Continuous glucose monitoring vs. self-monitoring in pregnant individuals with type 1 diabetes: an economic analysis.

BACKGROUND: In the United States, approximately 1% of pregnancies are complicated by pregestational diabetes. Individuals with type 1 diabetes have an increased risk of adverse maternal and neonatal outcomes. While continuous glucose monitoring has demonstrated benefits for patients with type 1 diabetes, its cost is higher than traditional intermittent fingerstick monitoring, particularly if used only during pregnancy. OBJECTIVE: To develop an economic analysis model to compare in silico the cost of continuous glucose monitoring and self-monitoring of blood glucose in a cohort of pregnant individuals with type 1 diabetes mellitus. STUDY DESIGN: We developed an economic analysis model to compare two glucose monitoring strategies in pregnant individuals with type 1 diabetes: continuous glucose monitoring and self-monitoring. The model considered hypertensive disorders of pregnancy, large for gestational age, cesarean delivery, neonatal intensive care unit (NICU) admission, and neonatal hypoglycemia. The primary outcome was the total cost per strategy in 2022 USD from a health system perspective, with self-monitoring as the reference group. Probabilities, relative risks, and costs were extracted from the literature, and the costs were adjusted to 2022 US dollars. Sensitivity analyses were conducted by varying parameters based on the probability, relative risk, and cost distributions. The robustness of the results was tested through 1000 Monte Carlo simulations. RESULTS: In the base-case analysis, the cost of pregnancy using continuous glucose monitoring was $26,837 compared to $29,039 for self-monitoring, resulting in a cost reduction of $2,202 per individual. The parameters with the greatest effect on the incremental cost included the relative risk of NICU admission, cost of NICU admission, continuous glucose monitoring costs, and usual care costs. Monte Carlo simulations indicated that continuous glucose monitoring was the optimal strategy 98.7% of the time. One-way sensitivity analysis showed that continuous glucose monitoring was more economical if the relative risk of NICU admission with continuous glucose monitoring vs. self-monitoring was below 1.15. CONCLUSION: Compared to self-monitoring, continuous glucose monitoring is an economical strategy for pregnant individuals with type 1 diabetes mellitus.

Regional impact assessment of air quality improvement: The air quality lifecourse assessment tool (AQ-LAT) for the West Midlands combined authority (WMCA) area.

Poor air quality is the largest environmental health risk in England. In the West Midlands, UK, ∼2.9 million people are affected by air pollution with an average loss in life expectancy of up to 6 months. The 2021 Environment Act established a legal framework for local authorities in England to develop regional air quality plans, generating a policy need for predictive environmental impact assessment tools. In this context, we developed a novel Air Quality Lifecourse Assessment Tool (AQ-LAT) to estimate electoral ward-level impacts of PM2.5 and NO2 exposure on outcomes of interest to local authorities, namely morbidity (asthma, coronary heart disease (CHD), stroke, lung cancer), mortality, and associated healthcare costs. We apply the Tool to assess the health economic burden of air pollutant exposure and estimate benefits that would be generated by meeting WHO 2021 Global Air Quality Guidelines (AQGs) (annual average concentrations) for NO2 (10 µg/m3) and PM2.5 (5 µg/m3) in the West Midlands Combined Authority Area. All West Midlands residents live in areas which exceed WHO AQGs, with 2070 deaths, 2070 asthma diagnoses, 770 CHD diagnoses, 170 lung cancers and 650 strokes attributable to air pollution exposure annually. Reducing PM2.5 and NO2 concentrations to WHO AQGs would save 10,700 lives reducing regional mortality by 1.8%, gaining 92,000 quality-adjusted life years (QALYs), and preventing 20,500 asthma, 7400 CHD, 1400 lung cancer, and 5700 stroke diagnoses, with economic benefits of £3.2 billion over 20 years. Significantly, we estimate 30% of QALY gains relate to reduced disease burden. The AQ-LAT has major potential to be replicated across local authorities in England and applied to inform regional investment decisions.

Can Robotic Arm-assisted Total Knee Arthroplasty Remain Cost-effective in Volume-based Procurement System in China? A Markov Model-based Study.

