Validating health economic models with the Probabilistic Analysis Check dashBOARD (PACBOARD).

OBJECTIVES: Health economic (HE) models are often considered as "black boxes" because they are not publicly available and lack transparency, which prevents independent scrutiny of HE models. Additionally, validation efforts and validation status of HE models are not systematically reported. Methods to validate HE models in absence of their full underlying code are therefore urgently needed to improve health policy making.This study aimed to develop and test a generic dashboard to systematically explore the workings of HE models and validate their model parameters and outcomes. METHODS: The Probabilistic Analysis Check dashBOARD (PACBOARD) was developed using insights from literature, health economists, and a data scientist.Functionalities of PACBOARD are 1) exploring and validating model parameters and outcomes using standardised validation tests and interactive plots, 2) visualising and investigating the relationship between model parameters and outcomes using metamodelling, and 3) predicting health economic outcomes using the fitted metamodel.To test PACBOARD, two mock HE models were developed and errors were introduced in these models, e.g. negative costs inputs, utility values exceeding 1. PACBOARD metamodelling predictions of incremental net monetary benefit were validated against the original model's outcomes. RESULTS: PACBOARD automatically identified all errors introduced in the erroneous HE models. Metamodel predictions were accurate compared to the original model outcomes. CONCLUSIONS: PACBOARD is a unique dashboard aiming at improving the feasibility and transparency of validation efforts of HE models. PACBOARD allows users to explore the working of HE models using metamodelling based on HE models' parameters and outcomes.

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

BACKGROUND: The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. METHODS: We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. RESULTS: In total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). CONCLUSIONS: MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.

A direct comparison in diagnostic performance of CDUS, FDG-PET/CT and MRI in patients suspected of giant cell arteritis.

OBJECTIVES: This study directly compares diagnostic performance of Colour Duplex Ultrasound (CDUS), Fluor-18-deoxyglucose Positron Emission Tomography Computed Tomography (FDG-PET/CT) and Magnetic Resonance Imaging (MRI) in patients suspected of giant cell arteritis (GCA). METHODS: Patients with suspected GCA were included in a nested-case control pilot study. CDUS, whole body FDG-PET/CT and cranial MRI were performed within 5 working days after initial clinical evaluation. Clinical diagnosis after six months follow-up by experienced rheumatologists in the field of GCA, blinded for imaging, was used as reference standard. Diagnostic performance of the imaging modalities was determined. Stratification for GCA subtype was performed and imaging results were evaluated in different risk stratification groups. RESULTS: In total, 23 patients with GCA and 19 patients suspected of but not diagnosed with GCA were included. Sensitivity was 69.6% (95%CI 50.4%-88.8%) for CDUS, 52.2% (95%CI 31.4%-73.0%) for FDG-PET/CT and 56.5% (95%CI 35.8%-77.2%) for MRI. Specificity was 100% for CDUS, FDG-PET/CT and MRI. FDG-PET/CT was negative for GCA in all isolated cranial GCA patients (n = 8), while MRI was negative in all isolated extracranial GCA patients (n = 4). In 4 GCA patients with false-negative (n = 2; intermediate and high risk) or inconclusive (n = 2; low and intermediate risk) CDUS results, further imaging confirmed diagnosis. CONCLUSIONS: Sensitivity of CDUS was highest, while specificity was excellent in all imaging modalities. Nevertheless, confidence intervals of all imaging modalities were overlapping. Following EULAR recommendations, CDUS can be used as a first test to diagnose GCA. With insufficient evidence for GCA, further testing considering GCA subtype is warranted.

Opportunities for personalised follow-up in breast cancer: the gap between daily practice and recurrence risk.

