Aim: To quantify the type and duration of physical activity performed by hospitalized adults.Background: Inactivity is pervasive among hospitalized patients and is associated with increased mortality, functional decline, and cognitive impairment. Objective measurement of activity is necessary to examine associations with clinical outcomes and quantify optimal inpatient mobility interventions. Methods:We used PRISMA guidelines to search three databases in December 2017 to retrieve original research evaluating activity type and duration among adult acute-care inpatients. We abstracted data on inpatient population, measurement method, monitoring time, activity duration, and study quality.Results: Thirty-eight articles were included in the review and 7 articles were included in the meta-analysis. Study populations included geriatric (n=5), surgical (n=5), medical (n=12), poststroke (n=10), psychiatric (n=2), and critical care inpatients (n=4). To measure activity, 29% of studies used human observation and 71% used activity monitors. Among inpatient populations, 87-100% of time was spent sitting or lying in-bed. Among medical inpatients monitored over a 24-hour period, 70 minutes per day was spent standing/walking (95% CI 57-83 minutes). Conclusions:This review provides a baseline assessment and benchmark of inpatient activity, which can be used to compare inpatient mobility practices. While there is substantial
Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide
Summary Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.
Asthma is a complex inflammatory disease with many triggers. The best understood asthma inflammatory pathways involve signals characterized by peripheral eosinophilia and elevated immunoglobulin E levels (called T2-high or allergic asthma), though other asthma phenotypes exist (eg, T2-low or non-allergic asthma, eosinophilic or neutrophilic-predominant). Common triggers that lead to poor asthma control and exacerbations include respiratory viruses, aeroallergens, house dust, molds, and other organic and inorganic substances. Increasingly recognized non-allergen triggers include tobacco smoke, small particulate matter (eg, PM2.5), and volatile organic compounds. The interaction between respiratory viruses and non-allergen asthma triggers is not well understood, though it is likely a connection exists which may lead to asthma development and/or exacerbations. In this paper we describe common respiratory viruses and non-allergen triggers associated with asthma. In addition, we aim to show the possible interactions, and potential synergy, between viruses and non-allergen triggers. Finally, we introduce a new clinical approach that collects exhaled breath condensates to identify metabolomics associated with viruses and non-allergen triggers that may promote the early management of asthma symptoms.
Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.
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