M. G. Myriam Hunink

Background: Coronary artery calcium scoring ( CACS ) by computed tomography could enhance risk assessment and decision making for preventive medication in patients with diabetes. We performed a microsimulation study to compare costs and health outcomes of guideline‐based periodic cardiovascular risk assessment with and without CACS . Methods: We modeled various US guideline‐based preventive approaches based on periodically assessed 10‐year risk by pooled cohort equations with and without CACS. We predicted cumulative costs and quality‐adjusted life years (QALYs) until age 100 years from the US health care sector perspective in MESA (Multi‐Ethnic Study of Atherosclerosis) participants aged 45 to 84 years with diabetes (n=853), who were weighted to represent the US general patient population. Probabilistic and deterministic sensitivity analyses were performed to address uncertainty. Results: Initiating high‐intensity statins regardless of risk and low‐dose aspirin if 10‐year risk ≥10% led to the largest QALY gains with incremental cost‐effectiveness ratios of $35 000 to $40 000/QALY. When omitting such universal approaches, allocating high‐intensity statins and low‐dose aspirin if CACS ≥100 led to incremental cost‐effectiveness ratios around $50 000/QALY. Ranking of strategies by cost‐effectiveness was generally robust against parameter uncertainty. The incremental cost‐effectiveness ratio of the CACS ≥100 strategy fell below $50 000/QALY if the fee of CACS fell below $75 or when statin continuation was assumed to significantly improve with nonzero CAC scores. Conclusions: Broadening the use of high‐intensity statins and low‐dose aspirin in patients aged 45 to 84 years with diabetes can be considered cost‐effective. If broad‐scale use of intensive preventive treatment is either not feasible or not desired, then CACS may be cost‐effective in refining preventive treatment decisions.

Mobile stroke units (MSUs) have the potential to improve functional outcome of ischemic stroke patients, through shortening onset-to-treatment times. Previous cost-effectiveness studies have limited generalizability to nonmetropolitan settings and did not evaluate cost-effectiveness over a lifetime horizon. We aimed to develop a regionally adaptable decision-analytic model, to evaluate cost-effectiveness of MSU implementation and to identify the optimal dispatch scenario. We developed a generalizable state-transition microsimulation model with modifiable region-specific parameters and dispatch characteristics to evaluate the lifetime cost-effectiveness from a health care perspective of 1-year MSU implementation. We used the southwest of the Netherlands (1,770,000 inhabitants, 1,592 km², 7 primary stroke centers, 2 thrombectomy-capable stroke centers) as an example. Region-specific input parameters for the model, such as population density, age distribution, and driving times, were obtained at the level of postal codes. We developed a virtual cohort of suspected stroke patients based on age-dependent stroke risks and the number of inhabitants per postal code. We compared the combined dispatch of an MSU and emergency medical services (EMS) with dispatch of EMS alone for patients with onset-to-alarm time <6 hours, living within the catchment area of the MSU. In the base case analysis, the MSU could be dispatched to all postal codes in the study region between 7.00 am and 11.00 pm from a central dispatch site. We assessed the long-term cost-effectiveness through incremental net monetary benefits (iNMBs). Discount rates were 1.5% for effects and 4.0% for costs. In the base case scenario, the MSU was dispatched to 2,080 of 3,628 patients (57.3%) with a suspected stroke and onset-to-alarm time <6 hours, resulting in a lifetime gain of 399 (95% CI 384-414) additional quality-adjusted life years, €3.9 million (95% CI €3.5 million-€4.3 million) cost savings, and an iNMB of €23.9 million (95% CI €22.8 million-€24.9 million). A smaller catchment area for MSU dispatch was associated with increased cost-effectiveness. Adding an MSU to the dispatch strategy for suspected stroke patients is expected to be cost-effective in our region. Our model facilitates evaluation of the cost-effectiveness of MSU implementation in different regions, settings, and scenarios with varying characteristics.

Decision-analytic models (DAMs) are essentially informative yet complex tools for solving questions in medical decision making. When their complexity grows, the need for causal inference techniques becomes evident as causal relationships between variables become unclear. In this methodological commentary, we argue that graphical representations of assumptions on such relationships, directed acyclic graphs (DAGs), can enhance the transparency of decision models and aid in parameter selection and estimation through visually specifying backdoor paths (i.e., potential biases in parameter estimates) and visually clarifying structural modeling choices of frontdoor paths (i.e., the effect of the model structure on the outcome). This commentary discusses the benefit of integrating DAGs and DAMs in medical decision making and in particular health economics with 2 applications: the first examines statin use for prevention of cardiovascular disease, and the second considers mindfulness-based interventions for students’ stress. Despite the potential application of DAGs in the decision science framework, challenges remain, including simplicity, defining the scope of a DAG, unmeasured confounding, noncausal aspects, and limited data availability or quality. Broader adoption of DAGs in decision science requires full-model applications and further debate. Highlights: Our commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making. The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness. This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.

