External validation of the 0/1h-algorithm and derivation of a 0/2h-algorithm using a new point-of-care Hs-cTnI assay.

BACKGROUND: The high-sensitivity cardiac troponin (hs-cTn) I point-of-care (POC) hs-cTnI-PATHFAST assay has recently become clinically available. METHODS: We aimed to externally validate the hs-cTnI-PATHFAST 0/1h-algorithm recently developed for the early diagnosis of non-ST-segment-elevation myocardial infarction (NSTEMI) and derive and validate a 0/2-algorithm in patients presenting to the emergency department with acute chest discomfort included in a multicenter diagnostic study. Two independent cardiologists centrally adjudicated the final diagnoses using all the clinical and study-specific information available including serial measurements of hs-cTnI-Architect. RESULTS: Among 1,532 patients (median age 60 years, 33% [n = 501] women), NSTEMI was the final diagnosis in 13%. External validation of the hs-cTnI-PATHFAST 0/1h-algorithm showed very high negative predictive value (NPV; 100% [95%CI, 99.5%-100%]) and sensitivity 100% (95%CI, 98.2%-100%) for rule-out of NSTEMI. Positive predictive value (PPV) and specificity for rule-in of NSTEMI were high (74.9% [95%CI, 68.3%-80.5%] and 96.4% [95%CI, 95.2%-97.3%], respectively). Among 1,207 patients (median age 61 years, 32% [n = 391] women) available for the derivation (n = 848) and validation (n = 359) of the hs-cTnI-PATHFAST 0/2h-algorithm, a 0h-concentration <3 ng/L or a 0h-concentration <4 ng/L with a 2h-delta <4ng/L ruled-out NSTEMI in 52% of patients with a NPV of 100% (95%CI, 98-100) and sensitivity of 100% (95%CI, 92.9%-100%) in the validation cohort. A 0h-concentration ≥90ng/L or a 2h-delta ≥ 55ng/L ruled-in 38 patients (11%): PPV 81.6% (95%CI, 66.6-90.8), specificity 97.7% (95%CI, 95.4-98.9%). CONCLUSIONS: The POC hs-cTnI-PATHFAST assay allows rapid and effective rule-out and rule-in of NSTEMI using both a 0/1h- and a 0/2h-algorithm with high NPV/sensitivity for rule-out and high PPV/specificity for rule-in. CLINICAL TRIAL REGISTRATION: NCT00470587.

A Smart Collar for Assessment of Activity Levels and Environmental Conditions for Guide Dogs.

This paper demonstrates the design and manufacturing of a smart and connected internet-of-things collar system for the collection of behavioral and environmental information from working canines. The environmental factors of ambient light, ambient temperature, ambient noise levels, barometric pressure and relative humidity are recorded by the smart collar system in addition to behavioral information about barking incidences and activity levels. The data are collected from the sensors and transmitted via Bluetooth to the handler's smartphone where the custom app also acquires GPS positioning using the on-board smartphone sensors. The stored data on the smartphone are uploaded to the IBM Cloud once the user is connected to a WiFi network. The low power design of the smart collar system permits it to be used continuously for 27 hours with a 290 mAh lithium polymer battery. The cost of the system is low enough to let the handlers have multiple collars and exchange it if needed or recharge it overnight when not in use. This system is currently being scaled up to be tested on hundreds of canine puppies by a preeminent guide dog school in the US. As a result, the design emphasis here has been on the cost and power reduction, comfortable ergonomics, user friendliness, and robustness of data streaming. We expect the system to provide continuous quantitative data for improving guide dog training programs in addition to contributing the well-being of other working dogs in the future.

A Systematic Literature Review of Animal-Assisted Interventions in Oncology (Part I): Methods and Results.

Animal-assisted interventions (AAIs) use human-animal interactions to positive effect in various contexts including cancer care. As the first installment of a 2-part series, this systematic literature review focuses on the research methods and quantitative results of AAI studies in oncology. We find methodological consistency in the use of canines as therapy animals, in the types of high-risk patients excluded from studies, and in the infection precautions taken with therapy animals throughout cancer wards. The investigated patient endpoints are not significantly affected by AAI, with the exceptions of improvements in oxygen consumption, quality of life, depression, mood, and satisfaction with therapy. The AAI field in oncology has progressed significantly since its inception and has great potential to positively affect future patient outcomes. To advance the field, future research should consistently improve the methodological design of studies, report data more completely, and focus more on the therapy animal's well-being.

A Systematic Literature Review of Animal-Assisted Interventions in Oncology (Part II): Theoretical Mechanisms and Frameworks.

Animal-assisted interventions (AAIs) can improve patients' quality of life as complementary medical treatments. Part I of this 2-paper systematic review focused on the methods and results of cancer-related AAIs; Part II discusses the theories of the field's investigators. Researchers cite animal personality, physical touch, physical movement, distraction, and increased human interaction as sources of observed positive outcomes. These mechanisms then group under theoretical frameworks such as the social support hypothesis or the human-animal bond concept to fully explain AAI in oncology. The cognitive activation theory of stress, the science of unitary human beings, and the self-object hypothesis are additional frameworks mentioned by some researchers. We also discuss concepts of neurobiological transduction connecting mechanisms to AAI benefits. Future researchers should base study design on theories with testable hypotheses and use consistent terminology to report results. This review aids progress toward a unified theoretical framework and toward more holistic cancer treatments.

A System for Assessment of Canine-Human Interaction during Animal-Assisted Therapies.

Animal-assisted therapies (AAT) are becoming increasingly common to help hospitalized patients, especially in oncology units. There is a critical need for methods and technologies that can enable a quantifiable understanding of AAT to objectively demonstrate its efficacy and improve its efficiency. In this paper, we present our preliminary efforts towards the development of wireless sensor systems to simultaneously detect the related behavioral (activity level, movement, stroking) and physiological signals (heart rate/variability) of humans and animals during their interaction. To detect heart rate, we tested two different techniques based on wearable or contactless electrocardiography. In this preliminary evaluation, we were able to assess these parameters successfully and identify the design challenges towards deployment of these systems in larger clinical studies.