Decipher test impacts decision making among patients considering adjuvant and salvage treatment after radical prostatectomy: Interim results from the Multicenter Prospective PRO-IMPACT study.

BACKGROUND: Patients with prostate cancer and their providers face uncertainty as they consider adjuvant radiotherapy (ART) or salvage radiotherapy (SRT) after undergoing radical prostatectomy. The authors prospectively evaluated the impact of the Decipher test, which predicts metastasis risk after radical prostatectomy, on decision making for ART and SRT. METHODS: A total of 150 patients who were considering ART and 115 who were considering SRT were enrolled. Providers submitted a management recommendation before processing the Decipher test and again at the time of receipt of the test results. Patients completed validated surveys on prostate cancer (PCa)-specific decisional effectiveness and PCa-related anxiety. RESULTS: Before the Decipher test, observation was recommended for 89% of patients considering ART and 58% of patients considering SRT. After Decipher testing, 18% (95% confidence interval [95% CI], 12%-25%) of treatment recommendations changed in the ART arm, including 31% among high-risk patients; and 32% (95% CI, 24%-42%) of management recommendations changed in the salvage arm, including 56% among high-risk patients. Decisional Conflict Scale (DCS) scores were better after viewing Decipher test results (ART arm: median DCS before Decipher, 25 and after Decipher, 19 [P<.001]; SRT arm: median DCS before Decipher, 27 and after Decipher, 23 [P<.001]). PCa-specific anxiety changed after Decipher testing; fear of PCa disease recurrence in the ART arm (P = .02) and PCa-specific anxiety in the SRT arm (P = .05) decreased significantly among low-risk patients. Decipher results reported per 5% increase in 5-year metastasis probability were associated with the decision to pursue ART (odds ratio, 1.48; 95% CI, 1.19-1.85) and SRT (odds ratio, 1.41; 95% CI, 1.09-1.81) in multivariable logistic regression analysis. CONCLUSIONS: Knowledge of Decipher test results was associated with treatment decision making and improved decisional effectiveness among men with PCa who were considering ART and SRT. Cancer 2017;123:2850-59. © 2017 American Cancer Society.

A Crossover Design for Comparative Efficacy: A 36-Week Randomized Trial of Bevacizumab and Ranibizumab for Diabetic Macular Edema.

PURPOSE: To investigate the comparative efficacy of bevacizumab (Avastin) and ranibizumab (Lucentis; both Genentech, Inc, South San Francisco, CA) for diabetic macular edema (DME) using a crossover study design. DESIGN: Randomized, double-masked, 36-week, 3-period crossover clinical trial. PARTICIPANTS: Fifty-six subjects with DME involving the center of the macula in one or both eyes. METHODS: Monthly intravitreous injections of bevacizumab (1.25 mg) or ranibizumab (0.3 mg). MAIN OUTCOME MEASURES: Comparison of mean changes in visual acuity and central retinal thickness, tested using a linear mixed-effects model. RESULTS: Based on the linear mixed-effects model, the 3-month estimated mean improvement in visual acuity was 5.3 letters for bevacizumab and 6.6 letters for ranibizumab (difference, 1.3 letters; P = 0.039). Estimated change in optical coherence tomography (OCT) central subfield mean thickness (CSMT) was -89 µm for bevacizumab and -137 µm for ranibizumab (difference, 48 µm; P < 0.001). Incorporating cumulative treatment benefit, the model yielded a predicted 36-week (9-month) average improvement in visual acuity of 7.1 letters (95% confidence interval [CI], 5.0-9.2) for bevacizumab and 8.4 letters (95% CI, 6.3-10.5) for ranibizumab, and a change in OCT CSMT of -128 µm (95% CI, -155 to -100) for bevacizumab and -176 µm (95% CI, -202 to -149) for ranibizumab. There was no significant treatment-by-period interaction (i.e., treatment difference was constant in all 3 periods), nor was there a significant differential carryover effect from one period to the next. CONCLUSIONS: This trial demonstrated a statistically significant but small relative clinical benefit of ranibizumab compared with bevacizumab for treatment of DME, using a markedly reduced sample size relative to a full comparative efficacy study. The effects on visual acuity and central retinal thickness for the 2 drugs are consistent with those reported at 1 year for the concurrent parallel-group trial by the Diabetic Retinopathy Clinical Research Network testing bevacizumab, ranibizumab, and aflibercept for DME. The 3-period crossover design allowed for meaningful and efficient comparison, suggesting that this approach may be useful for future comparative efficacy studies of anti-vascular endothelial growth factor drugs for DME.