Background: The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses. Methods: We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration’s policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method’s capacity to optimize health outcomes and resource allocation. Results: Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion). Conclusion: Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study’s findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses. Highlights: This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources. Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline. This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

Background: Prior research suggests clinical effects of glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) and sodium‐glucose cotransporter‐2 inhibitors (SGLT2is) are mediated by changes in glycated hemoglobin, body weight, systolic blood pressure, hematocrit, and urine albumin‐creatinine ratio. We aimed to confirm these findings using a meta‐analytic approach. Methods and Results: We updated a systematic review of 9 GLP‐1RA and 13 SGLT2i trials and summarized longitudinal mediator data. We obtained hazard ratios (HRs) for cardiovascular, renal, and mortality outcomes. We performed linear mixed‐effects modeling of LogHRs versus changes in potential mediators and investigated differences in meta‐regression associations among drug classes using interaction terms. HRs generally became more protective with greater glycated hemoglobin reduction among GLP‐1RA trials, with average HR improvements of 20% to 30%, reaching statistical significance for major adverse cardiovascular events (ΔHR, 23%; P =0.02). Among SGLT2i trials, associations with HRs were not significant and differed from GLP1‐RA trials for major adverse cardiovascular events ( P _interaction =0.04). HRs for major adverse cardiovascular events, myocardial infarction, and stroke became less efficacious (ΔHR, −15% to −34%), with more weight loss for SGLT2i but not for GLP‐1RA trials (ΔHR, 4%−7%; P _interaction P =0.09). No changes in HRs by systolic blood pressure (ΔHR, −11% to 9%) and urine albumin‐creatinine ratio (ΔHR, −1% to 4%) were found for any outcome. Conclusions: We confirmed increased efficacy findings for major adverse cardiovascular events with reduction in glycated hemoglobin for GLP1‐RAs. Further research is needed on the potential loss of cardiovascular benefits with increased weight loss and hematocrit for SGLT2i.

First-line treatment of diffuse large B-cell lymphoma (DLBCL) achieves durable remission in approximately 60% of patients. In relapsed or refractory disease, only about 20% achieve durable remission with salvage chemoimmunotherapy and consolidative autologous stem cell transplantation (ASCT). The ZUMA-7 (axicabtagene ciloleucel [axi-cel]) and TRANSFORM (lisocabtagene maraleucel [liso-cel]) trials demonstrated superior event-free survival (and, in ZUMA-7, overall survival) in primary-refractory or early-relapsed (high-risk) DLBCL with chimeric antigen receptor T-cell therapy (CAR-T) compared with salvage chemoimmunotherapy and consolidative ASCT; however, list prices for CAR-T exceed $400 000 per infusion. To determine the cost-effectiveness of second-line CAR-T versus salvage chemoimmunotherapy and consolidative ASCT. State-transition microsimulation model. ZUMA-7, TRANSFORM, other trials, and observational data. "High-risk" patients with DLBCL. Lifetime. Health care sector. Axi-cel or liso-cel versus ASCT. Incremental cost-effectiveness ratio (ICER) and incremental net monetary benefit (iNMB) in 2022 U.S. dollars per quality-adjusted life-year (QALY) for a willingness-to-pay (WTP) threshold of $200 000 per QALY. The increase in median overall survival was 4 months for axi-cel and 1 month for liso-cel. For axi-cel, the ICER was $684 225 per QALY and the iNMB was -$107 642. For liso-cel, the ICER was $1 171 909 per QALY and the iNMB was -$102 477. To be cost-effective with a WTP of $200 000, the cost of CAR-T would have to be reduced to $321 123 for axi-cel and $313 730 for liso-cel. Implementation in high-risk patients would increase U.S. health care spending by approximately $6.8 billion over a 5-year period. Differences in preinfusion bridging therapies precluded cross-trial comparisons. Neither second-line axi-cel nor liso-cel was cost-effective at a WTP of $200 000 per QALY. Clinical outcomes improved incrementally, but costs of CAR-T must be lowered substantially to enable cost-effectiveness. No research-specific funding.

Context Pituitary tumors are the third most common brain tumor and yet there is no standardization of the surveillance schedule and assessment modalities after transsphenoidal surgery. Evidence acquisition OVID, EMBASE and the Cochrane Library databases were systematically screened from database inception to March 5, 2020. Inclusion and exclusion criteria were designed to capture studies examining detection of pituitary adenoma recurrence in patients 18 years of age and older following surgical resection with curative intent. Evidence synthesis A total of 7936 abstracts were screened, with 812 articles reviewed in full text and 77 meeting inclusion criteria for data extraction. A pooled analysis demonstrated recurrence rates at 1 year, 5 years and 10 years for non-functioning pituitary adenomas (NFPA; N = 3533 participants) were 1%, 17%, and 33%, for prolactin-secreting adenomas (PSPA; N = 1295) were 6%, 21%, and 28%, and for growth-hormone pituitary adenomas (GHPA; N = 1257) were 3%, 8% and 13%, respectively. Rates of recurrence prior to 1 year were 0% for NFPA, 1–2% for PSPA and 0% for GHPA. The mean time to disease recurrence for NFPA, PSPA and GHPA were 4.25, 2.52 and 4.18 years, respectively. Conclusions This comprehensive review of the literature quantified the recurrence rates for commonly observed pituitary adenomas after transsphenoidal surgical resection with curative intent. Our findings suggest that surveillance within 1 year may be of low yield. Further clinical trials and cohort studies investigating cost-effectiveness of surveillance schedules and impact on quality of life of patients under surveillance will provide further insight to optimize follow-up.