Development and validation of a risk stratification model for immediate microvascular breast reconstruction

Operative Efficiency in Deep Inferior Epigastric Perforator Flap Reconstruction: Key Concepts and Implementation

The deep inferior epigastric perforator flap has become one of the most popular approaches for autologous breast reconstruction after mastectomy. As much of health care has moved to a value-based approach, reducing complications, operative time, and length of stay in deep inferior flap reconstruction is becoming increasingly important. In this article, we discuss important preoperative, intraoperative, and postoperative considerations to maximize efficiency when performing autologous breast reconstruction and offer tips on how to handle certain challenges.

Individualized Risk Prediction Tool for Serious Wound Complications After Mastectomy With and Without Immediate Reconstruction

BACKGROUND

A greater proportion of patients with surgical risk factors are undergoing immediate breast reconstruction after mastectomy, resulting in the need for better risk prediction to inform decisions about the procedure. The objective of this study was to leverage clinical data to restructure a previously developed risk model to predict serious infectious and noninfectious wound complications after mastectomy alone and mastectomy plus immediate reconstruction for use during a surgical consultation.

METHODS

The study established a cohort of women age 21 years or older treated with mastectomy from 1 July 2010 to 31 December 2015 using electronic health records from two hospitals. Serious infectious and non-infectious wound complications, defined as surgical-site infection, dehiscence, tissue necrosis, fat necrosis requiring hospitalization, or surgical treatment, were identified within 180 days after surgery. Risk factors for serious wound complications were determined using modified Poisson regression, with discrimination and calibration measures. Bootstrap validation was performed to correct for overfitting.

RESULTS

Among 2159 mastectomy procedures, 1410 (65.3%) included immediate implant or flap reconstruction. Serious wound complications were identified after 237 (16.8%) mastectomy-plus-reconstruction and 30 (4.0%) mastectomy-only procedures. Independent risk factors for serious wound complications included immediate reconstruction, bilateral mastectomy, higher body mass index, depression, and smoking. The optimism-corrected C statistic of the risk prediction model was 0.735.

CONCLUSIONS

Immediate reconstruction, bilateral mastectomy, obesity, depression, and smoking were significant risk factors for serious wound complications in this population of women undergoing mastectomy. Our risk prediction model can be used to counsel women before surgery concerning their individual risk of serious wound complications after mastectomy.

"In Free Flap Autologous Breast Reconstruction Frailty Is a More Accurate Predictor of Postoperative Complications than Age, Body Mass Index, or ASA class: A Retrospective Cohort Analysis on the ACS-NSQIP Database."

BACKGROUND

Free flap autologous breast reconstruction (f-ABR) improves quality of life in cancer survivors but has a 5-47% higher postoperative complication (PCs) rate in vulnerable patients, such as those with obesity or the elderly. Given the high (respectively: 43% and 16%) and rising prevalence of these conditions, operative risk prediction is critical to guide targeted care. Age, BMI, and ASA class have shown inaccuracies as predictive factors of PCs in f-ABR. Since frailty, a measure of vulnerability, was reported to be a reliable predictor of PCs in multiple other surgical fields, we hypothesized that it would be an accurate predictor of PCs also in f-ABR.

METHODS

Patients undergoing f-ABR (CPT: 19364) were identified using the ACS-NSQIP (American College of Surgeons-National Surgical Quality Improvement Program) database (01/2010-12/2018). Frailty was calculated using the validated modified Frailty Index (mFI). Rates of wound complications, bleeding episodes, readmissions, returns to operating room (ROR), and DVTs were compared across mFI score, BMI, age, and ASA class.

RESULTS

mFI ≥ 2 was associated with 22.22% (p <0.001) wound complications; 15.79% (p <0.001) bleeding episodes; 8.20% (p <0.001) readmissions; 17.19% (p <0.001) ROR; and 1.81% (p <0.05) DVTs. Higher BMI, age, and ASA class did not significantly correlate with increased rates in one or more PCs. Only a high mFI was consistently associated with significantly higher odds of complications in all complication types.