Longitudinal clinical findings and outcome among patients with Cryptococcus gattii infection in British Columbia.

BACKGROUND: Cryptococcus gattii (Cg) infection emerged in British Columbia in 1999. A longitudinal, clinical description of patients has not been reported. METHODS: Medical records were reviewed for Cg patients identified through surveillance (1999-2007). Risk factors for Cg mortality were explored using multivariate Cox regression; longitudinal patterns in serum cryptococcal antigen (SCrAg) titers and the probability of chest cryptococcomas over time were estimated using cubic B-splines in mixed-effects regression models. RESULTS: Among 152 patients, 111 (73.0%) were culture confirmed. Isolated lung infection was present in 105 (69.1%) patients; 47 (30.9%) had central nervous system infection, with or without lung involvement. Malignancy was the provisional diagnosis in 64 (42.1%) patients. Underlying diseases were present in 91 (59.9%) patients; 23 (15.1%) were immunocompromised, and 23 (15.1%) had asymptomatic disease. There were only 2 (1.8%) culture positive relapses, both within 12 months of follow-up. The estimated median time to resolution of lung cryptococcomas and decline in SCrAg titer to <1:8 was 2.8 and 2.9 years, respectively. Cg-related and all-cause mortality among culture-confirmed cases at 12 months' follow-up was 23.3% and 27.2%, respectively. Cg-related mortality was associated with age >50 years (hazard ratio [HR], 15.6; 95% confidence interval [CI], 1.9-130.5) and immunocompromise (HR, 5.8; CI, 1.5-21.6). All Cg-related mortality occurred among culture-positive cases within 1 year of diagnosis. CONCLUSIONS: Cryptococcomas and serum antigenemia were slow to resolve. However, late onset of failed therapy or relapse was uncommon, suggesting that delayed resolution of these findings does not require prolongation of treatment beyond that recommended by guidelines.

Impairments in Dark Adaptation Are Associated with Age-Related Macular Degeneration Severity and Reticular Pseudodrusen.

PURPOSE: We investigate whether ocular and person-based characteristics were associated with dark adaptation (DA). DESIGN: Cross-sectional, single-center, observational study. PARTICIPANTS: One hundred sixteen participants older than 50 years of age with a range of age-related macular degeneration (AMD) severity. METHODS: Participants underwent best-corrected visual acuity (BCVA) testing, ophthalmoscopic examination, and multimodal imaging. Presence of reticular pseudodrusen (RPD) was assessed by masked grading of fundus images and was confirmed with optical coherence tomography. Eyes also were graded for AMD features (drusen, pigmentary changes, late AMD) to generate person-based AMD severity groups. One eye was designated the study eye for DA testing. Nonparametric statistical testing was performed on all comparisons. MAIN OUTCOME MEASURES: The primary outcome of this study was the rod intercept time (RIT), which is defined as the time for a participant's visual sensitivity to recover to a stimulus intensity of 5×10(-3) cd/m(2) (a decrease of 3 log units), or until a maximum test duration of 40 minutes was reached. RESULTS: A total of 116 study eyes from 116 participants (mean age, 75.4±9.4 years; 58% female) were analyzed. Increased RIT was associated significantly with increasing AMD severity, increasing age (r = 0.34; P = 0.0002), decreasing BCVA (r = -0.54; P < 0.0001), pseudophakia (P = 0.03), and decreasing subfoveal choroidal thickness (r = -0.27; P = 0.003). Study eyes with RPD (15/116 [13%]) had a significantly greater mean RIT compared with eyes without RPD in any AMD severity group (P < 0.02 for all comparisons), with 80% reaching the DA test ceiling. CONCLUSIONS: Impairments in DA increased with age, worse visual acuity, presence of RPD, AMD severity, and decreased subfoveal choroidal thickness. Analysis of covariance found the multivariate model that best fit the data included age, AMD group, and presence of RPD (R(2) = 0.56), with the presence of RPD conferring the largest parameter estimate.

