Recent Publications by CFE Educators

BACKGROUND

Surgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.

METHODS

Cochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

RESULTS

Requirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.

CONCLUSION

Traditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

BACKGROUND

To determine if the number of endoscopic treatments of urethral stricture disease (USD) prior to urethroplasty has changed in the context of new AUA guidelines on management of USD. In addition to an increase in practicing reconstructive urologists and published reconstructive literature, the AUA guidelines regarding the management of male USD were presented in May 2016, advocating consideration of urethroplasty in patients with 1 prior failed endoscopic treatment.

METHODS

A retrospective review of a prospectively maintained, multi-institutional urethral stricture database of high volume, geographically diverse institutions was performed from 2006 to 2017. We performed a review of relevant literature and evaluated pre-urethroplasty endoscopic treatment patterns prior to and after the AUA male stricture guideline.

RESULTS

2964 urethroplasties were reviewed in 10 institutions. There was both a decrease in the number of endoscopic treatments prior to urethroplasty in the pre-May 2016 compared to post-May 2016 cohorts both for overall urethroplasties (2.3 vs 1.6, P = 0.0012) and a gradual decrease in the number of pre-urethroplasty endoscopic treatments over the entire study period.

CONCLUSION

There was a decrease in the number of endoscopic treatments of USD prior to urethroplasty in the observed period of interest. Declining endoscopic USD management is not likely to be a reflection of a solely unique influence of the guidelines as endoscopic treatment decreased over the entire study period. Further research is needed to determine if there will be a continued trend in the declining use of endoscopic treatment and elucidate the barriers to earlier urethroplasty in patients with USD.

PURPOSE

A primary objective of the Children's Oncology Group (COG) ANBL0532 phase III study was to assess the effect of increasing local dose of radiation to a residual primary tumor on the cumulative incidence of local progression (CILP) in patients with high-risk neuroblastoma.

PATIENTS AND METHODS

Newly diagnosed patients with high-risk neuroblastoma were randomly assigned or assigned to receive single or tandem autologous stem-cell transplantation (SCT) after induction chemotherapy. Local control consisted of surgical resection during induction chemotherapy and radiotherapy after last SCT. Patients received 21.6 Gy to the preoperative primary tumor volume. For patients with incomplete surgical resection, an additional boost of 14.4 Gy was delivered to the gross residual tumor, for a total dose of 36 Gy. CILP (primary end point) and event-free (EFS) and overall survival (OS; secondary end points) were compared with the COG A3973 historical cohort, in which all patients received single SCT and 21.6 Gy without a boost.

RESULTS

For all patients in ANBL0532 receiving radiotherapy (n = 323), 5-year CILP, EFS, and OS rates were 11.2% ± 1.8%, 56.2% ± 3.4%, and 68.4% ± 3.2% compared with 7.1% ± 1.4% ( = .0590), 47.0% ± 3.5% ( = .0090), and 57.4% ± 3.5% ( = .0088) for all patients in A3973 receiving radiotherapy (n = 328), respectively. Five-year CILP, EFS, and OS rates for patients in A3973 with incomplete resection and radiotherapy (n = 47) were 10.6% ± 4.6%, 48.9% ± 10.1%, and 56.9% ± 10.0%, respectively. In comparison, 5-year CILP, EFS, and OS rates for patients in ANBL0532 who were randomly assigned or assigned to single SCT and received boost radiotherapy (n = 74) were 16.3% ± 4.3% ( = .4126), 50.9% ± 7.0% ( = .5084), and 68.1% ± 6.7% ( = .2835), respectively.

CONCLUSION

Boost radiotherapy to gross residual tumor present at the end of induction did not significantly improve 5-year CILP. These results highlight the need for new strategies to decrease the risk of locoregional failure.

INTRODUCTION

Achievement of remission is a key treatment goal for patients with axial spondyloarthritis (axSpA). C-OPTIMISE assessed achievement of sustained clinical remission in patients with axSpA, including radiographic (r) and non-radiographic (nr) axSpA, during certolizumab pegol (CZP) treatment, and subsequent maintenance of remission following CZP dose continuation, dose reduction or withdrawal. Here, we report outcomes from the first 48 weeks (induction period) of C-OPTIMISE, during which patients received open-label CZP.

