Approaches for classifying the indications for colonoscopy using detailed clinical data

BACKGROUND: Accurate indication classification is critical for obtaining unbiased estimates of colonoscopy effectiveness and quality improvement efforts, but there is a dearth of published systematic classification approaches. The objective of this study was to evaluate the effects of data-source and adjudication on indication classification and on estimates of the effectiveness of screening colonoscopy on late-stage colorectal cancer diagnosis risk. METHODS: This was an observational study in members of four U.S. health plans. Eligible persons (n = 1039) were age 55-85 and had been enrolled for 5 years or longer in their health plans during 2006-2008. Patients were selected based on late-stage colorectal cancer diagnosis in a case-control design; each case patient was matched to 1-2 controls by study site, age, sex, and health plan enrollment duration. Reasons for colonoscopies received in the 10-year period before the reference date were collected from three medical records sources (progress notes; referral notes; procedure reports) and categorized using an algorithm, with committee adjudication of some tests. We evaluated indication classification concordance before and after adjudication and used logistic regressions with the Wald Chi-square test to compare estimates of the effects of screening colonoscopy on late-stage colorectal cancer diagnosis risk for each of our data sources to the adjudicated indication. RESULTS: Classification agreement between each data-source and adjudication was 78.8-94.0% (weighted kappa = 0.53-0.72); the highest agreement (weighted kappa = 0.86-0.88) was when information from all data sources was considered together. The choice of data-source influenced the association between screening colonoscopy and late-stage colorectal cancer diagnosis; estimates based on progress notes were closest to those based on the adjudicated indication (% difference in regression coefficients = 2.4%, p-value = 0.98), as compared to estimates from only referral notes (% difference in coefficients = 34.9%, p-value = 0.12) or procedure reports (% difference in coefficients = 27.4%, p-value = 0.23). CONCLUSION: There was no single gold-standard source of information in medical records. The estimates of colonoscopy effectiveness from progress notes alone were the closest to estimates using adjudicated indications. Thus, the details in the medical records are necessary for accurate indication classification

Effects of Transitioning From Conventional Methods to Liquid Based Methods on Unsatisfactory Pap Tests: Results from a Multicenter U.S. Study (poster)

Background: Pap testing has transitioned from conventional preparations (CP) to liquid based preparations (LBP) due to perceived superiority of LBPs. Many studies conclude LBPs reduce unsatisfactory (UNSAT) tests however some believe the evidence to substantiate this claim is weak. We studied the effect of the transition from CPs to LBPs on the proportion of UNSAT Pap tests (PT) in four health care systems in the United States participating in the NIH-funded SEARCH project. Methods: Our study cohort consisted of 548,174 women with 1,443,725 total PTs, ages 21-65 years, between 2000 and 2010. We used segmented regression analysis to estimate the effect of adopting LBPs on the proportion of UNSAT PTs after adjusting for age. Results: Three sites implementing SurePath LBP experienced significant reductions in UNSAT PTs (Site 1 estimated effect: -2.46% [95% CI: -1.47%, -3.45%], Site 2: -1.78% [95% CI: -1.54%, -2.02%], Site 3: -8.25% [95% CI: -7.33%, -9.17%]. The fourth site implementing ThinPrep LBP did not experience a reduction in UNSAT studies. The relative risk of an UNSAT PT in women \u3e 50 increased after the transition to LBPs (SurePath: RR 2.1 [95% CI: 1.9, 2.2] and ThinPrep: RR 1.7 [95% CI: 1.5, 2.0]). Conclusions: The observed changes in the proportion of UNSAT PTs varied across the participating sites and it was dependent on the type of LBP technology, age of women and the rates prior to the implementation of this technology

Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease.

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I(2) test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P \u3c .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I(2) = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P \u3c .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted

A novel biosignature identifies patients with DCIS with high risk of local recurrence after breast conserving surgery and radiation therapy

PURPOSE: There is an unmet need to identify women diagnosed with ductal carcinoma in situ (DCIS) with a low risk of in-breast recurrence (IBR) after breast conserving surgery (BCS), which could omit radiation therapy (RT), and also to identify those with elevated IBR risk remaining after BCS plus RT. We evaluated a novel biosignature for a residual risk subtype (RRt) to help identify patients with elevated IBR risk after BCS plus RT. METHODS AND MATERIALS: Women with DCIS treated with BCS with or without RT at centers in the US, Australia, and Sweden (n = 926) were evaluated. Patients were classified into 3 biosignature risk groups using the decision score (DS) and the RRt category: (1) Low Risk (DS ≤2.8 without RRt), (2) Elevated Risk (DS \u3e2.8 without RRt), and (3) Residual Risk (DS \u3e2.8 with RRt). Total and invasive IBR rates were assessed by risk group and treatment. RESULTS: In patients at low risk, there was no significant difference in IBR rates with or without RT (total, P = .8; invasive IBR, P = .7), and there were low overall 10-year rates (total, 5.1%; invasive, 2.7%). In patients with elevated risk, IBR rates were decreased with RT (total: hazard ratio [HR], 0.25; P \u3c .001; invasive: HR, 0.28; P = .005); 10-year rates were 20.6% versus 4.9% (total) and 10.9% versus 3.1% (invasive). In patients with residual risk, although IBR rates decreased with RT after BCS (total: HR, 0.21; P \u3c .001; invasive: HR, 0.29; P = .028), IBR rates remained significantly higher after RT compared with patients with elevated risk (HR, 2.5; 95% CI, 1.2-5.4; P = .018), with 10-year rates of 42.1% versus 14.7% (total) and 18.3% versus 6.5% (invasive). CONCLUSIONS: The novel biosignature identified patients with 3 distinct risk profiles: Low Risk patients with a low recurrence risk with or without adjuvant RT, Elevated Risk patients with excellent outcomes after BCS plus RT, and Residual Risk patients with an elevated recurrence risk remaining after BCS plus RT, warranting potential intensified or alternative treatment approaches