Effect of a genomic classifier test on clinical practice decisions for patients with high-risk prostate cancer after surgery.

OBJECTIVES: To evaluate the impact of a genomic classifier (GC) test for predicting metastasis risk after radical prostatectomy (RP) on urologists' decision-making about adjuvant treatment of patients with high-risk prostate cancer. SUBJECTS AND METHODS: Patient case history was extracted from the medical records of each of the 145 patients with pT3 disease or positive surgical margins (PSMs) after RP treated by six high-volume urologists, from five community practices. GC results were available for 122 (84%) of these patients. US board-certified urologists (n = 107) were invited to provide adjuvant treatment recommendations for 10 cases randomly drawn from the pool of patient case histories. For each case, the study participants were asked to make an adjuvant therapy recommendation without (clinical variables only) and with knowledge of the GC test results. Recommendations were made without knowledge of other participants' responses and the presentation of case histories was randomised to minimise recall bias. RESULTS: A total of 110 patient case histories were available for review by the study participants. The median patient age was 62 years, 71% of patients had pT3 disease and 63% had PSMs. The median (range) 5-year predicted probability of metastasis by the GC test for the cohort was 3.9 (1-33)% and the GC test classified 72% of patients as having low risk for metastasis. A total of 51 urologists consented to the study and provided 530 adjuvant treatment recommendations without, and 530 with knowledge of the GC test results. Study participants performed a mean of 130 RPs/year and 55% were from community-based practices. Without GC test result knowledge, observation was recommended for 57% (n = 303), adjuvant radiation therapy (ART) for 36% (n = 193) and other treatments for 7% (n = 34) of patients. Overall, 31% (95% CI: 27-35%) of treatment recommendations changed with knowledge of the GC test results. Of the ART recommendations without GC test result knowledge, 40% (n = 77) changed to observation (95% CI: 33-47%) with this knowledge. Of patients recommended for observation, 13% (n = 38 [95% CI: 9-17%]) were changed to ART with knowledge of the GC test result. Patients with low risk disease according to the GC test were recommended for observation 81% of the time (n = 276), while of those with high risk, 65% were recommended for treatment (n = 118; P < 0.001). Treatment intensity was strongly correlated with the GC-predicted probability of metastasis (P < 0.001) and the GC test was the dominant risk factor driving decisions in multivariable analysis (odds ratio 8.6, 95% CI: 5.3-14.3%; P < 0.001). CONCLUSIONS: Knowledge of GC test results had a direct effect on treatment strategies after surgery. Recommendations for observation increased by 20% for patients assessed by the GC test to be at low risk of metastasis, whereas recommendations for treatment increased by 16% for patients at high risk of metastasis. These results suggest that the implementation of genomic testing in clinical practice may lead to significant changes in adjuvant therapy decision-making for high-risk prostate cancer.

Best method for right atrial volume assessment by two-dimensional echocardiography: validation with magnetic resonance imaging.

AIM: Echocardiographic methods for estimating right atrial (RA) volume have not been standardized. Our aim was to evaluate two-dimensional (2D) echocardiographic methods of RA volume assessment, using RA volume by magnetic resonance imaging (MRI) as the reference. METHODS AND RESULTS: Right atrial volume was assessed in 51 patients (mean age 63 ± 14 years, 33 female) who underwent comprehensive 2D echocardiography and cardiac MRI for clinically indicated reasons. Echocardiographic RA volume methods included (1) biplane area length, using four-chamber view twice (biplane 4C-4C); (2) biplane area length, using four-chamber and subcostal views (biplane 4C-subcostal); and (3) single plane Simpson's method of disks (Simpson's). Echocardiographic RA volumes as well as linear RA major and minor dimensions were compared to RA volume by MRI using correlation and Bland-Altman methods, and evaluated for inter-observer reproducibility and accuracy in discriminating RA enlargement. All echocardiography volumetric methods performed well compared to MRI, with Pearson's correlation of 0.98 and concordance correlation ≥0.91 for each. For bias and limits of agreement, biplane 4C-4C (bias -4.81 mL/m(2) , limits of agreement ±9.8 mL/m(2) ) and Simpson's (bias -5.15 mL/m(2) , limits of agreement ±10.1 mL/m(2) ) outperformed biplane 4C-subcostal (bias -8.36 mL/m(2) , limits of agreement ±12.5 mL/m(2) ). Accuracy for discriminating RA enlargement was higher for all volumetric methods than for linear measurements. Inter-observer variability was satisfactory across all methods. CONCLUSIONS: Compared to MRI, biplane 4C-4C and single plane Simpson's are highly accurate and reproducible 2D echocardiography methods for estimating RA volume. Linear dimensions are inaccurate and should be abandoned.