PURPOSE: Follow-up guidelines barely diverge from a one-size-fits-all approach, even though the risk of recurrence differs per patient. However, the personalization of breast cancer care improves outcomes for patients. This study explores the variation in follow-up pathways in the Netherlands using real-world data to determine guideline adherence and the gap between daily practice and risk-based surveillance, to demonstrate the benefits of personalized risk-based surveillance compared with usual care. METHODS: Patients with stage I-III invasive breast cancer who received surgical treatment in a general hospital between 2005 and 2020 were selected from the Netherlands Cancer Registry and included all imaging activities during follow-up from hospital-based electronic health records. Process analysis techniques were used to map patients and activities to investigate the real-world utilisation of resources and identify the opportunities for improvement. The INFLUENCE 2.0 nomogram was used for risk prediction of recurrence. RESULTS: In the period between 2005 and 2020, 3478 patients were included with a mean follow-up of 4.9 years. In the first 12 months following treatment, patients visited the hospital between 1 and 5 times (mean 1.3, IQR 1-1) and received between 1 and 9 imaging activities (mean 1.7, IQR 1-2). Mammogram was the prevailing imaging modality, accounting for 70% of imaging activities. Patients with a low predicted risk of recurrence visited the hospital more often. CONCLUSIONS: Deviations from the guideline were not in line with the risk of recurrence and revealed a large gap, indicating that it is hard for clinicians to accurately estimate this risk and therefore objective risk predictions could bridge this gap.

Cost-effectiveness of home-based screening of the general population for albuminuria to prevent progression of cardiovascular and kidney disease.

Background: Chronic kidney disease (CKD) is often detected late, leading to substantial health loss and high treatment costs. Screening the general population for albuminuria identifies individuals at high risk of kidney events and cardiovascular disease (CVD) who may benefit from early start of preventive interventions. Previous studies on the cost-effectiveness of albuminuria population screening were inconclusive, but were based on survey or cohort data rather than an implementation study, modelled screening as performed by general practitioners rather than home-based screening, and often included only benefits with respect to kidney events. We evaluated the cost-effectiveness of home-based general population screening for increased albuminuria based on real-world data obtained from a prospective implementation study taking into account prevention of CKD as well as CVD events. Methods: We developed an individual-level simulation model to compare home-based screening using a urine collection device with usual care (no home-based screening) in individuals of the general population aged 45-80, based on the THOMAS study (Towards HOMe-based Albuminuria Screening). Cost-effectiveness was assessed from the Dutch healthcare perspective with a lifetime horizon. The costs of the screening process and benefits of preventing CKD progression (dialysis and kidney transplantation) and CVD events (non-fatal myocardial infarction, non-fatal stroke, fatal CVD event) were reflected. Albuminuria detection led to treatment of identified risk factors. The model subsequently simulated CKD progression, the occurrence of CVD events, and death. The risks of experiencing CVD events were calculated using the SCORE2 CKD risk prediction model and individual-level data from the THOMAS study. Relative treatment effectiveness, quality of life scores, resource use, and cost inputs were obtained from literature. Model outcomes were the number of CKD and CVD-related events, total costs, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER) per QALY gained by screening versus usual care. All results were obtained through probabilistic analysis. Findings: The absolute difference between screening versus usual care in lifetime probability of dialysis, kidney transplantation, non-fatal myocardial infarction, non-fatal stroke, and fatal CVD events were 0.2%, 0.05%, 0.6%, 0.6%, and 0.2%, respectively. This led to relative decreases compared to usual care in lifetime incidence of these events of 10.7%, 11.1%, 5.1%, 4.1%, and 1.6%, respectively. The incremental costs and QALYs of screening were €1607 and 0.17 QALY, respectively, which led to a corresponding ICER of €9225/QALY. The probability of screening being cost-effective for the Dutch willingness-to-pay threshold for preventive population screening of €20,000/QALY was 95.0%. Implementing the screening in the subgroup of 45-64 years old reduced the ICER (€7946/QALY), whereas implementing screening in the subgroup of 65-80 years old increased the ICER (€10,310/QALY). A scenario analysis assuming treatment optimization in all individuals with newly diagnosed risk factors or known risk factors not within target range reduced the ICER to €7083/QALY, resulting from the incremental costs and QALY gain of €2145 and 0.30, respectively. Interpretation: Home-based screening for increased albuminuria to prevent CVD and CKD events is likely cost-effective. More health benefits can be obtained by screening younger individuals and better optimization of care in individuals identified with newly diagnosed or known risk factors outside target range. Funding: Dutch Kidney Foundation, Top Sector Life Sciences & Health of the Dutch Ministry of Economic Affairs.

Introducing Open Science in Teaching Health Economic Modelling.