OBJECTIVE: The volume based procurement (VBP) program in China was initiated in 2022. The cost-effectiveness of robotic arm assisted total knee arthroplasty is yet uncertain after the initiation of the program. The objective of the study was to investigate the cost-effectiveness of robotic arm-assisted total knee arthroplasty and the influence of the VBP program to its cost-effectiveness in China. METHODS: The study was a Markov model-based cost-effectiveness study. Cases of primary total knee arthroplasty from January 2019 to December 2021 were included retrospectively. A Markov model was developed to simulate patients with advanced knee osteoarthritis. Manual and robotic arm-assisted total knee arthroplasties were compared for cost-effectiveness before and after the engagement of the VBP program in China. Probability and sensitivity analysis were conducted. RESULTS: Robotic arm-assisted total knee arthroplasty showed better recovery and lower revision rates before and after initiation of the VBP program. Robotic arm-based TKA was superior to manual total knee arthroplasty, with an increased effectiveness of 0.26 (16.87 vs 16.61) before and 0.52 (16.96 vs 16.43) after the application of Volume-based procurement, respectively. The procedure is more cost-effective in the new procurement system (17.13 vs 16.89). Costs of manual or robotic arm-assisted TKA were the most sensitive parameters in our model. CONCLUSION: Based on previous and current medical charging systems in China, robotic arm-assisted total knee arthroplasty is a more cost-effective procedure compared to traditional manual total knee arthroplasty. As the volume-based procurement VBP program shows, the procedure can be more cost-effective.

A cost-benefit analysis of mass prostate cancer screening.

BACKGROUND: Prostate cancer (PCa) causes a substantial health and financial burden worldwide, underscoring the need for efficient mass screening approaches. This study attempts to evaluate the Net Cost-Benefit Index (NCBI) of PCa screening in Iran to offer insights for informed decision-making and resource allocation. METHOD: The Net Cost-Benefit Index (NCBI) was calculated for four age groups (40 years and above) using a decision-analysis model. Two screening strategies, prostate-specific antigen (PSA) solely and PSA with Digital Rectal Examination (DRE), were evaluated from the health system perspective. A retrospective assessment of 1402 prostate cancer (PCa) patients' profiles were conducted, and direct medical and non-medical costs were calculated based on the 2021 official tariff rates, patient records, and interviews. The monetary value of mass screening was determined through Willingness to Pay (WTP) assessments, which served as a measure for the benefit aspect. RESULT: The combined PSA and DRE strategy of screening is cost-effective, yields up to $3 saving in costs per case and emerges as the dominant strategy over PSA alone. Screening for men aged 70 and above does not meet economic justification, indicated by a negative Net Cost-Benefit Index (NCBI). The 40-49 age group exhibits the highest net benefit, $13.81 based on basic information and $13.54 based on comprehensive information. Sensitivity analysis strongly supports the cost-effectiveness of the combined screening approach. CONCLUSION: This study advocates prostate cancer screening with PSA and DRE, is economically justified for men aged 40-69. The results of the study recommend that policymakers prioritize resource allocation for PCa screening programs based on age and budget constraints. Men's willingness to pay, especially for the 40-49 age group which had the highest net benefit, leverages their financial participation in screening services. Additionally, screening services for other age groups, such as 50-54 or 55-59, can be provided either for free or at a reduced cost.

A benchmarking framework for the accurate and cost-effective detection of clinically-relevant structural variants for cancer target identification and diagnosis.

BACKGROUND: Accurate clinical structural variant (SV) calling is essential for cancer target identification and diagnosis but has been historically challenging due to the lack of ground truth for clinical specimens. Meanwhile, reduced clinical-testing cost is the key to the widespread clinical utility. METHODS: We analyzed massive data from tumor samples of 476 patients and developed a computational framework for accurate and cost-effective detection of clinically-relevant SVs. In addition, standard materials and classical experiments including immunohistochemistry and/or fluorescence in situ hybridization were used to validate the developed computational framework. RESULTS: We systematically evaluated the common algorithms for SV detection and established an expert-reviewed SV call set of 1,303 tumor-specific SVs with high-evidence levels. Moreover, we developed a random-forest-based decision model to improve the true positive of SVs. To independently validate the tailored 'two-step' strategy, we utilized standard materials and classical experiments. The accuracy of the model was over 90% (92-99.78%) for all types of data. CONCLUSION: Our study provides a valuable resource and an actionable guide to improve cancer-specific SV detection accuracy and clinical applicability.

