Faculty Bibliography

OBJECTIVES/OBJECTIVE:Black race has been associated with increased 30-day morbidity and mortality following surgery for endometrial cancer. Black women are also less likely to undergo laparoscopy when compared to white women. With the development of improved laparoscopic techniques and equipment, including the robotic platform, we sought to evaluate whether there has been a change in surgical approach for black women, and in turn, improvement in perioperative outcomes. METHODS:Using the American College of Surgeons' National Surgical Quality Improvement Project's database, patients who underwent hysterectomy for endometrial cancer from 2010 to 2015 were identified. Comparative analyses stratified by race and hysterectomy approach were performed to assess the relationship between race and perioperative outcomes. RESULTS:A total of 17,692 patients were identified: of these, 13,720 (77.5%) were white and 1553 (8.8%) were black. Black women were less likely to undergo laparoscopic hysterectomy compared to white women (49.3% vs 71.3%, p<0.0001). Rates of laparoscopy in both races increased over the 6-year period; however these consistently remained lower in black women each year. Black women had higher 30-day postoperative complication rates compared to white women (22.5% vs 13.6%, p<0.0001). When laparoscopic hysterectomies were isolated, there was no difference in postoperative complication rates between black and white women (9.2% vs 7.5%, p=0.1). CONCLUSIONS:Overall black women incur more postoperative complications compared to white women undergoing hysterectomy for endometrial cancer. However, laparoscopy may mitigate this disparity. Efforts should be made to maximize the utilization of minimally invasive surgery for the surgical management of endometrial cancer.

BACKGROUND AND OBJECTIVES: Missed visits are common in office-based buprenorphine treatment (OBOT). The feasibility of text message (TM) appointment reminders among OBOT patients is unknown. METHODS: This 6-month prospective cohort study provided TM reminders to OBOT program patients (N = 93). A feasibility survey was completed following delivery of TM reminders and at 6 months. RESULTS: Respondents reported that the reminders should be provided to all OBOT patients (100%) and helped them to adhere to their scheduled appointment (97%). At 6 months, there were no reports of intrusion to their privacy or disruption of daily activities due to the TM reminders. Most participants reported that the TM reminders were helpful in adhering to scheduled appointments (95%), that the reminders should be offered to all clinic patients (95%), and favored receiving only TM reminders rather than telephone reminders (95%). Barriers to adhering to scheduled appointment times included transportation difficulties (34%), not being able to take time off from school or work (31%), long clinic wait-times (9%), being hospitalized or sick (8%), feeling sad or depressed (6%), and child care (6%). CONCLUSIONS: This study demonstrated the acceptability and feasibility of TM appointment reminders in OBOT. Older age and longer duration in buprenorphine treatment did not diminish interest in receiving the TM intervention. Although OBOT patients expressed concern regarding the privacy of TM content sent from their providers, privacy issues were uncommon among this cohort. Scientific Significance Findings from this study highlighted patient barriers to adherence to scheduled appointments. These barriers included transportation difficulties (34%), not being able to take time off from school or work (31%), long clinic lines (9%), and other factors that may confound the effect of future TM appointment reminder interventions. Further research is also required to assess 1) the level of system changes required to integrate TM appointment reminder tools with already existing electronic medical records and appointment records software; 2) acceptability among clinicians and administrators; and 3) financial and resource constraints to healthcare systems. (Am J Addict 2017;XX:1-6).

BACKGROUND: At NYU, as part of a comprehensive objective structured clinical skills exam, experienced medical educators judge clinical knowledge, decision-making, and clinical reasoning skills of trainees based on their patient notes. Despite being rubric-driven, this task requires tremendous time and effort to establish consistent scoring, delaying and limiting individualized feedback. We conducted pilot machine learning text classification studies to establish if accurate automated scoring of clinical notes is possible. METHODS: As a use case, we tested 100 student written clinical notes from7 standardized patient cases (Vision Loss, Tel Diarrhea, Difficulty Sleeping, Shoulder Pain, Failure To Thrive, Abdominal, Pain, Palpitations) that had been scored for quality of clinical reasoning by faculty on a 1-4 scale. In order to assess performance of NLP strategies to categorize students in meaningful groups we dichotomized students based on their faculty given scores by case into "failing" (score of 1, 5-18 students per case) and "passing" (score 2,3,4). We treated each task as a binary classification task in a text classification pipeline. First, we treated each note as a bag of tokens and weight each token with term frequency-inverse document frequency (TFIDF) a numerical statistic that reflects howimportant aword is to a document. We then applied 3 different classification algorithms (random forests, support vector machines, and Bayesian logistic regression) and measured discriminatory performance using Area Under Curve (AUC) in a cross validation evaluation design. RESULTS: TFDIF performed with AUCs between 0.669 and 0.905. Logistic regression provided the highestAUC in four cases: Difficulty Sleeping (0.905), Shoulder Pain (0.618), Failure To Thrive (0.717) and Abdominal Pain (0.892). As we observed the highest AUCs in Difficulty Sleeping and Abdominal Pain cases, we have begun to refine the algorithm for these two cases by identifying the importance features that lead faculty to give students to a higher grade and improve the accuracy of NLP based scoring. Promising features include the presence and sequence of certainwords in the problem representation, sentence length in the management section, ranking of the differential diagnosis, sequence between key words (e.g. rule out appendicitis), and evidence of "thinkingness" or what many call semantic qualifiers. CONCLUSIONS: With additional effort to build targeted case specific classifiers for clinical content and reasoning, a validated machine-learning model may achieve partial or full automation of grading of the notes. This work, which builds on decades of clinical decision-making and critical reasoning research, may provide medical trainees with more and potentially better feedback; facilitating learning of clinical reasoning, freeing faculty to coach this process, and in the long run impacting healthcare quality and patient safety

