Adverse health effects after breast cancer up to 14 years after diagnosis

BACKGROUND: The number of breast cancer survivors increases, but information about long-term adverse health effects in breast cancer survivors is sparse. We aimed to get an overview of the health effects for which survivors visit their general practitioner up to 14 years after diagnosis. METHODS: We retrieved data on 11,671 women diagnosed with breast cancer in 2000–2016 and 23,242 age and sex matched controls from the PSCCR-Breast Cancer, a database containing data about cancer diagnosis, treatment and primary healthcare. We built Cox regression models for 685 health effects, with time until the health effect as the outcome and survivor/control and cancer treatment as predictors. Models were built separately for four age groups (aged 18/44, 45/59, 60/74 and 75/89) and two follow-up periods (1/4 and 5/14 years after diagnosis). RESULTS: 229 health effects occurred statistically significantly more often in survivors than in controls (p < 0.05). Health effects varied by age, time since diagnosis and treatment, but coughing, respiratory and urinary infections, fatigue, sleep problems, osteoporosis and lymphedema were statistically significantly increased in breast cancer survivors. Osteoporosis and chest symptoms were associated with hormone therapy; respiratory and skin infections with chemotherapy and lymphedema and skin infections with axillary dissection. CONCLUSIONS: Breast cancer survivors may experience numerous adverse health effects up to 14 years after diagnosis. Insight in individual risks may assist healthcare professionals in managing patient expectations and improve monitoring, detection and treatment of adverse health effects

Cancer risk in DES daughters

We examined long-term risk of cancer in women exposed to diethylstilbestrol (DES) in utero. A total of 12,091 DES-exposed women in the Netherlands were followed prospectively from December 1992 till June 2008. Cancer incidence was assessed through linkage with the Dutch pathology database (PALGA) and the Netherlands Cancer Registry and compared with the Dutch female population. A total of 348 medically verified cancers occurred; median age at end of follow-up was 44.0 years. No overall increased risk of cancer was found (standardized incidence ratio [SIR] = 1.01; 95% confidence interval [CI] = 0.91, 1.13). The risk of clear cell adenocarcinoma of the vagina and cervix (CCA) was statistically significantly increased (SIR = 24.23; 95% CI = 8.89, 52.74); the elevated risk persisted above 40 years of age. The risk of melanoma diagnosed before age 40 was increased (SIR = 1.59; 95% CI = 1.08, 2.26). No excess risks were found for other sites, including breast cancer. Except for an elevated risk of CCA, persisting at older ages, and an increased risk of melanoma at young ages, we found no increased risk of cancer. Longer follow-up is warranted to examine cancer risk at ages when cancer occurs more frequently

Long-term Survival in Breast Cancer Patients Is Associated with Contralateral Parenchymal Enhancement at MRI: Outcomes of the SELECT Study

Background Several single-center studies found that high contralateral parenchymal enhancement (CPE) at breast MRI was associated with improved long-term survival in patients with estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Due to varying sample sizes, population characteristics, and follow-up times, consensus of the association is currently lacking. Purpose To confirm whether CPE is associated with long-term survival in a large multicenter retrospective cohort, and to investigate if CPE is associated with endocrine therapy effectiveness. Materials and Methods This multicenter observational cohort included women with unilateral ER-positive HER2-negative breast cancer (tumor size ≤50 mm and ≤three positive lymph nodes) who underwent MRI from January 2005 to December 2010. Overall survival (OS), recurrence-free survival (RFS), and distant RFS (DRFS) were assessed. Kaplan-Meier analysis was performed to investigate differences in absolute risk after 10 years, stratified according to CPE tertile. Multivariable Cox proportional hazards regression analysis was performed to investigate whether CPE was associated with prognosis and endocrine therapy effectiveness. Results Overall, 1432 women (median age, 54 years [IQR, 47-63 years]) were included from 10 centers. Differences in absolute OS after 10 years were stratified according to CPE tertile as follows: 88.5% (95% CI: 88.1, 89.1) in tertile 1, 85.8% (95% CI: 85.2, 86.3) in tertile 2, and 85.9% (95% CI: 85.4, 86.4) in tertile 3. CPE was independently associated with OS, with a hazard ratio (HR) of 1.17 (95% CI: 1.0, 1.36; P = .047), but was not associated with RFS (HR, 1.11; P = .16) or DRFS (HR, 1.11; P = .19). The effect of endocrine therapy on survival could not be accurately assessed; therefore, the association between endocrine therapy efficacy and CPE could not reliably be estimated. Conclusion High contralateral parenchymal enhancement was associated with a marginally decreased overall survival in patients with estrogen receptor-positive and human epidermal growth factor receptor 2-negative breast cancer, but was not associated with recurrence-free survival (RFS) or distant RFS. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Honda and Iima in this issue

Abstract P3-03-18: M1 registration: Signaling patients who develop metachronous metastases after primary breast cancer