CONCLUSIONS

As a reliable and accurate predictor of PCs in f-ABR, frailty could be used preoperatively to counsel patients and guide surgical care.

An Ounce of Prediction is Worth a Pound of Cure: Risk Calculators in Breast Reconstruction

Preoperative risk calculators provide individualized risk assessment and stratification for surgical patients. Recently, several general surgery–derived models have been applied to the plastic surgery patient population, and several plastic surgery–specific calculators have been developed. In this scoping review, the authors aimed to identify and critically appraise risk calculators implemented in postmastectomy breast reconstruction.

Predicted versus actual complications in Australian women undergoing post-mastectomy breast reconstruction: a retrospective cohort study using the BRA Score tool

INTRODUCTION

The Breast Reconstruction Risk Assessment (BRA) Score tool is a risk calculator developed to predict the risk of complications in individual patients undergoing breast reconstruction. It was developed in a North American population exclusively undergoing immediate breast reconstruction. This study sought to assess the predictions of the BRA Score tool against the measured outcomes of surgery for an Australian public hospital population, including both immediate and delayed reconstructions.

METHOD

This was a retrospective cohort study of data from women at a single Australian public teaching hospital unit. Data from the Flinders Breast Reconstruction Database was retrieved and compared to BRA Scores calculated for each patient. Receiver operating curve area under the curve analysis was performed as well as Brier scores to compare predicted versus observed complications.

RESULTS

BRA Score predictions were reasonable or good (C-statistic >0.7, Brier score <0.09) for the complications of overall surgical complications, surgical site infection (SSI) and seroma at 30 days, and similarly accurate for prediction of the same complications for implant reconstructions at 12 months. There were similar findings between delayed and immediate reconstructions.

CONCLUSION

The BRA Score risk calculator is valid to detect some risks in both patients undergoing immediate and delayed breast reconstruction in an Australian public hospital setting. SSI is the best predicted complication and is well-predicted across both autologous and prosthetic reconstruction types.

Inadequate prediction of postoperative complications in breast cancer surgery: An evaluation of the ACS Surgical Risk Calculator

BACKGROUND

The American College of Surgeon (ACS) Surgical Risk Calculator is an online tool that helps surgeons estimate the risk of postoperative complications for numerous surgical procedures across several surgical specialties.

METHODS

We evaluated the predictive performance of the calculator in 385 cancer patients undergoing breast surgery. Calculator-predicted complication rates were compared with observed complication rates; calculator performance was evaluated using calibration and discrimination analyses.

RESULTS

The mean calculator-predicted rates for any complication (4.1%) and serious complication (3.2%) were significantly lower than the observed rates (11.2% and 5.2%, respectively). The area under the curve was 0.617 for any complication and 0.682 for serious complications. p Values for the Hosmer-Lemeshow test were significant (<.05) for both outcomes. Brier scores were 0.102 for any complication and 0.048 for serious complication.

CONCLUSIONS

The ACS risk calculator is not an ideal tool for predicting individual risk of complications following breast surgery in a Mexican cohort. The most valuable use of the calculator may reside in its role as an aid for patient-led surgery planning. The possibility of introducing breast surgery-specific data could improve the performance of the calculator. Furthermore, a disease-specific calculator could provide more accurate predictions and include complications more frequently found in breast cancer surgery.