Open Science is gaining ground in all research fields, including health economics and outcomes research (HEOR). However, teaching Open Science is still in its infancy. This paper describes the design, implementation and evaluation of a teaching activity focusing on introducing Open Science during a Master's course during which participants have to develop a discrete event simulation. The teaching activity was organised as a series of lectures introducing different aspects of the Open Science philosophy and practices, such as good software coding practices, version control systems and reproducible research. The participants' increase in Open Science knowledge was elicited through a survey before and after the teaching innovation. After the teaching innovation, participants' knowledge of Open Science increased and they reported an improvement in Open Science-related skills, such as using a script-based statistical software, identifying and re-using open data, and collaborative script development. During the evaluation at the end of the course, the course participants mentioned that the Open Science-related content was interesting but would fit better within a course in which broader research-related content is taught. Based on this feedback, we will most likely narrow the scope of the Open-Science-related content in this course to Open Source Modelling which may better fit the scope of the course. This paper contains links to the teaching activities we developed and other resources which may be used to design teaching activities on Open Science. Herewith, we hope to inspire other teachers in including Open Science into their teaching.

Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer.

BACKGROUND: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs. OBJECTIVE: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs. METHODS: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate. RESULTS: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity >95%. CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity >95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.

Diagnostic yield of a proactive strategy for early detection of cardiovascular disease versus usual care in adults with type 2 diabetes or chronic obstructive pulmonary disease in primary care in the Netherlands (RED-CVD): a multicentre, pragmatic, cluster-randomised, controlled trial.

BACKGROUND: Progressive cardiovascular diseases (eg, heart failure, atrial fibrillation, and coronary artery disease) are often diagnosed late in high-risk individuals with common comorbidities that might mimic or mask symptoms, such as chronic obstructive pulmonary disease (COPD) and type 2 diabetes. We aimed to assess whether a proactive diagnostic strategy consisting of a symptom and risk factor questionnaire and low-cost and accessible tests could increase diagnosis of progressive cardiovascular diseases in patients with COPD or type 2 diabetes in primary care. METHODS: In this multicentre, pragmatic, cluster-randomised, controlled trial (RED-CVD), 25 primary care practices in the Netherlands were randomly assigned to usual care or a proactive diagnostic strategy conducted during routine consultations and consisting of a validated symptom questionnaire, followed by physical examination, N-terminal-pro-B-type natriuretic peptide measurement, and electrocardiography. We included adults (≥18 years) with type 2 diabetes, COPD, or both, who participated in a disease management programme. Patients with an established triple diagnosis of heart failure, atrial fibrillation, and coronary artery disease were excluded. In the case of abnormal findings, further work-up or treatment was done at the discretion of the general practitioner. The primary endpoint was the number of newly diagnosed cases of heart failure, atrial fibrillation, and coronary artery disease, adjudicated by an expert clinical outcome committee using international guidelines, at 1-year follow-up, in the intention-to-treat population. FINDINGS: Between Jan 31, 2019, and Oct 7, 2021, we randomly assigned 25 primary care centres: 11 to usual care and 14 to the intervention. We included patients between June 21, 2019, and Jan 31, 2022. Following exclusion of ineligible patients and those who did not give informed consent, 1216 participants were included: 624 (51%) in the intervention group and 592 (49%) in the usual care group. The mean age of participants was 68·4 years (SD 9·4), 482 (40%) participants were female, and 734 (60%) were male. During 1 year of follow-up, 50 (8%) of 624 participants in the intervention group and 18 (3%) of 592 in the control group were newly diagnosed with heart failure, atrial fibrillation, or coronary artery disease (adjusted odds ratio 2·97 [95% CI 1·66-5·33]). This trial is registered with the Netherlands Trial Registry, NTR7360, and was completed on Jan 31, 2023. INTERPRETATION: An easy-to-use, proactive, diagnostic strategy more than doubled the number of new diagnoses of heart failure, atrial fibrillation, and coronary artery disease in patients with type 2 diabetes or COPD in primary care compared with usual care. Although the effect on patient outcomes remains to be studied, our diagnostic strategy might contribute to improved early detection and timely initiation of treatment in individuals with cardiovascular disease. FUNDING: Dutch Heart Foundation.

