Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making?
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ABSTRACT:
SUBMITTER: Yarnell CJ
PROVIDER: S-EPMC8439199 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Because of practice variation and new developments in palliative pediatric care, the Dutch Association of Pediatrics decided to develop the clinical practice guideline (CPG) palliative care for children. With this guideline, the association also wanted to precipitate an attitude shift towards shared decision-making (SDM) and therefore integrated SDM in the CPG Palliative care for children. The aim was to gain insight if integrating SDM in CPGs can potentially encourage pediatricians to practice SDM. Its objectives were to explore pediatricians' attitudes and thoughts regarding (1) recommendations on SDM in CPGs in general and the guideline Palliative care for children specifically; (2) other SDM enhancing strategies or tools linked to CPGs. METHODS:Semi-structured face-to-face interviews. Pediatricians (15) were recruited through purposive sampling in three university-based pediatric centers in the Netherlands. The interviews were audio-recorded and transcribed verbatim, coded by at least two authors and analyzed with NVivo. RESULTS:Some pediatricians considered SDM a skill or attitude that cannot be addressed by clinical practice guidelines. According to others, however, clinical practice guidelines could enhance SDM. In case of the guideline Palliative care for children, the recommendations needed to focus more on how to practice SDM, and offer more detailed recommendations, preferring a recommendation stating multiple options. Most interviewed pediatricians felt that patient decisions aids were beneficial to patients, and could ensure that all topics relevant to the patient are covered, even topics the pediatrician might not consider him or herself, or deems less important. Regardless of the perceived benefit, some pediatricians preferred providing the information themselves instead of using a patient decision aid. CONCLUSIONS:For clinical practice guidelines to potentially enhance SDM, guideline developers should avoid blanket recommendations in the case of preference sensitive choices, and SDM should not be limited to recommendations on non-treatment decisions. Furthermore, preference sensitive recommendations are preferably linked with patient decision aids.
| S-EPMC6883587 | biostudies-literature
Project description:BackgroundElectronic medical records are widely used in family practices across Canada and can improve health outcomes. However, recent reports indicate that physicians using electronic medical records work longer and have less direct patient contact which may contribute to burnout. Therefore, new and innovative digital tools are essential to reduce physician workloads and improve patient-physician interaction to address physician burnout. The objective of this study was to assess the efficiency and accuracy of clinical decision-making when using a new preventive care point-of-care clinical decision support system (CDSS). An estimate of the potential annual time savings was also determined. This study also assessed physician reported perceived usefulness and ease of use of the CDSS.MethodsQuantitative and qualitative data were collected during this study. Each participant evaluated two simulated patient charts and identified which preventive care metrics were due. The participants recorded their decisions and the time required to assess each chart. Participants then completed a Technology Acceptance Model survey regarding the perceived usefulness and ease of use of the CDSS, which included qualitative feedback. The amount of time saved was determined and participants' clinical decision-making accuracy was scored against current Canadian preventive care guidelines. The number of preventive care specific visits completed per year was determined using clinic billing data.ResultsThe preventive care CDSS saved an average of 195.7 s of chart review time (249.5 s vs 445.2 s; P < 0.001). A total of 1520 preventive visits were performed at Primrose and Bruyère Family Medicine Centres. Extrapolated across the organization, implementation of the new tool could save 82.6 h per year. Decision-making accuracy was not affected by the new tool (78.4% vs 80.9%, P > 0.05). Participants rated the perceived ease of use and usefulness to be very high.ConclusionsNew digital tools may reduce providers' workload without impacting clinical decision-making accuracy. Participants indicated that the preventive care CDSS was useful and easy to use. Further software development and clinical studies are required to further improve and characterize the effect this new CDSS has when implemented in clinical practice.