Involution of Breast Lobules, Mammographic Breast Density and Prognosis Among Tamoxifen-Treated Estrogen Receptor-Positive Breast Cancer Patients

Mammographic breast density (MD) reflects breast fibroglandular content. Its decline following adjuvant tamoxifen treated, estrogen receptor (ER)-positive breast cancer has been associated with improved outcomes. Breast cancers arise from structures termed lobules, and lower MD is associated with increased age-related lobule involution. We assessed whether pre-treatment involution influenced associations between MD decline and risk of breast cancer-specific death. ER-positive tamoxifen treated patients diagnosed at Kaiser Permanente Northwest (1990–2008) were defined as cases who died of breast cancer (n = 54) and matched controls (remained alive over similar follow-up; n = 180). Lobule involution was assessed by examining terminal duct lobular units (TDLUs) in benign tissues surrounding cancers as TDLU count/mm2, median span and acini count/TDLU. MD (%) was measured in the unaffected breast at baseline (median 6-months before) and follow-up (median 12-months after tamoxifen initiation). TDLU measures and baseline MD were positively associated among controls (p < 0.05). In multivariable regression models, MD decline (≥10%) was associated with reduced risk of breast cancer-specific death before (odds ratio (OR): 0.41, 95% CI: 0.18–0.92) and after (OR: 0.41, 95% CI: 0.18–0.94) adjustment for TDLU count/mm2, TDLU span (OR: 0.34, 95% CI: 0.14–0.84), and acini count/TDLU (OR: 0.33, 95% CI: 0.13–0.81). MD decline following adjuvant tamoxifen is associated with reduced risk of breast cancer-specific death, irrespective of pre-treatment lobule involution

Establishing a Population-Based Cohort of Nonmuscle Invasive Bladder Cancer Cases to Improve Care by Enhancing Surveillance and Risk Stratification

Background: Among the ~75% of bladder cancer patients who present with superficial (stage \u3c T2) disease, there is substantial variability in the progression to muscle-invasive (stage ≥ T2) disease. An expert panel representing the American Urological Association has expressed the need for population-based data and better predictive tools for bladder cancer surveillance. However, an existing risk-calculator has faced limited adoption, possibly because of reliance on aggregate data from clinical trial populations and failure to design the risk-calculator for use in specific clinical contexts (“use cases”). Methods: We are developing a risk-assessment tool using a population-based cohort from Kaiser Permanente Northwest (KPNW). The cohort includes all superficial bladder cancer cases diagnosed and treated at KPNW from 1990 to 2014. Data from tumor registry, pathology and health plan membership files comprise our database. We conducted in-depth interviews with 8 urologists in a variety of practice settings to identify use cases for improving clinical care through personalized risk-assessment. Our analysis describes key characteristics of our cohort as well as stakeholder suggestions for how a risk-calculator could enhance care. Results: We identified 2,131 cases of superficial bladder cancer. The population had a median of 2.0 pathology records per case, ranging up to 24 records, with 95% of cases having 6 or fewer records. Half of the population did not have a recurrence or progression of bladder cancer during follow-up. Overall, 86% of pathology reports per case reported positive bladder cancer findings. Stakeholder interviews with urologists identified numerous use cases for personalized risk calculations of recurrence and progression. These included: enhancing patient communication and documentation about recurrence/progression; providing guidance about when to discontinue or reduce surveillance for very low-risk patients; identifying and creating scheduling and call-back supports for patients who have high priority for follow-up cystoscopy; and contextualizing bladder cancer mortality risk in the context of competing comorbid conditions. Urologists in general practice reported different use preferences than urologic oncologists. Conclusion: By interviewing a variety of clinicians, we have identified a range of clinical decision support and patient education uses for a personalized risk-calculator for bladder cancer surveillance based on a large population-based cohort reflecting “real world” practice

Calculation of Organ Doses for a Large Number of Patients Undergoing CT Examinations

OBJECTIVE: The objective of our study was to develop an automated calculation method to provide organ dose assessment for a large cohort of pediatric and adult patients undergoing CT examinations. MATERIALS AND METHODS: We adopted two dose libraries that were previously published: the volume CT dose index-normalized organ dose library and the tube current-exposure time product (100 mAs)-normalized weighted CT dose index library. We developed an algorithm to calculate organ doses using the two dose libraries and the CT parameters available from DICOM data. We calculated organ doses for pediatric (n = 2499) and adult (n = 2043) CT examinations randomly selected from four health care systems in the United States and compared the adult organ doses with the values calculated from the ImPACT calculator. RESULTS: The median brain dose was 20 mGy (pediatric) and 24 mGy (adult), and the brain dose was greater than 40 mGy for 11% (pediatric) and 18% (adult) of the head CT studies. Both the National Cancer Institute (NCI) and ImPACT methods provided similar organ doses (median discrepancy \u3c 20%) for all organs except the organs located close to the scanning boundaries. The visual comparisons of scanning coverage and phantom anatomies revealed that the NCI method, which is based on realistic computational phantoms, provides more accurate organ doses than the ImPACT method. CONCLUSION: The automated organ dose calculation method developed in this study reduces the time needed to calculate doses for a large number of patients. We have successfully used this method for a variety of CT-related studies including retrospective epidemiologic studies and CT dose trend analysis studies