Dataset Information

Pathology Evaluation of Reduction Mammaplasty Specimens and Subsequent Diagnosis of Malignant Breast Disease: A Claims-Based Analysis.

ABSTRACT: BACKGROUND:This study aimed to measure the use of pathology evaluation of breast specimens among patients undergoing reduction mammaplasty and assess rates of new diagnoses of breast disease and associated cost. METHODS:We analyzed the Truven MarketScan Databases from 2009 to 2015 to identify adult female patients undergoing reduction mammaplasty for macromastia. We recorded patient age, rates of obtaining pathology evaluation, new diagnoses of benign or malignant breast disease after pathology evaluation, and total cost for the surgery encounter. RESULTS:Among 17,738 macromastia patients undergoing reduction mammaplasty, 91.3% (n?=?16,193) received pathology evaluation. Pathology evaluation rates were clinically similar across age groups <70 years (90.8-92.1%) and slightly lower for patients ?70 (85.0%). Among 6987 patients less than 40 years who received pathology evaluation, 0.06% (n?=?4) were subsequently diagnosed with malignant breast disease within 3 months, compared to 0.23% in the entire cohort (n?=?37/16,193). Pathology claims resulted in an added $307 (SD 251) on average for the breast reduction surgery encounters. CONCLUSIONS:Breast tissue after reduction mammaplasty is routinely submitted for pathology evaluation, without consideration of age-based risk for breast cancer. Routine pathology evaluation of breast tissue in patients in lower risk age groups (less than 40 years) required an additional $536,000 on average to detect a single occult breast cancer compared to an added $85,600 to detect a new malignancy in patients 40 years and older. Clinicians and policy makers should consider whether routine pathology evaluation of breast tissue should be individualized based on risk factors for breast cancer.

SUBMITTER: Sears ED

PROVIDER: S-EPMC7422755 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Pathology Evaluation of Reduction Mammaplasty Specimens and Subsequent Diagnosis of Malignant Breast Disease: A Claims-Based Analysis.

Sears Erika D ED Lu Yu-Ting YT Chung Ting-Ting TT Momoh Adeyiza O AO Chung Kevin C KC

World journal of surgery 20190601 6

<h4>Background</h4>This study aimed to measure the use of pathology evaluation of breast specimens among patients undergoing reduction mammaplasty and assess rates of new diagnoses of breast disease and associated cost.<h4>Methods</h4>We analyzed the Truven MarketScan Databases from 2009 to 2015 to identify adult female patients undergoing reduction mammaplasty for macromastia. We recorded patient age, rates of obtaining pathology evaluation, new diagnoses of benign or malignant breast disease a ...[more]

PMID: 30719555

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Project description:Background:Ketorolac is a potent nonsteroidal anti-inflammatory drug that has valuable analgesic properties but also a hypothetical risk of increased bleeding due to inhibition of platelet activation. The clinical significance of this risk, however, is unclear when it is used after reduction mammaplasty. Our study objective was to therefore examine the association between ketorolac exposure and hematoma occurrence after breast reduction surgery. We hypothesized that there was no association between ketorolac exposure and hematoma occurrence in breast reduction surgery. Methods:A case-control design was used. Data from charts of all reduction mammaplasties that developed hematomas requiring surgical evacuation (cases) at our university-based hospitals were retrieved and matched to data from charts of reduction mammaplasty patients who did not indicate this complication (controls). Matching occurred in a 1:1 ratio based on 4 criteria: age, body mass index, institution, and preexisting hypertension. Charts were reviewed for retrospective information on exposure to ketorolac. Odds ratio (OR) was calculated with an OR > 1 favoring an association. Results:From 2002 to 2016, 40 cases of hematoma met inclusion criteria and were matched with 40 controls (N = 80). Cases had a significantly lower body mass index than controls; however, the other baseline patient demographics were similar between the 2 groups. There was an association between hematoma formation and exposure to ketorolac (OR, 2.4; 95% confidence interval, 0.8-7.4; P = 0.114) and a trend for greater risk of hematoma formation, although this was not statistically significant. Conclusions:Based on this level 3 evidence, there appears to be an association between perioperative ketorolac exposure and hematoma after breast reduction surgery, but it was not statistically significant. Although this study was adequately powered, the OR of 2.4 was associated with a wide confidence interval. A larger sample size may increase the precision of the results and may also make the association definitive.

Project description:The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.

Dataset Information

Pathology Evaluation of Reduction Mammaplasty Specimens and Subsequent Diagnosis of Malignant Breast Disease: A Claims-Based Analysis.

Publications

Pathology Evaluation of Reduction Mammaplasty Specimens and Subsequent Diagnosis of Malignant Breast Disease: A Claims-Based Analysis.

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