This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data repositories available for research. This article aims to invoke novel research ideas through a combination of examples of analyses and domain knowledge.

Complex medical data sometimes requires significant data preprocessing to prepare for analysis. The complexity can lead non-domain experts to apply simple filters of available data or to not use the data at all. The preprocessing choices can also have serious effects on the results of the study if incorrect decision or missteps are made. In this work, we present open-source data filters for an analysis motivated by understanding mortality in the context of sepsis- associated cardiomyopathy in the ICU. We report specific ICU filters and validations through chart review and graphs. These published filters reduce the complexity of using data in analysis by (1) encapsulating the domain expertise and feature engineering applied to the filter, by (2) providing debugged and ready code for use, and by (3) providing sensible validations. We intend these filters to evolve through pull requests and forks and serve as common starting points for specific analyses.

RATIONALE AND OBJECTIVES: This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. MATERIALS AND METHODS: We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations). The model having the highest area under the curve was selected to derive a list of article features (words) predicting high citation volume, which was iteratively reduced to identify the smallest possible core feature list maintaining predictive power. Overall themes were qualitatively assigned to the core features. RESULTS: The regularized logistic regression (Bayesian binary regression) model had highest performance, achieving an area under the curve of 0.814 in predicting articles in the top 10% of citation volume. We reduced the initial 14,083 features to 210 features that maintain predictivity. These features corresponded with topics relating to various imaging techniques (eg, diffusion-weighted magnetic resonance imaging, hyperpolarized magnetic resonance imaging, dual-energy computed tomography, computed tomography reconstruction algorithms, tomosynthesis, elastography, and computer-aided diagnosis), particular pathologies (prostate cancer; thyroid nodules; hepatic adenoma, hepatocellular carcinoma, non-alcoholic fatty liver disease), and other topics (radiation dose, electroporation, education, general oncology, gadolinium, statistics). CONCLUSIONS: Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature.

Objectives: Minimally invasive surgery is the preferred surgical method to treat women with endometrial cancer. Several single-institution reports have described the feasibility and safety of outpatient laparoscopic hysterectomies (LH) for both benign and malignant indications. The objective of this study is to identify patient and surgical factors associated with outpatient laparoscopic hysterectomies (OLH) and to compare outcomes between OLH and inpatient laparoscopic hysterectomies (ILH) in women with endometrial cancer.Methods: Data were obtained from the American College of Surgeons' National Surgical Quality Improvement Program (NSQIP) database. All patients who underwent hysterectomies for endometrial cancer from 2007 to 2013 were identified by ICD-9 and CPT codes. These patients were then filtered for LH. Comparative analyses were performed and stratified by admission status to evaluate demographics, preoperative and intraoperative variables, and surgical outcomes. Statistical tests were performed with R Studio version 0.99.442.Results: LH rates have been steadily increasing. (See Table 1.) Between 2010 and 2013, 5,851 patients underwent LH for endometrial cancer; of these, 3,428 (58.6%) were ILH and 2,423 (41.4%) were OLH. OLH rates increased each year from 30.0% in 2010 to 50.0% in 2013. OLH patients were on average 61.81 years old compared with 63.03 years for ILH patients (P <.001). Medical comorbidities were not different between the 2 groups. Total operating time and anesthesia time were both significantly shorter in the OLH group: average times were 161.3 and 187.0 minutes (P <.001) and 245.2 versus 256.3 minutes (P =.002), respectively. More lymph node dissections were performed in the ILH group than the OLH group: 2,074 (60.5%) versus 1,390 (57.4%, P =.016). There were more radical hysterectomies in the ILH group (n = 803; 23.4%) compared with the OLH group (n = 315; 13.1%) (P <.001). OLHs were assigned fewer relative value units than ILHs (mean 28.5 vs 30.6, respectively, P <.001). Postoperative complications were not different between the groups.Conclusions: Younger age, fewer RVUs, shorter operating and anesthesia times were associated with successful OLH in patients with endometrial cancer. Lymph node dissection and radical surgery were associated with an increased rate of ILH. There were no differences in postoperative complications between OLH and ILH. (table present)

BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. METHODS: Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. RESULTS: The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. CONCLUSIONS: The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum.