Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables

Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges

A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.

Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device

Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal participants (control group). We examined 172 eyes at the Ophthalmology Clinic of the "Elpis" General Hospital of Athens between October 2022 and September 2023. In addition, we investigated the glaucoma classification in terms of the following: (a) eye selection and (b) gender. Our methodology was based on the features extracted via two diagnostic optical systems: (i) conventional optical coherence tomography (OCT) and (ii) a modern RETeval portable device. The machine-learning approach comprised three different classifiers: the Bayesian, the Probabilistic Neural Network (PNN), and Support Vectors Machines (SVMs). For all cases examined, classification accuracy was found to be significantly higher when using the RETeval device with respect to the OCT system, as follows: 14.7% for all participants, 13.4% and 29.3% for eye selection (right and left, respectively), and 25.6% and 22.6% for gender (male and female, respectively). The most efficient classifier was found to be the SVM compared to the PNN and Bayesian classifiers. In summary, all aforementioned comparisons demonstrate that the RETeval device has the advantage over the OCT system for the classification of glaucoma patients by using the machine-learning approach.

Wearable Sensors as a Preoperative Assessment Tool: A Review

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

EVIDENCE FOR BENEFICIAL USE OF THE COMPENSATORY RESERVE MEASUREMENT IN GUIDING INTRAOPERATIVE RESUSCITATION: A PROSPECTIVE COHORT STUDY OF ORTHOTOPIC LIVER TRANSPLANT RECIPIENTS

ABSTRACT

Introduction: The compensatory reserve measurement (CRM) is a continuous noninvasive monitoring technology that provides an assessment of the integrated capacity of all physiological mechanisms associated with responses to a hypovolemic stressor such as hemorrhagic shock. No prior studies have analyzed its use for intraoperative resuscitation guidance. Methods: A prospective observational study was conducted of 23 patients undergoing orthotopic liver transplant. Chart review was performed to identify timing of various intraoperative events. Data were compared based on predefined thresholds for existence of hemorrhagic shock: CRM lower than 40%, systolic blood pressure (SBP) lower than 90 mm Hg (SBP90), and heart rate (HR) higher than 100 beats per minute (HR100). Regression analysis was performed for predicting resuscitation events, and nonlinear eXtreme Gradient Boosting (XGBoost) models were used to compare CRM with standard vital sign measures. Results: Events where CRM dropped lower than 40% were 2.25 times more likely to lead to an intervention, whereas HR100 and SBP90 were not associated with intraoperative interventions. XGBoost prediction models showed superior discriminatory capacity of CRM alone compared with the model with SBP and HR and no difference when all three were combined (CRM-HR-SBP). All XGBoost models outperformed equivalent linear regression models. Conclusion: These results demonstrate that CRM can provide an adjunctive clinical tool that can augment early and accurate of hemodynamic compromise and promote goal-directed resuscitation in the perioperative setting.