Astrocytic BDNF signaling within the ventromedial hypothalamus regulates energy homeostasis

Brain-derived neurotrophic factor (BDNF) is essential for maintaining energy and glucose balance within the central nervous system. Because the study of its metabolic actions has been limited to effects in neuronal cells, its role in other cell types within the brain remains poorly understood. Here we show that astrocytic BDNF signaling within the ventromedial hypothalamus (VMH) modulates neuronal activity in response to changes in energy status. This occurs via the truncated TrkB.T1 receptor. Accordingly, either fasting or central BDNF depletion enhances astrocytic synaptic glutamate clearance, thereby decreasing neuronal activity in mice. Notably, selective depletion of TrkB.T1 in VMH astrocytes blunts the effects of energy status on excitatory transmission, as well as on responses to leptin, glucose and lipids. These effects are driven by increased astrocytic invasion of excitatory synapses, enhanced glutamate reuptake and decreased neuronal activity. We thus identify BDNF/TrkB.T1 signaling in VMH astrocytes as an essential mechanism that participates in energy and glucose homeostasis.

Provider- and Patient-Related Barriers to and Facilitators of Digital Health Technology Adoption for Hypertension Management: Scoping Review

Background

The uptake of digital health technology (DHT) has been surprisingly low in clinical practice. Despite showing great promise to improve patient outcomes and disease management, there is limited information on the factors that contribute to the limited adoption of DHT, particularly for hypertension management.

Objective

This scoping review provides a comprehensive summary of barriers to and facilitators of DHT adoption for hypertension management reported in the published literature with a focus on provider- and patient-related barriers and facilitators.

Methods

This review followed the methodological framework developed by Arskey and O’Malley. Systematic literature searches were conducted on PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica database. Articles that reported on barriers to and/or facilitators of digital health adoption for hypertension management published in English between 2008 and 2017 were eligible. Studies not reporting on barriers or facilitators to DHT adoption for management of hypertension were excluded. A total of 2299 articles were identified based on the above criteria after removing duplicates, and they were assessed for eligibility. Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria), resulting in a final set of 32 articles. In addition, 4 handpicked articles were also included in the review, making it a total of 36 studies.

Results

A total of 36 studies were selected for data extraction after abstract and full-text screening by 2 independent reviewers. All conflicts were resolved by a third reviewer. Thematic analysis was conducted to identify major themes pertaining to barriers and facilitators of DHT from both provider and patient perspectives. The key facilitators of DHT adoption by physicians that were identified include ease of integration with clinical workflow, improvement in patient outcomes, and technology usability and technical support. Technology usability and timely technical support improved self-management and patient experience, and positive impact on patient-provider communication were most frequently reported facilitators for patients. Barriers to use of DHTs reported by physicians include lack of integration with clinical workflow, lack of validation of technology, and lack of technology usability and technical support. Finally, lack of technology usability and technical support, interference with patient-provider relationship, and lack of validation of technology were the most commonly reported barriers by patients.

Conclusions

Findings suggest the settings and context in which DHTs are implemented and individuals involved in implementation influence adoption. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide patients and providers with reliable and accurate information on both clinical outcomes and cost effectiveness.

Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study

BACKGROUND

Telehealth programs have been successful in reducing 30-day readmissions and emergency department visits. However, such programs often focus on the costliest patients with multiple morbidities and last for only 30 to 60 days postdischarge. Inexpensive monitoring of elderly patients via a personal emergency response system (PERS) to identify those at high risk for emergency hospital transport could be used to target interventions and prevent avoidable use of costly readmissions and emergency department visits after 30 to 60 days of telehealth use.

OBJECTIVE

The objectives of this study were to (1) develop and validate a predictive model of 30-day emergency hospital transport based on PERS data; and (2) compare the model's predictions with clinical outcomes derived from the electronic health record (EHR).

METHODS

We used deidentified medical alert pattern data from 290,434 subscribers to a PERS service to build a gradient tree boosting-based predictive model of 30-day hospital transport, which included predictors derived from subscriber demographics, self-reported medical conditions, caregiver network information, and up to 2 years of retrospective PERS medical alert data. We evaluated the model's performance on an independent validation cohort (n=289,426). We linked EHR and PERS records for 1815 patients from a home health care program to compare PERS-based risk scores with rates of emergency encounters as recorded in the EHR.