METHODS

C-OPTIMISE (NCT02505542) was a two-part, multicenter, phase 3b study in adult patients with early axSpA (r-/nr-axSpA), including a 48-week open-label induction period followed by a 48-week maintenance period. Patients with active adult-onset axSpA,  5 years' symptom duration, and fulfilling Assessment of SpondyloArthritis international Society classification criteria, were included. During the induction period, patients received a loading dose of CZP 400 mg at weeks 0, 2, and 4, followed by CZP 200 mg every 2 weeks (Q2W) up to week 48. The main outcome of the 48-week induction period was the achievement of sustained clinical remission (defined as an Ankylosing Spondylitis Disease Activity Score [ASDAS]  1.3 at week 32 and  2.1 at week 36 [or vice versa], and  1.3 at week 48).

RESULTS

In total, 736 patients (407 with r-axSpA, 329 with nr-axSpA) were enrolled into the study. At week 48, 43.9% (323/736) of patients achieved sustained remission, including 42.8% (174/407) of patients with r-axSpA and 45.3% (149/329) with nr-axSpA. Patients also demonstrated substantial improvements in axSpA symptoms, MRI outcomes and quality of life measures. Adverse events occurred in 67.9% (500/736) of patients, of which 6.0% (44/736) were serious.

CONCLUSIONS

Over 40% of patients with early axSpA achieved sustained remission during 48 weeks of open-label CZP treatment. Additionally, patients across the axSpA spectrum demonstrated substantial improvements in imaging outcomes and quality of life following treatment. No new safety signals were identified.

TRIAL REGISTRATION

NCT02505542.

OBJECTIVE

The number of therapies for axial spondyloarthritis (axSpA) is increasing. Thus, it has become more challenging for patients and physicians to navigate the risk-benefit profiles of the various treatment options. In this study, we used conjoint analysis-a form of trade-off analysis that elucidates how people make complex decisions by balancing competing factors-to examine patient decision-making surrounding medication options for axSpA.

METHODS

We conducted an adaptive choice-based conjoint analysis survey for patients with axSpA to assess the relative importance of medication attributes (eg, chance of symptom improvement, risk of side effects, route of administration, etc) in their decision-making. We also performed logistic regression to explore whether patient demographics and disease characteristics predicted decision-making.

RESULTS

Overall, 397 patients with axSpA completed the conjoint analysis survey. Patients prioritized medication efficacy (importance score 26.8%), cost (26.3%), and route of administration (13.9%) as most important in their decision-making. These were followed by risk of lymphoma (9.5%), dosing frequency (7.2%), risk of serious infection (6.0%), tolerability of side effects (5.3%), and clinic visit and laboratory test frequency (4.8%). In regression analyses, there were few significant associations between patients' treatment preferences and sociodemographic and axSpA characteristics.

CONCLUSIONS

Treatment decision-making in axSpA is highly individualized, and demographics and baseline disease characteristics are poor predictors of individual preferences. This calls for the development of online shared decision-making tools for patients and providers, with the goal of selecting a treatment that is consistent with patients' preferences.

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

BACKGROUND

The presentation of coronavirus 2019 (COVID-19) overlaps with common influenza symptoms. There is limited data on whether a specific symptom or collection of symptoms may be useful to predict test positivity.

METHODS

An anonymous electronic survey was publicized through social media to query participants with COVID-19 testing. Respondents were questioned regarding 10 presenting symptoms, demographic information, comorbidities, and COVID-19 test results. Stepwise logistic regression was used to identify predictors for COVID-19 positivity. Selected classifiers were assessed for prediction performance using receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 145 participants with positive COVID-19 testing and 157 with negative results were included. Participants had a mean age of 39 years, and 214 (72%) were female. Smell or taste change, fever, and body ache were associated with COVID-19 positivity, and shortness of breath and sore throat were associated with a negative test result (p 0.05). A model using all 5 diagnostic symptoms had the highest accuracy with a predictive ability of 82% in discriminating between COVID-19 results. To maximize sensitivity and maintain fair diagnostic accuracy, a combination of 2 symptoms, change in sense of smell or taste and fever was found to have a sensitivity of 70% and overall discrimination accuracy of 75%.

CONCLUSION

Smell or taste change is a strong predictor for a COVID-19-positive test result. Using the presence of smell or taste change with fever, this parsimonious classifier correctly predicts 75% of COVID-19 test results. A larger cohort of respondents will be necessary to refine classifier performance.