A field trial to assess a blood-stage malaria vaccine.

BACKGROUND: Blood-stage malaria vaccines are intended to prevent clinical disease. The malaria vaccine FMP2.1/AS02(A), a recombinant protein based on apical membrane antigen 1 (AMA1) from the 3D7 strain of Plasmodium falciparum, has previously been shown to have immunogenicity and acceptable safety in Malian adults and children. METHODS: In a double-blind, randomized trial, we immunized 400 Malian children with either the malaria vaccine or a control (rabies) vaccine and followed them for 6 months. The primary end point was clinical malaria, defined as fever and at least 2500 parasites per cubic millimeter of blood. A secondary end point was clinical malaria caused by parasites with the AMA1 DNA sequence found in the vaccine strain. RESULTS: The cumulative incidence of the primary end point was 48.4% in the malaria-vaccine group and 54.4% in the control group; efficacy against the primary end point was 17.4% (hazard ratio for the primary end point, 0.83; 95% confidence interval [CI], 0.63 to 1.09; P=0.18). Efficacy against the first and subsequent episodes of clinical malaria, as defined on the basis of various parasite-density thresholds, was approximately 20%. Efficacy against clinical malaria caused by parasites with AMA1 corresponding to that of the vaccine strain was 64.3% (hazard ratio, 0.36; 95% CI, 0.08 to 0.86; P=0.03). Local reactions and fever after vaccination were more frequent with the malaria vaccine. CONCLUSIONS: On the basis of the primary end point, the malaria vaccine did not provide significant protection against clinical malaria, but on the basis of secondary results, it may have strain-specific efficacy. If this finding is confirmed, AMA1 might be useful in a multicomponent malaria vaccine. (Funded by the National Institute of Allergy and Infectious Diseases and others; ClinicalTrials.gov number, NCT00460525.).

Impact of patient use of an online patient portal on diabetes outcomes.

OBJECTIVE: To assess the effect of patient use of an online patient portal on diabetes outcomes. METHODS: Patients included were those with diabetes who were newly referred to a Vancouver-based tertiary care diabetologist between April 2008 and October 2012. Each patient was assessed by the diabetologist, received initial diabetes education and was referred, as necessary, for further education and self-management training. All patients who provided an e-mail address at registration were invited to open an online patient portal account. The portal provided access to diabetes education material, personal laboratory values and a messaging system allowing communication with the diabetologist and staff. Patients who logged in 1 or more times were defined as portal users (n=50); patients who never logged in to the portal were defined as non-users (n=107). A1C was measured at 2 time points: at baseline (i.e. initial, in-clinic visit) and at last follow up (visit no less than 6 months and no more than 2 years after the initial visit). Because usership is self-selected, propensity score matching was used to create comparable user/non-user groups based on available baseline covariates. RESULTS: Compared to non-users, a higher proportion of users achieved A1C ≤7% at follow up (56% vs. 32%) (p=0.031). CONCLUSION: Accessing an online patient portal is associated with improved glycemic control.

Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population.

PURPOSE: Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population. MATERIALS AND METHODS: A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 219 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. RESULTS: The genomic classifier AUC was 0.79 for predicting 5-year metastasis after radical prostatectomy. Decision curves showed that the genomic classifier net benefit exceeded that of clinical only models. The genomic classifier was the predominant predictor of metastasis on multivariable analysis. The cumulative incidence of metastasis 5 years after radical prostatectomy was 2.4%, 6.0% and 22.5% in patients with low (60%), intermediate (21%) and high (19%) genomic classifier scores, respectively (p<0.001). CONCLUSIONS: Results indicate that genomic information from the primary tumor can identify patients with adverse pathological features who are most at risk for metastasis and potentially lethal prostate cancer.