Willingness to participate in combination screening for lung cancer, chronic obstructive pulmonary disease and cardiovascular disease in four European countries.

OBJECTIVES: Lung cancer screening (LCS), using low-dose computed tomography (LDCT), can be more efficient by simultaneously screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), the Big-3 diseases. This study aimed to determine the willingness to participate in (combinations of) Big-3 screening in four European countries and the relative importance of amendable participation barriers. METHODS: An online cross-sectional survey aimed at (former) smokers aged 50-75 years elicited the willingness of individuals to participate in Big-3 screening and used analytical hierarchy processing (AHP) to determine the importance of participation barriers. RESULTS: Respondents were from France (n = 391), Germany (n = 338), Italy (n = 399), and the Netherlands (n = 342), and consisted of 51.2% men. The willingness to participate in screening was marginally influenced by the diseases screened for (maximum difference of 3.1%, for Big-3 screening (73.4%) vs. lung cancer and COPD screening (70.3%)) and by country (maximum difference of 3.7%, between France (68.5%) and the Netherlands (72.3%)). The largest effect on willingness to participate was personal perceived risk of lung cancer. The most important barriers were the missed cases during screening (weight 0.19) and frequency of screening (weight 0.14), while diseases screened for (weight 0.11) ranked low. CONCLUSIONS: The difference in willingness to participate in LCS showed marginal increase with inclusion of more diseases and limited variation between countries. A marginal increase in participation might result in a marginal additional benefit of Big-3 screening. The amendable participation barriers are similar to previous studies, and the new criterion, diseases screened for, is relatively unimportant. CLINICAL RELEVANCE STATEMENT: Adding diseases to combination screening modestly improves participation, driven by personal perceived risk. These findings guide program design and campaigns for lung cancer and Big-3 screening. Benefits of Big-3 screening lie in long-term health and economic impact, not participation increase. KEY POINTS: • It is unknown whether or how combination screening might affect participation. • The addition of chronic obstructive pulmonary disease and cardiovascular disease to lung cancer screening resulted in a marginal increase in willingness to participate. • The primary determinant influencing individuals' engagement in such programs is their personal perceived risk of the disease.

Objectively assessed long-term wearing patterns and predictors of wearing orthopaedic footwear in people with diabetes at moderate-to-high risk of foot ulceration: a 12 months observational study.

BACKGROUND: Orthopaedic footwear can only be effective in preventing diabetic foot ulcers if worn by the patient. Robust data on long-term wearing time of orthopaedic footwear are not available, and needed to gain more insights into wearing patterns and associated factors (i.e. participants' demographic, disease-related characteristics, and footwear usability). We aimed to objectively assess long-term wearing patterns and identify factors associated with wearing orthopaedic footwear in people with diabetes at moderate-to-high risk of ulceration. METHODS: People diagnosed with diabetes mellitus type 1 and 2 with loss of protective sensation and/or peripheral artery disease and prescribed with orthopaedic footwear were included and followed for 12 months. The primary outcome was mean daily wearing time, continuously measured using a temperature sensor inside the footwear (Orthotimer®). Adherence to wearing orthopaedic footwear was calculated as percentage of wearing time of a total assumed 16 h out-of-bed daytime, where adherence < 60% was a pre-determined non-adherent threshold. Wearing time patterns were assessed by calculating participants' wearing (in)consistency. One-way analyses of variance tested for wearing time differences between subgroups, weekdays, and weekend days. Factors potentially associated with wearing time were collected by questionnaires and medical files. Univariately associated factors were included in multivariate linear regression analysis. RESULTS: Sixty one participants were included (mean (SD) age: 68.0 (7.4) years; females: n = 17; type 2 diabetes mellitus: n = 54). Mean (SD) overall daily wearing time was 8.3 (6.1) hours/day. A total of 40 (66%) participants were non-adherent. Participants with a consistent wearing pattern showed higher daily wearing times than participants with an inconsistent pattern. Mean (SD) wearing times were 12.7 (4.3) vs 3.6 (4.8) hours/day, respectively (P < 0.001). Mean (SD) wearing time was significantly higher (P < 0.010) during weekdays (8.7 (6.0) hours/day) compared to Saturday (8.0 (6.1) hours/day) and Sunday (6.9 (6.2) hours/day). In the multivariate model (R2 = 0.28), "satisfaction with my wear of orthopaedic footwear" was positively associated (P < 0.001) with wearing time. The other seven multivariate model factors (four demographic variables and three footwear usability variables) were not associated with wearing time. CONCLUSIONS: Only one out of three people at moderate to high risk of foot ulceration were sufficiently adherent to wearing their orthopaedic footwear. Changing people's wearing behaviour to a more stable pattern seems a potential avenue to improve long-term adherence to wearing orthopaedic footwear. Investigated factors are not associated with daily wearing time. Based on these factors the daily wearing time cannot be estimated in daily practice. TRIAL REGISTRATION: Netherlands Trial Register NL7710. Registered: 6 May 2019.

