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Abstract Schizophrenia is often characterized by recurring relapses, which are associated with a substantial clinical and economic burden. Early identification of individuals at the highest risk for relapse in real-world treatment settings could help improve outcomes and reduce healthcare costs. Prior work has identified a few consistent predictors of relapse in schizophrenia, however, studies to date have been limited to insurance claims data or small patient populations. Thus, this study used a large sample of health systems electronic health record (EHR) data to analyze relationships between patient-level factors and relapse and model a set of factors that can be used to identify the increased prevalence of relapse, a severe and preventable reality of schizophrenia. This retrospective, observational cohort study utilized EHR data extracted from the largest Midwestern U.S. non-profit healthcare system to identify predictors of relapse. The study included patients with a diagnosis of schizophrenia (ICD-10 F20) or schizoaffective disorder (ICD-10 F25) who were treated within the system between October 15, 2016, and December 31, 2021, and received care for at least 12 months. A relapse episode was defined as an emergency room or inpatient encounter with a pre-determined behavioral health-related ICD code. Patients’ baseline characteristics, comorbidities and healthcare utilization were described. Modified log-Poisson regression (i.e. log Poisson regression with a robust variance estimation) analyses were utilized to estimate the prevalence of relapse across patient characteristics, comorbidities and healthcare utilization and to ultimately identify an adjusted model predicting relapse. Among the 8119 unique patients included in the study, 2478 (30.52%) experienced relapse and 5641 (69.48%) experienced no relapse. Patients were primarily male (54.72%), White Non-Hispanic or Latino (54.23%), with Medicare insurance (51.40%), and had baseline diagnoses of substance use (19.24%), overweight/obesity/weight gain (13.06%), extrapyramidal symptoms (48.00%), lipid metabolism disorder (30.66%), hypertension (26.85%), and diabetes (19.08%). Many differences in patient characteristics, baseline comorbidities, and utilization were revealed between patients who relapsed and patients who did not relapse. Through model building, the final adjusted model with all significant predictors of relapse included the following variables: insurance, age, race/ethnicity, substance use diagnosis, extrapyramidal symptoms, number of emergency room encounters, behavioral health inpatient encounters, prior relapses episodes, and long-acting injectable prescriptions written. Prevention of relapse is a priority in schizophrenia care. Challenges related to historical health record data have limited the knowledge of real-world predictors of relapse. This study offers a set of variables that could conceivably be used to construct algorithms or models to proactively monitor demographic, comorbidity, medication, and healthcare utilization parameters which place patients at risk for relapse and to modify approaches to care to avoid future relapse.

Objective: Osteoporosis is a common skeletal disease that greatly increases the risk of pathologic fractures and accounts for approximately 700,000 vertebral compression fractures (VCFs) annually in the United States. Cement augmentation procedures such as balloon kyphoplasty (KP) and percutaneous vertebroplasty (VP) have demonstrated efficacy in the treatment of VCFs, however, some studies report rates of readmission as high as 10.8% following such procedures. The purpose of this study was to employ Machine Learning (ML) algorithms to predict 30-day hospital readmission following cement augmentation procedures for the treatment of VCFs using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Methods: ACS-NSQIP was queried to identify patients undergoing either KP or VP from 2011 to 2014. Three ML algorithms were constructed and tasked with predicting post-operative readmissions within this cohort of patients. Results: Postoperative pneumonia, ASA Class 2 designation, age, partially-dependent functional status, and a history of smoking were independently identified as highly predictive of readmission by all ML algorithms. Among these variables postoperative pneumonia (p < 0.01), ASA Class 2 designation (p < 0.01), age (p = 0.002), and partially-dependent functional status (p < 0.01) were found to be statistically significant. Predictions were generated with an average AUC value of 0.757 and an average accuracy of 80.5%. Conclusions: Postoperative pneumonia, ASA Class 2 designation, partially-dependent functional status, and age are perioperative variables associated with 30-day readmission following cement augmentation procedures. The use of ML allows for quantification of the relative contributions of these variables toward producing readmission.

Abstract Temperature in the body and the tumor reflects physiological and pathological conditions. A reliable, contactless, and simplistic measurement system can be used for long-term monitoring of disease progression and therapy response. In this study, miniaturized battery-free wireless chips implanted into growing tumors on small animals were used to capture both basal and tumor temperature dynamics. Three preclinical models: melanoma (B16), breast cancer (4T1), and colon cancer (MC-38), were treated with adoptive T cell transfer, AC-T chemotherapy, and anti-PD-1 immunotherapy respectively. Each model presents a distinctive pattern of temperature history dependent on the tumor characteristic and influenced by the administered therapy. Certain features are associated with positive therapeutic response, for instance the transient reduction of body and tumor temperature following adaptive T cell transfer, the elevation of tumor temperature following chemotherapy, and a steady decline of body temperature following anti-PD-1 therapy. Tracking in vivo thermal activity by cost-effective telemetric sensing has the potential of offering earlier treatment assessment to patients without requiring complex imaging or lab testing. Multi-parametric on-demand monitoring of tumor microenvironment by permanent implants and its integration into health information systems could further advance cancer management and reduce patient burden.

[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] In this paper, we describe the results of a research project whose goals were to (1) develop and implement video-based experimental investigations using the Investigative Science Learning Environment (ISLE) approach and (2) study how students who engage with video experiments develop scientific abilities and learn physics ideas in comparison to students who do the same investigations using physical apparatus. We developed six parallel ISLE-based investigations for the students to engage in, either with apparatus or with video arrays created in the Pivot platform. We found that substituting 30% of the apparatus-based activities with video-based activities did not affect student development of conceptual physics knowledge. On the other hand, the development of certain scientific abilities was significantly affected by whether students experimented with physical apparatus or used video experiments.