Monitoring policy in the context of preventive treatment of cardiovascular disease.

Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.

Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations.

OBJECTIVE: Occupational injuries (OIs) cause an immense burden on the US population. Prediction models help focus resources on those at greatest risk of a delayed return to work (RTW). RTW depends on factors that develop over time; however, existing methods only utilize information collected at the time of injury. We investigate the performance benefits of dynamically estimating RTW, using longitudinal observations of diagnoses and treatments collected beyond the time of initial injury. MATERIALS AND METHODS: We characterize the difference in predictive performance between an approach that uses information collected at the time of initial injury (baseline model) and a proposed approach that uses longitudinal information collected over the course of the patient's recovery period (proposed model). To control the comparison, both models use the same deep learning architecture and differ only in the information used. We utilize a large longitudinal observation dataset of OI claims and compare the performance of the two approaches in terms of daily prediction of future work state (working vs not working). The performance of these two approaches was assessed in terms of the area under the receiver operator characteristic curve (AUROC) and expected calibration error (ECE). RESULTS: After subsampling and applying inclusion criteria, our final dataset covered 294 103 OIs, which were split evenly between train, development, and test datasets (1/3, 1/3, 1/3). In terms of discriminative performance on the test dataset, the proposed model had an AUROC of 0.728 (90% confidence interval: 0.723, 0.734) versus the baseline's 0.591 (0.585, 0.598). The proposed model had an ECE of 0.004 (0.003, 0.005) versus the baseline's 0.016 (0.009, 0.018). CONCLUSION: The longitudinal approach outperforms current practice and shows potential for leveraging observational data to dynamically update predictions of RTW in the setting of OI. This approach may enable physicians and workers' compensation programs to manage large populations of injured workers more effectively.

Development and Validation of Dynamic Multivariate Prediction Models of Sexual Function Recovery in Patients with Prostate Cancer Undergoing Radical Prostatectomy: Results from the MUSIC Statewide Collaborative.

Background: Radical prostatectomy (RP) is the most common definitive treatment for men with intermediate-risk prostate cancer and is frequently complicated by erectile dysfunction. Objective: To develop and validate models to predict 12- and 24-month post-RP sexual function. Design setting and participants: Using Michigan Urological Surgery Improvement Collaborative (MUSIC) registry data from 2016 to 2021, we developed dynamic, multivariate, random-forest models to predict sexual function recovery following RP. Model factors (established a priori) included baseline patient characteristics and repeated assessments of sexual satisfaction, and Expanded Prostate Cancer Index Composite 26 (EPIC-26) overall scores and sexual domain questions. Outcome measurements and statistical analysis: We evaluated three outcomes related to sexual function: (1) the EPIC-26 sexual domain score (range 0-100); (2) the EPIC-26 sexual domain score dichotomized at ≥73 for "good" function; and (3) a dichotomized variable for erection quality at 12 and 24 months after RP. A gradient-boosting decision tree was used for the prediction models, which combines many decision trees into a single model. We evaluated the performance of our model using the root mean squared error (RMSE) and mean absolute error (MAE) for the EPIC-26 score as a continuous variable, and the area under the receiver operating characteristic curve (AUC) for the dichotomized EPIC-26 sexual domain score (SDS) and erection quality. All analyses were conducted using R v3.6.3. Results and limitations: We identified 3983 patients at 12 months and 2494 patients at 24 months who were randomized to the derivation cohort at 12 and 24 months, respectively. Using baseline information only, our model predicted the 12-month EPIC-26 SDS with RMSE of 24 and MAE of 20. The AUC for predicting EPIC-26 SDS ≥73 (a previously published threshold) was 0.82. Our model predicted 24-month EPIC-26 SDS with RMSE of 26 and MAE of 21, and AUC for SDS ≥73 of 0.81. Inclusion of post-RP data improved the AUC to 0.91 and 0.94 at 12 and 24 months, respectively. A web tool has also been developed and is available at https://ml4lhs.shinyapps.io/askmusic_prostate_pro/. Conclusions: Our model provides a valid way to predict sexual function recovery at 12 and 24 months after RP. With this dynamic, multivariate (multiple outcomes) model, accurate predictions can be made for decision-making and during survivorship, which may reduce decision regret. Patient summary: Our prediction model allows patients considering prostate cancer surgery to understand their probability before and after surgery of recovering their erectile function and may reduce decision regret.

