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Various models have been proposed for the estimation of blood pressure (BP) from pulse transit time (PTT). PTT is defined as the time delay of the pressure wave, produced by left ventricular contraction, measured between a proximal and a distal site along the arterial tree. Most researchers, when they measure the time difference between the peak of the R-wave in the electrocardiogram signal (corresponding to left ventricular depolarisation) and a fiducial point in the photoplethysmogram waveform (as measured by a pulse oximeter attached to the fingertip), describe this erroneously as the PTT. In fact, this is the pulse arrival time (PAT), which includes not only PTT, but also the time delay between the electrical depolarisation of the heart's left ventricle and the opening of the aortic valve, known as pre-ejection period (PEP). PEP has been suggested to present a significant limitation to BP estimation using PAT. This work investigates the impact of PEP on PAT, leading to a discussion on the best models for BP estimation using PAT or PTT. We conducted a clinical study involving 30 healthy volunteers (53.3% female, 30.9 ± 9.35 years old, with a body mass index of 22.7 ± 3.2 kg/m[Formula: see text]). Each session lasted on average 27.9 ± 0.6 min and BP was varied by an infusion of phenylephrine (a medication that causes venous and arterial vasoconstriction). We introduced new processing steps for the analysis of PAT and PEP signals. Various population-based models (Poon, Gesche and Fung) and a posteriori models (inverse linear, inverse squared and logarithm) for estimation of BP from PTT or PAT were evaluated. Across the cohort, PEP was found to increase by 5.5 ms ± 4.5 ms from its baseline value. Variations in PTT were significantly larger in amplitude, - 16.8 ms ± 7.5 ms. We suggest, therefore, that for infusions of phenylephrine, the contribution of PEP on PAT can be neglected. All population-based models produced large BP estimation errors, suggesting that they are insufficient for modelling the complex pathways relating changes in PTT or PAT to changes in BP. Although PAT is inversely correlated with systolic blood pressure (SBP), the gradient of this relationship varies significantly from individual to individual, from - 2946 to - 470.64 mmHg/s in our dataset. For the a posteriori inverse squared model, the root mean squared errors (RMSE) for systolic and diastolic blood pressure (DBP) estimation from PAT were 5.49 mmHg and 3.82 mmHg, respectively. The RMSEs for SBP and DBP estimation by PTT were 4.51 mmHg and 3.53 mmHg, respectively. These models take into account individual calibration curves required for accurate blood pressure estimation. The best performing population-based model (Poon) reported error values around double that of the a posteriori inverse squared model, and so the use of population-based models is not justified., (© 2021. The Author(s).)

Background: Timely recognition of the deteriorating inpatient remains challenging. Wearable monitoring systems (WMS) may augment current monitoring practices. However, there are many barriers to implementation in the hospital environment, and evidence describing the clinical impact of WMS on deterioration detection and patient outcome remains unclear., Objective: To assess the impact of vital-sign monitoring on detection of deterioration and related clinical outcomes in hospitalised patients using WMS, in comparison with standard care., Methods: A systematic search was conducted in August 2020 using MEDLINE, Embase, CINAHL, Cochrane Database of Systematic Reviews, CENTRAL, Health Technology Assessment databases and grey literature. Studies comparing the use of WMS against standard care for deterioration detection and related clinical outcomes in hospitalised patients were included. Deterioration related outcomes (primary) included unplanned intensive care admissions, rapid response team or cardiac arrest activation, total and major complications rate. Other clinical outcomes (secondary) included in-hospital mortality and hospital length of stay. Exploratory outcomes included alerting system parameters and clinical trial registry information., Results: Of 8706 citations, 10 studies with different designs met the inclusion criteria, of which 7 were included in the meta-analyses. Overall study quality was moderate. The meta-analysis indicated that the WMS, when compared with standard care, was not associated with significant reductions in intensive care transfers (risk ratio, RR 0.87; 95% confidence interval, CI 0.66-1.15), rapid response or cardiac arrest team activation (RR 0.84; 95% CI 0.69-1.01), total (RR 0.77; 95% CI 0.44-1.32) and major (RR 0.55; 95% CI 0.24-1.30) complications prevalence. There was also no statistically significant association with reduced mortality (RR 0.48; 95% CI 0.18-1.29) and hospital length of stay (mean difference, MD - 0.09; 95% CI - 0.43 to 0.44)., Conclusion: This systematic review indicates that there is no current evidence that implementation of WMS impacts early deterioration detection and associated clinical outcomes, as differing design/quality of available studies and diversity of outcome measures make it difficult to reach a definite conclusion. Our narrative findings suggested that alarms should be adjusted to minimise false alarms and promote rapid clinical action in response to deterioration. PROSPERO Registration number: CRD42020188633 ., (© 2021. The Author(s).)