Genomic Classifier Performance in Intermediate-Risk Prostate Cancer: Results From NRG Oncology/RTOG 0126 Randomized Phase 3 Trial.

PURPOSE: Intermediate-risk prostate cancer is a heterogeneous disease state with diverse treatment options. The 22-gene Decipher genomic classifier (GC) retrospectively has shown to improve risk stratification in these patients. We assessed the performance of the GC in men with intermediate-risk disease enrolled in NRG Oncology/RTOG 01-26 with updated follow-up. METHODS AND MATERIALS: After National Cancer Institute approval, biopsy slides were collected from NRG Oncology/RTOG 01-26, a randomized phase 3 trial of men with intermediate-risk prostate cancer randomized to 70.2 Gy versus 79.2 Gy of radiation therapy without androgen deprivation therapy. RNA was extracted from the highest-grade tumor foci to generate the locked 22-gene GC model. The primary endpoint for this ancillary project was disease progression (composite of biochemical failure, local failure, distant metastasis, prostate cancer-specific mortality, and use of salvage therapy). Individual endpoints were also assessed. Fine-Gray or cause-specific Cox multivariable models were constructed adjusting for randomization arm and trial stratification factors. RESULTS: Two-hundred fifteen patient samples passed quality control for analysis. The median follow-up was 12.8 years (range, 2.4-17.7). On multivariable analysis, the 22-gene GC (per 0.1 unit) was independently prognostic for disease progression (subdistribution hazard ratio [sHR], 1.12; 95% confidence interval [CI], 1.00-1.26; P = .04), biochemical failure (sHR, 1.22; 95% CI, 1.10-1.37; P < .001), distant metastasis (sHR, 1.28; 95% CI, 1.06-1.55; P = .01), and prostate cancer-specific mortality (sHR, 1.45; 95% CI, 1.20-1.76; P < .001). Ten-year distant metastasis in GC low-risk patients was 4% compared with 16% for GC high-risk patients. In patients with lower GC scores, the 10-year difference in metastasis-free survival rate between arms was -7%, compared with 21% for higher GC patients (P-interaction = .04). CONCLUSIONS: This study represents the first validation of a biopsy-based gene expression classifier, assessing both its prognostic and predictive value, using data from a randomized phase 3 trial of intermediate-risk prostate cancer. Decipher improves risk stratification and can aid in treatment decision-making in men with intermediate-risk disease.

Impact of Decipher on use of post-operative radiotherapy: Individual patient analysis of two prospective registries.

Objective: To assess the association between Genomic Classifier (GC)-risk group and post-radical prostatectomy treatment in clinical practice. Methods: Two prospective observational cohorts of men with prostate cancer (PCa) who underwent RP in two referral centers and had GC testing post-prostatectomy between 2013 and 2018 were included. The primary endpoint of the study was to assess the association between GC-risk group and time to secondary therapy. Univariable (UVA) and multivariable (MVA) Cox proportional hazards models were constructed to assess the association between GC-risk group and time to receipt of secondary therapy after RP, where secondary therapy is defined as receiving either RT or ADT after RP. Results: A total of 398 patients are included in the analysis. Patients with high-GC risk were more likely to receive any secondary therapy (OR: 6.84) compared to patients with low/intermediate-GC risk. The proportion of high-GC risk patients receiving RT at 2 years post-RP was 31.5%, compared to only 6.3% among the low/intermediate-GC risk patients. Conclusion: This study demonstrates that physicians in routine practice used GC to identify high risk patients who might benefit the most from secondary treatment. As such, GC score was independent predictor of receipt of secondary treatment.

Conversion of Central Subfield Thickness Measurements of Diabetic Macular Edema Across Cirrus and Spectralis Optical Coherence Tomography Instruments.