Prostate Cancer Patients Under Active Surveillance with a Suspicious Magnetic Resonance Imaging Finding Are at Increased Risk of Needing Treatment: Results of the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) Consortium.

BACKGROUND: The inclusion criterion for active surveillance (AS) is low- or intermediate-risk prostate cancer. The predictive value of the presence of a suspicious lesion at magnetic resonance imaging (MRI) at the time of inclusion is insufficiently known. OBJECTIVE: To evaluate the percentage of patients needing active treatment stratified by the presence or absence of a suspicious lesion at baseline MRI. DESIGN SETTING AND PARTICIPANTS: A retrospective analysis of the data from the multicentric AS GAP3 Consortium database was conducted. The inclusion criteria were men with grade group (GG) 1 or GG 2 prostate cancer combined with prostate-specific antigen <20 ng/ml. We selected a subgroup of patients who had MRI at baseline and for whom MRI results and targeted biopsies were used for AS eligibility. Suspicious MRI was defined as an MRI lesion with Prostate Imaging Reporting and Data System (PI-RADS)/Likert ≥3 and for which targeted biopsies did not exclude the patient for AS. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was treatment free survival (FS). The secondary outcomes were histological GG progression FS and continuation of AS (discontinuation FS). RESULTS AND LIMITATIONS: The study cohort included 2119 patients (1035 men with nonsuspicious MRI and 1084 with suspicious MRI) with a median follow-up of 23 (12-43) mo. For the whole cohort, 3-yr treatment FS was 71% (95% confidence interval [CI]: 69-74). For nonsuspicious MRI and suspicious MRI groups, 3-yr treatment FS rates were, respectively, 80% (95% CI: 77-83) and 63% (95% CI: 59-66). Active treatment (hazard ratio [HR] = 2.0, p < 0.001), grade progression (HR = 1.9, p < 0.001), and discontinuation of AS (HR = 1.7, p < 0.001) were significantly higher in the suspicious MRI group than in the nonsuspicious MRI group. CONCLUSIONS: The risks of switching to treatment, histological progression, and AS discontinuation are higher in cases of suspicious MRI at inclusion. PATIENT SUMMARY: Among men with low- or intermediate-risk prostate cancer who choose active surveillance, those with suspicious magnetic resonance imaging (MRI) at the time of inclusion in active surveillance are more likely to show switch to treatment than men with nonsuspicious MRI.

Development and Validation of Models to Predict Pathological Outcomes of Radical Prostatectomy in Regional and National Cohorts.

PURPOSE: Prediction models are recommended by national guidelines to support clinical decision making in prostate cancer. Existing models to predict pathological outcomes of radical prostatectomy (RP)-the Memorial Sloan Kettering (MSK) models, Partin tables, and the Briganti nomogram-have been developed using data from tertiary care centers and may not generalize well to other settings. MATERIALS AND METHODS: Data from a regional cohort (Michigan Urological Surgery Improvement Collaborative [MUSIC]) were used to develop models to predict extraprostatic extension (EPE), seminal vesicle invasion (SVI), lymph node invasion (LNI), and nonorgan-confined disease (NOCD) in patients undergoing RP. The MUSIC models were compared against the MSK models, Partin tables, and Briganti nomogram (for LNI) using data from a national cohort (Surveillance, Epidemiology, and End Results [SEER] registry). RESULTS: We identified 7,491 eligible patients in the SEER registry. The MUSIC model had good discrimination (SEER AUC EPE: 0.77; SVI: 0.80; LNI: 0.83; NOCD: 0.77) and was well calibrated. While the MSK models had similar discrimination to the MUSIC models (SEER AUC EPE: 0.76; SVI: 0.80; LNI: 0.84; NOCD: 0.76), they overestimated the risk of EPE, LNI, and NOCD. The Partin tables had inferior discrimination (SEER AUC EPE: 0.67; SVI: 0.76; LNI: 0.69; NOCD: 0.72) as compared to other models. The Briganti LNI nomogram had an AUC of 0.81 in SEER but overestimated the risk. CONCLUSIONS: New models developed using the MUSIC registry outperformed existing models and should be considered as potential replacements for the prediction of pathological outcomes in prostate cancer.

