Symptom Burden in Long-Term Survivors of Head and Neck Cancer: Patient-Reported Versus Clinical Data

Introduction: The symptom burden faced by long-term head and neck cancer survivors is not well understood. In addition, the accuracy of clinical data sources for symptom ascertainment is not clear. Objective: To 1) describe the prevalence of symptoms in 5-year survivors of head and neck cancer, and 2) to evaluate agreement between symptoms obtained via self-report and symptoms obtained from clinical data sources. Methods: We recruited 5-year survivors of head and neck cancer enrolled at Kaiser Permanente Washington (n = 54). Symptoms were assessed using the MD Anderson Symptom Inventory head and neck cancer module. For each symptom, we assessed the agreement of the patient\u27s survey response ( gold standard ) with the 1) medical chart and 2) administrative health care claims data. We computed the sensitivity, specificity, positive predictive value (PPV), and negative predictive value, along with their 95 percent confidence intervals, for each clinical data source. Results: Eighty percent of patients responded. Nearly all participants (95 percent) reported experiencing at least one symptom from the MDASI-HN, and 93 percent reported two or more symptoms. Among patients reporting a given symptom, there was generally no evidence of the symptom from either clinical data source (i.e., sensitivity was generally no greater than 40 percent). The specificity and PPV of the clinical data sources were generally higher than the sensitivity. Conclusion: Relying only on medical chart review and/or administrative health data would substantially underestimate symptom burden in long-term head and neck cancer survivors

Predicting suicide attempts and suicide deaths among adolescents following outpatient visits

BACKGROUND: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation

Multiple Myeloma Data in the Cancer Research Network

Background/Aims: Multiple myeloma is a rare plasma cell cancer that disproportionately affects men and blacks/African-Americans. Research supported by initiatives such as the Cancer Research Network (CRN) is needed to advance knowledge about this uncommon but increasingly prevalent disease. The CRN is a consortium funded by the National Cancer Institute to support cancer research among a subset of HCSRN sites. The data collected and maintained by CRN sites facilitate a variety of multiple myeloma research opportunities. Methods: Data from eight funded and three affiliate CRN sites were included in this analysis. Counts and characteristics of multiple myeloma diagnoses during 2003–2012 were obtained from the CRN Cancer Counter, an informatics tool that provides aggregate data to assist in study planning. Site-specific descriptive analyses included annual counts of incident cases as well as demographic distributions. Updated counts, enrollee retention metrics and cancer treatment information will be obtained from each site’s Virtual Data Warehouse in early 2016 via a centrally developed SAS software program. Results: During 2003–2012, 5,137 multiple myeloma cases were diagnosed among 11 CRN sites. Annual diagnoses increased steadily from 405 cases in 2003 to 610 in 2012. The majority of diagnoses (61%; n = 3,117) occurred among persons age 65 or older, although that percentage ranged from 37% to 70% across sites. Males accounted for 57% (n = 2,920) of diagnoses with a range of 49–63% across sites. Blacks/African-Americans comprised 13% (n = 686) of total diagnoses, although two sites contributed \u3e 80% of those cases. Among persons with known ethnicity, Hispanics accounted for 7% (n = 289) of diagnoses, with one site contributing nearly 80% of those cases. Conclusion: The CRN supports the infrastructure to maintain high-quality data (from tumor registries as well as internal health plan systems) on patients diagnosed with cancer at participating sites. Furthermore, the multisite CRN environment allows for the study of rare cancers among a large, demographically diverse patient population. Thus, the CRN provides a setting that is well suited for research in what is potentially one of the largest multiple myeloma cohorts with longitudinal data

Cardiovascular medication use and risks of colon cancer recurrences and additional cancer events: a cohort study

