Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.

OBJECTIVES: It has become increasingly common for multiple computable phenotypes from electronic health records (EHR) to be developed for a given phenotype. However, EHR-based association studies often focus on a single phenotype. In this paper, we develop a method aiming to simultaneously make use of multiple EHR-derived phenotypes for reduction of bias due to phenotyping error and improved efficiency of phenotype/exposure associations. MATERIALS AND METHODS: The proposed method combines multiple algorithm-derived phenotypes with a small set of validated outcomes to reduce bias and improve estimation accuracy and efficiency. The performance of our method was evaluated through simulation studies and real-world application to an analysis of colon cancer recurrence using EHR data from Kaiser Permanente Washington. RESULTS: In settings where there was no single surrogate performing uniformly better than all others in terms of both sensitivity and specificity, our method achieved substantial bias reduction compared to using a single algorithm-derived phenotype. Our method also led to higher estimation efficiency by up to 30% compared to an estimator that used only one algorithm-derived phenotype. DISCUSSION: Simulation studies and application to real-world data demonstrated the effectiveness of our method in integrating multiple phenotypes, thereby enhancing bias reduction, statistical accuracy and efficiency. CONCLUSIONS: Our method combines information across multiple surrogates using a statistically efficient seemingly unrelated regression framework. Our method provides a robust alternative to single-surrogate-based bias correction, especially in contexts lacking information on which surrogate is superior.

Stockholm3 in a Multiethnic Cohort for Prostate Cancer Detection (SEPTA): A Prospective Multicentered Trial.

PURPOSE: Asian, Black, and Hispanic men are underrepresented in prostate cancer (PCa) clinical trials. Few novel prostate cancer biomarkers have been validated in diverse cohorts. We aimed to determine if Stockholm3 can improve prostate cancer detection in a diverse cohort. METHODS: An observational prospective multicentered (17 sites) clinical trial (2019-2023), supplemented by prospectively recruited participants (2008-2020) in a urology clinic setting included men with suspicion of PCa and underwent prostate biopsy. Before biopsy, sample was collected for measurement of the Stockholm3 risk score. Parameters include prostate-specific antigen (PSA), free PSA, KLK2, GDF15, PSP94, germline risk (single-nucleotide polymorphisms), age, family history, and previous negative biopsy. The primary endpoint was detection of International Society of Urological Pathology (ISUP) Grade ≥2 cancer (clinically significant PCa, csPC). The two primary aims were to (1) demonstrate noninferior sensitivity (0.8 lower bound 95% CI noninferiority margin) in detecting csPC using Stockholm3 compared with PSA (relative sensitivity) and (2) demonstrate superior specificity by reducing biopsies with benign results or low-grade cancers (relative specificity). RESULTS: A total of 2,129 biopsied participants were included: Asian (16%, 350), Black or African American (Black; 24%, 505), Hispanic or Latino and White (Hispanic; 14%, 305), and non-Hispanic or non-Latino and White (White; 46%, 969). Overall, Stockholm3 showed noninferior sensitivity compared with PSA ≥4 ng/mL (relative sensitivity: 0.95 [95% CI, 0.92 to 0.99]) and nearly three times higher specificity (relative specificity: 2.91 [95% CI, 2.63 to 3.22]). Results were consistent across racial and ethnic subgroups: noninferior sensitivity (0.91-0.98) and superior specificity (2.51-4.70). Compared with PSA, Stockholm3 could reduce benign and ISUP 1 biopsies by 45% overall and between 42% and 52% across racial and ethnic subgroups. CONCLUSION: In a substantially diverse population, Stockholm3 significantly reduces unnecessary prostate biopsies while maintaining a similar sensitivity to PSA in detecting csPC.

Social Determinants of Health Mediate Racial Disparities in Cardiovascular Disease in Men With Prostate Cancer.

