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.

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.].

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.

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.

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.

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.

A Statewide Intervention Improves Appropriate Imaging in Localized Prostate Cancer.

PURPOSE: We implemented a statewide intervention to improve imaging utilization for the staging of patients with newly diagnosed prostate cancer. MATERIALS AND METHODS: MUSIC (Michigan Urological Surgery Improvement Collaborative) is a quality improvement collaborative comprising 42 diverse practices representing approximately 85% of the urologists in Michigan. MUSIC has developed imaging appropriateness criteria (prostate specific antigen greater than 20 ng/ml, Gleason score 7 or higher and clinical stage T3 or higher) which minimize unnecessary imaging with bone scan and computerized tomography. After baseline rates of radiographic staging were established in 2012 and 2013, we used multidimensional interventions to deploy these criteria in 2014. Imaging utilization was then remeasured in 2015 to evaluate for changes in practice patterns. RESULTS: A total of 10,554 newly diagnosed patients with prostate cancer were entered into the MUSIC registry from January 1, 2012 through December 31, 2013 and January 1, 2015 through December 31, 2015. Of these patients 7,442 (79%) and 7,312 (78%) met our criteria to avoid bone scan and computerized tomography imaging, respectively. The use of bone scan imaging when not indicated decreased from 11.0% at baseline to 6.5% after interventions (p <0.0001). The use of computerized tomography when not indicated decreased from 14.7% at baseline to 7.7% after interventions (p <0.0001). Variability among practices decreased substantially after the interventions as well. The use of recommended imaging remained stable during these periods. CONCLUSIONS: An intervention aimed at appropriate use of imaging was associated with decreased use of bone scans and computerized tomography among men at low risk for metastases.

Anticipatory Positive Urine Tests for Bladder Cancer.

PURPOSE: The aim of this study was to establish the criteria defining an anticipatory positive test for bladder cancer. METHODS: We reviewed all patients at our institution who underwent urine cytology or UroVysion fluorescence in situ hybridization (FISH) and cystoscopy from 2003 to 2012. Test performance and cancer anticipation was assessed using generalized linear mixed models, mixed-effects proportional hazards models, and cumulative incidence curves using tests performed within 30 days of each other as well as within a lag time of 1 year. RESULTS: Overall, 6729 urine tests (4729 cytology and 2040 UroVysion FISH) were paired with gold-standard cystoscopies. Sensitivity and specificity were 63 and 41% for cytology, and 37 and 84% for UroVysion FISH, respectively. A 1-year lag time allowed for cancer anticipation and neither test improved. Among patients with positive cytology and initially negative cystoscopy, the hazard ratio of developing a bladder tumor at 1 year was 1.83; 76% of these patients developed a tumor within 1 year. Similarly, among patients with a positive FISH and initially negative cystoscopy, the hazard ratio of developing a bladder tumor at 1 year was 1.56; 40% of these patients developed a tumor within 1 year. CONCLUSIONS: Urine-based tests for bladder cancer are frequently falsely positive. With further follow-up time, some of these false positive tests are vindicated as true (anticipatory) positive tests, although many will remain false positives. We developed statistical criteria to determine if a test anticipates future cancers or not.

Assessment of long-term outcomes associated with urinary prostate cancer antigen 3 and TMPRSS2:ERG gene fusion at repeat biopsy.

BACKGROUND: In men with clinically localized prostate cancer who have undergone at least 1 previous negative biopsy and have elevated serum prostate-specific antigen (PSA) levels, long-term health outcomes associated with the assessment of urinary prostate cancer antigen 3 (PCA3) and the transmembrane protease, serine 2 (TMPRSS2):v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene fusion (T2:ERG) have not been investigated previously in relation to the decision to recommend a repeat biopsy. METHODS: The authors performed a decision analysis using a decision tree for men with elevated PSA levels. The probability of cancer was estimated using the Prostate Cancer Prevention Trial Risk Calculator (version 2.0). The use of PSA alone was compared with the use of PCA3 and T2:ERG scores, with each evaluated independently, in combination with PSA to trigger a repeat biopsy. When PCA3 and T2:ERG score evaluations were used, predefined thresholds were established to determine whether the patient should undergo a repeat biopsy. Biopsy outcomes were defined as either positive (with a Gleason score of <7, 7, or >7) or negative. Probabilities and estimates of 10-year overall survival and 15-year cancer-specific survival were derived from previous studies and a literature review. Outcomes were defined as age-dependent and Gleason score-dependent 10-year overall and 15-year cancer-specific survival rates and the percentage of biopsies avoided. RESULTS: Incorporating the PCA3 score (biopsy threshold, 25; generated based on the urine PCA3 level normalized to the amount of PSA messenger RNA) or the T2:ERG score (biopsy threshold, 10; based on the urine T2:ERG level normalized to the amount of PSA messenger RNA) into the decision to recommend repeat biopsy would have avoided 55.4% or 64.7% of repeat biopsies for the base-case patient, respectively, and changes in the 10-year survival rate were only 0.93% or 1.41%, respectively. Multi-way sensitivity analyses suggested that these results were robust with respect to the model parameters. CONCLUSIONS: The use of PCA3 or T2:ERG testing for repeat biopsy decisions can substantially reduce the number of biopsies without significantly affecting 10-year survival.

