Search

Simple Summary: The breast cancer diagnostic process is a stressful period for patients. We looked at the length of the diagnostic interval within and across five Canadian provinces: British Columbia, Alberta, Manitoba, Ontario, and Nova Scotia. Our analysis was conducted separately for those who had their cancer detected through the respective provincial screening program versus those outside of the provincial screening program (symptom-detected). The diagnostic interval was shorter for patients who had their cancer detected through the screening program. Interprovincial diagnostic interval variation was 17 and 16 days for screen- and symptom-detected patients, respectively, at the median, and 14 and 41 days, respectively, at the 90th percentile. The diagnostic interval was longer for those with more comorbid disease among the symptom-detected group. Screen-detected patients living in rural areas also had a longer diagnostic interval. Having a regular primary care provider was not associated with a shorter diagnostic interval. The cancer diagnostic process can be protracted, and it is a time of great anxiety for patients. The objective of this study was to examine inter- and intra-provincial variation in diagnostic intervals and explore factors related to the variation. This was a multi-province retrospective cohort study using linked administrative health databases. All females with a diagnosis of histologically confirmed invasive breast cancer in British Columbia (2007–2010), Manitoba (2007–2011), Ontario (2007–2010), Nova Scotia (2007–2012), and Alberta (2004–2010) were included. The start of the diagnostic interval was determined using algorithms specific to whether the patient's cancer was detected through screening. We used multivariable quantile regression analyses to assess the association between demographic, clinical and healthcare utilization factors with the diagnostic interval outcome. We found significant inter- and intra-provincial variation in the breast cancer diagnostic interval and by screen-detection status; patients who presented symptomatically had longer intervals than screen-detected patients. Interprovincial diagnostic interval variation was 17 and 16 days for screen- and symptom-detected patients, respectively, at the median, and 14 and 41 days, respectively, at the 90th percentile. There was an association of longer diagnostic intervals with increasing comorbid disease in all provinces in non-screen-detected patients but not screen-detected. Longer intervals were observed across most provinces in screen-detected patients living in rural areas. Having a regular primary care provider was not associated with a shorter diagnostic interval. Our results highlight important findings regarding the length of the breast cancer diagnostic interval, its variation within and across provinces, and its association with comorbid disease and rurality. We conclude that diagnostic processes can be context specific, and more attention should be paid to developing tailored processes so that equitable access to a timely diagnosis can be achieved. [ABSTRACT FROM AUTHOR]

Cancer recurrence is the diagnosis of a second clinical episode of cancer after the first was considered cured. Identifying patients who had experienced cancer recurrence is an important task as it can be used to compare treatment effectiveness, measure recurrence-free survival, and plan and prioritize cancer control resources. We developed BERT-based natural language processing (NLP) contextual models for identifying cancer recurrence incidence and the recurrence time based on the records in progress notes. Using two datasets containing breast and colorectal cancer patients, we demonstrated the advantage of the contextual models over the traditional NLP models by overcoming the laborious and often unscalable tasks of composing keywords in a specific disease domain. [ABSTRACT FROM AUTHOR]

Background: Algorithms that use administrative health and electronic medical record (EMR) data to determine cancer recurrence have the potential to replace chart reviews. This study evaluated algorithms to determine breast and colorectal cancer recurrence in a Canadian province with a universal health care system.Methods: Individuals diagnosed with stage I-III breast or colorectal cancer diagnosed from 2004 to 2012 in Manitoba, Canada were included. Pre-specified and conditional inference tree algorithms using administrative health and structured EMR data were developed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) correct classification, and scaled Brier scores were measured.Results: The weighted pre-specified variable algorithm for the breast cancer validation cohort (N = 1181, 167 recurrences) demonstrated 81.1% sensitivity, 93.2% specificity, 61.4% PPV, 97.4% NPV, 91.8% correct classification, and scaled Brier score of 0.21. The weighted conditional inference tree algorithm demonstrated 68.5% sensitivity, 97.0% specificity, 75.4% PPV, 95.8% NPV, 93.6% correct classification, and scaled Brier score of 0.39. The weighted pre-specified variable algorithm for the colorectal validation cohort (N = 693, 136 recurrences) demonstrated 77.7% sensitivity, 92.8% specificity, 70.7% PPV, 94.9% NPV, 90.1% correct classification, and scaled Brier score of 0.33. The conditional inference tree algorithm demonstrated 62.6% sensitivity, 97.8% specificity, 86.4% PPV, 92.2% NPV, 91.4% correct classification, and scaled Brier score of 0.42.Conclusions: Algorithms developed in this study using administrative health and structured EMR data to determine breast and colorectal cancer recurrence had moderate sensitivity and PPV, high specificity, NPV, and correct classification, but low accuracy. The accuracy is similar to other algorithms developed to classify recurrence only (i.e., distinguished from second primary) and inferior to algorithms that do not make this distinction. The accuracy of algorithms for determining cancer recurrence only must improve before replacing chart reviews. [ABSTRACT FROM AUTHOR]

Background: Variation in breast cancer surgical practice patterns can lead to poor clinical outcomes. It is important to measure and reduce variation to ensure all women diagnosed with breast cancer receive equitable, high-quality care. A population-based assessment of the variation in breast cancer surgery treatment and quality has never been conducted in Manitoba. The objective of this study was to assess the variation in surgical treatment patterns, quality of care, and post-operative outcomes for women diagnosed with invasive breast cancer. Methods: This descriptive study used data from the Manitoba Cancer Registry, Hospital Discharge Abstracts Database, Medical Claims, Manitoba Health Insurance Registry, and Statistics Canada. The study included women in Manitoba aged 20+ and diagnosed with invasive breast cancer between 1 January 2010 and 31 December 2014. Results: Axillary lymph node dissection (ALND) for node-negative disease ranged from 11.8% to 33.3%, timeliness (surgery within 30 days of consult) ranged from 33.3% to 60.2%, and re-excision ranged from 14.7% to 24.6% between health authorities. Women who underwent breast-conserving surgery had the shortest median length of stay and women who underwent mastectomy with immediate reconstruction had the longest median length of stay. In-hospital post-operative complications were higher among women who received mastectomy with immediate reconstruction (9.9%). Conclusion: Variation in surgical treatment, quality, and outcomes exist in Manitoba. The findings from this study can be used to inform cancer service delivery planning, quality improvement efforts, and policy development. Influencing data-driven change at the health system level is paramount to ensuring Manitobans receive the highest quality of care. [ABSTRACT FROM AUTHOR]