Dynamic Simulation Modeling to Analyze the Impact of Whole Genome Sequencing National Implementation Scenarios in Lung cancer on Time-to-Treatment, Costs and Patient Demand

Background Although Whole Genome Sequencing (WGS) is increasingly proposed to unravel molecular origins of advanced cancers, it is less clear if and how WGS should be routinely offered in the health service. The objective of this study is to investigate how the cost per patient and time-to-treatment is affected if WGS were implemented in the national health system and how these outcomes differ among subgroups of patients with lung cancer. This first-ever study used health systems simulation modeling to analyze implementation scenarios ensuring sustainable access to cancer treatment.Methods A base case and three scenarios (varying stage of disease and hospitals offering WGS) the optimal placement of WGS in the diagnostic pathway was simulated using a dynamic simulation model. The model simulated lung cancer patients undergoing molecular diagnostic procedures in one or multiple hospitals. The model also included patient and healthcare provider heterogeneity as well as referral patterns of lung cancer (LC) patients using patient-level data obtained from the Netherlands Cancer Registry. Model outcomes were the time-to-treatment, total diagnostic cost, and the demand for WGS sequencing capacity including the expertise of a molecular tumor board.Results The time-to-treatment ranged between 20-46 days for all four scenarios considered. The cost of molecular diagnostic testing per patient ranged from €621 in the base case to €1930 in the scenario where all LC patients (stage I-IV) receive upfront WGS. Compared to the base case, upfront testing using WGS in all LC patients led to a 33% reduction in the time-to-treatment, a 210% increase in the cost per patient and a six-fold increase in total diagnostic costs.Conclusions This first-ever study investigating implementation scenario’s demonstrated that upfront WGS for all lung cancer patients can reduce the time to treatment yet at a higher cost. However, upfront WGS also reduces diagnostic pathway complexity, which may improve care planning and treatment efficiency. The model is versatile in its approach to study the impact of price discounts or the amount of actionable targets tested for and further analysis showed discounts on consumables up to 50% imply WGS would the preferred strategy

Incorporating published univariable associations in diagnostic and prognostic modeling

Background: Diagnostic and prognostic literature is overwhelmed with studies reporting univariable predictor-outcome associations. Currently, methods to incorporate such information in the construction of a prediction model are underdeveloped and unfamiliar to many researchers. Methods. This article aims to improve upon an adaptation method originally proposed by Greenland (1987) and Steyerberg (2000) to incorporate previously published univariable associations in the construction of a novel prediction model. The proposed method improves upon the variance estimation component by reconfiguring the adaptation process in established theory and making it more robust. Different variants of the proposed method were tested in a simulation study, where performance was measured by comparing estimated associations with their predefined values according to the Mean Squared Error and coverage of the 90% confidence intervals. Results: Results demonstrate that performance of estimated multivariable associations considerably improves for small datasets where external evidence is included. Although the error of estimated associations decreases with increasing amount of individual participant data, it does not disappear completely, even in very large datasets. Conclusions: The proposed method to aggregate previously published univariable associations with individual participant data in the construction of a novel prediction models outperforms established approaches and is especially worthwhile when relatively limited individual participant data are available

Real-world data on discordance between estrogen, progesterone, and HER2 receptor expression on diagnostic tumor biopsy versus tumor resection material

Purpose The estrogen (ER), progesterone (PR), and HER2 status are essential in guiding treatment decisions in breast cancer patients. In daily life, the ER/PR/HER2 status is expected to be commonly tested twice, i.e., at diagnosis using material from tumor needle biopsies, and after tumor resection using full tumor tissue material. This study explored the discordance of ER/PR/HER2 between tumor needle biopsies and full tumor resection material using real-world patient-level data from Dutch breast cancer patients. Methods Pathology reports of 11,054 breast cancer patients were derived from PALGA (Dutch Pathology Registry). Discordance was calculated for multiple combinations of the ER/PR/HER2 receptor status. The influence of patient and tumor characteristics on the probability of having discordant test results was analyzed using multiple logistic regression models (separately for ER, PR and HER2). Results For 1279 patients (14.4%), at least one of the receptors (ER/PR/HER2) was determined on both biopsy and tumor tissue material. The majority had concordant test results for ER (n=916; 94.8%), PR (n=1170; 86.7%), and HER2 (n=881; 98.1%). Patients having an ER- and HER2-positive but PR-negative biopsy classification, BR grade III, and <10% tumor tissue remaining after neoadjuvant therapy (NAT) have the highest probability of ER discordant test results (OR4.991; p=83.31%). The probability of discordance in PR is based on different sets of patient and tumor characteristics. Potential cost savings from omitting multiple tests if concordance can be perfectly predicted can be up to €205,000 yearly. Conclusions Double testing of ER/PR/HER2 is less common than expected. Discordance in ER/PR/HER2 test results between tumor needle biopsy taken at the time of diagnosis and tumor resection material is very low, especially in patients not receiving any form of neoadjuvant therapy. These results imply that a substantial number of tests can potentially be omitted in specific subgroups of breast cancer patient

