Medical Decision Making最新文献

Background: A previously developed risk prediction model needs to be validated before being used in a new population. The finite size of the validation sample entails that there is uncertainty around model performance. We apply value-of-information (VoI) methodology to quantify the consequence of uncertainty in terms of net benefit (NB).

Methods: We define the expected value of perfect information (EVPI) for model validation as the expected loss in NB due to not confidently knowing which of the alternative decisions confers the highest NB. We propose bootstrap-based and asymptotic methods for EVPI computations and conduct simulation studies to compare their performance. In a case study, we use the non-US subsets of a clinical trial as the development sample for predicting mortality after myocardial infarction and calculate the validation EVPI for the US subsample.

Results: The computation methods generated similar EVPI values in simulation studies. EVPI generally declined with larger samples. In the case study, at the prespecified threshold of 0.02, the best decision with current information would be to use the model, with an incremental NB of 0.0020 over treating all. At this threshold, the EVPI was 0.0005 (relative EVPI = 25%). When scaled to the annual number of heart attacks in the US, the expected NB loss due to uncertainty was equal to 400 true positives or 19,600 false positives, indicating the value of further model validation.

Conclusion: VoI methods can be applied to the NB calculated during external validation of clinical prediction models. While uncertainty does not directly affect the clinical implications of NB findings, validation EVPI provides an objective perspective to the need for further validation and can be reported alongside NB in external validation studies.

Highlights: External validation is a critical step when transporting a risk prediction model to a new setting, but the finite size of the validation sample creates uncertainty about the performance of the model.In decision theory, such uncertainty is associated with loss of net benefit because it can prevent one from identifying whether the use of the model is beneficial over alternative strategies.We define the expected value of perfect information for external validation as the expected loss in net benefit by not confidently knowing if the use of the model is net beneficial.The adoption of a model for a new population should be based on its expected net benefit; independently, value-of-information methods can be used to decide whether further validation studies are warranted.

Background: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare.

Purpose: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA.

Data sources: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking.

Study selection: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation.

Data extraction: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods.

Data synthesis: Across 18 methods identified from 22 studies, themes included use of informative prior(s) (n = 5), piecewise (n = 7), and general population adjustment (n = 9), plus a variety of "other" (n = 8) approaches. Most methods were applied in cancer populations (n = 13). No studies compared or validated their method against another method that also incorporated external evidence.

Limitations: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review.

Conclusions: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

Objective: Traditionally, validation of surrogate endpoints has been carried out using randomized controlled trial (RCT) data. However, RCT data may be too limited to validate surrogate endpoints. In this article, we sought to improve the validation of surrogate endpoints with the inclusion of real-world evidence (RWE).

Methods: We use data from comparative RWE (cRWE) and single-arm RWE (sRWE) to supplement RCT evidence for the evaluation of progression-free survival (PFS) as a surrogate endpoint to overall survival (OS) in metastatic colorectal cancer (mCRC). Treatment effect estimates from RCTs, cRWE, and matched sRWE, comparing antiangiogenic treatments with chemotherapy, were used to inform surrogacy patterns and predictions of the treatment effect on OS from the treatment effect on PFS.

Results: Seven RCTs, 4 cRWE studies, and 2 matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. The addition of RWE to RCTs also improved the accuracy and precision of predictions of the treatment effect on OS obtained using data on the observed effect on PFS.

Conclusion: The addition of RWE to RCT data improved the precision of the parameters describing the surrogate relationship between treatment effects on PFS and OS and the predicted clinical benefit of antiangiogenic therapies in mCRC.

Highlights: Regulatory agencies increasingly rely on surrogate endpoints when making licensing decisions, and for the decisions to be robust, surrogate endpoints need to be validated. In the era of precision medicine, when surrogacy patterns may depend on the drug's mechanism of action and trials of targeted therapies may be small, data from randomized controlled trials may be limited.Real-world evidence (RWE) is increasingly used at different stages of the drug development process. When used to enhance the evidence base for surrogate endpoint evaluation, RWE can improve inferences about the strength of surrogate relationships and the precision of predicted treatment effect on the final clinical outcome based on the observed effect on the surrogate endpoint in a new trial.Careful selection of RWE is needed to reduce risk of bias.

Introduction: Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19) may support clinical decision making, treatment, and communication. However, attitudes about using CPMs for COVID-19 decision making are unknown.

Methods: Online focus groups and interviews were conducted among health care providers, survivors of COVID-19, and surrogates (i.e., loved ones/surrogate decision makers) in the United States and the Netherlands. Semistructured questions explored experiences about clinical decision making in COVID-19 care and facilitators and barriers for implementing CPMs.

