Suspected sepsis: summary of NICE guidance

The UK Parliamentary and Health Service Ombudsman inquiry “Time to Act” found failures in the recognition, diagnosis, and early management of those who died from sepsis, which triggered this guidance. In sepsis the body’s immune and coagulation systems are switched on by an infection and cause one or more body organs to malfunction with variable severity. The condition is life threatening. Although most people with infection do not have and will not develop sepsis, non-specific signs and symptoms can lead to late recognition of people who might have sepsis. We would like clinicians to “think sepsis” and recognise symptoms and signs of potential organ failure when they assess someone with infection, in a similar way to thinking “Could this chest pain be cardiac in origin?”This guidance provides a pragmatic approach for patients with infection who are assessed in the community, emergency departments, and hospitals by a wide range of general and specialist healthcare professionals. It includes guidance on assessment of risk factors followed by a detailed structured assessment of potential clinical signs and symptoms of concern.Definitions of sepsis have been developed, but these offer limited explanation on how to confirm or rule out the diagnosis in general clinical settings or in the community. Current mechanisms to diagnose sepsis and guidelines for use largely apply to critical care settings such as intensive care. We recognised a need for better recognition of sepsis in non-intensive settings and for the diagnosis to be entertained sooner.While sepsis is multifactorial and rarely presents in the same way, the Guideline Development Group considered that use of an easy, structured risk assessment may help clinicians identify those most severely ill who require immediate potentially lifesaving treatment. This guideline ensures that patients defined as having sepsis by recent definitions are, as a minimum, assessed as moderate-high risk. This guidance is also about appropriate de-escalation if sepsis is unlikely and broad spectrum antibiotics or hospital admission are not appropriate.This article summarises recommendations from the National Institute for Health and Care Excellence (NICE) guideline for the recognition, diagnosis, and management of sepsis in children and adults. Recommendations and the clinical pathway are available via the NICE website, and the UK Sepsis Trust tools are being revised to align with this guidance. This article is accompanied by an infographic, which displays the NICE guideline as a decision making tool

Human activity recognition for emergency first responders via body-worn inertial sensors

Every year over 75 000 firefighters are injured and 159 die in the line of duty. Some of these accidents could be averted if first response team leaders had better information about the situation on the ground. The SAFESENS project is developing a novel monitoring system for first responders designed to provide response team leaders with timely and reliable information about their firefighters' status during operations, based on data from wireless inertial measurement units. In this paper we investigate if Gradient Boosted Trees (GBT) could be used for recognising 17 activities, selected in consultation with first responders, from inertial data. By arranging these into more general groups we generate three additional classification problems which are used for comparing GBT with k-Nearest Neighbours (kNN) and Support Vector Machines (SVM). The results show that GBT outperforms both kNN and SVM for three of these four problems with a mean absolute error of less than 7%, which is distributed more evenly across the target activities than that from either kNN or SVM

Sensor and feature selection for an emergency first responders activity recognition system

Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients’ recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality of a HAR problem in the context of an emergency first responders monitoring system. We empirically estimate the accuracy of k-Nearest Neighbours, Support Vector Machines, and Gradient Boosted Trees when using different combinations of (A)ccelerometer, (G)yroscope and (P)ressure sensors. We then apply Principal Component Analysis for dimensionality reduction, and the Kruskal-Wallis test for feature selection. Our results show that the best combination is that which includes all three sensors (MAE: 3.6%), followed by the A/G (MAE: 3.7%), and the A/P combination (MAE 4.3%): the same as that when using the accelerometer alone. Moreover, our results show that the Kruskal-Wallis test can be used to discard up to 50% of the features, and yet improve the performance of classification algorithms

Case reports: A helping hand to generalists

Clinical decision making can be challenging for both generalists and specialists. Case reports may assist the decision making process either by providing guidance to generalists on identifying rarer conditions or a searchable database for looking up seemingly disparate symptoms. This editorial highlights the innovations being implemented by Journal of Medical Case Reports and Cases Journal in developing an educational resource to help clinicians in decision-making

Development of a highly-miniaturised wireless ISE/pH sensor

The goal of this work is to fabricate robust, highly-miniaturised, wireless sensor modules that incorporates ion-selective electrodes (ISEs). pH is one of the main parameters in assessment of the quality of our environment (water, soil) and these ISE/pH sensors will be deployed in a miniaturised, programmable modular system. The simplicity of ISEs (low costs and low power requirements) allow for the preparation of sensors that are all very similar in construction but can at the same time be easily made for variety of different environmentally important ions (i.e. heavy metals). This is important because of the increasing focus on the impact of the quality of the environment on society, both locally, and globally. The work described will contribute to a widely distributed sensor network for monitoring the quality of our environment, focused mainly on soil and water quality

Medicines adherence: Involving patients in decisions about prescribed medicines and supporting adherence