Correction: Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials.

[This corrects the article DOI: 10.1371/journal.pmed.1002410.].

Comparison of biopsy under-sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies.

This study aimed to estimate the rates of biopsy undersampling and progression for four prostate cancer (PCa) active surveillance (AS) cohorts within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium. We used a hidden Markov model (HMM) to estimate factors that define PCa dynamics for men on AS including biopsy under-sampling and progression that are implied by longitudinal data in four large cohorts included in the GAP3 database. The HMM was subsequently used as the basis for a simulation model to evaluate the biopsy strategies previously proposed for each of these cohorts. For the four AS cohorts, the estimated annual progression rate was between 6%-13%. The estimated probability of a biopsy successfully sampling undiagnosed non-favorable risk cancer (biopsy sensitivity) was between 71% and 80%. In the simulation study of patients diagnosed with favorable risk cancer at age 50, the mean number of biopsies performed before age 75 was between 4.11 and 12.60, depending on the biopsy strategy. The mean delay time to detection of non-favorable risk cancer was between 0.38 and 2.17 years. Biopsy undersampling and progression varied considerably across study cohorts. There was no single best biopsy protocol that is optimal for all cohorts, because of the variation in biopsy under-sampling error and annual progression rates across cohorts. All strategies demonstrated diminishing benefits from additional biopsies.

Prospective monitoring of imaging guideline adherence by physicians in a surgical collaborative: comparison of statistical process control methods for detecting outlying performance.

BACKGROUND: Systematic, automated methods for monitoring physician performance are necessary if outlying behavior is to be detected promptly and acted on. In the Michigan Urological Surgery Improvement Collaborative (MUSIC), we evaluated several statistical process control (SPC) methods to determine the sensitivity and ease of interpretation for assessing adherence to imaging guidelines for patients with newly diagnosed prostate cancer. METHODS: Following dissemination of imaging guidelines within the Michigan Urological Surgery Improvement Collaborative (MUSIC) for men with newly diagnosed prostate cancer, MUSIC set a target of imaging < 10% of patients for which bone scan is not indicated. We compared four SPC methods using Monte Carlo simulation: p-chart, weighted binomial CUSUM, Bernoulli cumulative sum (CUSUM), and exponentially weighted moving average (EWMA). We simulated non-indicated bone scan rates ranging from 5.9% (within target) to 11.4% (above target) for a representative MUSIC practice. Sensitivity was determined using the average run length (ARL), the time taken to signal a change. We then plotted actual non-indicated bone scan rates for a representative MUSIC practice using each SPC method to qualitatively assess graphical interpretation. RESULTS: EWMA had the lowest ARL and was able to detect changes significantly earlier than the other SPC methodologies (p < 0.001). The p-chart had the highest ARL and thus detected changes slowest (p < 0.001). EWMA and p-charts were easier to interpret graphically than CUSUM methods due to their ability to display historical imaging rates. CONCLUSIONS: SPC methods can be used to provide informative and timely feedback regarding adherence to healthcare performance target rates in quality improvement collaboratives. We found the EWMA method most suited for detecting changes in imaging utilization.

Active Surveillance vs Immediate Treatment-Which Has a Greater Financial Incentive for Urologists?

INTRODUCTION: We compared cumulative reimbursement to urologists following implementation of surveillance vs immediate treatment. Active surveillance for prostate cancer is widely considered beneficial and cost-effective for low risk patients, although many still receive immediate therapy. It is unknown whether reduced reimbursement may be a barrier to urologists recommending surveillance. METHODS: We used Medicare claims and a validated natural history model for low risk prostate cancer to simulate annual reimbursements associated with active surveillance and immediate treatments, including surgery and radiation therapy. The model accounts for misclassification due to biopsy under sampling, grade progression and discontinuation of surveillance due to patient preferences. RESULTS: Active surveillance provided approximately $907 to $2,041 less in the net present value of expected cumulative reimbursements for urologists over 10 years ($1,711.80 to $2,740.40 less over 5 years) compared to initial treatment. Sensitivity analysis showed that use of magnetic resonance imaging/ultrasound fusion based biopsy and frequency of biopsies and clinic visits under surveillance are major sources of uncertainty regarding reimbursement. CONCLUSIONS: Urologists have little financial incentive to implement active surveillance. New payment models may be needed to bring financial incentives in line with the recommended treatment for patients with low risk prostate cancer.