RESULTS

In the validation cohort, 2.22% (6411/289,426) of patients had 1 or more emergency transports in 30 days. The performance of the predictive model of emergency hospital transport, as evaluated by the area under the receiver operating characteristic curve, was 0.779 (95% CI 0.774-0.785). Among the top 1% of predicted high-risk patients, 25.5% had 1 or more emergency hospital transports in the next 30 days. Comparison with clinical outcomes from the EHR showed 3.9 times more emergency encounters among predicted high-risk patients than low-risk patients in the year following the prediction date.

CONCLUSIONS

Patient data collected remotely via PERS can be used to reliably predict 30-day emergency hospital transport. Clinical observations from the EHR showed that predicted high-risk patients had nearly four times higher rates of emergency encounters than did low-risk patients. Health care providers could benefit from our validated predictive model by targeting timely preventive interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource utilization.

Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study

BACKGROUND

Big data solutions, particularly machine learning predictive algorithms, have demonstrated the ability to unlock value from data in real time in many settings outside of health care. Rapid growth in electronic medical record adoption and the shift from a volume-based to a value-based reimbursement structure in the US health care system has spurred investments in machine learning solutions. Machine learning methods can be used to build flexible, customized, and automated predictive models to optimize resource allocation and improve the efficiency and quality of health care. However, these models are prone to the problems of overfitting, confounding, and decay in predictive performance over time. It is, therefore, necessary to evaluate machine learning-based predictive models in an independent dataset before they can be adopted in the clinical practice. In this paper, we describe the protocol for independent, prospective validation of a machine learning-based model trained to predict the risk of 30-day re-admission in patients with heart failure.

OBJECTIVE

This study aims to prospectively validate a machine learning-based predictive model for inpatient admissions in patients with heart failure by comparing its predictions of risk for 30-day re-admissions against outcomes observed prospectively in an independent patient cohort.

METHODS

All adult patients with heart failure who are discharged alive from an inpatient admission will be prospectively monitored for 30-day re-admissions through reports generated by the electronic medical record system. Of these, patients who are part of the training dataset will be excluded to avoid information leakage to the algorithm. An expected sample size of 1228 index admissions will be required to observe a minimum of 100 30-day re-admission events. Deidentified structured and unstructured data will be fed to the algorithm, and its prediction will be recorded. The overall model performance will be assessed using the concordance statistic. Furthermore, multiple discrimination thresholds for screening high-risk patients will be evaluated according to the sensitivity, specificity, predictive values, and estimated cost savings to our health care system.

RESULTS

The project received funding in April 2017 and data collection began in June 2017. Enrollment was completed in July 2017. Data analysis is currently underway, and the first results are expected to be submitted for publication in October 2018.

CONCLUSIONS

To the best of our knowledge, this is one of the first studies to prospectively evaluate a predictive machine learning algorithm in a real-world setting. Findings from this study will help to measure the robustness of predictions made by machine learning algorithms and set a realistic benchmark for expectations of gains that can be made through its application to health care.

REGISTERED REPORT IDENTIFIER

RR1-10.2196/9466.

Health Care Cost Analyses for Exploring Cost Savings Opportunities in Older Patients: Longitudinal Retrospective Study

BACKGROUND

Half of Medicare reimbursement goes toward caring for the top 5% of the most expensive patients. However, little is known about these patients prior to reaching the top or how their costs change annually. To address these gaps, we analyzed patient flow and associated health care cost trends over 5 years.

OBJECTIVE

To evaluate the cost of health care utilization in older patients by analyzing changes in their long-term expenditures.

METHODS

This was a retrospective, longitudinal, multicenter study to evaluate health care costs of 2643 older patients from 2011 to 2015. All patients had at least one episode of home health care during the study period and used a personal emergency response service (PERS) at home for any length of time during the observation period. We segmented all patients into top (5%), middle (6%-50%), and bottom (51%-100%) segments by their annual expenditures and built cost pyramids based thereon. The longitudinal health care expenditure trends of the complete study population and each segment were assessed by linear regression models. Patient flows throughout the segments of the cost acuity pyramids from year to year were modeled by Markov chains.