Background: Cardiovascular disease (CVD) is a significant cause of morbidity and mortality in men with prostate cancer; however, data on racial disparities in CVD outcomes are limited. Objectives: We quantified the disparities in CVD according to self-identified race and the role of the structural social determinants of health in mediating disparities in prostate cancer patients. Methods: A retrospective cohort study of 3,543 prostate cancer patients treated with systemic androgen deprivation therapy (ADT) between 2008 and 2021 at a quaternary, multisite health care system was performed. The multivariable adjusted association between self-reported race (Black vs White) and incident major adverse cardiovascular events (MACE) after ADT initiation was evaluated using cause-specific proportional hazards. Mediation analysis determined the role of theme-specific and overall social vulnerability index (SVI) in explaining the racial disparities in CVD outcomes. Results: Black race was associated with an increased hazard of MACE (HR: 1.38; 95% CI: 1.16-1.65; P < 0.001). The association with Black race was strongest for incident heart failure (HR: 1.79; 95% CI: 1.32-2.43), cerebrovascular disease (HR: 1.98; 95% CI: 1.37-2.87), and peripheral artery disease (HR: 1.76; 95% CI: 1.26-2.45) (P < 0.001). SVI, specifically the socioeconomic status theme, mediated 98% of the disparity in MACE risk between Black and White patients. Conclusions: Black patients are significantly more likely to experience adverse CVD outcomes after systemic ADT compared with their White counterparts. These disparities are mediated by socioeconomic status and other structural determinants of health as captured by census tract SVI. Our findings motivate multilevel interventions focused on addressing socioeconomic vulnerability.

Racial Disparities and Strategies for Improving Equity in Diagnostic Follow-Up for Abnormal Screening Mammograms.

PURPOSE: Black and White women undergo screening mammography at similar rates, but racial disparities in breast cancer outcomes persist. To assess potential contributors, we investigated delays in follow-up after abnormal imaging by race/ethnicity. METHODS: Women who underwent screening mammography at our urban academic center from January 2015 to February 2018 and received a Breast Imaging Reporting and Data System 0 assessment were included. Kaplan-Meier estimates described distributions of time between diagnostic events from (1) screening to diagnostic imaging and (2) diagnostic imaging to biopsy. Multivariable logistic regression models estimated the associations between race/ethnicity and receipt of follow-up within 15 and 30 days. RESULTS: Two thousand five hundred and fifty-four women were included (48.6% non-Hispanic [NH] Black, 38.2% NH White, 13.1% other/unknown). Median time between screening and diagnostic imaging varied by race/ethnicity (White: 7 days [IQR, 2-14]; Black: 12 days [IQR, 7-23]; other/unknown: 9 days [IQR, 5-21]). There were similar disparities in days between diagnostic imaging and biopsy (White: 12 [IQR, 7-24]; Black: 21 [IQR, 13-37]; other/unknown: 16 [IQR, 9-30]) and between screening and biopsy (White: 20 [IQR, 11-41]; Black: 35 [IQR, 22-63]; other/unknown: 27.5 [IQR, 17-42]). After adjustment, odds of diagnostic imaging follow-up within 15 days of screening were lower for Black versus White women (odds ratio, 0.59 [95% CI, 0.44 to 0.80]; P < .001). CONCLUSION: In this diverse cohort, disparities in timely diagnostic follow-up after abnormal breast screening were observed, with Black women waiting 1.75 times as long as White women to obtain a tissue diagnosis. National guidelines for time to diagnostic follow-up may facilitate more timely breast cancer care and potentially affect outcomes.

Racial Differences in Germline Genetic Testing Completion Among Males With Pancreatic, Breast, or Metastatic Prostate Cancers.

BACKGROUND: Germline genetic testing is a vital component of guideline-recommended cancer care for males with pancreatic, breast, or metastatic prostate cancers. We sought to determine whether there were racial disparities in germline genetic testing completion in this population. PATIENTS AND METHODS: This retrospective cohort study included non-Hispanic White and Black males with incident pancreatic, breast, or metastatic prostate cancers between January 1, 2019, and September 30, 2021. Two nationwide cohorts were examined: (1) commercially insured individuals in an administrative claims database, and (2) Veterans receiving care in the Veterans Health Administration. One-year germline genetic testing rates were estimated by using Kaplan-Meier methods. Cox proportional hazards regression was used to test the association between race and genetic testing completion. Causal mediation analyses were performed to investigate whether socioeconomic variables contributed to associations between race and germline testing. RESULTS: Our cohort consisted of 7,894 males (5,142 commercially insured; 2,752 Veterans). One-year testing rates were 18.0% (95% CI, 16.8%-19.2%) in commercially insured individuals and 14.2% (95% CI, 11.5%-15.0%) in Veterans. Black race was associated with a lower hazard of testing among commercially insured individuals (adjusted hazard ratio [aHR], 0.73; 95% CI, 0.58-0.91; P=.005) but not among Veterans (aHR, 0.99; 95% CI, 0.75-1.32; P=.960). In commercially insured individuals, income (aHR, 0.90; 95% CI, 0.86-0.96) and net worth (aHR, 0.92; 95% CI, 0.86-0.98) mediated racial disparities, whereas education (aHR, 0.98; 95% CI, 0.94-1.01) did not. CONCLUSIONS: Overall rates of guideline-recommended genetic testing are low in males with pancreatic, breast, or metastatic prostate cancers. Racial disparities in genetic testing among males exist in a commercially insured population, mediated by net worth and household income; these disparities are not seen in the equal-access Veterans Health Administration. Alleviating financial and access barriers may mitigate racial disparities in genetic testing.

