A cloud-based medical device for predicting cardiac risk in suspected coronary artery disease: a rapid review and conceptual economic model.

Background: The CaRi-Heart® device estimates risk of 8-year cardiac death, using a prognostic model, which includes perivascular fat attenuation index, atherosclerotic plaque burden and clinical risk factors.

Objectives: To provide an Early Value Assessment of the potential of CaRi-Heart Risk to be an effective and cost-effective adjunctive investigation for assessment of cardiac risk, in people with stable chest pain/suspected coronary artery disease, undergoing computed tomography coronary angiography. This assessment includes conceptual modelling which explores the structure and evidence about parameters required for model development, but not development of a full executable cost-effectiveness model.

Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information.

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).

Second-Line Chimeric Antigen Receptor T-Cell Therapy in Diffuse Large B-Cell Lymphoma : A Cost-Effectiveness Analysis.

Background: 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.

Surgical prioritization based on decision model outcomes is not sensitive to differences between the health-related quality of life values estimates of physicians and citizens.

Purpose: Decision models can be used to support allocation of scarce surgical resources. These models incorporate health-related quality of life (HRQoL) values that can be determined using physician panels. The predominant opinion is that one should use values obtained from citizens.

Cost-effectiveness of postoperative imaging surveillance strategies for nonfunctional pituitary adenomas after resection with curative intent.

Objective: The aim of this study was to determine an optimal follow-up imaging surveillance strategy in terms of cost-effectiveness after resection of nonfunctioning pituitary adenomas with curative intent.

Methods: An individual-level state-transition microsimulation model was used to simulate costs and outcomes associated with three postoperative imaging strategies over a lifetime time horizon: 1) annual MRI surveillance, 2) tapered MRI surveillance (annual surveillance for 5 years followed by surveillance every 2 years), and 3) personalized surveillance (annual surveillance for 5 years followed by surveillance every 2 years when MRI shows remnant disease/postoperative changes, and surveillance at 7, 10, and 15 years for disease-free MRI). Transition probabilities, utilities, and costs were estimated from recent published data and discounted by 3% annually.

Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input.

Objectives: A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates.

Minimising population health loss in times of scarce surgical capacity: a modelling study for surgical procedures performed in nonacademic hospitals.

Background: The burden of the COVID-19 pandemic resulted in a reduction of available health care capacity for regular care. To guide prioritisation of semielective surgery in times of scarcity, we previously developed a decision model to quantify the expected health loss due to delay of surgery, in an academic hospital setting. The aim of this study is to validate our decision model in a nonacademic setting and include additional elective surgical procedures.

A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example.

Cost-effectiveness of direct surgery versus preoperative octreotide therapy for growth-hormone secreting pituitary adenomas.

Purpose: The objective of this study was to compare the cost-effectiveness of preoperative octreotide therapy followed by surgery versus the standard treatment modality for growth-hormone secreting pituitary adenomas, direct surgery (that is, surgery without preoperative treatment) from a public third-party payer perspective.

Methods: We developed an individual-level state-transition microsimulation model to simulate costs and outcomes associated with preoperative octreotide therapy followed by surgery and direct surgery for patients with growth-hormone secreting pituitary adenomas. Transition probabilities, utilities, and costs were estimated from recent published data and discounted by 3% annually over a lifetime time horizon.

An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time.

Emerging Therapies for COVID-19: The Value of Information From More Clinical Trials.

Objectives: The COVID-19 pandemic necessitates time-sensitive policy and implementation decisions regarding new therapies in the face of uncertainty. This study aimed to quantify consequences of approving therapies or pursuing further research: immediate approval, use only in research, approval with research (eg, emergency use authorization), or reject.

Methods: Using a cohort state-transition model for hospitalized patients with COVID-19, we estimated quality-adjusted life-years (QALYs) and costs associated with the following interventions: hydroxychloroquine, remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, tocilizumab, lopinavir-ritonavir, interferon beta-1a, and usual care.

Early Cost-Effectiveness of Onasemnogene Abeparvovec-xioi (Zolgensma) and Nusinersen (Spinraza) Treatment for Spinal Muscular Atrophy I in The Netherlands With Relapse Scenarios.

Objectives: Onasemnogene Abeparvovec-xioi (AVXS-101) is a gene therapy intended for curative treatment of spinal muscular atrophy (SMA) with an expected price of around €2 000 000. The goal of this study is to perform a cost-effectiveness analysis of treatment of SMA I patients with AVXS-101 in The Netherlands including relapse scenarios.

Methods: An individual-based state-transition model was used to model treatment effect and survival of SMA I patients treated with AVXS-101, nusinersen and best supportive care (BSC).

Prioritisation and design of clinical trials.

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials.

Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study.

Objectives: Coronavirus disease 2019 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model to estimate the impact of postponing semielective surgical procedures on health, to support prioritization of care from a utilitarian perspective.

Methods: A cohort state-transition model was developed and applied to 43 semielective nonpediatric surgical procedures commonly performed in academic hospitals.

A Multidimensional Array Representation of State-Transition Model Dynamics.

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics).

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals.

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial.

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling.

An Overview of R in Health Decision Sciences.

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition.