Assessing the feasibility of digital keypress statistics to detect seizures and capture cognitive impairment in patients with epilepsy: A pilot study.

BACKGROUND: Efficient, non-invasive monitoring may provide a more accurate and comprehensive understanding of seizure frequency and the development of some comorbidities in people with epilepsy. Novel keyboard technology measuring digital keypress statistics has demonstrated its practical value for neurodegenerative diseases including Parkinson's Disease and Dementia. Smartphones integrated into daily life may serve as a low-burden longitudinal monitoring system for patients with epilepsy. OBJECTIVE: This study aimed to assess the feasibility of keyboard statistics as an objective measure of seizure frequency for patients with epilepsy, in addition to tracking differences between cognitively normal and cognitively impaired patients. METHODS: Six adult patients admitted to the Epilepsy Monitoring Unit (EMU) at Mayo Clinic in Rochester, Minnesota were studied. The keyboard was installed on the patient's smartphone. In the EMU, typing statistics were correlated to electroencephalogram (EEG) confirmed seizures. After discharge, participants continued using their keyboards and kept a seizure log. We also analyzed the key press/release times and usage of participants' keyboards for adherence. RESULTS: Keyboard sessions during and after seizures assessed for key press/release differences versus baseline showed no statistically significant difference (p = 0.44). Using one-way ANOVA, cognitive impairment's potential impact on keyboard statistics was explored in patients who had neuropsychological testing (N = 3). Significant differences were found between patients with and without cognitive impairment (p < 0.001). No significant difference was noted between patients with mild intellectual disability and normal cognitive function (p = 0.55).

Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized AF. We pursued this study to determine if the AI-ECG model differentiates between patients with ESUS and those with known causes of stroke, and to evaluate whether the AF prediction by AI-ECG among patients with ESUS was associated with the results of prolonged ambulatory cardiac rhythm monitoring. MATERIALS AND METHODS: We reviewed consecutive patients admitted with acute ischemic stroke to a comprehensive stroke center between January 2018 and August 2019 and employed the TOAST classification to categorize the mechanisms of ischemia. Use and results of ambulatory cardiac rhythm monitoring after discharge were gathered. We ran the AI-ECG model to obtain AF probabilities from all ECGs acquired during the hospitalization and compared those probabilities in patients with ESUS versus those with known stroke causes (apart from AF), and between patients with and without AF detected by ambulatory cardiac rhythm monitoring. RESULTS: The study cohort had 930 patients, including 263 patients (28.3%) with known AF or AF diagnosed during the index hospitalization and 265 cases (28.5%) categorized as ESUS. Ambulatory cardiac rhythm monitoring was performed in 226 (85.3%) patients with ESUS. AF probability by AI-ECG was not associated with ESUS. However, among patients with ESUS, the probability of AF by AI-ECG was associated with a higher likelihood of AF detection by ambulatory monitoring (P = 0.004). A probability of AF by AI-ECG greater than 0.20 was associated with AF detection by ambulatory cardiac rhythm monitoring with an OR of 5.47 (95% CI 1.51-22.51). CONCLUSIONS: AI-ECG may help guide the use of prolonged ambulatory cardiac rhythm monitoring in patients with ESUS to identify those who might benefit from anticoagulation.

Examining the weekend effect across ICU performance metrics.

BACKGROUND: Known colloquially as the "weekend effect," the association between weekend admissions and increased mortality within hospital settings has become a highly contested topic over the last two decades. Drawing interest from practitioners and researchers alike, a sundry of works have emerged arguing for and against the presence of the effect across various patient cohorts. However, it has become evident that simply studying population characteristics is insufficient for understanding how the effect manifests. Rather, to truly understand the effect, investigations into its underlying factors must be considered. As such, the work presented in this manuscript serves to address this consideration by moving beyond identification of patient cohorts to examining the role of ICU performance. METHODS: Employing a comprehensive, publicly available database of electronic medical records (EMR), we began by utilizing multiple logistic regression to identify and isolate a specific cohort in which the weekend effect was present. Next, we leveraged the highly detailed nature of the EMR to evaluate ICU performance using well-established ICU quality scorecards to assess differences in clinical factors among patients admitted to an ICU on the weekend versus weekday. RESULTS: Our results demonstrate the weekend effect to be most prevalent among emergency surgery patients (OR 1.53; 95% CI 1.19, 1.96), specifically those diagnosed with circulatory diseases (P<.001). Differences between weekday and weekend admissions for this cohort included a variety of clinical factors such as ventilatory support and night-time discharges. CONCLUSIONS: This work reinforces the importance of accounting for differences in clinical factors as well as patient cohorts in studies investigating the weekend effect.

Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice.

Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team's technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encountered with real-world implementation and maintenance, and included the source code for the platform. Our monitoring platform was built as an R shiny application, developed and implemented over the course of 6 months. The platform has been used and maintained for 2 years and is still in use as of July 2023. The considerations necessary for the implementation of the monitoring platform center around 4 pillars: feasibility (what resources can be used for platform development?); design (through what statistics or models will the model be monitored, and how will these results be efficiently displayed to the end user?); implementation (how will this platform be built, and where will it exist within the IT ecosystem?); and policy (based on monitoring feedback, when and what actions will be taken to fix problems, and how will these problems be translated to clinical staff?). While much of the literature surrounding ML performance monitoring emphasizes methodological approaches for capturing changes in performance, there remains a battery of other challenges and considerations that must be addressed for successful real-world implementation.

Integrating 4 methods to evaluate physical function in patients with cancer (In4M): protocol for a prospective cohort study.

INTRODUCTION: Accurate, patient-centred evaluation of physical function in patients with cancer can provide important information on the functional impacts experienced by patients both from the disease and its treatment. Increasingly, digital health technology is facilitating and providing new ways to measure symptoms and function. There is a need to characterise the longitudinal measurement characteristics of physical function assessments, including clinician-reported outcome, patient-reported ported outcome (PRO), performance outcome tests and wearable data, to inform regulatory and clinical decision-making in cancer clinical trials and oncology practice. METHODS AND ANALYSIS: In this prospective study, we are enrolling 200 English-speaking and/or Spanish-speaking patients with breast cancer or lymphoma seen at Mayo Clinic or Yale University who will receive intravenous cytotoxic chemotherapy. Physical function assessments will be obtained longitudinally using multiple assessment modalities. Participants will be followed for 9 months using a patient-centred health data aggregating platform that consolidates study questionnaires, electronic health record data, and activity and sleep data from a wearable sensor. Data analysis will focus on understanding variability, sensitivity and meaningful changes across the included physical function assessments and evaluating their relationship to key clinical outcomes. Additionally, the feasibility of multimodal physical function data collection in real-world patients with breast cancer or lymphoma will be assessed, as will patient impressions of the usability and acceptability of the wearable sensor, data aggregation platform and PROs. ETHICS AND DISSEMINATION: This study has received approval from IRBs at Mayo Clinic, Yale University and the US Food and Drug Administration. Results will be made available to participants, funders, the research community and the public. TRIAL REGISTRATION NUMBER: NCT05214144; Pre-results.

Longitudinal trajectories of glycemic control among U.S. Adults with newly diagnosed diabetes.

AIMS: To identify longitudinal trajectories of glycemic control among adults with newly diagnosed diabetes, overall and by diabetes type. METHODS: We analyzed claims data from OptumLabs® Data Warehouse for 119,952 adults newly diagnosed diabetes between 2005 and 2018. We applied a novel Mixed Effects Machine Learning model to identify longitudinal trajectories of hemoglobin A1c (HbA1c) over 3 years of follow-up and used multinomial regression to characterize factors associated with each trajectory. RESULTS: The study population was comprised of 119,952 adults with newly diagnosed diabetes, including 696 (0.58%) with type 1 diabetes. Among patients with type 1 diabetes, 52.6% were diagnosed at very high HbA1c, partially improved, but never achieved control; 32.5% were diagnosed at low HbA1c and deteriorated over time; and 14.9% had stable low HbA1c. Among patients with type 2 diabetes, 67.7% had stable low HbA1c, 14.4% were diagnosed at very high HbA1c, partially improved, but never achieved control; 10.0% were diagnosed at moderately high HbA1c and deteriorated over time; and 4.9% were diagnosed at moderately high HbA1c and improved over time. CONCLUSIONS: Claims data identified distinct longitudinal trajectories of HbA1c after diabetes diagnosis, which can be used to anticipate challenges and individualize care plans to improve glycemic control.

Integrating 4 Measures to Evaluate Physical Function in Patients with Cancer (In4M): Protocol for a prospective study.