A Quantitative Bias Analysis Approach to Informative Presence Bias in Electronic Health Records.

Accurate outcome and exposure ascertainment in electronic health record (EHR) data, referred to as EHR phenotyping, relies on the completeness and accuracy of EHR data for each individual. However, some individuals, such as those with a greater comorbidity burden, visit the health care system more frequently and thus have more complete data, compared with others. Ignoring such dependence of exposure and outcome misclassification on visit frequency can bias estimates of associations in EHR analysis. We developed a framework for describing the structure of outcome and exposure misclassification due to informative visit processes in EHR data and assessed the utility of a quantitative bias analysis approach to adjusting for bias induced by informative visit patterns. Using simulations, we found that this method produced unbiased estimates across all informative visit structures, if the phenotype sensitivity and specificity were correctly specified. We applied this method in an example where the association between diabetes and progression-free survival in metastatic breast cancer patients may be subject to informative presence bias. The quantitative bias analysis approach allowed us to evaluate robustness of results to informative presence bias and indicated that findings were unlikely to change across a range of plausible values for phenotype sensitivity and specificity. Researchers using EHR data should carefully consider the informative visit structure reflected in their data and use appropriate approaches such as the quantitative bias analysis approach described here to evaluate robustness of study findings.

Comparing survival in trial- versus routine-care advanced urothelial cancer patients on immune checkpoint blockade.

PURPOSE: Although recent trials involving first-line immune checkpoint inhibitors have expanded treatment options for patients with advanced urothelial carcinoma (aUC) who are ineligible for standard cisplatin-based chemotherapy, there exists limited evidence for whether trial efficacy translates into real-world effectiveness for patients seen in routine care. This retrospective cohort study compares differences in overall survival (OS) between KEYNOTE-052 trial participants and routine-care patients receiving first-line pembrolizumab monotherapy. METHODS: A routine-care patient cohort was constructed from the Flatiron Health database using trial eligibility criteria and was weighted to balance EHR and trial patient characteristics using matching-adjusted indirect comparisons. RESULTS: The routine-care cohort was older, more likely to be female, and more often cisplatin-ineligible due to renal dysfunction. ECOG performance status was comparable between the cohorts. Median OS was 9 months (95% CI 7-16) in the weighted routine-care cohort and 11.3 months (9.7-13.1) in the trial cohort. No significant differences between the Kaplan-Meier OS curves were detected (p = 0.76). Survival probabilities were similar between the weighted routine-care and trial cohorts at 12-, 24-, and 36- months (0.45 vs. 0.47, 0.31 vs. 0.31, 0.26 vs. 0.23, respectively). Notably, routine care patients had modestly lower survival at 3 months compared to trial participants (0.69 vs. 0.83, respectively). CONCLUSION: Our results provide reassurance that cisplatin-ineligible aUC patients receiving first-line immunotherapy in routine care experience similar benefits to those observed in trial patients.

Real-world use, dose intensity, and adherence to enfortumab vedotin in locally advanced or metastatic urothelial cancer.

BACKGROUND: Enfortumab vedotin (EV) monotherapy is approved for the treatment of advanced urothelial cancer as later-line therapy (post-immunotherapy and -platinum-chemotherapy) and as earlier-line therapy (cisplatin-ineligible, at least 1 prior therapy). We examined real-world EV monotherapy use, dose intensity and adherence across 280 US cancer clinics. METHODS: This postmarketing study used data from a nationwide (United States) deidentified patient-level electronic health record-derived database. Included were patients with advanced urothelial cancer initiating EV on or after December 19, 2019 (date of accelerated approval). We summarized characteristics of EV users using descriptive statistics and computed metrics of EV use, EV dose intensity, and EV treatment adherence. RESULTS: We identified 416 advanced urothelial cancer patients initiating EV monotherapy. More than half of patients (55.3%) received EV as later-line therapy (3L+), and nearly half (44.7%) received EV as earlier line therapy (1 or 2L). Dosing frequency (mean [SD] 2.4 [0.5] treatments per 28 day cycle) and dose (1.1 [0.2] mg/kg) were lower than label indication guidelines (1.25 mg/kg, Day 1, 8, 15 of a 28 day cycle). Only 58.8% of patients received an average of >2 treatments per 28-day cycle. CONCLUSIONS: Among patients with advanced urothelial cancer treated with EV monotherapy in contemporary practice, EV dosing frequency, and dosage was lower in clinical practice than recommended in the product labeling. Further research is required to understand clinical factors and outcomes associated with the differences observed.

Predicting five-year interval second breast cancer risk in women with prior breast cancer.

BACKGROUND: Annual surveillance mammography is recommended for women with a personal history of breast cancer. Risk prediction models that estimate mammography failures such as interval second breast cancers could help to tailor surveillance imaging regimens to women's individual risk profiles. METHODS: In a cohort of women with a history of breast cancer receiving surveillance mammography in the Breast Cancer Surveillance Consortium in 1996-2019, we used Least Absolute Shrinkage and Selection Operator (LASSO)-penalized regression to estimate the probability of an interval second cancer (invasive cancer or ductal carcinoma in situ) in the 1 year after a negative surveillance mammogram. Based on predicted risks from this one-year risk model, we generated cumulative risks of an interval second cancer for the five-year period after each mammogram. Model performance was evaluated using cross-validation in the overall cohort and within race and ethnicity strata. RESULTS: In 173 290 surveillance mammograms, we observed 496 interval cancers. One-year risk models were well-calibrated (expected/observed ratio = 1.00) with good accuracy (area under the receiver operating characteristic curve = 0.64). Model performance was similar across race and ethnicity groups. The median five-year cumulative risk was 1.20% (interquartile range 0.93%-1.63%). Median five-year risks were highest in women who were under age 40 or pre- or perimenopausal at diagnosis and those with estrogen receptor-negative primary breast cancers. CONCLUSIONS: Our risk model identified women at high risk of interval second breast cancers who may benefit from additional surveillance imaging modalities. Risk models should be evaluated to determine if risk-guided supplemental surveillance imaging improves early detection and decreases surveillance failures.

Data gaps and opportunities for modeling cancer health equity.

Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. Data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change.

Lung Cancer Screening Decision Aid Designed for a Primary Care Setting: A Randomized Clinical Trial.

Importance: Guidelines recommend shared decision-making prior to initiating lung cancer screening (LCS). However, evidence is lacking on how to best implement shared decision-making in clinical practice. Objective: To evaluate the impact of an LCS Decision Tool (LCSDecTool) on the quality of decision-making and LCS uptake. Design, Setting, and Participants: This randomized clinical trial enrolled participants at Veteran Affairs Medical Centers in Philadelphia, Pennsylvania; Milwaukee, Wisconsin; and West Haven, Connecticut, from March 18, 2019, to September 29, 2021, with follow-up through July 18, 2022. Individuals aged 55 to 80 years with a smoking history of at least 30 pack-years who were current smokers or had quit within the past 15 years were eligible to participate. Individuals with LCS within 15 months were excluded. Of 1047 individuals who were sent a recruitment letter or had referred themselves, 140 were enrolled. Intervention: A web-based patient- and clinician-facing LCS decision support tool vs an attention control intervention. Main Outcome and Measures: The primary outcome was decisional conflict at 1 month. Secondary outcomes included decisional conflict immediately after intervention and 3 months after intervention, knowledge, decisional regret, and anxiety immediately after intervention and 1 and 3 months after intervention and LCS by 6 months. Results: Of 140 enrolled participants (median age, 64.0 [IQR, 61.0-69.0] years), 129 (92.1%) were men and 11 (7.9%) were women. Of 137 participants with data available, 75 (53.6%) were African American or Black and 62 (44.3%) were White; 4 participants (2.9%) also reported Hispanic or Latino ethnicity. Mean decisional conflict score at 1 month did not differ between the LCSDecTool and control groups (25.7 [95% CI, 21.4-30.1] vs 29.9 [95% CI, 25.6-34.2], respectively; P = .18). Mean LCS knowledge score was greater in the LCSDecTool group immediately after intervention (7.0 [95% CI, 6.3-7.7] vs 4.9 [95% CI, 4.3-5.5]; P < .001) and remained higher at 1 month (6.3 [95% CI, 5.7-6.8] vs 5.2 [95% CI, 4.5-5.8]; P = .03) and 3 months (6.2 [95% CI, 5.6-6.8] vs 5.1 [95% CI, 4.4-5.8]; P = .01). Uptake of LCS was greater in the LCSDecTool group at 6 months (26 of 69 [37.7%] vs 15 of 71 [21.1%]; P = .04). Conclusions and Relevance: In this randomized clinical trial of an LCSDecTool compared with attention control, no effect on decisional conflict occurred at 1 month. The LCSDecTool used in the primary care setting did not yield a significant difference in decisional conflict. The intervention led to greater knowledge and LCS uptake. These findings can inform future implementation strategies and research in LCS shared decision-making. Trial Registration: ClinicalTrials.gov Identifier: NCT02899754.