RESULTS

Total health care costs of the study population nearly doubled from US $17.7M in 2011 to US $33.0M in 2015 with an expected annual cost increase of US $3.6M (P=.003). This growth was primarily driven by a significantly higher cost increases in the middle segment (US $2.3M, P=.003). The expected annual cost increases in the top and bottom segments were US $1.2M (P=.008) and US $0.1M (P=.004), respectively. Patient and cost flow analyses showed that 18% of patients moved up the cost acuity pyramid yearly, and their costs increased by 672%. This was in contrast to 22% of patients that moved down with a cost decrease of 86%. The remaining 60% of patients stayed in the same segment from year to year, though their costs also increased by 18%.

CONCLUSIONS

Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis unveiled potential cost savings opportunities by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. To achieve this, data analytics integrating longitudinal data from electronic health records and home monitoring devices may help health care organizations optimize resources by enabling clinicians to proactively manage patients in their home or community environments beyond institutional settings and 30- and 60-day telehealth services.

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

BACKGROUND

Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission.

METHODS

We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system.

RESULTS

Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital.

CONCLUSIONS

Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.

Decreased caudate response to milkshake is associated with higher body mass index and greater impulsivity

Previous investigations consistently report a negative association between body mass index (BMI) and response in the caudate nucleus during the consumption of palatable and energy dense food. Since this response has also been linked to weight gain, we sought to replicate this finding and determine if the reduced response is associated with measures of impulsivity or food reward. Two studies were conducted in which fMRI was used to measure brain response to milkshake and a tasteless control solution. In Study 1 (n=25) we also assessed self-reported impulsivity, willingness to work for food, and subjective experiences of the pleasantness of milkshake taste and aroma. Replicating prior work, we report a negative association between BMI and brain response to milkshake vs. tasteless in the caudate nucleus. The opposite pattern was observed in the ventral putamen, with greater response observed in the 13 overweight compared to the 12 healthy weight subjects. Regression of brain response against impulsivity and food reward measures revealed one significant association: in the overweight but not healthy weight group self-reported impulsivity was negatively associated with caudate response to milkshake. In Study 2 (n=14), in addition to assessing brain response to milkshake and tasteless solutions subjects completed a go/no-go task outside the scanner. As predicted, we identified an inverse relationship between caudate response to milkshake vs. tasteless and failure to inhibit responses on the no-go trials. We conclude that the inverse correlation between BMI and caudate response to milkshake is associated with impulsivity but not food reward. These findings suggest that response to milkshake in the dorsal striatum may be related to weight gain by promoting impulsive eating behavior.

Altered hypothalamic response to food in smokers

BACKGROUND

Smoking cessation is often followed by weight gain. Eating behaviors and weight change have been linked to the brain response to food, but it is unknown whether smoking influences this response.

OBJECTIVE

We determined the influence of smoking status (smokers compared with nonsmokers) on the brain response to food in regions associated with weight changes in nonsmokers.

DESIGN

In study 1, we used functional MRI (fMRI) to identify regions of the brain associated with weight change in nonsmokers. BMI and the brain response to a milk shake, which is a palatable and energy-dense food, were measured in a group of 27 nonsmokers (5 men). Sixteen subjects (3 men) returned 1 y later for BMI reassessment. The change in BMI was regressed against the brain response to isolate regions associated with weight change. In study 2, to determine whether smokers showed altered responses in regions associated with weight change, we assessed the brain response to a milk shake in 11 smokers. The brain response to a milk shake compared with a tasteless control solution was assessed in 11 smokers (5 men) in comparison with a group of age-, sex- and body weight-matched nonsmokers selected from the pool of nonsmokers who participated in study 1.

RESULTS

The response in the midbrain, hypothalamus, thalamus, and ventral striatum was positively associated with weight change at the 1-y follow-up in 16 nonsmokers. Compared with nonsmokers, smokers had a greater response to milk shakes in the hypothalamus.

CONCLUSION

Smokers display an altered brain response to food in the hypothalamus, which is an area associated with long-term weight change in nonsmokers.