Individualized treatment decision model for inoperable elderly esophageal squamous cell carcinoma based on multi-modal data fusion.

BACKGROUND: This research aimed to develop a model for individualized treatment decision-making in inoperable elderly patients with esophageal squamous cell carcinoma (ESCC) using machine learning methods and multi-modal data. METHODS: A total of 189 inoperable elderly ESCC patients aged 65 or older who underwent concurrent chemoradiotherapy (CCRT) or radiotherapy (RT) were included. Multi-task learning models were created using machine learning techniques to analyze multi-modal data, including pre-treatment CT images, clinical information, and blood test results. Nomograms were constructed to predict the objective response rate (ORR) and progression-free survival (PFS) for different treatment strategies. Optimal treatment plans were recommended based on the nomograms. Patients were stratified into high-risk and low-risk groups using the nomograms, and survival analysis was performed using Kaplan-Meier curves. RESULTS: The identified risk factors influencing ORR were histologic grade (HG), T stage and three radiomic features including original shape elongation, first-order skewness and original shape flatness, while risk factors influencing PFS included BMI, HG and three radiomic features including high gray-level run emphasis, first-order minimum and first-order skewness. These risk factors were incorporated into the nomograms as independent predictive factors. PFS was substantially different between the low-risk group (total score ≤ 110) and the high-risk group (total score > 110) according to Kaplan-Meier curves (P < 0.05). CONCLUSIONS: The developed predictive models for ORR and PFS in inoperable elderly ESCC patients provide valuable insights for predicting treatment efficacy and prognosis. The nomograms enable personalized treatment decision-making and can guide optimal treatment plans for inoperable elderly ESCC patients.

Hierarchical decision model for in vitro bilirubin content prediction from absorption spectrum of whole blood.

Significance: Bilirubin forms by the breakdown of heme proteins in the liver, but a newborn's sluggish liver can lead to elevated serum bilirubin levels that cross the blood-brain barrier and result in kernicterus. Earlier studies have used the 400 to 500 nm optical wavelength range to characterize the bilirubin content. There is not a universally established correlation among other wavelengths and the amount of bilirubin in clinical whole blood samples. Aim: We demonstrated that the amount of bilirubin could be quantified with ∼82% accuracy in a label-free, self-referenced manner using only a few wavelengths, viz. 468, 492, 500, 560, 605, 645, 660, and 675 nm, wherein band-averaged absorption measurements are used. Approach: We addressed the above problem by conducting a preliminary study containing 50 neonates through an absorption spectrum measurement of whole blood in 3 to 5 µl samples from the neonates. We constructed a hierarchical decision method that first grossly divides the 30 neonates of the training set into <10 mg/dl and ≥10 mg/dl bilirubin level cohorts. A subsequent boundary condition further divides the ≥10 mg/dl group into two >15 mg/dl and ≤15 mg/dl bilirubin level cohorts. A finer measure later predicted the bilirubin content of each of these groups as low (<10 mg/dl), medium (10 to 15 mg/dl), and high (>15 mg/dl). Results: Using this hierarchical decision model statistical approach, we quantified the amount of bilirubin in the 20 testing set samples with 82% accuracy. Conclusions: We formulated a biostatistical model in which we automated the spectrometric determination of total bilirubin in the whole blood for patients of neonatal hyperbilirubinemia.

Prediction of complications in health economic models of type 2 diabetes: a review of methods used.

AIM: Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions. METHODS: PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated. RESULTS: The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly. CONCLUSIONS: The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Translating an Economic Analysis into a Tool for Public Health Resource Allocation in Cancer Survivorship.

