Faculty Bibliography

OBJECTIVE: To determine factors influencing discharge patterns after laparoscopic hysterectomy for endometrial cancer and to evaluate the safety of same-day discharge during the 30-day postoperative period. METHODS: Using the American College of Surgeons' National Surgical Quality Improvement Project's database, patients who underwent hysterectomy for endometrial cancer from 2010 to 2014 were identified and categorized by their hospital length of stay. Statistical analyses were performed to assess the relationship between hospital stay and demographics, medical comorbidities, intraoperative surgical factors and postoperative outcomes. RESULTS: A total of 9020 patients had laparoscopic hysterectomies for endometrial cancer and of these, 729 patients (8.1%) were successfully discharged on the day of surgery. These patients were younger and had lower body mass indexes and fewer medical comorbidities than patients who were admitted after their procedure. The same-day discharge group underwent surgical procedures of less complexity than the hospital admission group based on shorter operative times and fewer relative value units (RVUs). There was a lower rate of surgical site infections in the same-day discharge group, and no difference in rates of other postoperative complications including hospital readmissions and reoperations. CONCLUSIONS: Rates of laparoscopic hysterectomy for endometrial cancer are gradually increasing but the rates of same-day discharge have increased at a much slower rate. Same-day discharge has been successful despite differences in preoperative demographics, medical comorbidities and intraoperative surgical complexity. Overall postoperative complication rates were equivalent despite length of hospital stay, demonstrating the safety and feasibility of same-day discharge after laparoscopic hysterectomy for endometrial cancer.

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.

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: Multiple models have been developed to predict hospital admission for patients presenting to the ED. However, these tools suffer from multiple limitations including reliance on manual data entry (e.g. ED arrival mechanism), multiple types of data, and data that are not completely generalizable across institutions (e.g. triage score). An ideal solution would produce a disposition score that requires no data entry, employs variables already captured by all EDs, and provides a score far enough in advance to expedite admission processes. Objectives: Evaluate the discriminatory power of machine learning algorithms for predicting hospital admission at two hours of ED arrival through the sole use of the medication administration record (MAR). Methods: Our dataset included 27,757 encounters (26% admitted) from January 2013 to September 2014 and 2,109 medications encoded to RxNorm CUI numbers using MedEx. We included all medications in the MAR, including those given during prior ED visits. We employed classic and state[[Unsupported Character - Codename]]of [[Unsupported Character - Codename]]the[[Unsupported Character - Codename]]art classifiers including logistic regression, naive bayes, regularized logistic regression, classification and regression trees (CART), and linear support vector machine (SVM) with penalty parameter C. In all cases, we split the dataset into a training, validation, and test set. We used the validation set to optimize any parameters of the learning algorithm and used the test set to calculate performances. We employed 5[[Unsupported Character - Codename]]fold cross validation and reported AUC performances averaged across 5 folds. Results: The models performed with AUCs of 0.85 for linear SVM with penalty parameter C (95%CI 0.84-0.86), 0.83 for CART (95%CI 0.82-0.84), 0.79 for regularized logistic regression (95%CI 0.78-0.80), 0.70 for Naive Bayes (95%CI 0.69-0.72), and 0.68 for logistic regression (95%CI 0.67-0.69). Conclusion: MAR data is sufficient to reliably predict hospital admission two hours into the ED stay. Our models perform similarly to those from prior studies, but with the advantages of only requiring a single type of data and being highly generalizable to other institutions; MAR data is objective, does not require manual data entry, and is universally available across EDs

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.

Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system. To realize this use case, a foundational question is whether we can detect e-cigarette use at all. This work reports two pilot tasks using text classification to identify automatically Tweets that indicate e-cigarette use and/or e-cigarette use for smoking cessation. We build and define both datasets and compare performance of 4 state of the art classifiers and a keyword search for each task. Our results demonstrate excellent classifier performance of up to 0.90 and 0.94 area under the curve in each category. These promising initial results form the foundation for further studies to realize the ideal surveillance solution.

Study Objectives: Previously, we built machine-learned models to automatically identify Tweets indicating alcohol use from 34,563 labeled Tweets collected over 24 hours during New Year's Day. The models demonstrated an estimated area under the receiver operating curve (AUROC) of 0.94 for identifying alcohol use Tweets. In this study, we validated our alcohol use model in an independently collected dataset - the Electric Zoo music festival on New York City's Randall's Island. This event attracted over 130,000 people in 2013 and resulted in two substance-associated deaths. Methods: The initial dataset contained all Tweets and Instagrams geo-tagged within 5 miles of Randall's Island, covering all event days from August 29-31, 2014. Two authors independently reviewed Tweets for drug- or alcohol-related content. 10% of the Tweets were randomly selected for dual independent review to determine agreement using a weighted Cohen's kappa. Identified Tweets were then jointly reviewed to determine those indicative of alcohol use according to previous definitions. Tweets and Instagrams were considered indicators of alcohol use if they referred to: intention to drink, the act of drinking, location at a bar or liquor store, mention of a specific brand, drinking paraphernalia (eg, flask), consequences from drinking (eg, drunk, wasted, tipsy), or alcohol-related hashtags. Our Bayesian logistic regression machine learned model, which had been derived only from Tweets, was applied to a restricted dataset excluding Instagrams. Results: The complete geo-located collection included 11,071 Tweets and Instagrams. The restricted dataset containing only Tweets consisted of 2,928 elements, of which 82 Tweets were classified as drug- or alcohol-related (weighted kappa = 0.92). Of these, 23 Tweets explicitly referenced alcohol use (eg, "Wine at Zoo is the right play. Instadrunk;" "Wow. I am not sober;" "#clskipfridays #livesummer #Ezoo #were dumb #and drunk"). The model achieved an AUROC of 0.87 when applied to this independent Tweet validation set. Conclusion: Our machine-learned model automatically identified alcohol use at Electric Zoo with high discriminatory power. Differences between the previous estimated AUROC performance and the validated AUROC performance are likely due to language variations between the two groups. An in-depth error analysis may identify approaches to improve model performance. The ability to automate social media geosurveillance of substance behavior at events could be coupled with real-time data feeds. Model automation would allow these real-time data feeds to be analyzed for potential public health interventions (including messaging, Tweet geodensity dependent medical presence, or other measures) to further reduce harm

Aims: (1) Assess feasibility of a text message appointment reminder (TMR) intervention (2) Determine the clinical impact of the TMR on appointment adherence Methods: A 52-item survey was administered to 100 patients in an urban, public sector, office-based buprenorphine program between June 2013 and March 2014. Survey domains included: demographic characteristics, communication patterns, and content preferences for supportive, informational, and relapse prevention TM interventions. A TMR was then sent 7, 4, 1 day prior to the patients' upcoming appointment followed by a 16 item survey that assessed satisfaction and feedback for the TM reminders (n = 72). Results: Respondents were predominately African-American (42%), unemployed or reliant on public assistance (68%), and lacked permanent housing (52%). MP ownership was common (93%) with the caveat of a high turnover of phones (2) and phone numbers (2) in the past year. Most reported TM use (93%) and comfort with sending TM (79%). The feasibility survey demonstrated satisfaction with the TMR (100%) and most preferred receiving text reminders (88%) in place of telephone reminders at 6 months. There was no significant difference between participants receiving the TMR compared to patients that did not receive the reminders. Conclusions: TM based interventions are an acceptable and feasible strategy for enhancing the delivery of care in a safety net, office-based buprenorphine program