18F-Choline PET/mpMRI for Detection of Clinically Significant Prostate Cancer: Part 1. Improved Risk Stratification for MRI-Guided Transrectal Prostate Biopsies.

A prospective single-arm clinical trial was conducted to determine whether 18F-choline PET/mpMRI can improve the specificity of multiparametric MRI (mpMRI) of the prostate for Gleason ≥ 3+4 prostate cancer. Methods: Before targeted and systematic prostate biopsy, mpMRI and 18F-choline PET/CT were performed on 56 evaluable subjects with 90 Likert score 3-5 mpMRI target lesions, using a 18F-choline target-to-background ratio of greater than 1.58 to indicate a positive 18F-choline result. Prostate biopsies were performed after registration of real-time transrectal ultrasound with T2-weighted MRI. A mixed-effects logistic regression was applied to measure the performance of mpMRI (based on prospective Likert and retrospective Prostate Imaging Reporting and Data System, version 2 [PI-RADS], scores) compared with 18F-choline PET/mpMRI to detect Gleason ≥ 3+4 cancer. Results: The per-lesion accuracy of systematic plus targeted biopsy for mpMRI alone was 67.8% (area under receiver-operating-characteristic curve [AUC], 0.73) for Likert 4-5 and 70.0% (AUC, 0.76) for PI-RADS 3-5. Several PET/MRI models incorporating 18F-choline with mpMRI data were investigated. The most promising model selected all high-risk disease on mpMRI (Likert 5 or PI-RADS 5) plus low- and intermediate-risk disease (Likert 4 or PI-RADS 3-4), with an elevated 18F-choline target-to-background ratio greater than 1.58 as positive for significant cancer. Using this approach, the accuracy on a per-lesion basis significantly improved to 88.9% for Likert (AUC, 0.90; P < 0.001) and 91.1% for PI-RADS (AUC, 0.92; P < 0.001). On a per-patient basis, the accuracy improved to 92.9% for Likert (AUC, 0.93; P < 0.001) and to 91.1% for PI-RADS (AUC, 0.91; P = 0.009). Conclusion:18F-choline PET/mpMRI improved the identification of Gleason ≥ 3+4 prostate cancer compared with mpMRI, with the principal effect being improved risk stratification of intermediate-risk mpMRI lesions.

18F-Choline PET/mpMRI for Detection of Clinically Significant Prostate Cancer: Part 2. Cost-Effectiveness Analysis.