Background. The complexity of decision science models may prevent their use to assist in decision making. User-centered design (UCD) principles provide an opportunity to engage end users in model development and refinement, potentially reducing complexity and increasing model utilization in a practical setting. We report our experiences with UCD to develop a modeling tool for cancer control planners evaluating cancer survivorship interventions. Design. Using UCD principles (described in the article), we developed a dynamic cohort model of cancer survivorship for individuals with female breast, colorectal, lung, and prostate cancer over 10 y. Parameters were obtained from the National Program of Cancer Registries and peer-reviewed literature, with model outcomes captured in quality-adjusted life-years and net monetary benefit. Prototyping and iteration were conducted with structured focus groups involving state cancer control planners and staff from the Centers for Disease Control and Prevention and the American Public Health Association. Results. Initial feedback highlighted model complexity and unclear purpose as barriers to end user uptake. Revisions addressed complexity by simplifying model input requirements, providing clear examples of input types, and reducing complex language. Wording was added to the results page to explain the interpretation of results. After these updates, feedback demonstrated that end users more clearly understood how to use and apply the model for cancer survivorship resource allocation tasks. Conclusions. A UCD approach identified challenges faced by end users in integrating a decision aid into their workflow. This approach created collaboration between modelers and end users, tailoring revisions to meet the needs of the users. Future models developed for individuals without a decision science background could leverage UCD to ensure the model meets the needs of the intended audience. Highlights: Model complexity and unclear purpose are 2 barriers that prevent lay users from integrating decision science tools into their workflow.Modelers could integrate the user-centered design framework when developing a model for lay users to reduce complexity and ensure the model meets the needs of the users.

Duration discrimination: A diffusion decision modeling approach.

The human ability to discriminate the duration of two subsequently presented stimuli is often studied with tasks that involve a comparison between a standard stimulus (with fixed duration) and comparison stimuli (with varying durations). The performance in such tasks is influenced by the presentation order of these successively presented stimuli. The so-called Type A effect refers to the impact of presentation order on the point of subjective equality. The Type B effect describes effects of presentation order on the just-noticeable-difference. Cognitive models that account for these context effects assume that participants' duration estimation is influenced by the history of previously encountered stimuli. For example, the internal reference model assumes that the magnitude of a "typical" stimulus is represented by an internal reference. This internal reference evolves throughout an experiment and is updated on every trial. Different recent models have in common that they describe how the internal reference is computed but are agnostic to the decision process itself. In this study, we develop a new model that incorporates the mechanisms of perceptual discrimination models into a diffusion model. The diffusion model focuses on the dynamics of the decision process itself and accounts for choice and response times based on a set of latent cognitive variables. We show that our model accurately predicts the accuracy and response time distribution in a classical duration discrimination task. Further, model parameters were sensitive to the Type A and B effect. The proposed model opens up new opportunities for studying human discrimination performance (e.g., individual differences).

Cost-effectiveness of future lockdown policies against the COVID-19 pandemic.

AIM: While the European Union (EU) has approved several COVID-19 vaccines, new variants of concern may be able to escape immunity. The purpose of this study is to project the cost-effectiveness of future lockdown policies in conjunction with a variant-adapted vaccine booster. The exemplary scenario foresees a 25% decline in the vaccine protection against severe disease. METHODS: A decision model was constructed using, for example, information on age-specific fatality rates, intensive care unit (ICU) costs and outcomes, and herd protection threshold. The costs and benefits of a future lockdown strategy were determined from a societal viewpoint under three future scenarios-a booster shot's efficacy of 0%, 50%, and 95%. RESULTS: The cost-effectiveness ratio of a lockdown policy in conjunction with a booster dose with 95% efficacy is €44,214 per life year gained. A lockdown is cost-effective when the probability of approving a booster dose with 95% efficacy is at least 48% (76% when considering uncertainty in input factors). CONCLUSION: In this exemplary scenario, a future lockdown policy appears to be cost-effective if the probability of approving a variant-adapted vaccine booster with an efficacy of 95% is at least 48%.

Coupling an individual adaptive-decision model with a SIRV model of influenza vaccination reveals new insights for epidemic control.