Breast reconstruction in the high-risk population: current review of the literature and practice guidelines

Breast reconstruction is an important part of the cancer treatment paradigm and the psychosocial benefits are well described in the literature. Notably, breast reconstruction restores both the functional and emotional losses patients experience due to tumor resection. Post-cancer quality of life is an important benchmark of successful treatment; therefore, breast reconstruction is an essential component that should be offered whenever possible. Over time, reconstructive techniques and outcomes have improved dramatically resulting in better patient safety and decreased operative morbidity. When counseling a patient for surgery, the provider must consider all aspects of a patient's health. Ideally, breast cancer patients should be physically, emotionally, and oncologically appropriate candidates for reconstruction. However, in concerted effort to provide opportunities for as many patients as possible, the definition of who is a good candidate for reconstruction has evolved to include higher risk patients. These patients include those with advanced age, nicotine use, obesity, and significant ptosis. With improvements in surgical procedures and perioperative care, this population may also benefit from restorative surgery. However, the exact risk of complications and necessary counseling has gone largely undefined in this population. This article examines particular "high-risk" groups that may be challenging for extirpative and reconstructive surgeons and offers current guidelines for practice.

Factors that Influence Treatment Delay for Patients with Breast Cancer

INTRODUCTION

The diagnosis or treatment of breast cancer is sometimes delayed. A lengthy delay may have a negative psychological impact on patients. The aim of our study was to evaluate the sociodemographic, clinical and pathological factors associated with delay in the provision of surgical treatment for localised breast cancer, in a prospective cohort of patients.

METHODS

This observational, prospective, multicentre study was conducted in ten hospitals belonging to the Spanish national public health system, located in four Autonomous Communities (regions). The study included 1236 patients, diagnosed through a screening programme or found to be symptomatic, between April 2013 and May 2015. The study variables analysed included each patient's personal history, care situation, tumour history and data on the surgical intervention, pathological anatomy, hospital admission and follow-up. Treatment delay was defined as more than 30 days elapsed between biopsy and surgery.

RESULTS

Over half of the study population experienced surgical treatment delay. This delay was greater for patients with no formal education and among widows, persons not requiring assistance for usual activities, those experiencing anxiety or depression, those who had a high BMI or an above-average number of comorbidities, those who were symptomatic, who did not receive NMR spectroscopy, who presented a histology other than infiltrating ductal carcinoma or who had poorly differentiated carcinomas.

CONCLUSIONS

Certain sociodemographic and clinical variables are associated with surgical treatment delay. This study identifies factors that influence surgical delays, highlighting the importance of preventing these factors and of raising awareness among the population at risk and among health personnel.

External Validation of a Predictive Model for Acute Skin Radiation Toxicity in the REQUITE Breast Cohort

Background: Acute skin toxicity is a common and usually transient side-effect of breast radiotherapy although, if sufficiently severe, it can affect breast cosmesis, aftercare costs and the patient's quality-of-life. The aim of this study was to develop predictive models for acute skin toxicity using published risk factors and externally validate the models in patients recruited into the prospective multi-center REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve QUalITy of lifE in cancer survivors) study.

Methods: Patient and treatment-related risk factors significantly associated with acute breast radiation toxicity on multivariate analysis were identified in the literature. These predictors were used to develop risk models for acute erythema and acute desquamation (skin loss) in three Radiogenomics Consortium cohorts of patients treated by breast-conserving surgery and whole breast external beam radiotherapy (n = 2,031). The models were externally validated in the REQUITE breast cancer cohort (n = 2,057).

Results: The final risk model for acute erythema included BMI, breast size, hypo-fractionation, boost, tamoxifen use and smoking status. This model was validated in REQUITE with moderate discrimination (AUC 0.65), calibration and agreement between predicted and observed toxicity (Brier score 0.17). The risk model for acute desquamation, excluding the predictor tamoxifen use, failed to validate in the REQUITE cohort.

Conclusions: While most published prediction research in the field has focused on model development, this study reports successful external validation of a predictive model using clinical risk factors for acute erythema following radiotherapy after breast-conserving surgery. This model retained discriminatory power but will benefit from further re-calibration. A similar model to predict acute desquamation failed to validate in the REQUITE cohort. Future improvements and more accurate predictions are expected through the addition of genetic markers and application of other modeling and machine learning techniques.