PubMed Health⌕ Search

Biomedical subjects

R K Orr

Publications and source records attributed to R K Orr.

22 records · Page 2Linked to original sources
PubMedDate not supplied

Surgeon variability in treating nonpalpable breast cancer: surgical oncology as a value-added specialty.

BACKGROUND: Several studies have demonstrated a relatively low rate of breast conservation surgery (BCS) in the United States. Few have analyzed the impact of individual surgeon variability on the outcome of the procedure, and none have contrasted surgical oncologists versus general surgeons in the treatment of nonpalpable breast cancer. METHODS: A blinded review was done of 409 excisions for nonpalpable breast cancer performed by 11 board-certified general surgeons (GS, n = 221) and one surgical oncologist (SO, n = 185) in a teaching institution. We compared surgical margins, need for reexcision, and breast conservation rates. RESULTS: Although there were no significant differences in patient and tumor characteristics, there were surprising differences between the GS and SO, especially related to surgical margins and final treatment. The SO has a significantly higher rate of frozen section compared to GS (81% vs. 64%, P < 0.01) and a lower rate of positive margin at the time of original biopsy (25% vs. 41%, P < 0.01). These differences translated into lower necessity for reexcision of tumor (18% vs. 48%, P < 0.01) and higher rate of BCS (88% vs. 70%, P < 0.01). CONCLUSION: This study demonstrates marked differences among trained general surgeons. The additional experience of a surgical oncologist is valuable, because fewer positive margins lead to a higher likelihood of breast preservation and decreased costs related to fewer additional operative procedures.

Adult↗
PubMedDate not supplied

The impact of prophylactic axillary node dissection on breast cancer survival--a Bayesian meta-analysis.

BACKGROUND: Because of the general acceptance of the NSABP B-04 study, prophylactic axillary node dissection for women with clinically negative axillae is considered diagnostic, but not therapeutic, by many oncologists. Nevertheless, several authors have shown that B-04 did not include enough patients to exclude a small survival advantage. METHODS: A Bayesian meta-analysis of the available literature was performed comparing standard treatment to standard treatment without axillary node dissection. Six randomized controlled trials were identified, consisting of nearly 3000 patients and spanning four decades. RESULTS: All six trials showed that prophylactic axillary node dissection improved survival, ranging from 4% to 16%, corresponding to a risk reduction of 7%-46%. Combining the six trials showed an average survival benefit of 5.4% (95% CI = 2.7-8.0%, probability of survival benefit > 99.5%). Adjusting for biases in the individual studies did not alter the conclusions, nor did subset analysis of Stage I patients. CONCLUSIONS: Axillary node dissection improves survival in women with operable breast cancer. Nevertheless, two important limitations of this analysis are noteworthy. Few of the patients in the six trials had T1a tumors, so extrapolation of these results to this subset (and those with nonpalpable tumors) may be inappropriate. Essentially no patients in the six trials were treated with adjuvant therapy, as contrasted to current clinical practice. It is possible that the risk reduction seen in this meta-analysis may be diminished in patients receiving adjuvant chemotherapy. Despite these limitations, this study suggests that axillary dissection should be performed in most women with palpable tumors for diagnostic, as well as therapeutic, purposes.

Axilla↗
PubMedDate not supplied

Use of a probabilistic neural network to estimate the risk of mortality after cardiac surgery.

OBJECTIVE: To develop a probabilistic neural network (PNN) to estimate mortality risk following cardiac surgery. DESIGN AND SETTING: The PNN model was created using an institutional database obtained as part of routine quality assurance activity. Patient records (from 1991 to 1993) were randomly divided into training (n = 732) and validation (n = 380) sets. The model uses seven variables, each obtainable during routine clinical patient care. After completion of the initial validation phase, newer data (1994) became available and were used as an independent source of validation (n = 365). PATIENTS: 1,477 consecutive cardiac surgery patients operated on in a teaching hospital during a four-year period (1991-94). RESULTS: The overall accuracy of the neural network was 91.5% in the training set; it was 92.3% in the validation set. The model was well calibrated (p = 0.21 for the Hosmer-Lemeshow goodness-of-fit test) and discriminated well (areas under the ROC curves were 0.72 and 0.81 for the training and validation sets). The trained network also performed well on the 1994 data (ROC = 0.74, p = 0.19 for the Hosmer-Lemeshow test), albeit with a slight decrement in overall accuracy (88.2%). CONCLUSION: A neural network may be implemented to estimate mortality risk following cardiac surgery. Implementation is relatively rapid, and it is an alternative to standard statistical approaches.

Adult↗