Development and Validation of a Model to Predict Regression of Large Size Hepatocellular Adenoma.

INTRODUCTION: Surgery is advocated in hepatocellular adenomas (HCA) >5 cm that do not regress to <5 cm after 6-12 months. The aim of this study was to develop a model for these patients, estimating the probability of HCA regression to <5 cm at 1 and 2 years follow-up. METHODS: Data were derived from a multicenter retrospective cohort of female patients diagnosed with HCA >5 cm at first follow-up. Potential predictors included age, body mass index, and HCA diameter at diagnosis (T0), HCA-subtype (hepatocyte nuclear factor 1α inactivated HCA, inflammatory-HCA, unclassified HCA) and "T0-T1 regression-over-time" (percentage of regression between T0 and first follow-up (T1) divided by weeks between T0 and T1). Cox proportional hazards regression was used to develop a multivariable model with time to regression of HCA < 5 cm as outcome. Probabilities at 1 and 2 years follow-up were calculated. RESULTS: In total, 180 female patients were included. Median HCA diameter at T0 was 82.0 mm and at T1 65.0 mm. Eighty-one patients (45%) reached the clinical endpoint of regression to <5 cm after a median of 34 months. No complications occurred during follow-up. In multivariable analysis, the strongest predictors for regression to <5 cm were HCA diameter at T0 (logtransformed, hazard ratio (HR) 0.05), T0-T1 regression-over-time (HR 2.15) and HCA subtype inflammatory-HCA (HR 2.93) and unclassified HCA (HR 2.40), compared to hepatocyte nuclear factor 1α inactivated HCA (reference). The model yielded an internally validated c-index of 0.79. DISCUSSION: In patients diagnosed with HCA > 5 cm that still exceed 5 cm at first follow-up, regression to <5 cm can be predicted at 1 and 2 years follow-up using this model. Although external validation in an independent population is required, this model may aid in decision-making and potentially avoid unnecessary surgery.

The effect of cost-sharing design characteristics on use of health care recommended by the treating physician; a discrete choice experiment.

BACKGROUND: Cost-sharing programs are often too complex to be easily understood by the average insured individual. Consequently, it is often difficult to determine the amount of expenses in advance. This may preclude well-informed decisions of insured individuals to adhere to medical treatment advised by the treating physician. Preliminary research has showed that the uncertainty in these cost-sharing payments are affected by four design characteristics, i.e. 1) type of payments (copayments, coinsurances or deductibles), 2) rate of payments, 3) annual caps on cost-sharing and 4) moment that these payments must be made (directly at point of care or billed afterwards by the insurer). METHODS: An online discrete choice experiment was used to assess the extent to which design characteristics of cost-sharing programs affect the decision of individuals to adhere to recommended care (prescribed medications, ordered diagnostic tests and referrals to medical specialist care). Analyses were performed using mixed multinomial logits. RESULTS: The questionnaire was completed by 7921 members of a patient organization. Analyses showed that 1) cost-sharing programs that offer clear information in advance on actual expenses that are billed afterwards, stimulate adherence to care recommended by the treating physician; 2) the relative importance of the design characteristics differed between respondents who reported to have forgone health care due to cost-sharing and those who did not; 3) price-awareness among respondents was limited; 4) the utility derived from attributes and respondents' characteristics were positively correlated; 5) an optimized cost-sharing program revealed an adherence of more than 72.9% among those who reported to have forgone health care. CONCLUSIONS: The analyses revealed that less complex cost-sharing programs stimulate adherence to recommended care. If these programs are redesigned accordingly, individuals who had reported to have forgone a health service recommended by their treating physician due to cost-sharing, would be more likely to use this service. Such redesigned programs provide a policy option to reduce adverse health effects of cost-sharing in these groups. Considering the upcoming shift from volume-based to value-based health care provision, insights into the characteristics of a cost-sharing program that stimulates the use of recommended care may help to design value-based insurance plans.

Cost-effectiveness of prophylactic hysterectomy in first-degree female relatives with Lynch syndrome of patients diagnosed with colorectal cancer in the United States: a microsimulation study.

BACKGROUND: To evaluate the cost-effectiveness of prophylactic hysterectomy (PH) in women with Lynch syndrome (LS). METHODS: We developed a microsimulation model incorporating the natural history for the development of hyperplasia with and without atypia into endometrial cancer (EC) based on the MISCAN-framework. We simulated women identified as first-degree relatives (FDR) with LS of colorectal cancer patients after universal testing for LS. We estimated costs and benefits of offering this cohort PH, accounting for reduced quality of life after PH and for having EC. Three minimum ages (30/35/40) and three maximum ages (70/75/80) were compared to no PH. RESULTS: In the absence of PH, the estimated number of EC cases was 300 per 1,000 women with LS. Total associated costs for treatment of EC were $5.9 million. Offering PH to FDRs aged 40-80 years was considered optimal. This strategy reduced the number of endometrial cancer cases to 5.4 (-98%), resulting in 516 quality-adjusted life years (QALY) gained and increasing the costs (treatment of endometrial cancer and PH) to $15.0 million (+154%) per 1,000 women. PH from earlier ages was more costly and resulted in fewer QALYs, although this finding was sensitive to disutility for PH. CONCLUSIONS: Offering PH to 40- to 80-year-old women with LS is expected to add 0.5 QALY per person at acceptable costs. Women may decide to have PH at a younger age, depending on their individual disutility for PH and premature menopause.