Temporal Trends and Factors Associated with Receipt of Post-mastectomy Radiation After Neoadjuvant Chemotherapy in Women with cT3 Breast Cancer.

INTRODUCTION: Given the potential impact of increasingly effective neoadjuvant chemotherapy (NACT) on post-mastectomy radiotherapy (PMRT) recommendations, we examined temporal trends in post-NACT PMRT for cT3 breast cancer. METHODS: We identified women ≥ 18 years in the National Cancer Database (NCDB) diagnosed 2004-2019 with cT3N0-1M0 breast cancer treated with chemotherapy and mastectomy. Multivariable logistic regression and Cox proportional hazards models were used to estimate associations between pathologic NACT response [complete response (CR), partial response (PR), or no response (NR); or disease progression (DP)] and PMRT and between PMRT and overall survival (OS), respectively. RESULTS: We identified 39,901 women (Asian/Pacific Islander 1731, Black 5875, Hispanic 3265, White 27,303). Among cN0 patients with CR, PMRT rates declined from 67% in 2004 to 35% in 2019 but remained unchanged for patients with DP. Relative to NR, CR [odds ratio (OR) 0.36, 95% confidence interval (CI) 0.29-0.46] and PR (OR 0.44, 95% CI 0.36-0.55) in cN0 patients were associated with lower odds of PMRT while DP (OR 1.33, 95% CI 1.05-1.69) was associated with higher odds. Among cN1 patients, PMRT rates decreased from 90% to 73% for CR between 2005 and 2019 and increased from 76% to 82% for DP between 2004 and 2019. Relative to NR, CR (OR 0.78, 95% CI 0.63-0.95) was associated with lower odds of PMRT while DP (OR 1.93, 95% CI 1.58-2.37) was associated with higher odds. PMRT was associated with improved OS among cN1 patients (hazard ratio (HR) 0.77, 95% CI 0.67-0.88). CONCLUSION: CR was associated with decreased PMRT receipt over time, while temporal trends following PR and DP differed by cN status, suggesting that nodal involvement guided PMRT receipt more than in-breast disease.

Sources of Disparities in Surveillance Mammography Performance and Risk-Guided Recommendations for Supplemental Breast Imaging: A Simulation Study.

BACKGROUND: Surveillance mammography is recommended for all women with a history of breast cancer. Risk-guided surveillance incorporating advanced imaging modalities based on individual risk of a second cancer could improve cancer detection. However, personalized surveillance may also amplify disparities. METHODS: In simulated populations using inputs from the Breast Cancer Surveillance Consortium (BCSC), we investigated race- and ethnicity-based disparities. Disparities were decomposed into those due to primary breast cancer and treatment characteristics, social determinants of health (SDOH) and differential error in second cancer ascertainment by modeling populations with or without variation across race and ethnicity in the distribution of these characteristics. We estimated effects of disparities on mammography performance and supplemental imaging recommendations stratified by race and ethnicity. RESULTS: In simulated cohorts based on 65,446 BCSC surveillance mammograms, when only cancer characteristics varied by race and ethnicity, mammograms for Black women had lower sensitivity compared with the overall population (64.1% vs. 71.1%). Differences between Black women and the overall population were larger when both cancer characteristics and SDOH varied by race and ethnicity (53.8% vs. 71.1%). Basing supplemental imaging recommendations on high predicted second cancer risk resulted in less frequent recommendations for Hispanic (6.7%) and Asian/Pacific Islander women (6.4%) compared with the overall population (10.0%). CONCLUSIONS: Variation in cancer characteristics and SDOH led to disparities in surveillance mammography performance and recommendations for supplemental imaging. IMPACT: Risk-guided surveillance imaging may exacerbate disparities. Decision-makers should consider implications for equity in cancer outcomes resulting from implementing risk-guided screening programs. See related In the Spotlight, p. 1479.