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 &gt;95%.CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity &gt;95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.</p

Improving early diagnosis of cardiovascular disease in patients with type 2 diabetes and COPD:Protocol of the RED-CVD cluster randomised diagnostic trial

Introduction: The early stages of chronic progressive cardiovascular disease (CVD) generally cause non-specific symptoms that patients often do not spontaneously mention to their general practitioner, and are therefore easily missed. A proactive diagnostic strategy has the potential to uncover these frequently missed early stages, creating an opportunity for earlier intervention. This is of particular importance for chronic progressive CVDs with evidence-based therapies known to improve prognosis, such as ischaemic heart disease, atrial fibrillation and heart failure. Patients with type 2 diabetes or chronic obstructive pulmonary disease (COPD) are at particularly high risk of developing CVD. In the current study, we will demonstrate the feasibility and effectiveness of screening these high-risk patients with our early diagnosis strategy, using tools that are readily available in primary care, such as symptom questionnaires (to be filled out by the patients themselves), natriuretic peptide measurement and electrocardiography. Methods and analysis: The Reviving the Early Diagnosis-CVD trial is a multicentre, cluster randomised diagnostic trial performed in primary care practices across the Netherlands. We aim to include 1300 (2×650) patients who participate in a primary care disease management programme for COPD or type 2 diabetes. Practices will be randomised to the intervention arm (performing the early diagnosis strategy during the routine visits that are part of the disease management programmes) or the control arm (care as usual). The main outcome is the number of newly detected cases with CVDs in both arms, and the subsequent therapies they received. Secondary endpoints include quality of life, cost-effectiveness and the added diagnostic value of family and reproductive history questionnaires and three (novel) biomarkers (high-sensitive troponin-I, growth differentiation factor-15 and suppressor of tumourigenicity 2). Finally newly initiated treatments will be compared in both groups. Ethics and dissemination: The protocol was approved by the Medical Ethical Committee of the University Medical Center Utrecht, the Netherlands. Results are expected in 2022 and will be disseminated through international peer-reviewed publications. Trial registration number NTR7360

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.</p

Proactive screening for symptoms:A simple method to improve early detection of unrecognized cardiovascular disease in primary care. Results from the Lifelines Cohort Study

Cardiovascular disease (CVD) often goes unrecognized, despite symptoms frequently being present. Proactive screening for symptoms might improve early recognition and prevent disease progression or acute cardiovascular events. We studied the diagnostic value of symptoms for the detection of unrecognized atrial fibrillation (AF), heart failure (HF), and coronary artery disease (CAD) and developed a corresponding screening questionnaire. We included 100,311 participants (mean age 52 ± 9 years, 58% women) from the population-based Lifelines Cohort Study. For each outcome (unrecognized AF/HF/CAD), we built a multivariable model containing demographics and symptoms. These models were combined into one 'three-disease' diagnostic model and questionnaire for all three outcomes. Results were validated in Lifelines participants with chronic obstructive pulmonary disease (COPD) and diabetes mellitus (DM). Unrecognized CVD was identified in 1325 participants (1.3%): AF in 131 (0.1%), HF in 599 (0.6%), and CAD in 687 (0.7%). Added to age, sex, and body mass index, palpitations were independent predictors for unrecognized AF; palpitations, chest pain, dyspnea, exercise intolerance, health-related stress, and self-expected health worsening for unrecognized HF; smoking, chest pain, exercise intolerance, and claudication for unrecognized CAD. Area under the curve for the combined diagnostic model was 0.752 (95% CI 0.737-0.766) in the total population and 0.757 (95% CI 0.734-0.781) in participants with COPD and DM. At the chosen threshold, the questionnaire had low specificity, but high sensitivity. In conclusion, a short questionnaire about demographics and symptoms can improve early detection of CVD and help pre-select people who should or should not undergo further screening for CVD