Past seasonal influenza epidemics and vaccination experience may affect individuals' decisions on whether to be vaccinated or not, decisions that may be constantly reassessed in relation to recent influenza related experience. To understand the potentially complex interaction between experience and decisions and whether the vaccination rate is likely to reach a critical coverage level or not, we construct an adaptive-decision model. This model is then coupled with an influenza vaccination dynamics (SIRV) model to explore the interaction between individuals' decision-making and an influenza epidemic. Nonlinear least squares estimation is used to obtain the best-fit parameter values in the SIRV model based on data on new influenza-like illness (ILI) cases in Texas. Uncertainty and sensitivity analyses are then carried out to determine the impact of key parameters of the adaptive decision-making model on the ILI epidemic. The results showed that the necessary critical coverage rate of ILI vaccination could not be reached by voluntary vaccination. However, it could be reached in the fourth year if mass media reports improved individuals' memory of past vaccination experience. Individuals' memory of past vaccination experience, the proportion with histories of past vaccinations and the perceived cost of vaccination are important factors determining whether an ILI epidemic can be effectively controlled or not. Therefore, health authorities should guide people to improve their memory of past vaccination experience through media reports, publish timely data on annual vaccination proportions and adjust relevant measures to appropriately reduce vaccination perceived cost, in order to effectively control an ILI epidemic.

Intelligent recommendation system based on decision model of archive translation tasks.

How to recruit, test, and train the intelligent recommendation system users, and how to assign the archive translation tasks to all intelligent recommendation system users according to the intelligent matching principles are still a problem that needs to be solved. With the help of proper names and terms in China's Imperial Maritime Customs archives, this manuscript aims to solve the problem. When the corresponding translation, domain or attributes of a proper name or term is known, it will be easier for some archive translation tasks to be completed, and the adaptive archive intelligent recommendation system will also improve the efficiency of intelligent recommendation quality of archive translation tasks. These related domains or attributes are different labels of these archives. To put it simply, multi-label classification means that the same instance can have multiple labels or be labelled into multiple categories, which is called multi-label classification. With the multi-label classification, archives can be classified into different categories, such as the trade archives, preventive archives, personnel archives, etc. The system users are divided into different professional domains by some tests, for instance, system users who are good at economic knowledge and users who have higher language skills. With these labels, the intelligent recommendation system can make the intelligent match between the archives and system users, so as to improve the efficiency and quality of intelligent archive translation tasks. In this manuscript, through multi-label classification, the intelligent recommendation system can realize the intelligent allocation of archive translation tasks to the system users. The intelligent allocation is realized through the construction of intelligent control model, and verifies that the intelligent recommendation system can improve the performance of task allocation over time without the participation of task issuers.

Inductively coupled plasma mass spectrometry based urine metallome to construct clinical decision models for autism spectrum disorder.

BACKGROUND: The global prevalence of autism spectrum disorder (ASD) is on the rise, and high levels of exposure to toxic heavy metals may be associated with this increase. Urine analysis is a noninvasive method for investigating the accumulation and excretion of heavy metals. The aim of this study was to identify ASD-associated urinary metal markers. METHODS: Overall, 70 children with ASD and 71 children with typical development (TD) were enrolled in this retrospective case-control study. In this metallomics investigation, inductively coupled plasma mass spectrometry was performed to obtain the urine profile of 27 metals. RESULTS: Children with ASD could be distinguished from children with TD based on the urine metal profile, with ASD children showing an increased urine metal Shannon diversity. A metallome-wide association analysis was used to identify seven ASD-related metals in urine, with cobalt, aluminum, selenium, and lithium significantly higher, and manganese, mercury, and titanium significantly lower in the urine of children with ASD than in children with TD. The least absolute shrinkage and selection operator (LASSO) machine learning method was used to rank the seven urine metals in terms of their effect on ASD. On the basis of these seven urine metals, we constructed a LASSO regression model for ASD classification and found an area under the receiver operating characteristic curve of 0.913. We also constructed a clinical prediction model for ASD based on the seven metals that were different in the urine of children with ASD and found that the model would be useful for the clinical prediction of ASD risk. CONCLUSIONS: The study findings suggest that altered urine metal concentrations may be an important risk factor for ASD, and we recommend further exploration of the mechanisms and clinical treatment measures for such alterations.

