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Abstract P5-16-01: Is breast reconstruction safe in women over 70? An analysis of the national surgical quality improvement program (NSQIP) database

2019· article· en· W2919532882 on OpenAlexaff
F Angarita, DR McCready, Tulin Cil

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineBreast reconstructionBreast cancerMastectomySurgeryDemographicsDatabaseLymph nodeCancerInternal medicineDemography

Abstract

fetched live from OpenAlex

Abstract Background:Less than 14% of older women undergo post-mastectomy breast reconstruction. A major reason for the low rate is the concern about post-operative complications. A thorough analysis of surgical complications by age group is limited in previous studies. The aim of this study is to determine the surgical complication rates of older women (≥70 years old) with breast cancer who underwent breast reconstruction and compare them to younger women (18–69 years old). Methods: Data from the National Surgical Quality Improvement Program (NSQIP) database were used to identify women with carcinoma in situ and invasive breast cancer who underwent delayed or immediate breast reconstruction (2005-2016). The primary outcome was 30-day post-operative surgical complications; the secondary outcome was 30-day mortality.Patient demographics, comorbidities, and 30-day postoperative complications and mortality rates were compared across age groups and each type of reconstruction. Results: Of 42,929 women who underwent breast reconstruction, 2,615 (6.1%) were older women. Although compared to young women, older women were more likely to have medical comorbidities their American Society of Anesthesiologists' (ASA) classification was lower.Tumor histology distribution was similar in both groups. Lymph node surgery and neoadjuvant chemotherapy was significantly less frequent among older women. Compared to young women, older women more frequently underwent immediate breast reconstruction (IBR) [n=2,405 (92%) versus n=33,580 (88.3%), p<0.0001] but less frequently underwent delayed breast reconstruction [n=209 (8%) versus n=4,734 (11.7%), p<0.0001]. Prosthesis-based reconstruction was the most common technique in both age groups. Autologous reconstruction was significantly less common amongst older women than young women [n=517 (19.8%) versus n=10,011 (24.8%), p<0.0001]. Older women experienced higher rates of superficial surgical site infection (SSI) [n=69 (2.6%) versus n=716, (1.8%), p=0.002] and urinary tract infection [n=15 (0.6%) versus n=101 (0.3%) p =0.005]. However, the rates of deep SSI, dehiscence, pneumonia, thromboembolism, renal complications, cardiac events, and sepsis were similar between both groups. Older women had significantly lower rates of events of bleeding requiring transfusion [n=27 (1%) versus n=736 (1.8%), p=0.002] and flap failure [n=2 (0.4%) versus n=210 (2.1%), p=0.006). Return to the operating room within 30-days was similar between older and young women [n=171 (6.5%) versus n=2,821 (7.0%,) p=0.4]. Thirty-day deaths were rare events [older n=3 (0.1%) and young n=10 (0.02%), p=0.05]. Conclusions: Overall, 30-day postoperative complications in older women who undergo breast reconstruction are extremely low. Infection rates were slightly higher in the older group however; severe complications such as flap failure, bleeding, reoperation, and death were more common in young women. Age alone did not confer an increased risk of complications after breast reconstruction. Breast reconstruction can be safely offered to older women undergoing breast cancer treatment. Citation Format: Angarita FA, McCready DR, Cil T. Is breast reconstruction safe in women over 70? An analysis of the national surgical quality improvement program (NSQIP) database [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-16-01.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.441
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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