Medical Decision Making最新文献

BackgroundPrognostic models are crucial for predicting patient outcomes and aiding clinical decision making. Despite their availability in acute neurologic care, their use in clinical practice is limited, with insufficient reflection on reasons for this scarce implementation.PurposeTo summarize facilitators and barriers among clinicians affecting the use of prognostic models in acute neurologic care.Data SourcesSystematic searches were conducted in Embase, Medline ALL, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials from inception until February 2024.Study SelectionEligible studies included those providing clinicians' perspectives on the use of prognostic models in acute neurologic care.Data ExtractionData were extracted concerning study characteristics, study aim, data collection and analysis, prognostic models, participant characteristics, facilitators, and barriers. Risk of bias was assessed using the Qualsyst tool.Data SynthesisFindings were structured around the Unified Theory of Acceptance and Use of Technology framework. Identified facilitators included improved communication with patients and surrogate decision makers (n = 9), reassurance of clinical judgment (n = 6) perceived improved patient outcomes (n = 4), standardization of care (n = 4), resource optimization (n = 3), and extension of clinical knowledge (n = 3). Barriers included perceived misinterpretation during risk communication (n = 3), mistrust in data (n = 3), perceived reduction of clinicians' autonomy (n = 3), and ethical considerations (n = 2). In total, 15 studies were included, with all but 1 demonstrating good methodological quality. None were excluded due to poor quality ratings.LimitationsThis review identifies limitations, including study heterogeneity, exclusion of gray literature, and the scarcity of evaluations on model implementation.ConclusionsUnderstanding facilitators and barriers may enhance prognostic model development and implementation. Bridging the gap between development and clinical use requires improved collaboration among researchers, clinicians, patients, and surrogate decision makers.HighlightsThis is the first systematic review to summarize published facilitators and barriers affecting the use of prognostic models in acute neurologic care from the clinicians' perspective.Commonly reported barriers and facilitators were consistent with several domains of the Unified Theory of Acceptance and Use of Technology model, including effort expectancy, social influence, and facilitating conditions, with the focus on the performance expectancy domain.Future implementation research including collaboration with researchers from different fields, clinicians, patients, and their surrogate decision makers may be highly valuable for future model development and implementation.

BackgroundAlthough sterilization is one of the most effective methods of birth control, some physicians may hesitate to perform postpartum sterilizations on patients after preterm birth, as preterm labor and delivery may preclude adequate counseling.MethodsThis is a cross-sectional study conducted at a single, tertiary care, academic institution of adult pregnant patients who experienced a spontaneous or iatrogenic preterm delivery between March 15, 2011, and May 10, 2014 and underwent postpartum female surgical sterilization within 12 wk of delivery. A validated Decision Regret Scale was administered 7 to 11 y later. Univariate and bivariate analyses were conducted. Unadjusted and multivariate logistic regression analyses identified factors associated with moderate to severe decision regret.ResultsMost participants (75.5%) with a preterm delivery reported no or mild regret associated with their sterilization. Circumstances surrounding the sterilization decision were positive, as 85.7% reported having enough information, 81.6% reported enough emotional support, and 75.5% reported adequate decision time. Adjusting for maternal and gestational age at delivery plus other covariates, only those reporting they had adequate time to make their sterilization decision remained significantly associated with no or mild regret (odds ratio: 0.002, 95% confidence interval: <0.001-0.61).DiscussionStudy results indicated high confidence in the sterilization decision, which was not affected by maternal age at delivery or the fact that the individual had a preterm delivery, emphasizing the importance of individualized counseling and support for patients during the decision-making process.ConclusionProviding adequate time for patients to decide on postpartum surgical sterilization was the most important factor for decreased sterilization regret.ImplicationsThe decision for sterilization should be made using a patient-centered, shared decision-making framework.HighlightsAmong patients with a preterm delivery who underwent postpartum surgical sterilization, maternal age at delivery was not associated with increased decision regret.Providing adequate time for patients to decide on postpartum surgical sterilization was the most important factor for decreased sterilization regret among patients with a preterm delivery.We must trust the patient knows they are making the right decision for themselves in that moment, even if this is at the time of a preterm delivery.