The objective of this study was to evaluate the cost-effectiveness of 18F-choline PET/multiparametric MRI (mpMRI) versus mpMRI alone for the detection of primary prostate cancer with a Gleason score of greater than or equal to 3 + 4 in men with elevated prostate-specific antigen levels. Methods: A Markov model of prostate cancer onset and progression was used to estimate the health and economic consequences of 18F-choline PET/mpMRI for the detection of primary prostate cancer with a Gleason score of greater than or equal to 3 + 4 in men with elevated prostate-specific antigen levels. Multiple simultaneous hybrid 18F-choline PET/mpMRI strategies were evaluated using Likert or Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) scoring; the first was biopsy for Likert 5 mpMRI lesions or Likert 3-4 lesions with 18F-choline target-to-background ratios of greater than or equal to 1.58, and the second was biopsy for PI-RADSv2 5 mpMRI lesions or PI-RADSv2 3-4 mpMRI lesions with 18F-choline target-to-background ratios of greater than or equal to 1.58. These strategies were compared with universal standard biopsy, mpMRI alone with biopsy only for PI-RADSv2 3-5 lesions, and mpMRI alone with biopsy only for Likert 4-5 lesions. For each mpMRI strategy, either no biopsy or standard biopsy could be performed after negative mpMRI results were obtained. Deaths averted, quality-adjusted life years (QALYs), cost, and incremental cost-effectiveness ratios were estimated for each strategy. Results: When the results of 18F-choline PET/mpMRI were negative, performing a standard biopsy was more expensive and had lower QALYs than performing no biopsy. The best screening strategy among those considered in this study performed hybrid 18F-choline PET/mpMRI with Likert scoring on men with elevated PSA, performed combined biopsy (targeted biopsy and standard 12-core biopsy) for men with positive imaging results, and no biopsy for men with negative imaging results ($22,706/QALY gained relative to mpMRI alone); this strategy reduced the number of biopsies by 35% in comparison to mpMRI alone. When the same policies were compared using PI-RADSv2 instead of Likert scoring, hybrid 18F-choline PET/mpMRI cost $46,867/QALY gained relative to mpMRI alone. In a threshold analysis, the best strategy among those considered remained cost-effective when the sensitivity and specificity of PET/mpMRI and combined biopsy (targeted biopsy and standard 12-core biopsy) were simultaneously reduced by 20 percentage points. Conclusion:18F-choline PET/mpMRI for the detection of primary prostate cancer with a Gleason score of greater than or equal to 3 + 4 is cost-effective and can reduce the number of unneeded biopsies in comparison to mpMRI alone.

Temporary Health Impact of Prostate MRI and Transrectal Prostate Biopsy in Active Surveillance Prostate Cancer Patients.

PURPOSE: To assess the temporary health impact of prostate multiparametric MRI (mpMRI) and transrectal prostate biopsy in an active surveillance prostate cancer population. METHODS: A two-arm institutional review board-approved HIPAA-compliant prospective observational patient-reported outcomes study was performed from November 2017 to July 2018. Inclusion criteria were men with Gleason 6 prostate cancer in active surveillance undergoing either prostate mpMRI or transrectal prostate biopsy. A survey instrument was constructed using validated metrics in consultation with the local patient- and family-centered care organization. Study subjects were recruited at the time of diagnostic testing and completed the instrument by phone 24 to 72 hours after testing. The primary outcome measure was summary testing-related quality of life (summary utility score), derived from the testing morbidities index (TMI) (scale: 0 = death and 1 = perfect health). TMI is stratified into seven domains, with each domain scored from 1 (no health impact) to 5 (extreme health impact). Testing-related quality-of-life measures in the two cohorts were compared with Mann-Whitney U test. RESULTS: In all, 122 subjects were recruited, and 90% (110 of 122 [MRI 55 of 60, biopsy 55 of 62]) successfully completed the survey instrument. The temporary quality-of-life impact of transrectal biopsy was significantly greater than that of prostate mpMRI (0.82, 95% confidence interval [CI] 0.79-0.85, versus 0.95, 95% CI 0.94-0.97; P < .001). The largest mean domain-level difference was for intraprocedural pain (transrectal biopsy 2.6, 95% CI 2.4-2.8, versus mpMRI 1.3, 95% CI 1.1-1.5; P < .001). CONCLUSION: Transrectal prostate biopsy has greater temporary health impact (lower testing-related quality-of-life measure) than prostate mpMRI.

Probabilistic sensitivity analysis on Markov models with uncertain transition probabilities: an application in evaluating treatment decisions for type 2 diabetes.

Markov models are commonly used for decision-making studies in many application domains; however, there are no widely adopted methods for performing sensitivity analysis on such models with uncertain transition probability matrices (TPMs). This article describes two simulation-based approaches for conducting probabilistic sensitivity analysis on a given discrete-time, finite-horizon, finite-state Markov model using TPMs that are sampled over a specified uncertainty set according to a relevant probability distribution. The first approach assumes no prior knowledge of the probability distribution, and each row of a TPM is independently sampled from the uniform distribution on the row's uncertainty set. The second approach involves random sampling from the (truncated) multivariate normal distribution of the TPM's maximum likelihood estimators for its rows subject to the condition that each row has nonnegative elements and sums to one. The two sampling methods are easily implemented and have reasonable computation times. A case study illustrates the application of these methods to a medical decision-making problem involving the evaluation of treatment guidelines for glycemic control of patients with type 2 diabetes, where natural variation in a patient's glycated hemoglobin (HbA1c) is modeled as a Markov chain, and the associated TPMs are subject to uncertainty.

askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men.