Inverse Probability of Treatment Weighting and Confounder Missingness in Electronic Health Record-based Analyses: A Comparison of Approaches Using Plasmode Simulation.

BACKGROUND: Electronic health record (EHR) data represent a critical resource for comparative effectiveness research, allowing investigators to study intervention effects in real-world settings with large patient samples. However, high levels of missingness in confounder variables is common, challenging the perceived validity of EHR-based investigations. METHODS: We investigated performance of multiple imputation and propensity score (PS) calibration when conducting inverse probability of treatment weights (IPTW)-based comparative effectiveness research using EHR data with missingness in confounder variables and outcome misclassification. Our motivating example compared effectiveness of immunotherapy versus chemotherapy treatment of advanced bladder cancer with missingness in a key prognostic variable. We captured complexity in EHR data structures using a plasmode simulation approach to spike investigator-defined effects into resamples of a cohort of 4361 patients from a nationwide deidentified EHR-derived database. We characterized statistical properties of IPTW hazard ratio estimates when using multiple imputation or PS calibration missingness approaches. RESULTS: Multiple imputation and PS calibration performed similarly, maintaining ≤0.05 absolute bias in the marginal hazard ratio even when ≥50% of subjects had missing at random or missing not at random confounder data. Multiple imputation required greater computational resources, taking nearly 40 times as long as PS calibration to complete. Outcome misclassification minimally increased bias of both methods. CONCLUSION: Our results support multiple imputation and PS calibration approaches to missingness in missing completely at random or missing at random confounder variables in EHR-based IPTW comparative effectiveness analyses, even with missingness ≥50%. PS calibration represents a computationally efficient alternative to multiple imputation.

Uptake of Maintenance Immunotherapy and Changes in Upstream Treatment Selection Among Patients With Urothelial Cancer.

This cohort study compares the proportion of patients with urothelual cancer who initiated first-line chemotherapy and immune checkpoint inhibitors during the periods before and after avelumab approval.

Cumulative 6-Year Risk of Screen-Detected Ductal Carcinoma In Situ by Screening Frequency.

Importance: Detection of ductal carcinoma in situ (DCIS) by mammography screening is a controversial outcome with potential benefits and harms. The association of mammography screening interval and woman's risk factors with the likelihood of DCIS detection after multiple screening rounds is poorly understood. Objective: To develop a 6-year risk prediction model for screen-detected DCIS according to mammography screening interval and women's risk factors. Design, Setting, and Participants: This Breast Cancer Surveillance Consortium cohort study assessed women aged 40 to 74 years undergoing mammography screening (digital mammography or digital breast tomosynthesis) from January 1, 2005, to December 31, 2020, at breast imaging facilities within 6 geographically diverse registries of the consortium. Data were analyzed between February and June 2022. Exposures: Screening interval (annual, biennial, or triennial), age, menopausal status, race and ethnicity, family history of breast cancer, benign breast biopsy history, breast density, body mass index, age at first birth, and false-positive mammography history. Main Outcomes and Measures: Screen-detected DCIS defined as a DCIS diagnosis within 12 months after a positive screening mammography result, with no concurrent invasive disease. Results: A total of 916 931 women (median [IQR] age at baseline, 54 [46-62] years; 12% Asian, 9% Black, 5% Hispanic/Latina, 69% White, 2% other or multiple races, and 4% missing) met the eligibility criteria, with 3757 screen-detected DCIS diagnoses. Screening round-specific risk estimates from multivariable logistic regression were well calibrated (expected-observed ratio, 1.00; 95% CI, 0.97-1.03) with a cross-validated area under the receiver operating characteristic curve of 0.639 (95% CI, 0.630-0.648). Cumulative 6-year risk of screen-detected DCIS estimated from screening round-specific risk estimates, accounting for competing risks of death and invasive cancer, varied widely by all included risk factors. Cumulative 6-year screen-detected DCIS risk increased with age and shorter screening interval. Among women aged 40 to 49 years, the mean 6-year screen-detected DCIS risk was 0.30% (IQR, 0.21%-0.37%) for annual screening, 0.21% (IQR, 0.14%-0.26%) for biennial screening, and 0.17% (IQR, 0.12%-0.22%) for triennial screening. Among women aged 70 to 74 years, the mean cumulative risks were 0.58% (IQR, 0.41%-0.69%) after 6 annual screens, 0.40% (IQR, 0.28%-0.48%) for 3 biennial screens, and 0.33% (IQR, 0.23%-0.39%) after 2 triennial screens. Conclusions and Relevance: In this cohort study, 6-year screen-detected DCIS risk was higher with annual screening compared with biennial or triennial screening intervals. Estimates from the prediction model, along with risk estimates of other screening benefits and harms, could help inform policy makers' discussions of screening strategies.