Consolidated Health Economic Evaluation Reporting Standards - Value of Information (CHEERS-VOI): Explanation and Elaboration.

OBJECTIVES: Although the ISPOR Value of Information (VOI) Task Force's reports outline VOI concepts and provide good-practice recommendations, there is no guidance for reporting VOI analyses. VOI analyses are usually performed alongside economic evaluations for which the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Statement provides reporting guidelines. Thus, we developed the CHEERS-VOI checklist to provide reporting guidance and checklist to support the transparent, reproducible, and high-quality reporting of VOI analyses. METHODS: A comprehensive literature review generated a list of 26 candidate reporting items. These candidate items underwent a Delphi procedure with Delphi participants through 3 survey rounds. Participants rated each item on a 9-point Likert scale to indicate its relevance when reporting the minimal, essential information about VOI methods and provided comments. The Delphi results were reviewed at 2-day consensus meetings and the checklist was finalized using anonymous voting. RESULTS: We had 30, 25, and 24 Delphi respondents in rounds 1, 2, and 3, respectively. After incorporating revisions recommended by the Delphi participants, all 26 candidate items proceeded to the 2-day consensus meetings. The final CHEERS-VOI checklist includes all CHEERS items, but 7 items require elaboration when reporting VOI. Further, 6 new items were added to report information relevant only to VOI (eg, VOI methods applied). CONCLUSIONS: The CHEERS-VOI checklist should be used when a VOI analysis is performed alongside economic evaluations. The CHEERS-VOI checklist will help decision makers, analysts and peer reviewers in the assessment and interpretation of VOI analyses and thereby increase transparency and rigor in decision making.

Health Economic Research Assessing the Value of Early Detection of Cardiovascular Disease: A Systematic Review.

INTRODUCTION: Cardiovascular disease (CVD) is the most prominent cause of death worldwide and has a major impact on healthcare budgets. While early detection strategies may reduce the overall CVD burden through earlier treatment, it is unclear which strategies are (most) efficient. AIM: This systematic review reports on the cost effectiveness of recent early detection strategies for CVD in adult populations at risk. METHODS: PubMed and Scopus were searched to identify scientific articles published between January 2016 and May 2022. The first reviewer screened all articles, a second reviewer independently assessed a random 10% sample of the articles for validation. Discrepancies were solved through discussion, involving a third reviewer if necessary. All costs were converted to 2021 euros. Reporting quality of all studies was assessed using the CHEERS 2022 checklist. RESULTS: In total, 49 out of 5552 articles were included for data extraction and assessment of reporting quality, reporting on 48 unique early detection strategies. Early detection of atrial fibrillation in asymptomatic patients was most frequently studied (n = 15) followed by abdominal aortic aneurysm (n = 8), hypertension (n = 7) and predicted 10-year CVD risk (n = 5). Overall, 43 strategies (87.8%) were reported as cost effective and 11 (22.5%) CVD-related strategies reported cost reductions. Reporting quality ranged between 25 and 86%. CONCLUSIONS: Current evidence suggests that early CVD detection strategies are predominantly cost effective and may reduce CVD-related costs compared with no early detection. However, the lack of standardisation complicates the comparison of cost-effectiveness outcomes between studies. Real-world cost effectiveness of early CVD detection strategies will depend on the target country and local context. REGISTRATION OF SYSTEMATIC REVIEW: CRD42022321585 in International Prospective Registry of Ongoing Systematic Reviews (PROSPERO) submitted at 10 May 2022.