BACKGROUND: Clinical registries provide physicians with a means for making data-driven decisions but few opportunities exist for patients to interact with registry data to help make decisions. OBJECTIVE: We sought to develop a web-based system that uses a prostate cancer (CaP) registry to provide newly diagnosed men with a platform to view predicted treatment decisions based on patients with similar characteristics. DESIGN, SETTING, AND PARTICIPANTS: The Michigan Urological Surgery Improvement Collaborative (MUSIC) is a quality improvement consortium of urology practices that maintains a prospective registry of men with CaP. We used registry data from 45 MUSIC urology practices from 2015 to 2017 to develop and validate a random forest machine learning model. After fitting the random forest model to a derivation cohort consisting of a random two-thirds sample of patients after stratifying by practice location, we evaluated the model performance in a validation cohort consisting of the remaining one-third of patients using a multiclass area under the curve (AUC) measure and calibration plots. RESULTS AND LIMITATIONS: We identified 7543 men diagnosed with CaP, of whom 45% underwent radical prostatectomy, 30% surveillance, 17% radiation therapy, 5.6% androgen deprivation, and 1.8% watchful waiting. The personalized prediction for patients in the validation cohort was highly accurate (AUC 0.81). CONCLUSIONS: Using clinical registry data and machine learning methods, we created a web-based platform for patients that generates accurate predictions for most CaP treatments. PATIENT SUMMARY: We have developed and tested a tool to help men newly diagnosed with prostate cancer to view predicted treatment decisions based on similar patients from our registry. We have made this tool available online for patients to use.

Grade Groups Provide Improved Predictions of Pathological and Early Oncologic Outcomes Compared with Gleason Score Risk Groups.

PURPOSE: The GG (Grade Group) system was introduced in 2013. Data from academic centers suggest that GG better distinguishes between prostate cancer risk groups than the Gleason score (GS) risk groups. We compared the performance of the 2 systems to predict pathological/recurrence outcomes using data from the MUSIC (Michigan Urological Surgery Improvement Collaborative). MATERIALS AND METHODS: Patients who underwent biopsy and radical prostatectomy in the MUSIC from March 2012 to June 2017 were classified according to GG and GS. Outcomes included the presence or absence of extraprostatic extension, seminal vesical invasion, positive lymph nodes, positive surgical margins and time to cancer recurrence (defined as postoperative prostate specific antigen 0.2 ng/ml or greater). Logistic and Cox regression models were used to compare the difference in outcomes. RESULTS: A total of 8,052 patients were identified. When controlling for patient characteristics, significantly higher risks of extraprostatic extension, seminal vesical invasion and positive lymph nodes were observed for biopsy GG 3 vs 2 and for GG 5 vs 4 (p <0.001). Biopsy GGs 3, 4 and 5 also showed shorter time to biochemical recurrence than GGs 2, 3 and 4, respectively (p <0.001). GGs 3, 4 and 5 at radical prostatectomy were each associated with a greater probability of recurrence compared to the next lower GG (p <0.001). GG (vs GS) had better predictive power for extraprostatic extension, seminal vesical invasion, positive lymph nodes and biochemical recurrence. CONCLUSIONS: GG at biopsy and radical prostatectomy allows for better discrimination of recurrence-free survival between individual risk groups than GS risk groups with GGs 2, 3, 4 and 5 each incrementally associated with increased risk.

The Challenge of Measuring Surgeon Spending for Payment Policies.