Exposure to US Cancer Drugs With Lack of Confirmed Benefit After US Food and Drug Administration Accelerated Approval.

This cross-sectional study evaluates patient exposure to oncology drugs withdrawn from the US Food and Drug Administration (FDA) Accelerated Approval program.

Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data.

BACKGROUND: Missing data are common in studies using electronic health records (EHRs)-derived data. Missingness in EHR data is related to healthcare utilization patterns, resulting in complex and potentially missing not at random missingness mechanisms. Prior research has suggested that machine learning-based multiple imputation methods may outperform traditional methods and may perform well even in settings of missing not at random missingness. METHODS: We used plasmode simulations based on a nationwide EHR-derived de-identified database for patients with metastatic urothelial carcinoma to compare the performance of multiple imputation using chained equations, random forests, and denoising autoencoders in terms of bias and precision of hazard ratio estimates under varying proportions of observations with missing values and missingness mechanisms (missing completely at random, missing at random, and missing not at random). RESULTS: Multiple imputation by chained equations and random forest methods had low bias and similar standard errors for parameter estimates under missingness completely at random. Under missingness at random, denoising autoencoders had higher bias than multiple imputation by chained equations and random forests. Contrary to results of prior studies of denoising autoencoders, all methods exhibited substantial bias under missingness not at random, with bias increasing in direct proportion to the amount of missing data. CONCLUSIONS: We found no advantage of denoising autoencoders for multiple imputation in the setting of an epidemiologic study conducted using EHR data. Results suggested that denoising autoencoders may overfit the data leading to poor confounder control. Use of more flexible imputation approaches does not mitigate bias induced by missingness not at random and can produce estimates with spurious precision.

Variation in second breast cancer risk after primary invasive cancer by time since primary cancer diagnosis and estrogen receptor status.

BACKGROUND: In women with previously treated breast cancer, occurrence and timing of second breast cancers have implications for surveillance. The authors examined the timing of second breast cancers by primary cancer estrogen receptor (ER) status in the Breast Cancer Surveillance Consortium. METHODS: Women who were diagnosed with American Joint Commission on Cancer stage I-III breast cancer were identified within six Breast Cancer Surveillance Consortium registries from 2000 to 2017. Characteristics collected at primary breast cancer diagnosis included demographics, ER status, and treatment. Second breast cancer events included subsequent ipsilateral or contralateral breast cancers diagnosed >6 months after primary diagnosis. The authors examined cumulative incidence and second breast cancer rates by primary cancer ER status during 1-5 versus 6-10 years after diagnosis. RESULTS: At 10 years, the cumulative second breast cancer incidence was 11.8% (95% confidence interval [CI], 10.7%-13.1%) for women with ER-negative disease and 7.5% (95% CI, 7.0%-8.0%) for those with ER-positive disease. Women with ER-negative cancer had higher second breast cancer rates than those with ER-positive cancer during the first 5 years of follow-up (16.0 per 1000 person-years [PY]; 95% CI, 14.2-17.9 per 1000 PY; vs. 7.8 per 1000 PY; 95% CI, 7.3-8.4 per 1000 PY, respectively). After 5 years, second breast cancer rates were similar for women with ER-negative versus ER-positive breast cancer (12.1 per 1000 PY; 95% CI, 9.9-14.7; vs. 9.3 per 1000 PY; 95% CI, 8.4-10.3 per 1000 PY, respectively). CONCLUSIONS: ER-negative primary breast cancers are associated with a higher risk of second breast cancers than ER-positive cancers during the first 5 years after diagnosis. Further study is needed to examine the potential benefit of more intensive surveillance targeting these women in the early postdiagnosis period.