Purpose: Develop equations to convert Cirrus central subfield thickness (CST) to Spectralis CST equivalents and vice versa in eyes with diabetic macular edema (DME). Methods: The DRCR Retina Network Protocol O data were split randomly to train (70% sample) and validate (30% sample) conversion equations. Data from an independent study (CADME) also validated the equations. Bland-Altman 95% limits of agreement between predicted and observed values evaluated the equations. Results: Protocol O included 374 CST scan pairs from 187 eyes (107 participants). The CADME study included 150 scan pairs of 37 eyes (37 participants). Proposed conversion equations are Spectralis = 40.78 + 0.95 × Cirrus and Cirrus = 1.82 + 0.94 × Spectralis regardless of age, sex, or CST. Predicted values were within 10% of observed values in 101 (90%) of Spectralis and 99 (88%) of Cirrus scans in the validation data; and in 136 (91%) of the Spectralis and 148 (99%) of the Cirrus scans in the CADME data. Adjusting for within-eye correlations, 95% of conversions are estimated to be within 17% (95% confidence interval, 14%-21%) of CST on Spectralis and within 22% (95% confidence interval, 18%-28%) of CST on Cirrus. Conclusions: Conversion equations developed in this study allow the harmonization of CST measurements for eyes with DME using a mix of current Cirrus and Spectralis device images. Translational Relevance: The CSTs measured on Cirrus and Spectralis devices are not directly comparable owing to outer boundary segmentation differences. Converting CST values across spectral domain optical coherence tomography instruments should benefit both clinical research and standard care efforts.

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Validation of a 22-Gene Genomic Classifier in Patients With Recurrent Prostate Cancer: An Ancillary Study of the NRG/RTOG 9601 Randomized Clinical Trial.

Importance: Decipher (Decipher Biosciences Inc) is a genomic classifier (GC) developed to estimate the risk of distant metastasis (DM) after radical prostatectomy (RP) in patients with prostate cancer. Objective: To validate the GC in the context of a randomized phase 3 trial. Design, Setting, and Participants: This ancillary study used RP specimens from the phase 3 placebo-controlled NRG/RTOG 9601 randomized clinical trial conducted from March 1998 to March 2003. The specimens were centrally reviewed, and RNA was extracted from the highest-grade tumor available in 2019 with a median follow-up of 13 years. Clinical-grade whole transcriptomes from samples passing quality control were assigned GC scores (scale, 0-1). A National Clinical Trials Network-approved prespecified statistical plan included the primary objective of validating the independent prognostic ability of GC for DM, with secondary end points of prostate cancer-specific mortality (PCSM) and overall survival (OS). Data were analyzed from September 2019 to December 2019. Intervention: Salvage radiotherapy (sRT) with or without 2 years of bicalutamide. Main Outcomes and Measures: The preplanned primary end point of this study was the independent association of the GC with the development of DM. Results: In this ancillary study of specimens from a phase 3 randomized clinical trial, GC scores were generated from 486 of 760 randomized patients with a median follow-up of 13 years; samples from a total of 352 men (median [interquartile range] age, 64.5 (60-70) years; 314 White [89.2%] participants) passed microarray quality control and comprised the final cohort for analysis. On multivariable analysis, the GC (continuous variable, per 0.1 unit) was independently associated with DM (hazard ratio [HR], 1.17; 95% CI, 1.05-1.32; P = .006), PCSM (HR, 1.39; 95% CI, 1.20-1.63; P < .001), and OS (HR, 1.17; 95% CI, 1.06-1.29; P = .002) after adjusting for age, race/ethnicity, Gleason score, T stage, margin status, entry prostate-specific antigen, and treatment arm. Although the original planned analysis was not powered to detect a treatment effect interaction by GC score, the estimated absolute effect of bicalutamide on 12-year OS was less when comparing patients with lower vs higher GC scores (2.4% vs 8.9%), which was further demonstrated in men receiving early sRT at a prostate-specific antigen level lower than 0.7 ng/mL (-7.8% vs 4.6%). Conclusions and Relevance: This ancillary validation study of the Decipher GC in a randomized trial cohort demonstrated association of the GC with DM, PCSM, and OS independent of standard clinicopathologic variables. These results suggest that not all men with biochemically recurrent prostate cancer after surgery benefit equally from the addition of hormone therapy to sRT. Trial Registration: ClinicalTrials.gov identifier: NCT00002874.

Evaluation of the age-related eye disease study clinical lens grading system AREDS report No. 31.

