Mortality in Older Patients with Breast Cancer Undergoing Breast Surgery: How Low is “Low Risk”?

Dillon, Jacquelyn; Thomas, Samantha M.; Rosenberger, Laura H.; DiLalla, Gayle; Menendez, Carolyn S.; Hwang, E. Shelley; Plichta, Jennifer K.

doi:10.1245/s10434-021-10502-3

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…A personalized patient‐centred approach is essential in older women to ensure all patients who are fit for a BR and would like to access a BR are able to make a well‐informed decision. In the treatment planning phase, a patient's concerns and beliefs, breast cancer treatment options, including BR (both implant‐based and autologous), existing medical comorbidities and associated risk, should be discussed and guide surgical decision‐making among older women 27,31,32 . Older women may avoid breast cancer surgery for a variety of individual beliefs.…”

Section: Discussionmentioning

confidence: 99%

“…In the treatment planning phase, a patient's concerns and beliefs, breast cancer treatment options, including BR (both implant-based and autologous), existing medical comorbidities and associated risk, should be discussed and guide surgical decision-making among older women. 27,31,32 Older women may avoid breast cancer surgery for a variety of individual beliefs. It is necessary to recognize patient values and provide all relevant information for a patient to make a well-informed healthcare decision.…”

Section: Discussionmentioning

confidence: 99%

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Trends and variations in post‐mastectomy breast reconstruction rates in Australia over 10 years

Dayaratna

Nguyen

Spillane

et al. 2023

ANZ Journal of Surgery

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Background Offering breast reconstruction (BR) at the time of mastectomy is standard of care in Australia with proven quality‐of‐life benefits. Previously BR rates in Australia have been low compared to similar countries. Accurate up‐to‐date information is needed to promote equity in access to BR and inform future planning of services. This study analysed recent trends and variations of BR uptake in Australia. Method Data from the BreastSurgANZ Quality Audit (BQA) were used to identify patients who underwent mastectomy with or without reconstruction for invasive or in situ breast carcinoma from 2010 to 2019. The association between BR uptake and the variables of jurisdiction (state or territory), age, hospital type and remoteness, and remoteness of patients' home addresses were analysed. Results A total 41 880 women underwent mastectomy between 2010 to 2019. The national BR rate steadily increased from 12.8% in 2010 to 29% in 2019, with a 10‐year national average of 21.3%. Statistically significant differences in BR uptake (P < 0.001) were found between states with higher rates in New South Wales and Victoria, with BR more likely in private hospitals and in younger women (P < 0.001), and less likely in remote areas (P < 0.001). Conclusion The Australian BR rate has increased over the 10‐year period, but significant variation still exists between states. BR is lower in older women and those living in regional and remote areas. While the steady increase in BR uptake is encouraging, barriers that exist to equitable provision of reconstructive surgical services for all women living with breast cancer still need to be corrected.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Trends and variations in post‐mastectomy breast reconstruction rates in Australia over 10 years

Dayaratna

Nguyen

Spillane

et al. 2023

ANZ Journal of Surgery

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“…Predicted survival benefits, disease progress risk, effect on anticancer therapy toxicity, life expectancy, quality of life, and patient preferences should be considered carefully when making decision for older BC patients. The treatment decision making of breast cancer in older individuals should involve geriatric assessment and survival estimates 47–50 . Our model can provide the BC metastasis risk to contribute to make decision in the therapy of older BC patients.…”

Section: Discussionmentioning

confidence: 99%

“…The treatment decision making of breast cancer in older individuals should involve geriatric assessmentF I G U R E 7 Receiver operating characteristic curve depicting performance of the developed random survival forest prognostic model in the validation set.F I G U R E 8 Kaplan-Meier curves for metastasis-free survival for the validation set. and survival estimates [47][48][49][50]. Our model can provide the BC metastasis risk to contribute to make decision in the therapy of older BC patients.…”

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confidence: 99%

A novel machine learning prediction model for metastasis in breast cancer

Li,

Liu,

Long

et al. 2024

Cancer Reports

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BackgroundBreast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models.AimWe developed a model to predict BC metastasis using the random survival forest (RSF) method.MethodsBased on demographic data and routine clinical data, we used RSF‐recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan–Meier survival (KM) analyses were plotted to validate the predictive effect when C‐index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model.ResultsWe developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF‐recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C‐indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model.ConclusionsThis model relies on routine data and examination indicators in real‐time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.

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“…Another interesting study was by Lemij et al, which reports on a model to predict the development of postoperative complications [ 17 ], a problem that is especially pertinent to the surgical management of older patients with breast cancer, although mortality from breast cancer related surgery is generally quite low [ 35 ]. However, this study had a high ROB due to participant selection.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Tools for Older Women with Breast Cancer: A Systematic Review

Record,

Chanenchuk,

Parrish

et al. 2023

Medicina

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Background: Breast cancer is the most common cancer in women, and older patients comprise an increasing proportion of patients with this disease. The older breast cancer population is heterogenous with unique factors affecting clinical decision making. While many models have been developed and tested for breast cancer patients of all ages, tools specifically developed for older patients with breast cancer have not been recently reviewed. We systematically reviewed prognostic models developed and/or validated for older patients with breast cancer. Methods: We conducted a systematic search in 3 electronic databases. We identified original studies that were published prior to 8 November 2022 and presented the development and/or validation of models based mainly on clinico-pathological factors to predict response to treatment, recurrence, and/or mortality in older patients with breast cancer. The PROBAST was used to assess the ROB and applicability of each included tool. Results: We screened titles and abstracts of 7316 records. This generated 126 studies for a full text review. We identified 17 eligible articles, all of which presented tool development. The models were developed between 1996 and 2022, mostly using national registry data. The prognostic models were mainly developed in the United States (n = 7; 41%). For the derivation cohorts, the median sample size was 213 (interquartile range, 81–845). For the 17 included modes, the median number of predictive factors was 7 (4.5–10). Conclusions: There have been several studies focused on developing prognostic tools specifically for older patients with breast cancer, and the predictions made by these tools vary widely to include response to treatment, recurrence, and mortality. While external validation was rare, we found that it was typically concordant with interval validation results. Studies that were not validated or only internally validated still require external validation. However, most of the models presented in this review represent promising tools for clinical application in the care of older patients with breast cancer.

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Mortality in Older Patients with Breast Cancer Undergoing Breast Surgery: How Low is “Low Risk”?

Cited by 5 publications

References 21 publications

Trends and variations in post‐mastectomy breast reconstruction rates in Australia over 10 years

Trends and variations in post‐mastectomy breast reconstruction rates in Australia over 10 years

A novel machine learning prediction model for metastasis in breast cancer

Prognostic Tools for Older Women with Breast Cancer: A Systematic Review

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