An examination of the dynamic changes in prostate-specific antigen occurring in a population-based cohort of men over time.

OBJECTIVE: • To determine whether prostate-specific antigen velocity (PSA-V), PSA doubling time (PSA-DT), or PSA percentage change (PSA-PC) add incremental information to PSA alone for community-based men undergoing prostate cancer (PCa) screening. PARTICIPANTS AND METHODS: • A population-based cohort of 11 872 men from Olmsted County, MN undergoing PSA screening for PCa from 1993 to 2005 was analysed for PSA, PSA-DT, PSA-PC and PSA-V and subsequent PCa. • Receiver-operating characteristics curves and logistic regression were used to calculate the area under the curve (AUC) and Aikaike's information criterion. • Reclassification analysis was performed and the net reclassification improvement and integrated discrimination improvement were measured. • The method of Begg and Greenes was used to adjust for verification bias. RESULTS: • The single best predictor of future PCa was PSA (AUC = 0.773) with PSA-V (AUC = 0.729) and PSA-DT/PSA-PC (AUC = 0.689) performing worse. • After age adjustment, combining PSA with PSA-V (AUC = 0.773) or PSA-DT/PSA-PC (AUC = 0.773) resulted in no better predictions than PSA alone. • Reclassification analysis showed that adding PSA-V or PSA-DT/PSA-PC to PSA did not result in a meaningful amount of reclassification. CONCLUSIONS: • PSA is a better predictor of future PCa than PSA-V, PSA-DT, or PSA-PC. • Adding PSA-V, PSA-DT, or PSA-PC to PSA does not result in clinically relevant improvements in the ability to predict future PCa.

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.

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.

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.

Optimizing the start time of statin therapy for patients with diabetes.

BACKGROUND: Clinicians often use validated risk models to guide treatment decisions for cardiovascular risk reduction. The most common risk models for predicting cardiovascular risk are the UKPDS, Framingham, and Archimedes models. In this article, the authors propose a model to optimize the selection of patients for statin therapy of hypercholesterolemia, for patients with type 2 diabetes, using each of the risk models. For each model,they evaluate the role of age, gender, and metabolic state on the optimal start time for statins. METHOD: Using clinical data from the Mayo Clinic electronic medical record, the authors construct a Markov decision process model with health states composed of cardiovascular events and metabolic factors such as total cholesterol and high-density lipoproteins. They use it to evaluate the optimal start time of statin treatment for different combinations of cardiovascular risk models and patient attributes. RESULTS: The authors find that treatment decisions depend on the cardiovascular risk model used and the age, gender, and metabolic state of the patient. Using the UKPDS risk model to estimate the probability of coronary heart disease and stroke events, they find that all white male patients should eventually start statin therapy; however, using Framingham and Archimedes models in place of UKPDS, they find that for male patients at lower risk, it is never optimal to initiate statins. For white female patients, the authors also find some patients for whom it is never optimal to initiate statins. Assuming that age 40 is the earliest possible start time, the authors find that the earliest optimal start times for UKPDS, Framingham, and Archimedes are 50, 46, and 40, respectively, for women. For men, the earliest optimal start times are 40, 40, and 40, respectively. CONCLUSIONS: In addition to age, gender, and metabolic state, the choice of cardiovascular risk model influences the apparent optimal time for starting statins in patients with diabetes.

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.

Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan.

OBJECTIVE: To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0. MATERIALS AND METHODS: With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort. RESULTS: Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population. CONCLUSION: The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.

Two-Stage Biomarker Protocols for Improving the Precision of Early Detection of Prostate Cancer.

BACKGROUND: New cancer biomarkers are being discovered at a rapid pace; however, these tests vary in their predictive performance characteristics, and it is unclear how best to use them. METHODS: We investigated 2-stage biomarker-based screening strategies in the context of prostate cancer using a partially observable Markov model to simulate patients' progression through prostate cancer states to mortality from prostate cancer or other causes. Patients were screened every 2 years from ages 55 to 69. If the patient's serum prostate-specific antigen (PSA) was over a specified threshold in the first stage, a second stage biomarker test was administered. We evaluated design characteristics for these 2-stage strategies using 7 newly discovered biomarkers as examples. Monte Carlo simulation was used to estimate the number of screening biopsies, prostate cancer deaths, and quality-adjusted life-years (QALYs) per 1000 men. RESULTS: The all-cancer biomarkers significantly underperformed the high-grade cancer biomarkers in terms of QALYs. The screening strategy that used a PSA threshold of 2 ng/mL and a second biomarker test with high-grade sensitivity and specificity of 0.86 and 0.62, respectively, maximized QALYs. This strategy resulted in a prostate cancer death rate within 1% of using PSA alone with a threshold of 2 ng/mL, while reducing the number of biopsies by 20%. Sensitivity analysis suggests that the results are robust with respect to variation in model parameters. CONCLUSIONS: Two-stage biomarker screening strategies using new biomarkers with risk thresholds optimized for high-grade cancer detection may increase quality-adjusted survival and reduce unnecessary biopsies.

From Data to Improved Decisions: Operations Research in Healthcare Delivery.

BACKGROUND: The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. METHODS: Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. EXAMPLES: We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. CONCLUSIONS: There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.