Use of endocrine therapy for estrogen receptor-positive breast cancer among American Indians and Alaska natives

BACKGROUND

American Indian/Alaska Native (AI/AN) women with estrogen receptor-positive (ER +) breast cancer have higher mortality compared to non-Hispanic whites (NHW). The purpose of this study is to compare rates of initiation of endocrine therapy (ET) between AI/AN and NHW and further determine survival outcomes for ER + breast cancer.

METHODS

We used the National Cancer Database to identify patients diagnosed with ER + breast cancer, stage I-III, between 2004 and 2017. Multivariable logistic regression was performed to determine factors associated with initiation of adjuvant ET. Overall survival was estimated using the Kaplan-Meier analysis and Cox proportional hazards modeling.

RESULTS

We identified a total of 771,619 patients (AI/AN, n = 2473; NHW, n = 769,146). Compared to NHW, AI/AN patients were more likely to live in rural areas, be younger, and have tumors that were higher grade, node positive, and larger. Initiation of adjuvant ET was high in both groups and not significantly different between AI/AN and NHW. Independent predictors of ET initiation included rural location, age, higher tumor grade, node-positive disease, larger tumor size, and progesterone receptor-positive status. Initiation of ET was significantly associated with improved overall survival among all patients. Overall survival was significantly worse among the AI/AN population.

CONCLUSION

AI/AN race was significantly and independently associated with worse overall survival after diagnosis of ER + breast cancer. We did not find a significant difference in the initiation of adjuvant ET between AI/AN and NHW. Exact reasons why AI/AN women with ER + breast cancer have higher mortality rates remain elusive but are probably multifactorial.

Distalization of the Tibial Tubercle for Patellar Stabilization Combined With Medial Patellofemoral Ligament Reconstruction: Length of Distalization or Residual Patella Alta and its Affect on Outcome

BACKGROUND

Previous research supports that distal translation of the tibial tubercle osteotomy (dTTO) for patients with recurrent lateral patellar dislocation (R-LPD) and patella alta is effective for surgical patellar stabilization.

HYPOTHESIS/PURPOSE

The main purpose of this article is to evaluate (1) the results of modifying the surgical threshold and postoperative goal of patellar height measurements for surgical stabilization originated in the "menu à la carte" approach to patellar surgical stabilization and (2) the relationship between the distance distalized in millimeters and postoperative complications. Our hypothesis was that dTTO with medial patellofemoral ligament reconstruction (MPFL-R) will successfully stabilize the patella with improvement in outcome scores and few complications, using a modification of the original menu à la carte as our surgical algorithm.

STUDY DESIGN

Case series; Level of evidence, 4.

METHODS

A total of 68 consecutive patients with R-LPD underwent dTTO and MPFL-R for surgical patellar stabilization by a single surgeon between May 2009 and September 2015. Surgical indications were R-LPD combined with patella alta. The surgical threshold for dTTO was Caton-Deschamps index (CDI) or Insall-Salvati ratio ≥1.4 and/or a patellar trochlear index <0.15. The postoperative surgical goal for patellar height was a CDI of 1.1 to 1.2. Length of the distalization was computed by using the CDI measurement as the primary intraoperative guide and measured intraoperatively with a ruler. Clinical, radiographic, and patient outcome measures were reviewed.

RESULTS

The mean CDI preoperatively was 1.40 and postoperatively it was 1.09. The mean distalization was 9.9 mm (range, 4-15 mm). Three patients (4.4%) had frank R-LPD postoperatively. Ten patients had residual patella alta (CDI >1.2), with 1 redislocation. Mean postoperative CDI in the recurrent dislocation group was 1.13 (range, 1.06-1.25) as compared with 1.09 (range, 0.92-1.35) in the nonrecurrent group (P = 0.65). Complications included 3 tibial fractures (4.4%) and postoperative knee arthrofibrosis in 6 patients (8.8%), with mean distalization greater in the arthrofibrosis group (P = .04). Knee injury and Osteoarthritis Outcome Score (KOOS) values improved in all domains, including a 31-point increase in Quality of Life.

CONCLUSION

dTTO with MPFL-R for patients with patella alta leads to a high rate of normalization of patellar height measurements (87%) and patellar stabilization (95.6%). Residual patella alta is not associated with an increased risk of recurrence. The length of dTTO up to 15 mm is not associated with an increase in postoperative complications, except for an increased prevalence of arthrofibrosis (8.8%).

tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports

BACKGROUND

The manual extraction of valuable data from electronic medical records is cumbersome, error-prone, and inconsistent. By automating extraction in conjunction with standardized terminology, the quality and consistency of data utilized for research and clinical purposes would be substantially improved. Here, we set out to develop and validate a framework to extract pertinent clinical conditions for traumatic brain injury (TBI) from computed tomography (CT) reports.

METHODS

We developed tbiExtractor, which extends pyConTextNLP, a regular expression algorithm using negation detection and contextual features, to create a framework for extracting TBI common data elements from radiology reports. The algorithm inputs radiology reports and outputs a structured summary containing 27 clinical findings with their respective annotations. Development and validation of the algorithm was completed using two physician annotators as the gold standard.

RESULTS

tbiExtractor displayed high sensitivity (0.92-0.94) and specificity (0.99) when compared to the gold standard. The algorithm also demonstrated a high equivalence (94.6%) with the annotators. A majority of clinical findings (85%) had minimal errors (F1 Score ≥ 0.80). When compared to annotators, tbiExtractor extracted information in significantly less time (0.3 sec vs 1.7 min per report).

CONCLUSION

tbiExtractor is a validated algorithm for extraction of TBI common data elements from radiology reports. This automation reduces the time spent to extract structured data and improves the consistency of data extracted. Lastly, tbiExtractor can be used to stratify subjects into groups based on visible damage by partitioning the annotations of the pertinent clinical conditions on a radiology report.