It is thought that between a third and a half of all medicines1 There are many causes of non-adherence but they fall into two overlapping categories: intentional and unintentional. Unintentional non-adherence occurs when the patient wants to follow the agreed treatment but is prevented from doing so by barriers that are beyond their control. Examples include poor recall or difficulties in understanding the instructions, problems with using the treatment, inability to pay for the treatment, or simply forgetting to take it. prescribed for long-term conditions are not taken as recommended. If the prescription is appropriate, then this may represent a loss to patients, the healthcare system and society. The costs are both personal and economic. Adherence presumes an agreement between prescriber and patient about the prescriber’s recommendations. Adherence to medicines is defined as the extent to which the patient’s action matches the agreed recommendations. Non-adherence may limit the benefits of medicines, resulting in lack of improvement, or deterioration, in health. The economic costs are not limited to wasted medicines but also include the knock-on costs arising from increased demands for healthcare if health deteriorates. Non-adherence should not be seen as the patient’s problem. It represents a fundamental limitation in the delivery of healthcare, often because of a failure to fully agree the prescription in the first place or to identify and provide the support that patients need later on. Addressing non-adherence is not about getting patients to take more medicines per se. Rather, it starts with an exploration of patients’ perspectives of medicines and the reasons why they may not want or are unable to use them. Healthcare professionals have a duty to help patients make informed decisions about treatment and use appropriately prescribed medicines to best effec

What aspects of periods are most bothersome for women reporting heavy menstrual bleeding? Community survey and qualitative study

Background: Heavy menstrual bleeding is a common symptom amongst women of reproductive age, yet questions remain about why some women experience this as a problem while others do not. We investigated the concerns of women who reported heavy menstrual bleeding on questionnaire. Methods: A cross-sectional postal survey and qualitative interviews were carried out amongst a community-based sample of women in Lothian, Scotland. 906 women aged 25 to 44 reported heavy or very heavy periods in response to a postal survey of 2833 women registered with 19 general practices. Amongst those who had reported heavy menstrual bleeding, analysis was carried out of responses to the free text questionnaire item, "What bothers you most about your periods?" In addition, 32 of these women participated in qualitative interviews and their accounts were analysed to explore how menstrual symptoms and 'problems' with periods were experienced. Results: Even amongst this subgroup of women, selected on the basis of having reported their periods as heavy in the survey, pain was the aspect of their periods that 'most bothered' them, followed by heaviness, mood changes or tiredness, and irregularity or other issues of timing. Interviewees' accounts similarly suggested that a range of menstrual symptoms were problematic and some women did not disentangle which was worst. Judgements of periods as a problem were based on the impact of menstrual symptoms on daily life and this was contingent on social circumstances such as type of paid work and other responsibilities. Although women spoke readily of whether their periods were a problem, there was less clarity in accounts of whether or not menstrual loss was 'heavy'; women said they made judgements based on what was normal for them, degree of difficulty in containing blood loss and pattern of loss. Conclusion: Women with heavy periods are bothered by a range of menstrual symptoms and their impact on everyday life. Clinical emphasis should be on clarifying the presenting problem and providing help and advice for this, as well as on excluding serious disease. Sometimes simple approaches, such as help with analgesia, may be all that is required

Theoretical models for underwater RFID

Underwater wireless communications pose challenges due to the characteristics of water as a propagation channel medium. Regardless, it is needed for a range of systems that operate underwater. Commonly used technologies for these use cases (radio-frequency, acoustic and optical communications) are lacking, as they usually suffer from strong attenuation, multipath and propagation delays. In this context, we explore Radio Frequency Identification (RFID) systems underwater and the feasibility of their application. This paper aims to discuss the theoretical transmission models for RFID systems underwater, separating them into near-field systems -- which use Magnetic Induction (MI) to communicate -- and far-field systems -- that transfer data via Radio Frequency (RF). We determine the path loss for each case, explore its value for different system configurations and present preliminary measurements of magnetic field strength

Using domain knowledge for interpretable and competitive multi-class human activity recognition

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task that a machine learning algorithm is trained for requires separating multiple activities such as walking, running, sitting, and falling from each other. Despite a large body of work on multi-class HAR, and the well-known fact that the performance on a multi-class problem can be significantly affected by how it is decomposed into a set of binary problems, there has been little research into how the choice of multi-class decomposition method affects the performance of HAR systems. This paper presents the first empirical comparison of multi-class decomposition methods in a HAR context by estimating the performance of five machine learning algorithms when used in their multi-class formulation, with four popular multi-class decomposition methods, five expert hierarchies—nested dichotomies constructed from domain knowledge—or an ensemble of expert hierarchies on a 17-class HAR data-set which consists of features extracted from tri-axial accelerometer and gyroscope signals. We further compare performance on two binary classification problems, each based on the topmost dichotomy of an expert hierarchy. The results show that expert hierarchies can indeed compete with one-vs-all, both on the original multi-class problem and on a more general binary classification problem, such as that induced by an expert hierarchy’s topmost dichotomy. Finally, we show that an ensemble of expert hierarchies performs better than one-vs-all and comparably to one-vs-one, despite being of lower time and space complexity, on the multi-class problem, and outperforms all other multi-class decomposition methods on the two dichotomous problems