Tremorolytic effects of adenosine A2A antagonists: implications for parkinsonism

Drug-induced tremulous jaw movements in rats have been used as a model of parkinsonian tremor. Because adenosine A2A antagonists have antiparkinsonian effects, the present experiments were conducted to study the ability of adenosine A2A antagonism to reverse the tremulous jaw movements produced by the antipsychotic drugs pimozide, haloperidol and reserpine. In one group of studies, rats received daily injections of the dopamine antagonist pimozide, and on day 8 they received injections of pimozide plus various doses of the A2A antagonists KW 6002 or MSX-3. KW 6002 and MSX-3 suppressed pimozide-induced tremulous jaw movements, reduced catalepsy, and increased locomotion. MSX-3 also suppressed the jaw movements induced by haloperidol and reserpine. In addition, local injections of MSX-3 into the ventrolateral neostriatum suppressed pimozide-induced tremulous jaw movements. Thus, adenosine A2A antagonism can reverse the tremulous movements induced by antipsychotic drugs, which is consistent with the hypothesis that antagonism of adenosine A2A receptors can result in antiparkinsonian effects. Adenosine A2A antagonists may be useful for their tremorolytic effects, and may help in treating both idiopathic and antipsychotic-induced parkinsonian symptoms.

Separable substrates for anticipatory and consummatory food chemosensation

Perception of the smell of a food precedes its ingestion and perception of its flavor. The neurobiological underpinnings of this association are not well understood. Of central interest is whether the same neural circuits code for anticipatory and consummatory phases. Here, we show that the amygdala and mediodorsal thalamus respond preferentially to food odors that predict immediate arrival of their associated drink (FO+) compared to food odors that predict delivery of a tasteless solution (FO-) and compared to the receipt of the drink. In contrast, the left insula/operculum responds preferentially to the drink, whereas the right insula/operculum and left orbitofrontal cortex respond to FO+ and drink. These findings indicate separable and overlapping representation of anticipatory and consummatory chemosensation. Moreover, since ratings of perceived pleasantness of FO+, FO-, and drink were similar, the response in the amygdala and thalamus cannot reflect acquired affective value but rather predictive meaning or biological relevance.

The Role of the Human Orbitofrontal Cortex in Taste and Flavor Processing

The human orbitofrontal cortex (OFC) plays an important role in representing taste, flavor, and food reward. The primary role of the OFC in taste is thought to be the encoding of affective value and the computation of perceived pleasantness. The OFC also encodes retronasal olfaction and oral somatosensation. During eating, distinct sensory inputs fuse into a unitary flavor percept, and there is evidence that this percept is encoded in the orbital cortex. Studies examining the effect of internal state on neural representation of food and drink further suggest that processing in the OFC is critical for representing the reward value of foods. Thus, it is likely that, in addition to serving as higher-order gustatory cortex, the OFC integrates multiple sensory inputs and computes reward value to guide feeding behavior.

Validation of the tremulous jaw movement model for assessment of the motor effects of typical and atypical antipychotics: effects of pimozide (Orap) in rats

Drug-induced tremulous jaw movements (TJMs) in rats have been used as a model of parkinsonian tremor. Previous studies demonstrated that the typical antipsychotic haloperidol induced TJMs after acute or subchronic administration, while atypical antipsychotics did not. Moreover, it has been suggested that the relative potency for suppression of tacrine-induced TJMs relative to the suppression of lever pressing can be used to discriminate between typical and atypical antipsychotics. In order to validate this model with additional drugs, the present studies assessed the effects of the typical antipsychotic pimozide. In the first series of experiments, the effects of acute pimozide on tacrine-induced TJMs and lever pressing were examined. As with haloperidol, pimozide failed to suppress tacrine-induced TJMs, even at doses considerably higher than those that suppressed lever pressing. In the second group of experiments, rats were given single daily injections of pimozide (0.125-1.0 mg/kg) or tartaric acid vehicle for 13 days, and were observed for TJMs on days 1, 7, and 13. Pimozide induced TJMs in a dose-related manner on all days. The jaw movements occurred largely in the 3-7 Hz frequency range characteristic of parkinsonian tremor. These data support the hypothesis that typical antipsychotics can induce TJMs in rats, and demonstrate that chronic administration of typical antipsychotics is not necessary for induction of TJMs. TJMs induced by acute or subchronic pimozide may be related to early-onset motor syndromes such as drug-induced parkinsonism.