Autonomy Challenges in Epigenetic Risk-Stratified Cancer Screening: How Can Patient Decision Aids Support Informed Consent?

Information of an individual's epigenome can be useful in cancer screening to enable personalised decision making on participation, treatment options and further screening strategies. However, adding this information might result in complex risk predictions on multiple diseases, unsolicited findings and information on (past) environmental exposure and behaviour. This complicates informed consent procedures and may impede autonomous decision-making. In this article we investigate and identify the specific features of epigenetic risk-stratified cancer screening that challenge the current informed consent doctrine. Subsequently we describe current and new informed consent models and the principle of respect for autonomy and argue for a specific informed consent model for epigenetic risk-stratified screening programmes. Next, we propose a framework that guides the development of Patient Decision Aids (PDAs) to support informed consent and promote autonomous choices in the specific context of epigenetic cancer screening programmes.

Development of a stratification tool to identify pancreatic intraductal papillary mucinous neoplasms at lowest risk of progression.

BACKGROUND: Because most pancreatic intraductal papillary mucinous neoplasms (IPMNs) will never become malignant, currently advocated long-term surveillance is low-yield for most individuals. AIM: To develop a score chart identifying IPMNs at lowest risk of developing worrisome features or high-risk stigmata. METHODS: We combined prospectively maintained pancreatic cyst surveillance databases of three academic institutions. Patients were included if they had a presumed side-branch IPMN, without worrisome features or high-risk stigmata at baseline (as defined by the 2012 international Fukuoka guidelines), and were followed ≥ 12 months. The endpoint was development of one or more worrisome features or high-risk stigmata during follow-up. We created a multivariable prediction model using Cox-proportional logistic regression analysis and performed an internal-external validation. RESULTS: 875 patients were included. After a mean follow-up of 50 months (range 12-157), 116 (13%) patients developed worrisome features or high-risk stigmata. The final model included cyst size (HR 1.12, 95% CI 1.09-1.15), cyst multifocality (HR 1.49, 95% CI 1.01-2.18), ever having smoked (HR 1.40, 95% CI 0.95-2.04), history of acute pancreatitis (HR 2.07, 95% CI 1.21-3.55), and history of extrapancreatic malignancy (HR 1.34, 95% CI 0.91-1.97). After validation, the model had good discriminative ability (C-statistic 0.72 in the Mayo cohort, 0.71 in the Columbia cohort, 0.64 in the Erasmus cohort). CONCLUSION: In presumed side branch IPMNs without worrisome features or high-risk stigmata at baseline, the Dutch-American Risk stratification Tool (DART-1) successfully identifies pancreatic lesions at low risk of developing worrisome features or high-risk stigmata.

Prediction models for endometrial cancer for the general population or symptomatic women: A systematic review.

OBJECTIVE: To provide an overview of prediction models for the risk of developing endometrial cancer in women of the general population or for the presence of endometrial cancer in symptomatic women. METHODS: We systematically searched the Embase and Pubmed database until September 2017 for relevant publications. We included studies describing the development, the external validation, or the updating of a multivariable model for predicting endometrial cancer in the general population or symptomatic women. RESULTS: Out of 2756 references screened, 14 studies were included. We found two prediction models for developing endometrial cancer in the general population (risk models) and one extension. Eight studies described the development of models for symptomatic women (diagnostic models), one comparison of the performance of two diagnostic models and two external validation. Sample size varied from 60 (10 with cancer) to 201,811 (855 with cancer) women. The age of the women was included as a predictor in almost all models. The risk models included epidemiological variables related to the reproductive history of women, hormone use, BMI, and smoking history. The diagnostic models also included clinical predictors, such as endometrial thickness and recurrent bleeding. The concordance statistic (c), assessing the discriminative ability, varied from 0.68 to 0.77 in the risk models and from 0.73 to 0.957 in the diagnostic models. Methodological information was often limited, especially on the handling of missing data, and the selection of predictors. One risk model and four diagnostic models were externally validated. CONCLUSIONS: Only a few models have been developed to predict endometrial cancer in asymptomatic or symptomatic women. The usefulness of most models is unclear considering methodological shortcomings and lack of external validation. Future research should focus on external validation and extension with new predictors or biomarkers, such as genetic and epigenetic markers.