BackgroundEconomic models often require extrapolation of clinical time-to-event data for multiple events. Two modeling approaches in oncology that incorporate time dependency include partitioned survival models (PSM) and semi-Markov decision models estimated using multistate modeling (MSM). The objective of this simulation study was to assess the performance of PSM and MSM across datasets with varying sample size and degrees of censoring.MethodsWe generated disease trajectories of progression and death for multiple hypothetical populations with advanced cancers. These populations served as the sampling pool for simulated trial cohorts with multiple sample sizes and various levels of follow-up. We estimated MSM and PSM by fitting survival models to these simulated datasets with different approaches to incorporating general population mortality (GPM) and selected best-fitting models using statistical criteria. Mean survival was compared with "true" population values to assess error.ResultsWith near complete follow-up, both PSMs and MSMs accurately estimated mean population survival, while smaller samples and shorter follow-up times were associated with a larger error across approaches and clinical scenarios, especially for more distant clinical endpoints. MSMs were slightly more often not estimable when informed by studies with small sample sizes or short follow-up, due to low numbers at risk for the downstream transition. However, when estimable, the MSM models more commonly produced a smaller error in mean survival than the PSMs did.ConclusionsCaution should be taken with all modeling approaches when the underlying data are very limited, particularly PSMs, due to the large errors produced. When estimable and for selections based on statistical criteria, MSMs performed similar to or better than PSMs in estimating mean survival with limited data.HighlightsCaution should be taken with all modeling approaches when underlying data are very limited.Partitioned survival models (PSMs) can lead to significant errors, particularly with limited follow-up. Incorporating general population mortality (GPM) via internal additive hazards improved estimates of mean survival, but the effects were modest.When estimable, decision models based on multistate modeling (MSM) produced similar or smaller error in mean survival compared with PSM, but small samples or limited deaths after progression produce additional challenges for fitting MSMs; more research is needed to improve estimation of MSMs and similar state transition-based modeling methods with limited data.Future studies are needed to assess the applicability of these findings to comparative analyses estimating incremental survival benefits.

ObjectivesThe EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS®) preference score (PROPr) are preference-based measures. This study compares mapping and linking approaches to align the PROPr and the PROMIS domains included in PROPr plus Anxiety with EQ-5D-5L item responses and preference scores.MethodsA general population sample of 983 adults completed the online survey. Regression-based mapping methods and item response theory (IRT) linking methods were used to align scores. Mapping was used to predict EQ-5D-5L item responses or preference scores using PROMIS domain scores. Equating strategies were applied to address regression to the mean. The linking approach estimated item parameters of EQ-5D-5L based on the PROMIS score metric and generated bidirectional crosswalks between EQ-5D-5L item responses and relevant PROMIS domain scores.ResultsEQ-5D-5L item responses were significantly accounted for by PROMIS domains of Anxiety, Depression, Fatigue, Pain Interference, Physical Function, Social Roles, and Sleep Disturbance. EQ-5D-5L preference scores were accounted for by the same PROMIS domains, excluding Anxiety and Fatigue, and by the PROPr preference scores. IRT-linking crosswalks were generated between EQ-5D-5L item responses and PROMIS domains of Physical Function, Pain, and Depression. Small differences were found between observed and predicted scores for all 3 methods. The direct mapping approach (directly predicting EQ-5D-5L scores) with the equipercentile equating strategy proved superior to the linking method due to improved prediction accuracy and comparable score range coverage.ConclusionsThe PROPr and the PROMIS domains included in the PROMIS-29+2 predict EQ-5D-5L preference scores or item responses. Both methods can generate acceptably precise EQ-5D-5L preference scores, with the direct mapping approach using the equating strategy offering better precision. We summarized recommended score conversion tables based on available and desired scores.HighlightsThis study compares mapping (score prediction) and IRT-based linking approaches to align the PROPr and the PROMIS domains with EQ-5D-5L item responses and preference scores.Researchers, clinicians, and stakeholders can use this study's regression formulas and score crosswalks to convert scores between PROMIS and EQ-5D-5L.Mapping can generate more precise scores, while linking offers greater flexibility in score estimation when fewer PROMIS domain scores are collected.

BackgroundMany contributing factors can influence individuals' health, and these factors may not affect health outcomes equally. This study compared the importance of 38 predictors of health-related quality of life (HRQOL) and 2-y mortality for US older adults.MethodsData were from the Medicare Health Outcome Survey Cohort 23 (baseline 2020, follow-up 2022). This study included participants ≥65 y (N = 142,551). HRQOL measures included physically unhealthy days (PUD), mentally unhealthy days (MUD), and activity limitation days (ALD) from the Healthy Days questions and 3 measures from the Veterans RAND 12-Item Health Survey (VR-12). A variable's importance was measured as the average gain in R² after adding the variable in all submodels.ResultsFor physical health (PUD), pain interfered with daily activities was the most important predictor with an importance score (I) of 8.4, indicating that this variable contributed 8.4% variance of PUD. Other leading predictors included pain interfered with socializing (I = 7.3) and pain rating (I = 6.7). For mental health (MUD), depression (I = 11.6) was far more important than any of the other predictors, contributing 38% of the total importance. For perceived disability (ALD), pain interfered with socializing was the most important predictor (I = 8.3), followed by difficulty doing errands (I = 6.1) and pain interfered with activities (I = 6.0). Of note, this general pattern was consistent for VR-12 HRQOL measures. Variables' importance scores for 2-y morality were very different from that for HRQOL. Age (I = 2.8) and difficulty doing errands (I = 2.6) were the most important variables.ConclusionsThis study demonstrated a large discrepancy in the variables' importance for HRQOL and 2-y mortality. Functional limitations/disabilities and geriatric syndromes were more important for the prediction of HRQOL than were chronic conditions and other factors combined.HighlightsFor older adults, large differences were found in variable importance for explaining health-related quality of life (HRQOL) and 2-y mortality among 38 explanatory variables, including functional limitations, geriatric syndromes, chronic conditions, and other factors.Pain and pain interference, difficulty doing errands, difficulty concentrating, memory problems, problems with walking/balance, and depression were the most important predictors of HRQOL.Age, marital status, education, difficulty doing errands, congestive heart failure, chronic obstructive pulmonary disease, and any cancer were more important for 2-y mortality than HRQOL.Health care providers and policy makers should focus on the impact of multimorbidity and the interaction between often multifactorial conditions, as opposed to focusing only on individual diseases.