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Project description:Characterizing human variability in susceptibility to chemical toxicity is a critical issue in regulatory decision-making, but is usually addressed by a default 10-fold safety/uncertainty factor. Feasibility of population-based in vitro experimental approaches to more accurately estimate human variability was demonstrated recently using a large (~1000) panel of lymphoblastoid cell lines. However, routine use of such a large population-based model poses cost and logistical challenges. We hypothesize that a Bayesian approach embedded in a tiered workflow provides efficient estimation of variability and enables a tailored and sensible approach to selection of appropriate sample size. We used the previously collected lymphoblastoid cell line in vitro toxicity data to develop a data-derived prior distribution for the uncertainty in the degree of population variability. The resulting prior for the toxicodynamic variability factor (the ratio between the median and 1% most sensitive individuals) has a median (90% CI) of 2.5 (1.4-9.6). We then performed computational experiments using a hierarchical Bayesian population model with lognormal population variability with samples sizes of n = 5 to 100 to determine the change in precision and accuracy with increasing sample size. We propose a tiered Bayesian strategy for fit-for-purpose population variability estimates: (1) a default using the data-derived prior distribution; (2) a pilot experiment using samples sizes of ~20 individuals that reduces prior uncertainty by > 50% with > 80% balanced accuracy for classification; and (3) a high confidence experiment using sample sizes of ~50-100. This approach efficiently uses in vitro data on population variability to inform decision-making.
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Project description:Physicians face a challenge in promoting colorectal cancer screening (CRCS) in the face of multiple competing demands. A decision aid (DA) that clarifies patient preferences and improves decision quality could aid shared decision making (SDM) and be effective at increasing CRCS rates. However, exactly how such DA improves SDM is not clear. This 4-year R01 study funded by the National Cancer Institute seeks to provide detailed understanding of how an interactive DA affects patient-physician communication and SDM, and ultimately CRCS adherence.
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Project description:Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions.
| S-EPMC7682528 | biostudies-literature
Project description:When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal's performance. We present experimental evidence consistent with this prediction and discuss other predictions applicable to more general settings.
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Project description:Little is known about how primary care physicians (PCPs) in routine outpatient practice use paid price information (i.e., the amount that insurers finally pay providers) in daily clinical practice.To describe the experiences of PCPs who have had paid price information on tests and procedures for at least 1 year.Cross-sectional study using semi-structured interviews and the constant comparative method of qualitative analysis.Forty-six PCPs within an accountable care organization.Via the ordering screen of their electronic health record, PCPs were presented with the median paid price for commonly ordered tests and procedures (e.g., blood tests, x-rays, CTs, MRIs).We asked PCPs for (a) their "gut reaction" to having paid price information, (b) the situations in which they used price information in clinical decision-making separate from or jointly with patients, (c) their thoughts on who bore the chief responsibility for discussing price information with patients, and (d) suggestions for improving physician-targeted price information interventions.Among "gut reactions" that ranged from positive to negative, all PCPs were more interested in having patient-specific price information than paid prices from the practice perspective. PCPs described that when patients' out-of-pocket spending concerns were revealed, price information helped them engage patients in conversations about how to alter treatment plans to make them more affordable. PCPs stated that having price information only slightly altered their test-ordering patterns and that they avoided mentioning prices when advising patients against unnecessary testing. Most PCPs asserted that physicians bear the chief responsibility for discussing prices with patients because of their clinical knowledge and relationships with patients. They wished for help from patients, practices, health plans, and society in order to support price transparency in healthcare.Physician-targeted price transparency efforts may provide PCPs with the information they need to respond to patients' concerns regarding out-of-pocket affordability rather than that needed to change test-ordering habits.
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Project description:Drug development can be associated with slow timelines, particularly for rare or difficult-to-treat solid tumors such as glioblastoma. The use of external data in the design and analysis of trials has attracted significant interest because it has the potential to improve the efficiency and precision of drug development. A recurring challenge in the use of external data for clinical trials, however, is the difficulty in accessing high-quality patient-level data. Academic research groups generally do not have access to suitable datasets to effectively leverage external data for planning and analyses of new clinical trials. Given the need for resources to enable investigators to benefit from existing data assets, we have developed the Glioblastoma External (GBM-X) Data Platform which will allow investigators in neuro-oncology to leverage our data collection and obtain analyses. GBM-X strives to provide an uncomplicated process to use external data, contextualize single-arm trials, and improve inference on treatment effects early in drug development. The platform is designed to welcome interested collaborators and integrate new data into the platform, with the expectation that the data collection can continue to grow and remain updated. With such features, GBM-X is designed to help to accelerate evaluation of therapies, to grow with collaborations, and to serve as a model to improve drug discovery for rare and difficult-to-treat tumors in oncology.
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Project description:Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people's confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality.
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Project description:Plasmatic microRNA sequencing was conducted in specific matched subgroups of subjects (n = 53) with and without OSA using HTG EdgeSeq miRNA WTA Assay technology.
2024-04-01 | GSE229362 | GEO