Integrated diffusion models for distance effects in number memory.

I evaluated three models for the representation of numbers in memory. These were integrated with the diffusion decision model to explain accuracy and response time (RT) data from a recognition memory experiment in which the stimuli were two-digit numbers. The integrated models accounted for distance/confusability effects: when a test number was numerically close to a studied number, accuracy was lower and RTs were longer than when a test number was numerically far from a studied number. For two of the models, the representations of numbers are distributed over number (with Gaussian or exponential distributions) and the overlap between the distributions of a studied number and a test number provides the evidence (drift rate) on which a decision is made. For the third, the exponential gradient model, drift rate is an exponential function of the numerical distance between studied and test numbers. The exponential gradient model fit the data slightly better than the two overlap models. Monte Carlo simulations showed that the variability in the important parameter estimates from fitting data collected over 30-40 min is smaller than the variability among individuals, allowing differences among individuals to be studied. A second experiment compared number memory and number discrimination tasks and results showed different distance effects. Number memory had an exponential-like distance-effect and number discrimination had a linear function which shows radically different representations drive the two tasks.

Nursing students' decision-making regarding postpartum hemorrhage: An exploration using the recognition-primed decision model.

AIM: To explore the knowledge content and structure of nursing students' decision-making in a high-stake clinical situation of postpartum hemorrhage using the Recognition-Primed Decision Model. BACKGROUND: According to research on clinical judgment, a nurse's expectations for a patient situation are central to the clinical decision-making process. However, little research has addressed the expectation concept and its relationship with the nurse's knowledge. Grounded in the naturalistic decision-making paradigm, the Recognition-Primed Decision Model provides a potential framework to describe the content and structure of nurses' knowledge and expectations as they unfold in high-stake clinical situations, such as postpartum hemorrhage. As it is typically used in studies of expert decision-making, it is crucial to test the adequacy of the Model with a student population and refine the research methods for using this framework. DESIGN: Descriptive design where qualitative data were analyzed using qualitative and quantitative methods. METHODS: A convenience sample of 53 students enrolled in a maternal and child health course in the Fall of 2021 was formed. As part of an online exercise to prepare for a simulation, they read a vignette presenting the story of a woman experiencing postpartum hemorrhage and recorded their answers to questions designed to probe their decision-making. Recordings were transcribed and subjected to content analysis based on the four components of recognition according to the Recognition-Primed Decision Model (i.e., cues, expectations, goals and actions). FINDINGS: All participants recognized the postpartum hemorrhage. Their knowledge was organized into clusters representing the potential causes (i.e., tone, trauma, tissue and thrombin) and consequences (i.e., hemodynamic instability) of postpartum hemorrhage, as well as other potential issues (e.g., pain and comfort, baby and partner, infection). Although students could identify relevant cues and actions, they had difficulties articulating their longer-term goals and expectations for the mother and care outcomes. CONCLUSIONS: This study showed the potential of the Recognition-Primed Decision Model to organize the content and structure of the knowledge that supported nursing students' decision-making in a high-stake situation. The findings suggest that their knowledge disproportionately focuses on the cause-and-effect relations between cues and actions. They invite further consideration of longer-term goals and expectations in nursing education to prepare students to anticipate events and assess patient responses appropriately.

Development of a Knowledge Base for Chronic Wound Management Using the Decision Model & Notation.

Chronic wounds have significant impacts on patient health-related quality of life (HRQoL) and the healthcare expenditures. Various complex decision-making scenarios arise from wound management. Clinical decision-making systems (CDSS) can assist in relieving healthcare providers in these complex decision-making processes and improve the quality of care. In our study, we used the Decision Model & Notation (DMN) standard as a knowledge representation format to implement a knowledge base for chronic wound material recommendation in phase-based therapy. The resulting decision model is theoretically consistent and sustainable. With this study, we also emphasized the need of a semantic interoperability framework. This opens further research possibilities regarding the improvement of the model and the interest of DMN for decision models in clinical fields.