Abstract Background Cardiovascular medications may be associated with cancer development, but little is known about their association with cancer recurrence. Medications such as statins and antihypertensives may be commonly used among colon cancer survivors, who are, on average, diagnosed in their mid-60s. We described the associations between statins and antihypertensive medications and colon cancer recurrence in a large, population-based study. Methods We conducted a cohort study among adults with stage I-IIIA colon cancer diagnosed in 1995–2014 in two Kaiser Permanente regions, Colorado and Washington. Statin and antihypertensive use were obtained from electronic pharmacy dispensing data. People were classified as medication users on the date of their first dispensing after cohort entry, which started 90 days after completing cancer treatment, continuing through the earliest of death, health plan disenrollment, or chart abstraction. We collected outcome information from medical record abstraction and tumor registries on colon cancer recurrences and second primary cancers. Using Cox proportional hazards multivariable models, we estimated hazard ratios (HRs) with 95% confidence intervals (CIs) for colon cancer recurrences and any cancer event (recurrences and new primaries at any anatomic site) comparing medication users to non-users. Results Among 2039 people, 937 (46%) used statins and 1425 (70%) used antihypertensives at any point during a median of 4.9 years of follow-up; 460 people had any additional cancer event, including 152 with a colon cancer recurrence. Statin use was not associated with colon cancer recurrence (HR = 1.09, 95%CI = 0.65–1.85) or any cancer event (HR = 1.12, 95%CI = 0.85–1.47), nor was antihypertensive use associated with recurrence (HR = 0.73, 95%CI = 0.44–1.21) or any cancer event (HR = 0.93, 95%CI = 0.70–1.24). Conclusions Our results suggest no association between cardiovascular medication use and the risk of recurrence or any additional cancer, and may provide reassurance to colon cancer survivors

Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data

BACKGROUND: Suicide risk prediction models have been developed by using information from patients\u27 electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data. OBJECTIVES: A set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models\u27 performance with contemporary data (2014-2017) from these systems. METHODS: We evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value. RESULTS: Models were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860-0.864) and 0.864 (95% CI: 0.860-0.869) for suicide attempt, and 0.806 (95% CI: 0.790-0.822) and 0.804 (95% CI: 0.782-0.829) for suicide death. CONCLUSION: Performance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today

Evaluating and Improving Cancer Screening Process Quality in a Multilevel Context: The PROSPR II Consortium Design and Research Agenda

BACKGROUND: Cancer screening is a complex process involving multiple steps and levels of influence (e.g., patient, provider, facility, health care system, community, or neighborhood). We describe the design, methods, and research agenda of the Population-based Research to Optimize the Screening Process (PROSPR II) consortium. PROSPR II Research Centers (PRC), and the Coordinating Center aim to identify opportunities to improve screening processes and reduce disparities through investigation of factors affecting cervical, colorectal, and lung cancer screening in U.S. community health care settings. METHODS: We collected multilevel, longitudinal cervical, colorectal, and lung cancer screening process data from clinical and administrative sources on \u3e9 million racially and ethnically diverse individuals across 10 heterogeneous health care systems with cohorts beginning January 1, 2010. To facilitate comparisons across organ types and highlight data breadth, we calculated frequencies of multilevel characteristics and volumes of screening and diagnostic tests/procedures and abnormalities. RESULTS: Variations in patient, provider, and facility characteristics reflected the PROSPR II health care systems and differing target populations. PRCs identified incident diagnoses of invasive cancers, in situ cancers, and precancers (invasive: 372 cervical, 24,131 colorectal, 11,205 lung; in situ: 911 colorectal, 32 lung; precancers: 13,838 cervical, 554,499 colorectal). CONCLUSIONS: PROSPR II\u27s research agenda aims to advance: (i) conceptualization and measurement of the cancer screening process, its multilevel factors, and quality; (ii) knowledge of cancer disparities; and (iii) evaluation of the COVID-19 pandemic\u27s initial impacts on cancer screening. We invite researchers to collaborate with PROSPR II investigators. IMPACT: PROSPR II is a valuable data resource for cancer screening researchers