Wireless, soft electronics for rapid, multisensor measurements of hydration levels in healthy and diseased skin.

Precise, quantitative measurements of the hydration status of skin can yield important insights into dermatological health and skin structure and function, with additional relevance to essential processes of thermoregulation and other features of basic physiology. Existing tools for determining skin water content exploit surrogate electrical assessments performed with bulky, rigid, and expensive instruments that are difficult to use in a repeatable manner. Recent alternatives exploit thermal measurements using soft wireless devices that adhere gently and noninvasively to the surface of the skin, but with limited operating range (∼1 cm) and high sensitivity to subtle environmental fluctuations. This paper introduces a set of ideas and technologies that overcome these drawbacks to enable high-speed, robust, long-range automated measurements of thermal transport properties via a miniaturized, multisensor module controlled by a long-range (∼10 m) Bluetooth Low Energy system on a chip, with a graphical user interface to standard smartphones. Soft contact to the surface of the skin, with almost zero user burden, yields recordings that can be quantitatively connected to hydration levels of both the epidermis and dermis, using computational modeling techniques, with high levels of repeatability and insensitivity to ambient fluctuations in temperature. Systematic studies of polymers in layered configurations similar to those of human skin, of porcine skin with known levels of hydration, and of human subjects with benchmarks against clinical devices validate the measurement approach and associated sensor hardware. The results support capabilities in characterizing skin barrier function, assessing severity of skin diseases, and evaluating cosmetic and medication efficacy, for use in the clinic or in the home.

Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention.

STUDY OBJECTIVE: The Geriatric Emergency Department Innovations (GEDI) program is a nurse-based geriatric assessment and care coordination program that reduces preventable admissions for older adults. Unfortunately, only 5% of older adults receive GEDI care because of resource limitations. The objective of this study was to predict the likelihood of hospitalization accurately and consistently with and without GEDI care using machine learning models to better target patients for the GEDI program. METHODS: We performed a cross-sectional observational study of emergency department (ED) patients between 2010 and 2018. Using propensity-score matching, GEDI patients were matched to other older adult patients. Multiple models, including random forest, were used to predict hospital admission. Multiple second-layer models, including random forest, were then used to predict whether GEDI assessment would change predicted hospital admission. Final model performance was reported as the area under the curve using receiver operating characteristic models. RESULTS: We included 128,050 patients aged over 65 years. The random forest ED disposition model had an area under the curve of 0.774 (95% confidence interval [CI] 0.741 to 0.806). In the random forest GEDI change-in-disposition model, 24,876 (97.3%) ED visits were predicted to have no change in disposition with GEDI assessment, and 695 (2.7%) ED visits were predicted to have a change in disposition with GEDI assessment. CONCLUSION: Our machine learning models could predict who will likely be discharged with GEDI assessment with good accuracy and thus select a cohort appropriate for GEDI care. In addition, future implementation through integration into the electronic health record may assist in selecting patients to be prioritized for GEDI care.

A wireless, skin-interfaced biosensor for cerebral hemodynamic monitoring in pediatric care.

The standard of clinical care in many pediatric and neonatal neurocritical care units involves continuous monitoring of cerebral hemodynamics using hard-wired devices that physically adhere to the skin and connect to base stations that commonly mount on an adjacent wall or stand. Risks of iatrogenic skin injuries associated with adhesives that bond such systems to the skin and entanglements of the patients and/or the healthcare professionals with the wires can impede clinical procedures and natural movements that are critical to the care, development, and recovery of pediatric patients. This paper presents a wireless, miniaturized, and mechanically soft, flexible device that supports measurements quantitatively comparable to existing clinical standards. The system features a multiphotodiode array and pair of light-emitting diodes for simultaneous monitoring of systemic and cerebral hemodynamics, with ability to measure cerebral oxygenation, heart rate, peripheral oxygenation, and potentially cerebral pulse pressure and vascular tone, through the utilization of multiwavelength reflectance-mode photoplethysmography and functional near-infrared spectroscopy. Monte Carlo optical simulations define the tissue-probing depths for source-detector distances and operating wavelengths of these systems using magnetic resonance images of the head of a representative pediatric patient to define the relevant geometries. Clinical studies on pediatric subjects with and without congenital central hypoventilation syndrome validate the feasibility for using this system in operating hospitals and define its advantages relative to established technologies. This platform has the potential to substantially enhance the quality of pediatric care across a wide range of conditions and use scenarios, not only in advanced hospital settings but also in clinics of lower- and middle-income countries.

The Accuracy of Wearable Photoplethysmography Sensors for Telehealth Monitoring: A Scoping Review.

Background and Objectives: Photoplethysmography (PPG) sensors have been increasingly used for remote patient monitoring, especially during the COVID-19 pandemic, for the management of chronic diseases and neurological disorders. There is an urgent need to evaluate the accuracy of these devices. This scoping review considers the latest applications of wearable PPG sensors with a focus on studies that used wearable PPG sensors to monitor various health parameters. The primary objective is to report the accuracy of the PPG sensors in both real-world and clinical settings. Methods: This scoping review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Studies were identified by querying the Medline, Embase, IEEE, and CINAHL databases. The goal was to capture eligible studies that used PPG sensors to monitor various health parameters for populations with a minimum of 30 participants, with at least some of the population having relevant health issues. A total of 2,996 articles were screened and 28 are included in this review. Results: The health parameters and disorders identified and investigated in this study include heart rate and heart rate variability, atrial fibrillation, blood pressure (BP), obstructive sleep apnea, blood glucose, heart failure, and respiratory rate. An overview of the algorithms used, and their limitations is provided. Conclusion: Some of the barriers identified in evaluating the accuracy of multiple types of wearable devices include the absence of reporting standard accuracy metrics and a general paucity of studies with large subject size in real-world settings, especially for parameters such as BP.

Catalytic alkylation of remote C-H bonds enabled by proton-coupled electron transfer.

Despite advances in hydrogen atom transfer (HAT) catalysis, there are currently no molecular HAT catalysts that are capable of homolysing the strong nitrogen-hydrogen (N-H) bonds of N-alkyl amides. The motivation to develop amide homolysis protocols stems from the utility of the resultant amidyl radicals, which are involved in various synthetically useful transformations, including olefin amination and directed carbon-hydrogen (C-H) bond functionalization. In the latter process-a subset of the classical Hofmann-Löffler-Freytag reaction-amidyl radicals remove hydrogen atoms from unactivated aliphatic C-H bonds. Although powerful, these transformations typically require oxidative N-prefunctionalization of the amide starting materials to achieve efficient amidyl generation. Moreover, because these N-activating groups are often incorporated into the final products, these methods are generally not amenable to the direct construction of carbon-carbon (C-C) bonds. Here we report an approach that overcomes these limitations by homolysing the N-H bonds of N-alkyl amides via proton-coupled electron transfer. In this protocol, an excited-state iridium photocatalyst and a weak phosphate base cooperatively serve to remove both a proton and an electron from an amide substrate in a concerted elementary step. The resultant amidyl radical intermediates are shown to promote subsequent C-H abstraction and radical alkylation steps. This C-H alkylation represents a catalytic variant of the Hofmann-Löffler-Freytag reaction, using simple, unfunctionalized amides to direct the formation of new C-C bonds. Given the prevalence of amides in pharmaceuticals and natural products, we anticipate that this method will simplify the synthesis and structural elaboration of amine-containing targets. Moreover, this study demonstrates that concerted proton-coupled electron transfer can enable homolytic activation of common organic functional groups that are energetically inaccessible using traditional HAT-based approaches.

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. METHODS: Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. RESULTS: 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. CONCLUSIONS: The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

Monitoring and responding to signals of suicidal ideation in pragmatic clinical trials: Lessons from the GRACE trial for Chronic Sickle Cell Disease Pain.

Sickle cell disease (SCD) is a hemoglobin disorder and the most common genetic disorder that affects 100,000 Americans and millions worldwide. Adults living with SCD have pain so severe that it often requires opioids to keep it in control. Depression is a major global public health concern associated with an increased risk in chronic medical disorders, including in adults living with sickle cell disease (SCD). A strong relationship exists between suicidal ideation, suicide attempts, and depression. Researchers enrolling adults living with SCD in pragmatic clinical trials are obligated to design their methods to deliberately monitor and respond to symptoms related to depression and suicidal ideation. This will offer increased protection for their participants and help clinical investigators meet their fiduciary duties. This article presents a review of this sociotechnical milieu that highlights, analyzes, and offers recommendations to address ethical considerations in the development of protocols, procedures, and monitoring activities related to suicidality in depressed patients in a pragmatic clinical trial.

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat. OBJECTIVE: The purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission. DESIGN: Prospective clinical outcomes evaluation. SETTING: Evaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres. PARTICIPANTS: Analyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay ('sepsis-related' patients). INTERVENTIONS: Machine learning algorithm for severe sepsis prediction. OUTCOME MEASURES: In-hospital mortality, length of stay and 30-day readmission rates. RESULTS: Hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis. CONCLUSIONS: Reductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings. TRIAL REGISTRATION NUMBER: NCT03960203.