Results: In the United States, we conducted 4 online focus groups with 1) providers and 2) surrogates and survivors of COVID-19 between January 2021 and July 2021. In the Netherlands, we conducted 3 focus groups and 4 individual interviews with 1) providers and 2) surrogates and survivors of COVID-19 between May 2021 and July 2021. Providers expressed concern about CPM validity and the belief that patients may interpret CPM predictions as absolute. They described CPMs as potentially useful for resource allocation, triaging, education, and research. Several surrogates and people who had COVID-19 were not given prognostic estimates but believed this information would have supported and influenced their decision making. A limited number of participants felt the data would not have applied to them and that they or their loved ones may not have survived, as poor prognosis may have suggested withdrawal of treatment.

Conclusions: Many providers had reservations about using CPMs for people with COVID-19 due to concerns about CPM validity and patient-level interpretation of the outcome predictions. However, several people who survived COVID-19 and their surrogates indicated that they would have found this information useful for decision making. Therefore, information provision may be needed to improve provider-level comfort and patient and surrogate understanding of CPMs.

Highlights: While clinical prediction models (CPMs) may provide an objective means of assessing COVID-19 prognosis, provider concerns about CPM validity and the interpretation of CPM predictions may limit their clinical use.Providers felt that CPMs may be most useful for resource allocation, triage, research, or educational purposes for COVID-19.Several survivors of COVID-19 and their surrogates felt that CPMs would have been informative and may have aided them in making COVID-19 treatment decisions, while others felt the data would not have applied to them.

Background: It has been reported that a substantial number of COVID-19 infections are asymptomatic, with both symptomatic and asymptomatic infections contributing to transmission dynamics. Yet, the share of asymptomatic cases varies greatly across studies. One reason for this could be the measurement of symptoms in medical studies and surveys.

Design: In 2 experimental survey studies (total N > 3,000) with participants from Germany and the United Kingdom, respectively, we varied the inclusion of a filter question on whether participants who tested positive for COVID-19 had experienced symptoms prior to presenting a checklist of symptoms. We measured the reporting of asymptomatic (versus symptomatic) COVID-19 infections.

Results: The inclusion of a filter question increased the reporting of asymptomatic (versus symptomatic) COVID-19 infections. Particularly mild symptoms were underreported when using a filter question.

Conclusions and implications: Filter questions affect the reporting of (a)symptomatic COVID-19 cases. To account for such differences in the estimation of population infection rates, future studies should transparently report the applied question format.

Highlights: Both symptomatic and asymptomatic infections are important for COVID-19 transmission dynamics.In previous research, symptoms have been assessed either with or without a filter question prior to presenting a symptom list.We show that filter questions reduce the reporting of asymptomatic infections.Particularly mild symptoms are underreported when using a filter question.

Purpose: Values are critical to how individuals make decisions and cope, yet the values of heart failure (HF) patients and their family caregivers (FCGs) remain understudied. We sought to report the state of the science on how values are discussed, reflected upon, and acted on by patients with HF, their FCGs, or both related to health-related decision making and coping.

Method: A scoping review was conducted of empirical studies using the following keywords: "heart failure," "values," "decision-making," and "coping." PubMed, PsycINFO, and Scopus were searched from inception to June 2022 in English. Included articles reported values as a key finding (outcome/theme) in their abstract.

Results: Of 448 articles screened for eligibility, 16 met the inclusion criteria. Twelve articles reported findings addressing patient values, 3 addressed patient and FCG values, and 1 addressed FCG values. Values were reported to influence patient self-care behaviors and left ventricular assist device (LVAD) implantation decisions, although their prioritization varied across time and contexts. When prioritized values conflicted with recommended self-care activities, some patients modified their approach to achieving the value. Others modified or abandoned tasks in favor of the value and accompanying goals. Low motivation and alignment between unhealthy behaviors and values often led to nonadherent decisions. Five of 8 articles focusing on cardiac devices reported patient survival as the most prioritized value during implantation decisions. FCG values were rarely reported or evaluated separately from patient values. Patients leveraged several coping strategies, although the processes through which values affected coping was not described.

Conclusions: Prioritized values influenced HF-related decisions, including self-care and LVAD implantation. While several articles reported on coping and values, none described processes through which values affect coping, which highlights a research gap.

Highlights: Family caregiver values were rarely reported or evaluated separately from patient values, highlighting a gap in the literature.

Background: The promise of precision medicine could be stymied if people do not accept the legitimacy of personalized risk information. We tested 4 explanations for skepticism of personalized diabetes risk information.

Method: We recruited participants (N = 356; M_age = 48.6 [s = 9.8], 85.1% women, 59.0% non-Hispanic white) from community locations (e.g., barbershops, churches) for a risk communication intervention. Participants received personalized information about their risk of developing diabetes and heart disease, stroke, colon cancer, and/or breast cancer (women). Then they completed survey items. We combined 2 items (recalled risk, perceived risk) to create a trichotomous risk skepticism variable (acceptance, overestimation, underestimation). Additional items assessed possible explanations for risk skepticism: 1) information evaluation skills (education, graph literacy, numeracy), 2) motivated reasoning (negative affect toward the information, spontaneous self-affirmation, information avoidance); 3) Bayesian updating (surprise), and 4) personal relevance (racial/ethnic identity). We used multinomial logistic regression for data analysis.

