Analysis of Missingness Scenarios for Observational Health Data.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into the missing data methods. In this paper, we argue that the remedy is to identify the key scenarios that lead to data missingness and investigate their theoretical implications. Based on this proposal, we first introduce an analysis framework where we investigate how different observation agents, such as physicians, influence the data availability and then scrutinize each scenario with respect to the steps in the missing data analysis.

Delineating morbidity patterns in preterm infants at near-term age using a data-driven approach.

Background: Long-term survival after premature birth is significantly determined by development of morbidities, primarily affecting the cardio-respiratory or central nervous system. Existing studies are limited to pairwise morbidity associations, thereby lacking a holistic understanding of morbidity co-occurrence and respective risk profiles.

Methods: Our study, for the first time, aimed at delineating and characterizing morbidity profiles at near-term age and investigated the most prevalent morbidities in preterm infants: bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), mild cardiac defects, perinatal brain pathology and retinopathy of prematurity (ROP).

Correlation Between Early Trends of a Prognostic Biomarker and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.

Purpose: Overall survival (OS) is the primary end point in phase III oncology trials. Given low success rates, surrogate end points, such as progression-free survival or objective response rate, are used in early go/no-go decision making. Here, we investigate whether early trends of OS prognostic biomarkers, such as the ROPRO and DeepROPRO, can also be used for this purpose.

Assessable and interpretable sensitivity analysis in the pattern graph framework for nonignorable missingness mechanisms.

The pattern graph framework solves a wide range of missing data problems with nonignorable mechanisms. However, it faces two challenges of assessability and interpretability, particularly important in safety-critical problems such as clinical diagnosis: (i) How can one assess the validity of the framework's a priori assumption and make necessary adjustments to accommodate known information about the problem? (ii) How can one interpret the process of exponential tilting used for sensitivity analysis in the pattern graph framework and choose the tilt perturbations based on meaningful real-world quantities? In this paper, we introduce Informed Sensitivity Analysis, an extension of the pattern graph framework that enables us to incorporate substantive knowledge about the missingness mechanism into the pattern graph framework. Our extension allows us to examine the validity of assumptions underlying pattern graphs and interpret sensitivity analysis results in terms of realistic problem characteristics.

Proteomics reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients.

SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Previous studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited.

Improved Macro- and Micronutrient Supply for Favorable Growth and Metabolomic Profile with Standardized Parenteral Nutrition Solutions for Very Preterm Infants.

Very preterm infants are at high risk for suboptimal nutrition in the first weeks of life leading to insufficient weight gain and complications arising from metabolic imbalances such as insufficient bone mineral accretion. We investigated the use of a novel set of standardized parenteral nutrition (PN; MUC PREPARE) solutions regarding improving nutritional intake, accelerating termination of parenteral feeding, and positively affecting growth in comparison to individually prescribed and compounded PN solutions. We studied the effect of MUC PREPARE on macro- and micronutrient intake, metabolism, and growth in 58 very preterm infants and compared results to a historic reference group of 58 very preterm infants matched for clinical characteristics.

Leveraging Machine Learning to Predict 30-Day Hospital Readmission After Cardiac Surgery.

Background: Hospital readmission within 30 days of discharge is a well-studied outcome. Predicting readmission after cardiac surgery, however, is notoriously challenging; the best-performing models in the literature have areas under the curve around .65.

Artificial Intelligence for Prognostic Scores in Oncology: a Benchmarking Study.

Prognostic scores are important tools in oncology to facilitate clinical decision-making based on patient characteristics. To date, classic survival analysis using Cox proportional hazards regression has been employed in the development of these prognostic scores. With the advance of analytical models, this study aimed to determine if more complex machine-learning algorithms could outperform classical survival analysis methods.

Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks.

Purpose: Automatically segmenting and classifying surgical activities is an important prerequisite to providing automated, targeted assessment and feedback during surgical training. Prior work has focused almost exclusively on recognizing gestures, or short, atomic units of activity such as pushing needle through tissue, whereas we also focus on recognizing higher-level maneuvers, such as suture throw. Maneuvers exhibit more complexity and variability than the gestures from which they are composed, however working at this granularity has the benefit of being consistent with existing training curricula.

Association Between Surgical Trainee Daytime Sleepiness and Intraoperative Technical Skill When Performing Septoplasty.

Importance: Daytime sleepiness in surgical trainees can impair intraoperative technical skill and thus affect their learning and pose a risk to patient safety.

Objective: To determine the association between daytime sleepiness of surgeons in residency and fellowship training and their intraoperative technical skill during septoplasty.

Design, Setting, And Participants: This prospective cohort study included 19 surgical trainees in otolaryngology-head and neck surgery programs at 2 academic institutions (Johns Hopkins University School of Medicine and MedStar Georgetown University Hospital).

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery.

Objective: State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging.

Methods: In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes.

Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task.

Background: Surgical tasks are performed in a sequence of steps, and technical skill evaluation includes assessing task flow efficiency. Our objective was to describe differences in task flow for expert and novice surgeons for a basic surgical task.

Methods: We used a hierarchical semantic vocabulary to decompose and annotate maneuvers and gestures for 135 instances of a surgeon's knot performed by 18 surgeons.

Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment.

Objective: Task-level metrics of time and motion efficiency are valid measures of surgical technical skill. Metrics may be computed for segments (maneuvers and gestures) within a task after hierarchical task decomposition. Our objective was to compare task-level and segment (maneuver and gesture)-level metrics for surgical technical skill assessment.

Automated objective surgical skill assessment in the operating room from unstructured tool motion in septoplasty.

Purpose: Previous work on surgical skill assessment using intraoperative tool motion has focused on highly structured surgical tasks such as cholecystectomy and used generic motion metrics such as time and number of movements. Other statistical methods such as hidden Markov models (HMM) and descriptive curve coding (DCC) have been successfully used to assess skill in structured activities on bench-top tasks. Methods to assess skill and provide effective feedback to trainees for unstructured surgical tasks in the operating room, such as tissue dissection in septoplasty, have yet to be developed.

String motif-based description of tool motion for detecting skill and gestures in robotic surgery.

The growing availability of data from robotic and laparoscopic surgery has created new opportunities to investigate the modeling and assessment of surgical technical performance and skill. However, previously published methods for modeling and assessment have not proven to scale well to large and diverse data sets. In this paper, we describe a new approach for simultaneous detection of gestures and skill that can be generalized to different surgical tasks.

Robotic path planning for surgeon skill evaluation in minimally-invasive sinus surgery.

We observe that expert surgeons performing MIS learn to minimize their tool path length and avoid collisions with vital structures. We thus conjecture that an expert surgeon's tool paths can be predicted by minimizing an appropriate energy function. We hypothesize that this reference path will be closer to an expert with greater skill, as measured by an objective measurement instrument such as objective structured assessment of technical skill (OSATS).

An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data.

Background: Assessment of surgical skill plays a crucial role in determining competency, monitoring educational programs, and providing trainee feedback. With the changing health care environment, it will likely play an important role in credentialing and maintenance of certification. The ideal skill assessment tool should be unbiased, objective, and accurate.

Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery.

In the context of minimally invasive surgery, clinical risks are highly associated with surgeons' skill in manipulating surgical tools and their knowledge of the closed anatomy. A quantitative surgical skill assessment can reduce faulty procedures and prevent some surgical risks. In this paper focusing on sinus surgery, we present two methods to identify both skill level and task type by recording motion data of surgical tools as well as the surgeon's eye gaze location on the screen.