Introduction: Accurate, patient-centered evaluation of physical function in patients with cancer can provide important information on the functional impacts experienced by patients both from the disease and its treatment. Increasingly, digital health technology is facilitating and providing new ways to measure symptoms and function. There is a need to characterize the longitudinal measurement characteristics of physical function assessments, including clinician-reported physical function (ClinRo), patient-reported physical function (PRO), performance outcome tests (PerfO) and wearable data, to inform regulatory and clinical decision-making in cancer clinical trials and oncology practice. Methods and analysis: In this prospective study, we are enrolling 200 English- and/or Spanish-speaking patients with breast cancer or lymphoma seen at Mayo Clinic or Yale University who will receive standard of care intravenous cytotoxic chemotherapy. Physical function assessments will be obtained longitudinally using multiple assessment modalities. Participants will be followed for 9 months using a patient-centered health data aggregating platform that consolidates study questionnaires, electronic health record data, and activity and sleep data from a wearable sensor. Data analysis will focus on understanding variability, sensitivity, and meaningful changes across the included physical function assessments and evaluating their relationship to key clinical outcomes. Additionally, the feasibility of multi-modal physical function data collection in real-world patients with cancer will be assessed, as will patient impressions of the usability and acceptability of the wearable sensor, data aggregation platform, and PROs. Ethics and dissemination: This study has received approval from IRBs at Mayo Clinic, Yale University, and the U.S. Food & Drug Administration. Results will be made available to participants, funders, the research community, and the public. Registration Details: The trial registration number for this study is NCT05214144. Strengths & Limitations: This study addresses an important unmet need by characterizing the performance characteristics of multiple patient-centered physical function measures in patients with cancerPhysical function is an important and undermeasured clinical outcome. Scientifically rigorous capture and measurement of physical function constitutes a key component of cancer treatment tolerability assessment both from a regulatory and clinical perspective.This study will include patients with lymphoma or breast cancer receiving a broad range of cytotoxic chemotherapy regimens. While recruitment will occur at two academic sites, patients who ultimately receive treatment at local community sites will be included.A patient-centered health data aggregating platform facilitates the delivery of patient-reported outcome measures and collection of wearable data to researchers, while reducing patient burden compared to traditional patient-generated data collection and aggregation methodsHeterogeneity in patient willingness or comfort engaging with mobile products including smartphones and wearables, enrollment primarily at large academic centers, and the modest sample size are potential limitations to the external validity of the study.

Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records.

Idiopathic pulmonary fibrosis (IPF) is a lethal fibrosing interstitial lung disease with a mean survival time of less than 5 years. Nonspecific presentation, a lack of effective early screening tools, unclear pathobiology of early-stage IPF and the need for invasive and expensive procedures for diagnostic confirmation hinder early diagnosis. In this study, we introduce a new screening tool for IPF in primary care settings that requires no new laboratory tests and does not require recognition of early symptoms. Using subtle comorbidity signatures identified from the history of medical encounters of individuals, we developed an algorithm, called the zero-burden comorbidity risk score for IPF (ZCoR-IPF), to predict the future risk of an IPF diagnosis. ZCoR-IPF was trained on a national insurance claims database and validated on three independent databases, comprising a total of 2,983,215 participants, with 54,247 positive cases. The algorithm achieved positive likelihood ratios greater than 30 at a specificity of 0.99 across different cohorts, for both sexes, and for participants with different risk states and history of confounding diseases. The area under the receiver-operating characteristic curve for ZCoR-IPF in predicting IPF exceeded 0.88 and was approximately 0.84 at 1 and 4 years before a conventional diagnosis, respectively. Thus, if adopted, ZCoR-IPF can potentially enable earlier diagnosis of IPF and improve outcomes of disease-modifying therapies and other interventions.

Examining Response to Negative Life Events Through Fitness Tracker Data.

