Multimodal prehabilitation and postoperative outcomes in upper abdominal surgery: systematic review and meta-analysis.

The impact of multimodal prehabilitation on postoperative complications in upper abdominal surgeries is understudied. This review analyzes randomized trials on multimodal prehabilitation with patient and hospital outcomes. MEDLINE, Embase, CINAHL, and Cochrane CENTRAL were searched for trials on prehabilitation before elective (non-emergency) abdominal surgery. Two reviewers independently screened studies, extracted data, and assessed study quality. Primary outcomes of interest were postoperative pulmonary complications (PPCs) and all-cause complications; secondary outcomes included hospital and intensive care length of stay. A meta-analysis with random-effect models was performed, and heterogeneity was evaluated with I-square and Cochran's Q test. Dichotomous variables were reported in log-odds ratio and continuous variables were presented as mean difference. Ten studies (total 1503 patients) were included. Odds of developing complications after prehabilitation were significantly lower compared to various control groups (- 0.38 [- 0.75- - 0.004], P = 0.048). Five studies described PPCs, and participants with prehabilitation had decreased odds of PPC (- 0.96 [- 1.38- - 0.54], P < 0.001). Prehabilitation did not significantly reduce length of stay, unless exercise was implemented; with exercise, hospital stay decreased significantly (- 0.91 [- 1.67- - 0.14], P = 0.02). Multimodal prehabilitation may decrease complications in upper abdominal surgery, but not necessarily length of stay; research should address heterogeneity in the literature.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.

Understanding Heart Rate Reactions to Post-Traumatic Stress Disorder (PTSD) Among Veterans: A Naturalistic Study.

OBJECTIVE: We collected naturalistic heart rate data from veterans diagnosed with post-traumatic stress disorder (PTSD) to investigate the effects of various factors on heart rate. BACKGROUND: PTSD is prevalent among combat veterans in the United States. While a positive correlation between PTSD and heart rate has been documented, specific heart rate profiles during the onset of PTSD symptoms remain unknown. METHOD: Veterans were recruited during five cycling events in 2017 and 2018 to record resting and activity-related heart rate data using a wrist-worn device. The device also logged self-reported PTSD hyperarousal events. Regression analyses were performed on demographic and behavioral covariates including gender, exercise, antidepressants, smoking habits, sleep habits, average heart rate during reported hyperarousal events, age, glucocorticoids consumption, and alcohol consumption. Heart rate patterns during self-reported PTSD hyperarousal events were analyzed using Auto Regressive Integrated Moving Average (ARIMA). Heart rate data were also compared to an open-access non-PTSD representative case. RESULTS: Of 99 veterans with PTSD, 91 participants reported at least one hyperarousal event, with a total of 1023 events; demographic information was complete for 38 participants who formed the subset for regression analyses. The results show that factors including smoking, sleeping, gender, and medication significantly affect resting heart rate. Moreover, unique heart rate patterns associated with PTSD symptoms in terms of stationarity, autocorrelation, and fluctuation characteristics were identified. CONCLUSION: Our findings show distinguishable heart rate patterns and characteristics during PTSD hyperarousal events. APPLICATION: These findings show promise for future work to detect the onset of PTSD symptoms.

Atherosclerotic Cardiovascular Disease, Cancer, and Financial Toxicity Among Adults in the United States.

BACKGROUND: Financial toxicity (FT) is a well-established side-effect of the high costs associated with cancer care. In recent years, studies have suggested that a significant proportion of those with atherosclerotic cardiovascular disease (ASCVD) experience FT and its consequences. OBJECTIVES: This study aimed to compare FT for individuals with neither ASCVD nor cancer, ASCVD only, cancer only, and both ASCVD and cancer. METHODS: From the National Health Interview Survey, we identified adults with self-reported ASCVD and/or cancer between 2013 and 2018, stratifying results by nonelderly (age <65 years) and elderly (age ≥65 years). We defined FT if any of the following were present: any difficulty paying medical bills, high financial distress, cost-related medication nonadherence, food insecurity, and/or foregone/delayed care due to cost. RESULTS: The prevalence of FT was higher among those with ASCVD when compared with cancer (54% vs. 41%; p < 0.001). When studying the individual components of FT, in adjusted analyses, those with ASCVD had higher odds of any difficulty paying medical bills (odds ratio [OR]: 1.22; 95% confidence interval [CI]: 1.09 to 1.36), inability to pay bills (OR: 1.25; 95% CI: 1.04 to 1.50), cost-related medication nonadherence (OR: 1.28; 95% CI: 1.08 to 1.51), food insecurity (OR: 1.39; 95% CI: 1.17 to 1.64), and foregone/delayed care due to cost (OR: 1.17; 95% CI: 1.01 to 1.36). The presence of ≥3 of these factors was significantly higher among those with ASCVD and those with both ASCVD and cancer when compared with those with cancer (23% vs. 30% vs. 13%, respectively; p < 0.001). These results remained similar in the elderly population. CONCLUSIONS: Our study highlights that FT is greater among patients with ASCVD compared with those with cancer, with the highest burden among those with both conditions.

Deriving Information Requirements for a Smart Nursing System for Intensive Care Units.

The workplace environment for intensive care nursing is highly stressful, with long working hours and a dynamic workload that may induce fatigue. The resulting stress and fatigue may reduce nurses' efficiency and may contribute to medical errors. A smart wearable system is being designed to help nurses who experience high levels of stress and fatigue at work. This article documents the systematic process of deriving information requirements from 2 focus groups conducted separately with nurses and nurse managers working in various Southeastern Texas hospitals. While nurses expected functionality such as memory aid tools, health assessment, and stress-reducing exercises, nurse managers expected information about the overall status of the unit's fatigue/stress levels as well as nurses' communication and movement patterns. The derived information requirements will act as an objective assessment of needs and would set the stage for the design of a stress-monitoring tool.