In a landscape of accelerated approvals and a less mature evidence base, constrained health systems make reimbursement decisions based on uncertain evidence about the expected clinical and economic value of a health technology. Uncertain decisions require expert judgments, and there has recently been a drive to improve the accountability and transparency in the way these judgments are collected and reported. Structured expert elicitation (SEE) refers to formal methods to quantify experts' judgments. Protocols for conducting SEE exist; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation. This article describes the development of Structured Expert Elicitation Resources (STEER), a collection of open access resources based on a published protocol for SEE specific to the health care decision-making (HCDM) setting. The resources cover the entire SEE process from design to reporting. The resources include an overview and a practical guide for conducting SEE in this setting, adaptable tools for building bespoke SEE exercises, training materials for experts taking part in SEE, resources used in previous SEE exercises, and examples of published SEE in HCDM. The materials cover practical considerations such as timelines team skills requirements, and administrative requirements such as contracting. The use of off-the-shelf resources can streamline the SEE process in HCDM while maintaining robustness.HighlightsThere is a drive to improve accountability and transparency in the way expert judgments are used in health care decision making; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation.Structured Expert Elicitation Resources (STEER) is a collection of open access resources for conducting SEE in health care decision making, based on a published methods protocol for SEE specific to this setting.The use of off-the-shelf resources can streamline the SEE process in health care decision making while maintaining robustness.

BackgroundEconomic evaluations for life-extending treatments frequently require clinical trial data to be extrapolated beyond the trial duration to estimate changes in life expectancy. Conventional survival models often display hazard profiles that do not rise as expected in an aging population and require the incorporation of external data to ensure plausibility. Relative survival (RS) models can enable the incorporation of external data at model fitting. A comparison was performed between RS and "standard" all-cause survival (ACS) in modeling outcomes from the tafamidis for the treatment of transthyretin amyloid cardiomyopathy (ATTR-ACT) trial.MethodsPatient-level data from the 30-mo ATTR-ACT trial were used to develop survival models based on parametric ACS and RS models. The latter was composed of an expected hazard and an independent excess hazard. Models were selected according to statistical goodness of fit and clinical plausibility, with extrapolation up to 72 mo validated against ATTR-ACT long-term extension (LTE) data.ResultsInformation criteria were too similar to discriminate between RS or ACS models. Several ACS models were affected by capping with general population mortality rates and considered implausible. Selected RS models matched the empirical hazard function, could not fall below general population hazards, and predicted well compared with the LTE data. The preferred RS model predicted the restricted mean survival (RMST) to 72 mo of 51.0 mo (95% confidence interval [CI]: 46.1, 55.3); this compared favorably to the LTE RMST of 50.9 mo (95% CI: 47.7, 53.9).DiscussionRS models can improve the accuracy for modeling populations with high background mortality rates (e.g., the ATTR-CM trial). RS modeling enforces a plausible long-term hazard profile, enables flexibility in medium-term hazard profiles, and increases the robustness of medical decision making.HighlightsTo inform survival extrapolations for health technology assessment, a relative survival model incorporating external data per the recommendations of the National Institute for Health and Care Excellence (NICE) Decision Support Unit was used in support of the NICE evaluation of tafamidis for treatment of transthyretin amyloid cardiomyopathy (ATTR-CM).Relative survival modeling allowed selection of a broader range of hazard profiles compared with all-cause survival modeling by ensuring plausible long-term predictions.Predictions from plausible relative survival models of overall survival in patients with ATTR-CM, extrapolated from the ATTR-ACT trial, validated very well to outcomes after a doubling of follow-up and demonstrated improved precision and accuracy versus parametric all-cause survival models.

BackgroundEffective shared decision making (SDM) in health care involves thorough discussions of options, pros, cons, and patient preferences. While SDM is recommended for engaging adults aged 76 to 85 y in colorectal cancer (CRC) screening decisions, the extent of SDM documentation in clinical notes remains unclear.ObjectiveThis study aimed to evaluate the current state of SDM documentation in clinical notes regarding CRC screening discussions for adults aged 76 to 85 y. It also sought to assess the impact of an SDM training intervention on documentation quality and compare documented SDM elements with physician- and patient-reported SDM.MethodsData from 465 patient participants and 58 primary care physicians in a multisite cluster randomized trial were analyzed. Physicians in the intervention arm underwent a 2-h SDM skills training and received support tools, including an electronic health record SmartPhrase. Coders analyzed clinical notes using content analysis to identify SDM elements. Linear multilevel models and multilevel partial correlations were used for analysis.ResultsOverall, SDM Note scores were low ($\overline{x}$ = 0.80, s = 0.99). The intervention arm exhibited higher SDM Note scores than the comparator arm did (adjusted mean 1.02 v. 0.66; P = 0.006), with more frequent documentation of stool-based tests (52% v. 33%; P = 0.02) and colonoscopy cons (28% v. 8%; P = 0.001). No significant differences were observed in patient preference documentation. SDM Note scores correlated moderately with patient- and physician-reported SDM.ConclusionDocumentation of CRC screening discussions with older adults lacks comprehensive SDM elements. The intervention improved SDM documentation, particularly regarding alternative screening options and potential cons. Given the limited documentation of SDM even after a training intervention, attention to more robust SDM documentation, including patient preferences and discussion of stopping CRC screening, is needed.HighlightsShared decision-making (SDM) documentation in clinical notes is limited for discussions on colon cancer screening among older adults.SDM training improves SDM documentation of screening options for colorectal cancer, specifically documentation of stool-based testing and the downsides of screening options.SDM documentation in clinical notes is related to patient and provider reports of SDM.

IntroductionEpidemiological models benefit from incorporating detailed time-to-event data to understand how disease risk evolves. For example, decompensation risk in liver cirrhosis depends on sojourn time spent with cirrhosis. Semi-Markov and related models capture these details by modeling time-to-event distributions based on published survival data. However, implementations of semi-Markov processes rely on Monte Carlo sampling methods, which increase computational requirements and introduce stochastic variability. Explicitly calculating the evolving transition likelihood can avoid these issues and provide fast, reliable estimates.MethodsWe present the sojourn time density framework for computing semi-Markov and related models by calculating the evolving sojourn time probability density as a system of partial differential equations. The framework is parametrized by commonly used hazard and models the distribution of current disease state and sojourn time. We describe the mathematical background, a numerical method for computation, and an example model of liver disease.ResultsModels developed with the sojourn time density framework can directly incorporate time-to-event data and serial events in a deterministic system. This increases the level of potential model detail over Markov-type models, improves parameter identifiability, and reduces computational burden and stochastic uncertainty compared with Monte Carlo methods. The example model of liver disease was able to accurately reproduce targets without extensive calibration or fitting and required minimal computational burden.ConclusionsExplicitly modeling sojourn time distribution allows us to represent semi-Markov systems using detailed survival data from epidemiological studies without requiring sampling, avoiding the need for calibration, reducing computational time, and allowing for more robust probabilistic sensitivity analyses.HighlightsTime-inhomogeneous semi-Markov models and other time-to-event-based modeling approaches can capture risks that evolve over time spent with a disease.We describe an approach to computing these models that represents them as partial differential equations representing the evolution of the sojourn time probability density.This sojourn time density framework incorporates complex data sources on competing risks and serial events while minimizing computational complexity.

This study outlines methods for modeling disability-adjusted life-years (DALYs) in common decision-modeling frameworks. Recognizing the wide spectrum of experience and programming comfort level among practitioners, we outline 2 approaches for modeling DALYs in its constituent parts: years of life lost to disease (YLL) and years of life lived with disability (YLD). Our beginner approach draws on the Markov trace, while the intermediate approach facilitates more efficient estimation by incorporating non-Markovian tracking elements into the transition probability matrix. Drawing on an existing disease progression discrete time Markov cohort model, we demonstrate the equivalence of DALY estimates and cost-effectiveness analysis results across our methods and show that other commonly used "shortcuts" for estimating DALYs will not, in general, yield accurate estimates of DALY levels nor incremental cost-effectiveness ratios in a modeled population.HighlightsThis study introduces 2 DALY estimation methods-beginner and intermediate approaches-that produce similar results, expanding the toolkit available to decision modelers.These methods can be adapted to estimate other outcomes (e.g., QALYs, life-years) and applied to other common decision-modeling frameworks, including microsimulation models with patient-level attributes and discrete event simulations that estimate YLDs and YLLs based on time to death and disease duration.Our findings further reveal that commonly used shortcut methods for DALY calculations may lead to differing results, particularly for DALY levels and incremental cost-effectiveness ratios.