PURPOSE: To examine the grading (interrater) reliability of the Age-Related Eye Disease Study (AREDS) Clinical Lens Grading System (ARLNS). DESIGN: Evaluation of diagnostic test or technology. PARTICIPANTS: One hundred fifty volunteers (284 eyes). METHODS: Participants with lens opacities of varying severity were independently graded at the slit lamp for cataract severity by 2 examiners (retinal or anterior segment specialists) using the ARLNS, which employs 3 standard photographs of increasing severity for classifying each of the 3 major types of opacity. Lens photographs were taken and graded at a reading center using the more detailed AREDS System for Classifying Cataracts from photographs. MAIN OUTCOME MEASURES: The Pearson correlation, weighted-kappa, and limits-of-agreement statistics were used to assess the interrater agreement of the gradings. RESULTS: Examinations were performed on 284 lenses (150 participants). Tests of interrater reliability between pairs of clinicians showed substantial agreement between clinicians for cortical and posterior subcapsular opacities and moderate agreement for nuclear opacities. A similar pattern and strength of agreement was present when comparing scores of retinal versus anterior segment specialists. Interrater agreement between clinical and reading center gradings was not as great as inter-clinician agreement. CONCLUSIONS: Interrater agreements were in the moderate to substantial range for the clinical assessment of lens opacities. Inherent differences in cataract classification systems that rely on slit lamp vs photographic assessments of lens opacities may explain some of the disagreement noted between slit lamp and photographic gradings. Given the interrater reliability statistics for clinicians and the simplicity of the grading procedure, ARLNS is presented for use in studies requiring a simple, inexpensive method for detecting the presence and severity of the major types of lens opacities. FINANCIAL DISCLOSURE(S): The authors have no proprietary or commercial interest in any of the materials discussed in this article.

Preliminary assessment of celecoxib and microdiode pulse laser treatment of diabetic macular edema.

PURPOSE: Inflammation may play an important role in the pathogenesis of diabetic macular edema, a major cause of vision loss in persons with diabetes. The purpose of this study was to evaluate combined antiinflammatory therapy and laser approaches for treating patients with diabetic macular edema. METHODS: In this prospective, factorial, randomized, multicenter trial, we compared cyclo-oxygenase-2 inhibitor (celecoxib) with placebo and diode grid laser with standard Early Treatment Diabetic Retinopathy Study focal laser treatment in 86 participants with diabetic macular edema. The primary outcome is change in visual acuity of > or = 15 letters from baseline, and the secondary outcomes include a 50% reduction in the retinal thickening of diabetic macular edema measured by optical coherence tomography and a 50% reduction in leakage severity on fluorescein angiography. RESULTS: Visual acuity and retinal thickening data from >2 years of follow-up did not show evidence of differences between the medical and laser treatments. However, participants assigned to the celecoxib group were more likely to have a reduction in fluorescein leakage when compared with the placebo group (odds ratio = 3.6; P < 0.01). CONCLUSION: This short-term study did not find large visual function benefits of treatment with celecoxib or diode laser compared with those of standard laser treatment. A suggestive effect of celecoxib in reducing fluorescein leakage was observed.

Clinical Utility of a Genomic Classifier in Men Undergoing Radical Prostatectomy: The PRO-IMPACT Trial.

PURPOSE: The optimal management of men with prostate cancer at high risk of recurrence postradical prostatectomy is controversial. The clinical utility of the Decipher test was evaluated prospectively on postoperative treatment decisions and patient-reported outcomes. METHODS AND MATERIALS: In the study, 246 eligible men across 19 centers were enrolled. Patients were dichotomized into those considering adjuvant or salvage radiation therapy (ART or SRT). Participating providers submitted a management recommendation before and after receiving the Decipher test results. Treatment received within 12 months and a validated survey on prostate cancer-related anxiety were collected longitudinally. RESULTS: Pre-Decipher, treatment was recommended for 12% and 40% for the ART and SRT arms, respectively. Post-Decipher, 17% and 30% of treatment recommendations changed in the ART and SRT arms, respectively. Post-Decipher treatment recommendation was administered 78% and 76% of the time in the ART and SRT arms, respectively. Multivariable analysis confirmed that the Decipher score was an independent predictor for change in management for both adjuvant and salvage patients. The number needed to test to change management for one patient was 4. Cancer-specific anxiety decreased among Decipher risk categories in both arms. CONCLUSIONS: Use of Decipher postradical prostatectomy test was associated with postoperative treatment decisions. Overall, high Decipher risk was associated with an increase in treatment intensity whereas low risk scores were associated with a decrease in therapy administered independent of clinical and pathologic risk factors.

Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.

Importance: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies. Objectives: To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts. Design, Setting, and Participants: This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019. Main Outcomes and Measures: The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic. Results: On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it was 0.60. Conclusions and Relevance: Results of this study suggest that in prostate cancer classification from histopathologic images, patch-wise CV and single-expert training and evaluation may lead to a biased estimation of classifier's performance. To allow reproducibility and facilitate comparison between automatic classification methods, studies in the field should evaluate their performance using patient-based CV and multiexpert data. Some of these conclusions may be generalizable to other histopathologic applications and to other applications of machine learning in medicine.

Measuring the impact of influenza vaccination on healthcare worker absenteeism in the context of a province-wide mandatory vaccinate-or-mask policy.

OBJECTIVES: In 2012, British Columbia (BC) implemented a province-wide vaccinate-or-mask influenza prevention policy for healthcare workers (HCWs) with the aim of improving HCW coverage, and reducing illness in patients and staff. We assess post-policy impacts of HCW vaccination status on their absenteeism. METHODS: We matched individual HCW payroll data from December 1, 2012 to March 31, 2017 with annually self-reported vaccination status for BC health authority employees to assess sick rates (sick time as a proportion of sick time and productive time). We modelled adjusted odds ratios (OR) of taking any sick time, relative rates (RR) of sick time taken, and predicted mean sick rates by vaccination status in influenza (December 1-March 31) and non-influenza seasons (April 1 to November 30). We used two methods to assess changes in influenza season sick rates for HCWs who had a change in their vaccination status over the five years. RESULTS: HCWs who reported 'early' vaccination (before December 1 when the policy is in effect) were less likely to take sick time (OR 0.874, 95%CI: 0.866-0.881) and took less sick time (RR 0.907, 95%CI: 0.901-0.912) in influenza season compared to HCWs who did not report vaccination; whereas HCWs who reported 'late' (between December 1 and March 31, and subject to masking until vaccinated) had similar sick rates to HCWs who did not report vaccination. These trends were also observed in non-influenza season. Influenza season sick rates were similar for HCWs that had at least one year of 'early' vaccination and one year where vaccination was not reported over the five year period. CONCLUSIONS: Overall absenteeism is lower among HCWs who report vaccination versus those who do not report. However, absenteeism behaviours appear to be influenced by individual level factors other than vaccination status.

Measuring the impact of a mandatory province-wide vaccinate-or-mask policy on healthcare worker absenteeism in British Columbia, Canada.

OBJECTIVES: Vaccinate-or-mask (VOM) policies aim to improve influenza vaccine coverage among healthcare workers (HCW) and reduce influenza-related illness among patients and staff. In 2012, British Columbia (BC) implemented a province-wide VOM influenza prevention policy. This study describes an evaluation of policy impacts on HCW absenteeism rates from before to after policy implementation. METHODS: Using payroll data from regional and provincial Health Authorities (HA), we assessed all-cause sick rates (sick time as a proportion of sick time and productive time) before (2007-2011, excluding 2009-2010) and after (2012-2017) policy implementation, and during influenza season (December 1-March 31) and non-influenza season (April 1-November 30). We used a two-part negative binomial hurdle model to calculate odds ratios (OR) of taking any sick time, relative rates (RR) of sick time taken, and predicted mean sick rates, adjusting for age group, sex, job type, job classification, HA, year and vaccine effectiveness. RESULTS: During influenza season, HCWs in the post-policy period were less likely to take any sick time (OR 0.989, 95%CI: 0.979-0.999) but had higher rates of sick time (RR 1.038, 95%CI: 1.030-1.045). However, during non-influenza season, HCWs in the post-policy period were more likely to take any sick time (OR 1.015, 95%CI: 1.008-1.022) but had lower rates of sick time (RR 0.971, 95%CI: 0.966-0.976). There was an overall increase in predicted mean sick rate from pre to post-policy in influenza season (4.392% to 4.508%) and non-influenza season (3.815% to 3.901%). CONCLUSIONS: The observed year-round increase in sick rates from pre-to-post policy was likely influenced by other factors; however, opposite trends in how HCWs took sick time in the influenza and non-influenza seasons may reflect policy influences and need further research to explore reasons for these differences.