Commercial insurance covers a follow-up colonoscopy (COL) after a positive colorectal cancer (CRC) screening test with no patient cost-sharing. Instituting a similar policy for Medicare beneficiaries may increase screening adherence and improve outcomes. The cost-effectiveness of stool-based CRC screening was compared across adherence scenarios that assumed Medicare coinsurance status quo (20% for follow-up COL) or waived coinsurance. The CRC-AIM model simulated previously unscreened eligible Medicare beneficiaries undergoing stool-based CRC screening at age 65 for 10 years. Medicare costs, CRC cases, CRC deaths, life-years gained (LYG), and quality adjusted life-years (QALYs) were estimated versus no screening. Scenario 1 (S1) assumed 20% coinsurance for follow-up COL. Scenario 2 (S2) assumed waived coinsurance without adherence changes. Scenarios 3-7 (S3-S7) assumed that waiving coinsurance increased real-world stool-based screening and/or follow-up COL adherence by 5% or 10%. Sensitivity analyses assumed 1%-4% increased adherence. Cost-effectiveness threshold was ≤$100,000/QALY. Waiving coinsurance without adherence changes (S2) did not affect outcomes versus S1. S3-S7 versus S1 over 10 years estimated up to 3.6 fewer CRC cases/1000 individuals, up to 2.1 fewer CRC deaths, up to 20.7 more LYG, and had comparable total costs per-patient (≤$6,478 vs $6,449, respectively) as reduced CRC medical costs offset increased screening and COL costs. In sensitivity analyses, any increase in adherence after waiving coinsurance was cost-effective and increased LYG. In simulated Medicare beneficiaries, waiving coinsurance for follow-up COL after a positive stool-based test improved outcomes and was cost-effective when assumed to modestly increase CRC screening and/or follow-up COL adherence.

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Background and Aims We conducted a systematic literature review to understand the evidence supporting treatment decisions for cholestatic pruritus associated with primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Methods Studies that enrolled ≥ 75% participants with PBC or PSC and reported ≥ 1 endpoint(s) related to efficacy, safety, health-related quality of life (HRQoL) or other patient-reported outcomes were included. Bias was assessed using the Cochrane risk of bias tool for randomised controlled trials (RCTs) and the Quality of Cohort studies tool for non-RCTs. Results Thirty-nine publications were identified, covering 42 studies and six treatment classes (including investigational and approved products): anion-exchange resins, antibiotics (rifampicin/derivatives), opiates, selective serotonin reuptake inhibitors, fibrates, ileal bile acid transporter inhibitors and other agents not categorised in these six classes. Across studies, median sample size was small ( n = 18), 20 studies were over 20 years old, 25 followed patients for ≤ 6 weeks, only 25 were RCTs. Pruritus was assessed using several different tools, with inconsistencies in their application. Cholestyramine, considered first-line therapy for moderate-severe cholestatic pruritus, was assessed in six studies (two RCTs) including 56 patients with PBC and 2 with PSC, with evidence of efficacy demonstrated in only three studies, among which, two RCTs were assessed as having a high risk of bias. Findings were similar for other drug classes. Conclusions There is a lack of consistent and reproducible evidence available on efficacy, impact on HRQoL, and safety of cholestatic pruritus treatments, leaving physicians to rely on clinical experience rather than evidence-based medicine for treatment selection. Supplementary Information The online version contains supplementary material available at 10.1007/s10620-023-07862-z.

Objectives Machine learning (ML)–based emulators improve the calibration of decision-analytical models, but their performance in complex microsimulation models is yet to be determined. Methods We demonstrated the use of an ML-based emulator with the Colorectal Cancer (CRC)-Adenoma Incidence and Mortality (CRC-AIM) model, which includes 23 unknown natural history input parameters to replicate the CRC epidemiology in the United States. We first generated 15,000 input combinations and ran the CRC-AIM model to evaluate CRC incidence, adenoma size distribution, and the percentage of small adenoma detected by colonoscopy. We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants (i.e., XGBoost, LightGBM, CatBoost) and compared their performance. We evaluated 10 million potential input combinations using the selected emulator and examined input combinations that best estimated observed calibration targets. Furthermore, we cross-validated outcomes generated by the CRC-AIM model with those made by CISNET models. The calibrated CRC-AIM model was externally validated using the United Kingdom Flexible Sigmoidoscopy Screening Trial (UKFSST). Results The DNN with proper preprocessing outperformed other tested ML algorithms and successfully predicted all 8 outcomes for different input combinations. It took 473 s for the trained DNN to predict outcomes for 10 million inputs, which would have required 190 CPU-years without our DNN. The overall calibration process took 104 CPU-days, which included building the data set, training, selecting, and hyperparameter tuning of the ML algorithms. While 7 input combinations had acceptable fit to the targets, a combination that best fits all outcomes was selected as the best vector. Almost all of the predictions made by the best vector laid within those from the CISNET models, demonstrating CRC-AIM’s cross-model validity. Similarly, CRC-AIM accurately predicted the hazard ratios of CRC incidence and mortality as reported by UKFSST, demonstrating its external validity. Examination of the impact of calibration targets suggested that the selection of the calibration target had a substantial impact on model outcomes in terms of life-year gains with screening. Conclusions Emulators such as a DNN that is meticulously selected and trained can substantially reduce the computational burden of calibrating complex microsimulation models. Highlights Calibrating a microsimulation model, a process to find unobservable parameters so that the model fits observed data, is computationally complex. We used a deep neural network model, a popular machine learning algorithm, to calibrate the Colorectal Cancer Adenoma Incidence and Mortality (CRC-AIM) model. We demonstrated that our approach provides an efficient and accurate method to significantly speed up calibration in microsimulation models. The calibration process successfully provided cross-model validation of CRC-AIM against 3 established CISNET models and also externally validated against a randomized controlled trial.

Supplementary Table from Cost-Effectiveness of Waiving Coinsurance for Follow-Up Colonoscopy after a Positive Stool-Based Colorectal Screening Test in a Medicare Population