Abstract Background At the moment, over 180,000 breast cancer survivors are living in the Netherlands and survival after a breast cancer diagnosis and treatment is still increasing. Therefore, the number of survivors is rising, and these patients are at risk of metachronous metastases. Data at a national level on the development of metachronous metastases are limited, as this is not registered in the Netherlands Cancer Registry on a regular basis. Due to the vast amount of survivors, it is not feasible to actively monitor all patients to signal and register metachronous metastases. Applying machine learning may be an option to overcome this issue. The aim of our study is to develop a M1 detection algorithm based on hospital data to signal patients who developed metachronous metastases after their primary breast cancer treatment. Methods The Netherlands Cancer Registry (NCR) collects data on all individuals newly diagnosed with cancer in one of the 76 hospitals in the Netherlands since 1989. Dutch Hospital Data (DHD) collects and processes data from hospitals, including data on diagnosis and treatment. DHD data from 2019-2020 were linked to the NCR using a probabilistic matching method. We matched on date of birth, gender, diagnosis, postal code, hospital and patient ID. Column values that matched were weighted inversely proportional to the respective column’s probability distribution, where a match on a rare column value (e.g. postal codes with a relatively small population) increased the probability that the match was correct. Scores for each column were combined and patients with high matching scores were included in the algorithm development, validation and deployment. Actively signaled and manually registered data on metastases were available for subgroups of breast cancer patients included in previous studies (‘the golden standard’). First, 80% of these data was used to train the model, 20% was used to validate the model. Second, a pilot study was performed in which patients files were checked for 928 patients, sampled with variance in prediction probability, to evaluate a diverse range of cases. Results We included 4,395 patients. Variables that were included to predict metastases were i.e. specific medication for metastatic disease (palbociblib), counselling for metastatic breast cancer, Carcinoembryonic Antigen test, a confirmed diagnosis of metastases, and number of patient visits. The first validation step (including 20% of known data) showed that the model had a precision of 0.91 to predict metachronous metastases, 0.93 to predict free of metastases. The pilot study confirmed that a higher prediction probability of &amp;gt;0.8 correlated with a higher chance that a patient has metachronous metastases. However, false positive predictions did occur. Conclusion We developed a M1 detection algorithm to signal patients with metachronous metastases after breast cancer treatment on a national level. With this algorithm we are one step closer to identify all patients with metachronous metastases and to reach a complete registration of all breast cancer metastases reusing existing data sources. After review of patients with a high prediction probability, the model will be re-trained using these data and updated data from DHD. Citation Format: Linda de Munck, Daan Knoors, Harm Buisman, Koen Scholman, Janneke Verloop, Sabine Siesling. M1 registration: Signaling patients who develop metachronous metastases after primary breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-18.</jats:p

Opportunities and obstacles in linking large health care registries: the primary secondary cancer care registry - breast cancer

Abstract Background The growing volume of health data provides new opportunities for medical research. By using existing registries, large populations can be studied over a long period of time. Patient-level linkage of registries leads to even more detailed and extended information per patient, but brings challenges regarding responsibilities, privacy and security, and quality of data linkage. In this paper we describe how we dealt with these challenges when creating the Primary Secondary Cancer Care Registry (PSCCR)- Breast Cancer. Methods The PSCCR – Breast Cancer was created by linking two existing registries containing data on 1) diagnosis, tumour and treatment characteristics of all Dutch breast cancer patients (NCR), and 2) consultations and diagnoses from primary care electronic health records of about 10% of Dutch GP practices (Nivel-PCD). The existing registry governance structures and privacy regulations were incorporated in those of the new registry. Privacy and security risks were reassessed. Data were restricted to females and linked using postal code and date of birth. The breast cancer diagnosis was verified in both registries and for a subsample of 44 patients with the GP as well. Results A collaboration agreement was signed in which the organisations retained data responsibility and accountability for ‘their’ registry. A Trusted Third Party performed the record linkage. Ten percent of the patients with breast cancer could be linked to the primary care registry, as was expected based on the coverage of Nivel-PCD, and finally 7 % could be included. The breast cancer diagnosis was verified by the GP in 42 of the 44 patients. Conclusions We developed and validated a procedure for patient-level linkage of health data registries without a unique identifier, while preserving the integrity and privacy of the original registries. The method described may help researchers wishing to link existing health data registries. </jats:sec

Opportunities and obstacles in linking large health care registries: the primary secondary cancer care registry - breast cancer

Background: The growing volume of health data provides new opportunities for medical research. By using existing registries, large populations can be studied over a long period of time. Patient-level linkage of registries leads to even more detailed and extended information per patient, but brings challenges regarding responsibilities, privacy and security, and quality of data linkage. In this paper we describe how we dealt with these challenges when creating the Primary Secondary Cancer Care Registry (PSCCR)- Breast Cancer. Methods: The PSCCR – Breast Cancer was created by linking two existing registries containing data on 1) diagnosis, tumour and treatment characteristics of all Dutch breast cancer patients (NCR), and 2) consultations and diagnoses from primary care electronic health records of about 10% of Dutch GP practices (Nivel-PCD). The existing registry governance structures and privacy regulations were incorporated in those of the new registry. Privacy and security risks were reassessed. Data were restricted to females and linked using postal code and date of birth. The breast cancer diagnosis was verified in both registries and for a subsample of 44 patients with the GP as well. Results: A collaboration agreement was signed in which the organisations retained data responsibility and accountability for ‘their’ registry. A Trusted Third Party performed the record linkage. Ten percent of the patients with breast cancer could be linked to the primary care registry, as was expected based on the coverage of Nivel-PCD, and finally 7 % could be included. The breast cancer diagnosis was verified by the GP in 42 of the 44 patients. Conclusions: We developed and validated a procedure for patient-level linkage of health data registries without a unique identifier, while preserving the integrity and privacy of the original registries. The method described may help researchers wishing to link existing health data registries