OBJECTIVE: Our objective was to understand the reliability of profiling surgeons on average health care spending. SUMMARY OF BACKGROUND DATA: Under its Merit-based Incentive Payment System (MIPS), Medicare will measure surgeon spending and tie performance to payments. Although the intent of this cost-profiling is to reward low-cost surgeons, it is unknown whether surgeons can be accurately distinguished from their peers. METHODS: We used Michigan Medicare and commercial payer claims data to construct episodes of surgical care and to calculate average annual spending for individual surgeons. We then estimated the "reliability" (ie, the ability to distinguish surgeons from their peers) of these cost-profiles and the case-volume that surgeons would need in order to achieve high reliability [intraclass correlation coefficient (ICC) >0.8]. Finally, we calculated the reliability of 2 alternative methods of profiling surgeons (ie, using multiple years of data and grouping surgeons by hospitals). RESULTS: We found that annual cost-profiles of individual surgeons had poor reliability; the ICC ranged from <0.001 for CABG to 0.061 for cholecystectomy. We found that few surgeons in the state of Michigan have sufficient case-volume to be reliably compared; 1% had the minimum yearly case. Finally, we found that the reliability of the cost-profiles can be improved by measuring spending at the hospital-level and/or by incorporating additional years of data. CONCLUSION: These findings suggest that the Medicare program should measure surgeon spending at a group level or incorporate multiple years of data to reduce misclassification of surgeon performance in the MIPS program.

Cost-effectiveness of magnetic resonance imaging and targeted fusion biopsy for early detection of prostate cancer.

OBJECTIVE: To determine how best to use magnetic resonance imaging (MRI) and targeted MRI/ultrasonography fusion biopsy for early detection of prostate cancer (PCa) in men with elevated prostate-specific antigen (PSA) concentrations and whether it can be cost-effective. METHODS: A Markov model of PCa onset and progression was developed to estimate the health and economic consequences of PCa screening with MRI. Patients underwent PSA screening from ages 55 to 69 years. Patients with elevated PSA concentrations (>4 ng/mL) underwent MRI, followed by targeted fusion or combined (standard + targeted fusion) biopsy on positive MRI, and standard or no biopsy on negative MRI. Prostate Imaging Reporting and Data System (PI-RADS) score on MRI was used to determine biopsy decisions. Deaths averted, quality-adjusted life-years (QALYs), cost and incremental cost-effectiveness ratio (ICER) were estimated for each strategy. RESULTS: With a negative MRI, standard biopsy was more expensive and had lower QALYs than performing no biopsy. The optimum screening strategy (ICER $23 483/QALY) recommended combined biopsy for patients with PI-RADS score ≥3 and no biopsy for patients with PI-RADS score <3, and reduced the number of screening biopsies by 15%. Threshold analysis suggests MRI continues to be cost-effective when the sensitivity and specificity of MRI and combined biopsy are simultaneously reduced by 19 percentage points. CONCLUSIONS: Our analysis suggests MRI followed by targeted MRI/ultrasonography fusion biopsy can be a cost-effective approach to the early detection of PCa.

Optimizing active surveillance strategies to balance the competing goals of early detection of grade progression and minimizing harm from biopsies.

BACKGROUND: Active surveillance (AS) for prostate cancer includes follow-up with serial prostate biopsies. The optimal biopsy frequency during follow-up has not been determined. The goal of this investigation was to use longitudinal AS biopsy data to assess whether the frequency of biopsy could be reduced without substantially prolonging the time to the detection of disease with a Gleason score ≥ 7. METHODS: With data from 1375 men with low-risk prostate cancer enrolled in AS at Johns Hopkins, a hidden Markov model was developed to estimate the probability of undersampling at diagnosis, the annual probability of grade progression, and the 10-year cumulative probability of reclassification or progression to Gleason score ≥ 7. It simulated 1024 potential AS biopsy strategies for the 10 years after diagnosis. For each of these strategies, the model predicted the mean delay in the detection of disease with a Gleason score ≥ 7. RESULTS: The model estimated the 10-year cumulative probability of reclassification from a Gleason score of 6 to a Gleason score ≥ 7 to be 40.0%. The probability of undersampling at diagnosis was 9.8%, and the annual progression probability for men with a Gleason score of 6 was 4.0%. On the basis of these estimates, a simulation of an annual biopsy strategy estimated the mean time to the detection of disease with a Gleason score ≥ 7 to be 14.1 months; however, several strategies eliminated biopsies with only small delays (<12 months) in detecting grade progression. CONCLUSIONS: Although annual biopsy for low-risk men on AS is associated with the shortest time to the detection of disease with a Gleason score ≥ 7, several alternative strategies may allow less frequent biopsying without sizable delays in detecting grade progression. Cancer 2018;124:698-705. © 2017 American Cancer Society.

Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials.

BACKGROUND: Intensive blood pressure (BP) treatment can avert cardiovascular disease (CVD) events but can cause some serious adverse events. We sought to develop and validate risk models for predicting absolute risk difference (increased risk or decreased risk) for CVD events and serious adverse events from intensive BP therapy. A secondary aim was to test if the statistical method of elastic net regularization would improve the estimation of risk models for predicting absolute risk difference, as compared to a traditional backwards variable selection approach. METHODS AND FINDINGS: Cox models were derived from SPRINT trial data and validated on ACCORD-BP trial data to estimate risk of CVD events and serious adverse events; the models included terms for intensive BP treatment and heterogeneous response to intensive treatment. The Cox models were then used to estimate the absolute reduction in probability of CVD events (benefit) and absolute increase in probability of serious adverse events (harm) for each individual from intensive treatment. We compared the method of elastic net regularization, which uses repeated internal cross-validation to select variables and estimate coefficients in the presence of collinearity, to a traditional backwards variable selection approach. Data from 9,069 SPRINT participants with complete data on covariates were utilized for model development, and data from 4,498 ACCORD-BP participants with complete data were utilized for model validation. Participants were exposed to intensive (goal systolic pressure < 120 mm Hg) versus standard (<140 mm Hg) treatment. Two composite primary outcome measures were evaluated: (i) CVD events/deaths (myocardial infarction, acute coronary syndrome, stroke, congestive heart failure, or CVD death), and (ii) serious adverse events (hypotension, syncope, electrolyte abnormalities, bradycardia, or acute kidney injury/failure). The model for CVD chosen through elastic net regularization included interaction terms suggesting that older age, black race, higher diastolic BP, and higher lipids were associated with greater CVD risk reduction benefits from intensive treatment, while current smoking was associated with fewer benefits. The model for serious adverse events chosen through elastic net regularization suggested that male sex, current smoking, statin use, elevated creatinine, and higher lipids were associated with greater risk of serious adverse events from intensive treatment. SPRINT participants in the highest predicted benefit subgroup had a number needed to treat (NNT) of 24 to prevent 1 CVD event/death over 5 years (absolute risk reduction [ARR] = 0.042, 95% CI: 0.018, 0.066; P = 0.001), those in the middle predicted benefit subgroup had a NNT of 76 (ARR = 0.013, 95% CI: -0.0001, 0.026; P = 0.053), and those in the lowest subgroup had no significant risk reduction (ARR = 0.006, 95% CI: -0.007, 0.018; P = 0.71). Those in the highest predicted harm subgroup had a number needed to harm (NNH) of 27 to induce 1 serious adverse event (absolute risk increase [ARI] = 0.038, 95% CI: 0.014, 0.061; P = 0.002), those in the middle predicted harm subgroup had a NNH of 41 (ARI = 0.025, 95% CI: 0.012, 0.038; P < 0.001), and those in the lowest subgroup had no significant risk increase (ARI = -0.007, 95% CI: -0.043, 0.030; P = 0.72). In ACCORD-BP, participants in the highest subgroup of predicted benefit had significant absolute CVD risk reduction, but the overall ACCORD-BP participant sample was skewed towards participants with less predicted benefit and more predicted risk than in SPRINT. The models chosen through traditional backwards selection had similar ability to identify absolute risk difference for CVD as the elastic net models, but poorer ability to correctly identify absolute risk difference for serious adverse events. A key limitation of the analysis is the limited sample size of the ACCORD-BP trial, which expanded confidence intervals for ARI among persons with type 2 diabetes. Additionally, it is not possible to mechanistically explain the physiological relationships explaining the heterogeneous treatment effects captured by the models, since the study was an observational secondary data analysis. CONCLUSIONS: We found that predictive models could help identify subgroups of participants in both SPRINT and ACCORD-BP who had lower versus higher ARRs in CVD events/deaths with intensive BP treatment, and participants who had lower versus higher ARIs in serious adverse events.