we defined an analysis plan to assess treatment effect among American Indians and Alaska Natives (AI/AN) enrolled in the study. Patients were randomized into two arms using Bayesian adaptive design. For the opt-in arm, clinicians offered a cessation treatment plan after verifying that a patient was ready to quit. For the opt-out arm, clinicians provided all participants with free cessation medications and referred them to a Quitline. The study was powered to test a hypothesis of significantly higher quit rates for the opt-out arm at one-month post randomization. Overall, one-month abstinence rates were 15.9% and 21.5% (opt-in and opt-out arm, respectively). For AI/AN, one-month abstinence rates were 10.2% and 22.0% (opt-in and opt-out arm, respectively). The posterior probability that the abstinence rate in the treatment arm is higher is 0.96, indicating that AI/AN demonstrate response to treatment at almost the same probability as the whole population. [ABSTRACT FROM AUTHOR]