Design and testing of a health economic Markov model for treatment of anorexia nervosa.

OBJECTIVES: Anorexia Nervosa (AN) is a severe psychiatric disorder and knowledge about the cost-effectiveness of potential interventions is limited. The aim of this paper is to introduce the Trimbos Institute health economic cost-effectiveness model for Anorexia Nervosa (AnoMod-TI), a flexible modeling tool for assessing the long-term cost-effectiveness of interventions for AN in late adolescent and adult patients, which could support clinical decision making. METHODS: AnoMod-TI is a state-transition cohort simulation (Markov) model developed from a Dutch societal perspective, which consists of four health states - namely full remission (FR), partial remission (PR), AN and death. Results are expressed as total healthcare costs, QALYs and incremental cost-effectiveness ratio. RESULTS: For the purpose of demonstrating AnoMod-TI and how it could be used to estimate cost-effectiveness over a 20-year time horizon, it was applied to a hypothetical treatment scenario. Results illustrate how a relatively costly intervention with only modest effects can still be cost-effective in the long term. CONCLUSIONS: AnoMod-TI can be used to examine long-term cost-effectiveness of various interventions aimed at either treating AN or preventing relapse from a state of partial or full remission. AnoMod-TI is freely available upon request to the authors.

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. Model outcomes included lifetime costs [2020 United States (US) Dollars], quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs). RESULTS: Under base case assumptions, direct surgery was found to be the dominant strategy as it yielded lower costs and greater health effects (QALYs) compared to preoperative octreotide strategy in the second-order Monte Carlo microsimulation. The ICER was most sensitive to probability of remission following primary therapy and duration of preoperative octreotide therapy. Accounting for joint parameter uncertainty, direct surgery had a higher probability of demonstrating a cost-effective profile compared to preoperative octreotide treatment at 77% compared to 23%, respectively. CONCLUSIONS: Using standard benchmarks for cost-effectiveness in the US ($100,000/QALY), preoperative octreotide therapy followed by surgery may not be cost-effective compared to direct surgery for patients with growth-hormone secreting pituitary adenomas but the result is highly sensitive to initial treatment failure and duration of preoperative treatment.

Event rate effects on children with attention-deficit/hyperactive disorder: Test predictions from the moderate brain arousal model and the neuro-energetics theory using the diffusion decision model.

BACKGROUND: Converging evidence has found that the inhibitory control of children with attention-deficit/hyperactive disorder (ADHD) is context-dependent and particularly susceptible to the event rate. The Moderate Brain Arousal (MBA) model predicts a U-shaped curve between event rate and performance as a modulation of brain arousal. The neuroenergetics theory (NeT) proposes that a smaller event rate results in neuronal fatigue and subsequent descent performance. However, previous work applied the traditional one-dimensional index of performance, such as accuracy rate and response time, which might limit the exploration of the event rate effect on the specific underlying process. AIMS: We used a diffusion decision model (DDM) to study the influence of event rate on inhibition control in children with ADHD and verified the explanation of the MBA model and the NeT. METHODS AND PROCEDURES: The Stop Signal Task manipulated by four event rate conditions was conducted with 24 children with ADHD (mean age=8.5, males=16) and 29 typical developmental children (TDC) (mean age=9.0, males=12). DDM was applied to compare the differences in the DDM parameters across different event rates. OUTCOMES AND RESULTS: Compared with TDC, children with ADHD had a smaller drift rate, longer non-decision time, and smaller boundary separation. Although the event rate had little influence on ADHD, the drift rate of the TDC was approximately linear with an increased event rate, and the Ter had a quadratic function relationship with the event rate. CONCLUSIONS AND IMPLICATIONS: The event rate effect may influence children's performance through dual mechanisms. Neuronal energy supply could regulate information processing and brain arousal to regulate the activation of primary stimuli encoding and motor control. Insight into the multi-mechanism of ADHD cognition deficits would be helpful for clinicians in making objective diagnoses and effective targeted treatments.