Negative life events, such as the death of a loved one, are an unavoidable part of life. These events can be overwhelmingly stressful and may lead to the development of mental health disorders. To mitigate these adverse developments, prior literature has utilized measures of psychological responses to negative life events to better understand their effects on mental health. However, psychological changes represent only one aspect of an individual's potential response. We posit measuring additional dimensions of health, such as physical health, may also be beneficial, as physical health itself may be affected by negative life events and measuring its response could provide context to changes in mental health. Therefore, the primary aim of this work was to quantify how an individual's physical health changes in response to negative life events by testing for deviations in their physiological and behavioral state (PB-state). After capturing post-event, PB-state responses, our second aim sought to contextualize changes within known factors of psychological response to negative life events, namely coping strategies. To do so, we utilized a cohort of professionals across the United States monitored for 1 year and who experienced a negative life event while under observation. Garmin Vivosmart-3 devices provided a multidimensional representation of one's PB-state by collecting measures of resting heart rate, physical activity, and sleep. To test for deviations in PB-state following negative life events, One-Class Support Vector Machines were trained on a window of time prior to the event, which established a PB-state baseline. The model then evaluated participant's PB-state on the day of the life event and each day that followed, assigning each day a level of deviance relative to the participant's baseline. Resulting response curves were then examined in association with the use of various coping strategies using Bayesian gamma-hurdle regression models. The results from our objectives suggest that physical determinants of health also deviate in response to negative life events and that these deviations can be mitigated through different coping strategies. Taken together, these observations stress the need to examine physical determinants of health alongside psychological determinants when investigating the effects of negative life events.

Deviations from normal bedtimes are associated with short-term increases in resting heart rate.

Despite proper sleep hygiene being critical to our health, guidelines for improving sleep habits often focus on only a single component, namely, sleep duration. Recent works, however, have brought to light the importance of another aspect of sleep: bedtime regularity, given its ties to cognitive and metabolic health outcomes. To further our understanding of this often-neglected component of sleep, the objective of this work was to investigate the association between bedtime regularity and resting heart rate (RHR): an important biomarker for cardiovascular health. Utilizing Fitbit Charge HRs to measure bedtimes, sleep and RHR, 255,736 nights of data were collected from a cohort of 557 college students. We observed that going to bed even 30 minutes later than one's normal bedtime was associated with a significantly higher RHR throughout sleep (Coeff +0.18; 95% CI: +0.11, +0.26 bpm), persisting into the following day and converging with one's normal RHR in the early evening. Bedtimes of at least 1 hour earlier were also associated with significantly higher RHRs throughout sleep; however, they converged with one's normal rate by the end of the sleep session, not extending into the following day. These observations stress the importance of maintaining proper sleep habits, beyond sleep duration, as high variability in bedtimes may be detrimental to one's cardiovascular health.

Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data.

BACKGROUND: Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data. OBJECTIVE: The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies. METHODS: Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks' output in supporting MVPA behavior change studies, we applied it to 2 case studies. RESULTS: Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes. CONCLUSIONS: By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.

Social network structure is predictive of health and wellness.

Social networks influence health-related behavior, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behavior, it is less understood how the structure or topology of the network, in itself, impacts an individual's health behavior and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual's health and wellness. We develop a method called the Network-Driven health predictor (NetCARE) that leverages features representative of social network structure. Using a large longitudinal data set of students enrolled in the NetHealth study at the University of Notre Dame, we show that the NetCARE method improves the overall prediction performance over the baseline models-that use demographics and physical attributes-by 38%, 65%, 55%, and 54% for the wellness states-stress, happiness, positive attitude, and self-assessed health-considered in this paper.

Findings from a mobile application-based cohort are consistent with established knowledge of the menstrual cycle, fertile window, and conception.

OBJECTIVE: To investigate the validity of self-reported fertility data generated by a mobile application-based cohort in comparison with data collected by traditional clinical methodologies. DESIGN: Data were collected from July 2013 to July 2018 through a mobile application designed to track fertility. Bayesian hierarchical models were used to assess day-specific pregnancy probabilities. Descriptive statistics were used to estimate differences in day of ovulation and lengths of menstrual phases and to assess changes in the cervix and ovulation-related symptoms drawing closer to the day of ovulation. SETTING: Not applicable. PATIENT(S): Data consisted of 225,596 menstrual cycles from 98,903 women. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Day-specific probabilities of pregnancy, variability in lengths of the follicular and luteal phases, trends in prevalence of symptoms and cervix changes across the fertile window. RESULT(S): Analyses were consistent with established clinical knowledge. Probability of conception was highest during the 5 days before and day of ovulation, with the highest probability occurring the day before ovulation. The average cycle length was 29.6 days, and average lengths of the follicular and luteal phases were 15.8 and 13.7 days, respectively. Closer to day of ovulation, women were more likely to report changes in the cervix corresponding to fluid consistency, feel, position, and openness and symptoms associated with ovulation, including pelvic pain, tender breasts, increased sex drive, and cramps. CONCLUSION(S): Components of the menstrual cycle and fertile window, when re-evaluated with a mobile application-based cohort, were found to be consistent with established clinical knowledge, suggesting an agreement between traditional and modern data collection methodologies.