MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data.

BACKGROUND: Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each 'omic' type. RESULTS: Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal single-cell data. CONCLUSIONS: MOSCATO is a useful analytical technique for supervised feature selection in multimodal single-cell data. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments.

Outcomes following robot-assisted versus laparoscopic sleeve gastrectomy: the New York State experience.

INTRODUCTION: Laparoscopic sleeve gastrectomy (LSG) represents more than half of all bariatric procedures in the USA, and robot-assisted sleeve gastrectomy (RSG) is becoming increasingly common. There is a paucity of evidence regarding postoperative surgical outcomes (> 30 days) in RSG patients, especially as these patients move between multiple hospital systems. METHODS: Using 2012-2018 New York State's inpatient and ambulatory data from the Statewide Planning and Research Cooperative System, bivariate and multivariate analyses were employed to examine patient long-term outcomes, postoperative complications, and charges following RSG versus LSG in unmatched and propensity score-matched (PSM) samples. RESULTS: Among the 72,157 minimally invasive sleeve gastrectomies identified, 2365 (2.6%) were RSGs. In the PSM sample (2365 RSG matched to 23,650 LSG), RSG cases were more likely to be converted to an open procedure (2.3% vs 0.2% LSG patients, p < 0.01) and had a longer mean length of stay (LOS; 2.1 vs. 1.8 days LSG, p < 0.01). Postoperative complications were not different between RSG and LSG patients, but the proportion of emergency room visits resulting in inpatient readmissions was higher among RSG patients (5.5% vs. 4.2% in LSG patients, p < .01). Among the super obese (body mass index ≥ 50) patients, conversions to open procedure and LOS were also significantly higher for RSG versus LSG cases. Average hospital charges for the index admission ($47,623 RSG vs $35,934 LSG) and cumulative changes for 1 year from the date of surgery ($57,484 RSG vs $43,769 LSG) were > 30% higher for RSG patients. CONCLUSIONS: RSG patients were more likely to have conversions to open procedures, longer postoperative stay, readmissions, and higher charges for both the index admission and beyond, compared to LSG patients. No clear advantages emerged for the utilization of the robotic platform for either average risk or extremely obese patients.

The Early (2009-2017) Experience With Robot-assisted Cholecystectomy in New York State.

OBJECTIVE: The aim of this study was to examine real-life patterns of care and patient outcomes associated with robot-assisted cholecystectomy (RAC) in New York State (NYS). BACKGROUND: Although robotic assistance may offer some technological advantages, RACs are associated with higher procedural costs and longer operating times compared to traditional laparoscopic cholecystectomies (LCs). Evidence on long-term patient outcomes after RAC from large population-based datasets remains limited and inconsistent. METHODS: Using NYS inpatient and ambulatory surgery data from the Statewide Planning and Research Cooperative System (2009-2017), we conducted bivariate and multivariate analyses to examine patterns of utilization, complications, and secondary procedures following cholecystectomies. RESULTS: Among 299,306 minimally invasive cholecystectomies performed in NYS between 2009 and 2017, one thousand one hundred eighteen (0.4%) were robot-assisted. Compared to those undergoing LC, RAC patients were older, travelled further for surgery, and were more likely to have public insurance and preoperative comorbidities. RAC versus LC patients were more significantly likely to have conversions to open procedure (4.9% vs 2.8%), bile duct injuries (1.3% vs 0.4%), and major reconstructive interventions (0.6% vs 0.1%), longer median length of stay (3 vs 1 day), readmissions (7.3% vs 4.4%), and higher 12-month post-index surgery hospital charges (P < 0.01 for all estimates). Other postoperative complications decreased over time for LC but remained unchanged for RAC patients. CONCLUSIONS: Patients receiving RAC in NYS experienced higher rates of complications compared to LC patients. Addressing patient-, surgeon-, and system-level factors associated with intra/postoperative complications and applying recently promulgated safe cholecystectomy strategies coupled with advanced imaging modalities like fluorescence cholangiography to RAC may improve patient outcomes.

SuMO-Fil: Supervised multi-omic filtering prior to performing network analysis.

Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings. We accomplish this by implementing variable filters based on low similarity across the datasets in conjunction with low similarity with the outcome. This approach can improve accuracy, as well as reduce run times for a variety of computationally expensive downstream analyses. This method has applications in a setting where the downstream analysis may include sparse canonical correlation analysis. Filtering methods specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. The SuMO-Fil method performs favorably by eliminating non-network features while maintaining important biological signal under a variety of different signal settings as compared to popular filtering techniques based on low means or low variances. We show that the speed and accuracy of methods such as supervised sparse canonical correlation are increased after using SuMO-Fil, thus greatly improving the scalability of these approaches.

Balanced Functional Module Detection in genomic data.

Motivation: High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion. Of special interest are functional modules, cooperating sets of variables affecting the target which can be characterized by a graph. In applications such as social networks, the concept of balance in undirected signed graphs characterizes the consistency of associations within the network. This property requires that the module variables have a joint effect on the target outcome with no internal conflict, an efficiency that may be applied to biological networks. Results: In this paper, we model genomic variables in signed undirected graphs for applications where the set of predictor variables influences an outcome. Consequences of the balance property are exploited to implement a new module discovery algorithm, balanced Functional Module Detection (bFMD), which selects a subset of variables from high-dimensional data that compose a balanced functional module. Our bFMD algorithm performed favorably in simulations as compared to other module detection methods. Additionally, bFMD detected interpretable results in an application using RNA-seq data obtained from subjects with Uterine Corpus Endometrial Carcinoma using the percentage of tumor invasion as the outcome of interest. The variables selected by bFMD have improved interpretability due to the logical consistency afforded by the balance property. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Teaching surgery novices and trainees advanced laparoscopic suturing: a trial and tribulations.

INTRODUCTION: The benefits of minimally invasive surgery are numerous; however, considerable variability exists in its application and there is a lack of standardized training for important advanced skills. Our goal was to determine whether participation in an advanced laparoscopic curriculum (ALC) results in improved laparoscopic suturing skills. METHODS AND PROCEDURES: Study design was a prospective, randomized controlled trial. Surgery novices and trainees underwent baseline FLS training and were pre-tested on bench models. Participants were stratified by pre-test score and randomized to undergo either further FLS training (control group) or ALC training (intervention group). All were post-tested on the same bench model. Tests for differences between post-test scores of cohorts were performed using least squared means. Multivariable regression identified predictors of post-test score, and Wilcoxon rank sum test assessed for differences in confidence improvement in laparoscopic suturing ability between groups. RESULTS: Between November 2018 and May 2019, 25 participants completed the study (16 females; 9 males). After adjustment for relevant variables, participants randomized to the ALC group had significantly higher post-test scores than those undergoing FLS training alone (mean score 90.50 versus 82.99, p = 0.001). The only demographic or other variables found to predict post-test score include level of training (p = 0.049) and reported years of video gaming (p = 0.034). There was no difference in confidence improvement between groups. CONCLUSIONS: Training using the ALC as opposed to basic laparoscopic skills training only is associated with superior advanced laparoscopic suturing performance without affecting improvement in reported confidence levels. Performance on advanced laparoscopic suturing tasks may be predicted by lifetime cumulative video gaming history and year of training but does not appear to be associated with other factors previously studied in relation to basic laparoscopic skills, such as surgical career aspiration or musical ability.

Association of state vision screening requirements with parent-reported vision testing in young children.

PURPOSE: To examine the relationship between state school vision screening requirements and the likelihood that children 3-5 years of age receive vision testing. METHODS: We obtained nationally representative data from the 2016 National Survey of Children's Health on children ages 3-5 (n = 7,567) and used available sources to compile state policies that mandate childhood vision testing. We calculated the rates of parent-reported vision testing for each state and fit logistic regression models using survey-based estimation methods with nationally representative weights. Our models controlled for factors such as age, sex, race/ethnicity, and insurance coverage. Additional analyses added comorbidities that may lead to an eye care provider referral. RESULTS: Parent-reported vision testing rates by state ranged from 41% to 84%. A significant association was found between the presence of state-level vision screening requirements and parent-reported vision testing, which remained after controlling for comorbidities (aOR = 1.374; P = 0.016). Of these comorbidities, arthritis, blindness, and very low birth weight were associated with a higher rate of vision screening (all P < 0.05). CONCLUSIONS: The presence of a state-level school vision screening requirement is associated with increased parent-reported vision testing in children 3-5 years of age. This suggests that state policy may ensure timely screening for amblyopia and other sight-threatening complications.