Clinical decision rules in primary care: necessary investments for sustainable healthcare.

Clinical judgement in primary care is more often decisive than in the hospital. Clinical decision rules (CDRs) can help general practitioners facilitating the work-through of differentials that follows an initial suspicion, resulting in a concrete 'course of action': a 'rule-out' without further testing, a need for further testing, or a specific treatment. However, in daily primary care, the use of CDRs is limited to only a few isolated rules. In this paper, we aimed to provide insight into the laborious path required to implement a viable CDR. At the same time, we noted that the limited use of CDRs in primary care cannot be explained by implementation barriers alone. Through the case study of the Oudega rule for the exclusion of deep vein thrombosis, we concluded that primary care CDRs come out best if they are tailor-made, taking into consideration the specific context of primary health care. Current CDRs should be evaluated frequently, and future decision rules should anticipate the latest developments such as the use of point-of-care (POC) tests. Hence, such new powerful diagnostic CDRs could improve and expand the possibilities for patient-oriented primary care.

External validation of 87 clinical prediction models supporting clinical decisions for breast cancer patients.

INTRODUCTION: Numerous prediction models have been developed to support treatment-related decisions for breast cancer patients. External validation, a prerequisite for implementation in clinical practice, has been performed for only a few models. This study aims to externally validate published clinical prediction models using population-based Dutch data. METHODS: Patient-, tumor- and treatment-related data were derived from the Netherlands Cancer Registry (NCR). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), scaled Brier score, and model calibration. Net benefit across applicable risk thresholds was evaluated with decision curve analysis. RESULTS: After assessing 922 models, 87 (9%) were included for validation. Models were excluded due to an incomplete model description (n = 262 (28%)), lack of required data (n = 521 (57%)), previously validated or developed with NCR data (n = 45 (5%)), or the associated NCR sample size was insufficient (n = 7 (1%)). The included models predicted survival (33 (38%) overall, 27 (31%) breast cancer-specific, and 3 (3%) other cause-specific), locoregional recurrence (n = 7 (8%)), disease free survival (n = 7 (8%)), metastases (n = 5 (6%)), lymph node involvement (n = 3 (3%)), pathologic complete response (n = 1 (1%)), and surgical margins (n = 1 (1%)). Seven models (8%) showed poor (AUC<0.6), 39 (45%) moderate (AUC:0.6-0.7), 38 (46%) good (AUC:0.7-0.9), and 3 (3%) excellent (AUC≥0.9) discrimination. Using the scaled Brier score, worse performance than an uninformative model was found in 34 (39%) models. CONCLUSION: Comprehensive registry data supports broad validation of published prediction models. Model performance varies considerably in new patient populations, affirming the importance of external validation studies before applying models in clinical practice. Well performing models could be clinically useful in a Dutch setting after careful impact evaluation.

Population-Based Screening Using Low-Dose Chest Computed Tomography: A Systematic Review of Health Economic Evaluations.

BACKGROUND: Chest low-dose computed tomography (LDCT) is a promising technology for population-based screening because it is non-invasive, relatively inexpensive, associated with low radiation and highly sensitive to lung cancer. To improve the cost-effectiveness of lung cancer screening, simultaneous screening for other diseases could be considered. This systematic review was conducted to analyse studies that published evidence on the cost-effectiveness of chest LDCT screening programs for different diseases. METHODS: Scopus and PubMed were searched for English publications (1 January 2011-22 July 2022) using search terms related to screening, computed tomography and cost-effectiveness. An additional search specifically searched for the cost-effectiveness of screening for lung cancer, chronic obstructive pulmonary disease or cardiovascular disease. Included publications should present a full health economic evaluation of population screening with chest LDCT. The extracted data included the disease screened for, model type, country context of screening, inclusion of comorbidities or incidental findings, incremental costs, incremental effects and the resulting cost-effectiveness ratio amongst others. Reporting quality was assessed using the 2022 Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. RESULTS: The search yielded 1799 unique papers, of which 43 were included. Most papers focused on lung cancer screening (n = 40), and three were on coronary calcium scoring. Microsimulation was the most commonly applied modelling type (n = 16), followed by life table analysis (n = 10) and Markov cohort models (n = 10). Studies reflected the healthcare context of the US (n = 15), Canada (n = 4), the UK (n = 3) and 13 other countries. The reported incremental cost-effectiveness ratio ranged from US$10,000 to US$90,000/quality-adjusted life year (QALY) for lung cancer screening compared to no screening and was US$15,900/QALY-US$45,300/QALY for coronary calcium scoring compared to no screening. DISCUSSION: Almost all health economic evaluations of LDCT screening focused on lung cancer. Literature regarding the health economic benefits of simultaneous LDCT screening for multiple diseases is absent. Most studies suggest LDCT screening is cost-effective for current and former smokers aged 55-74 with a minimum of 30 pack-years of smoking history. Consequently, more evidence on LDCT is needed to support further cost-effectiveness analyses. Preferably evidence on simultaneous screening for multiple diseases is needed, but alternatively, on single-disease screening. REGISTRATION OF SYSTEMATIC REVIEW: Prospective Register of Ongoing Systematic Reviews registration CRD42021290228 can be accessed https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=290228 .

Drivers of healthcare costs in patients with ischaemic stroke: a hospital-based retrospective cohort study.

OBJECTIVES: To evaluate how costs of healthcare can be reduced, there is an increasing need to gain insight into the main drivers of such costs. We evaluated drivers of costs of predefined subgroups of patients who had a stroke by linking cost registration with clinical data. METHODS: We retrospectively selected 555 consecutive patients with ischaemic stroke participating between June 2011 and December 2016 in the Dutch Parelsnoer Initiative. Patient characteristics and costs of healthcare activities during hospital admission and the first 3 months after discharge were linked. Patients were divided in subgroups based on age, severity of stroke, stroke subtype, discharge destination and functional outcome. Unit cost per healthcare activity was based on 2018 rates for mutual service in euros. Mean total costs per subgroup were calculated. Multivariate analysis was performed to identify factors associated with costs. RESULTS: Number of admitted days was the main driver of total hospital costs (range 82%-93%) in all predefined subgroups of patients. Second driver was radiological diagnostic investigations (range 2%-9%). Highest costs were observed in patients with a younger age at the time of admission, a higher modified Rankin Scale at the time of discharge and a nursing home as discharge destination. The distribution of costs over the different healthcare activities was associated with stroke subtype; for example, in patients with a cardiac embolism most costs were spent on cardiology-related healthcare activities. CONCLUSION: The number of admitted days was the most important driver of costs in all subgroups of patients with ischaemic stroke. This implicates that to reduce healthcare costs for patients who had a stroke, focus should be on reducing length of hospital stay.

The societal impact of implementing an at-home blood sampling device for chronic care patients: patient preferences and cost impact.

BACKGROUND: Diabetes mellitus, cardiovascular diseases, chronic kidney disease, and thyroid diseases are chronic diseases that require regular monitoring through blood tests. This paper first investigates the experiences of chronic care patients with venipuncture and their expectations of an at-home blood-sampling device, and then assesses the impact on societal costs of implementing such a device in current practice. METHODS: An online survey was distributed among chronic care patients to gain insight into their experience of blood sampling in current practice, and their expectations of an at-home blood-sampling device. The survey results were used as input parameters in a patient-level monte carlo analysis developed to represent a hypothetical cohort of Dutch chronically ill patients to investigate the impact on societal costs compared to usual care. RESULTS: In total, 1311 patients participated in the survey, of which 31% experience the time spent on the phlebotomy appointment as a burden. Of all respondents, 71% prefer to use an at-home blood-sampling device to monitor their chronic disease. The cost analysis indicated that implementing an at-home blood-sampling device increases the cost of phlebotomy itself by €27.25 per patient per year, but it reduces the overall societal costs by €24.86 per patient per year, mainly due to limiting productivity loss. CONCLUSIONS: Patients consider an at-home blood-sampling device to be more user-friendly than venous phlebotomy on location. Long waiting times and crowded locations can be avoided by using an at-home blood-sampling device. Implementing such a device is likely cost-saving as it is expected to reduce societal costs.