Results: Of the participants, 18% believed that their diabetes risk was lower than the information provided, 40% believed their risk was higher, and 42% accepted the information. Information evaluation skills were not supported as a risk skepticism explanation. Motivated reasoning received some support; higher diabetes risk and more negative affect toward the information were associated with risk underestimation, but spontaneous self-affirmation and information avoidance were not moderators. For Bayesian updating, more surprise was associated with overestimation. For personal relevance, belonging to a marginalized racial/ethnic group was associated with underestimation.

Conclusion: There are likely multiple cognitive, affective, and motivational explanations for risk skepticism. Understanding these explanations and developing interventions that address them will increase the effectiveness of precision medicine and facilitate its widespread implementation.

Background: Hypertension (high blood pressure) is a modifiable risk factor for cardiovascular disease. However, patients may lack confidence in their understanding of what constitutes normal/healthy blood pressure, potentially affecting intentions to seek necessary care. The American Heart Association defines normal/healthy blood pressure as <120/80 mm Hg, with a 130/80 mm Hg threshold for hypertension diagnosis.

Methods: Our US sample (N = 6,592) included 1,342 adults with hypertension alone and 795 with hypertension and relevant comorbidities (heart disease, kidney disease, and diabetes mellitus). We assessed confidence in understanding blood pressure numbers, knowledge of thresholds for normal/healthy blood pressure ("normal or healthy blood pressure is below . . ."; counting 120-130/80 mm Hg as correct), and intentions to seek care for randomly assigned blood pressure readings of 142/91 (stage 2 hypertension), 132/69 (stage 1 hypertension), or 118/78 mm Hg (normal/healthy blood pressure).

Results: Among nonhypertensive participants, 55% expressed confidence in their understanding of blood pressure numbers, but only 36% knew the upper thresholds for normal/healthy blood pressure. Among participants with hypertension alone, 78% were confident while 47% were knowledgeable. Among participants with hypertension and comorbidities, 81% were confident and 40% were knowledgeable. Participants who were confident (v. not) were more likely to express intentions to act on stage 2 hypertension readings but less likely to express intentions to act on stage 1 readings, even after adjustment for knowledge, hypertension diagnosis, and sociodemographics.

Limitations: Confidence, knowledge, and intentions were each measured with 1 question.

Conclusions: Independent of knowledge, confidence was associated with greater willingness to act on stage 2 hypertension readings but reduced willingness to act on stage 1 hypertension readings. Interventions aiming to improve hypertension care-seeking behavior should improve confidence in accurate knowledge.

Highlights: Hypertension or high blood pressure is a major risk factor for heart disease.Most Americans do not know that normal/healthy blood pressure levels are ≤120/80 mm Hg, yet they are confident that they know this information.Inappropriate confidence in understanding of blood pressure numbers undermines intentions to seek care for stage 1 hypertension blood pressure readings.

Background: General practitioners (GPs) provide the most antidepressant prescriptions and psychologist referrals in Australia, yet little is known about how they decide between treatments for depressive symptoms.

Aims: This study examined the decision cues that GPs use when deciding how to treat depressive symptoms and the meaning they attribute to these associations.

Methods: Structured interviews were conducted with 16 Australian GPs in a "think-aloud" verbal protocol analysis format. The transcripts were analyzed using content coding and thematic analysis, informed by the dual processes model of decision making.

Results: Participants associated prescribing antidepressants with severe depressive symptoms, and psychologist referrals were the preferred initial treatment provided patients were willing to engage. Four main themes emerged from the thematic analysis: 1) psychologist as default, 2) the risk is just too high, 3) medication as supplement, and 4) drivers of antidepressants. Contrary to previous findings, participants identified a strong heuristic association between depressive symptoms and psychologist referral. Antidepressant prescription was associated with severe symptoms, higher risk, and a cluster of cues that lead them away from psychologist referral.

Conclusions: Participants demonstrated an oversensitivity to depressive symptom severity, associating psychologist referrals with subclinical depressive symptoms, and starting antidepressants for suicidal ideation and significant functional decline.

Highlights: Psychologist referrals were the default preference for GPs when treating depressive symptoms, whereas antidepressants were reserved for patients with elevated risk from severe depressive symptoms and suicidal ideation.Participants' conceptualization of depression severity was oversensitive compared with treatment guidelines.Contrary to treatment guidelines, GPs in this study demonstrated their discomfort for referring patients with suicidal ideation to a psychologist without initiating antidepressants.GPs should consider whether patients within their mild to moderate assessment range can be treated exclusively with more cost-effective means than psychological therapy such as e-mental health programs, guided self-help, and lifestyle changes alone.