A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford. Video was recorded for a total of 84 dialysis sessions from 40 patients during the course of 1 year, comprising an overall video recording time of approximately 304.1 h. Reference values were provided by two devices in regular clinical use. The mean absolute error between the heart rate estimates from the camera and the average from two reference pulse oximeters (positioned at the finger and earlobe) was 2.8 beats/min for over 65% of the time the patient was stable. The mean absolute error between the respiratory rate estimates from the camera and the reference values (computed from the Electrocardiogram and a thoracic expansion sensor-chest belt) was 2.1 breaths/min for over 69% of the time for which the reference signals were valid. To increase the robustness of the algorithms, novel methods were devised for cancelling out aliased frequency components caused by the artificial light sources in the hospital, using auto-regressive modelling and pole cancellation. Maps of the spatial distribution of heart rate and respiratory rate information were developed from the coefficients of the auto-regressive models. Most of the periods for which the camera could not produce a reliable heart rate estimate lasted under 3 min, thus opening the possibility to monitor heart rate continuously in a clinical environment.

Introduction: Skin perfusion varies in response to changes in the circulatory status. Blood flow to skin is reduced during haemodynamic collapse secondary to peripheral vasoconstriction, whereas increased skin perfusion is frequently observed when haemodynamics improve with resuscitation. These changes in perfusion may be monitored using non-contact image-based methods. Previous camera-derived physiological measurements have focused on accurate vital signs monitoring and extraction of physiological signals from environmental noise. One of the biggest challenges of camera-derived monitoring is artefacts from motion, which limits our understanding of what parameters may be derived from skin. In this study, we use phenylephrine and glyceryl trinitrate (GTN) to cause vasoconstriction and vasodilation in stationary healthy volunteers to describe directional changes in skin perfusion pattern., Methods and Analysis: We aim to recruit 30 healthy volunteers who will undergo protocolised infusions of phenylephrine and GTN, followed by the monitored and timed release of a thigh tourniquet. The experimental timeline will be identical for all participants. Measurements of traditionally used haemodynamic markers (heart rate, blood pressure and stroke volume) and camera-derived measurements will be taken concurrently throughout the experimental period. The parameters of interest from the image data are skin colour and pattern, skin surface temperature, pulsatile signal detected at the skin surface and skin perfusion index., Ethics and Dissemination: This study was reviewed and approved by the Oxford University Research and Ethics Committee and Clinical Trials and Research Governance teams (R63796/RE001). The results of this study will be presented at scientific conferences and published in peer-reviewed journals., Trial Registration Number: ISRCTN10417167., Competing Interests: Competing interests: PJW is Chief Medical Officer for Sensyne Health and holds share options in the company. His organisation has received research funding from Sensyne Health not associated with this work. LT is a non-executive Director of Sensyne Health and holds share options in the company. LT is a non-executive Director of Oxehealth and holds shares in the company., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO 2 ), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospital ward, the main challenges in the development of continuous and robust non-contact monitoring techniques are the identification of time periods and the segmentation of skin regions of interest (ROIs) from which vital signs can be estimated. We propose a deep learning framework to tackle these challenges., Approach: This paper presents two convolutional neural network (CNN) models. The first network was designed for detecting the presence of a patient and segmenting the patient's skin area. The second network combined the output from the first network with optical flow for identifying time periods of clinical intervention so that these periods can be excluded from the estimation of vital signs. Both networks were trained using video recordings from a clinical study involving 15 pre-term infants conducted in the high dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford, UK., Main Results: Our proposed methods achieved an accuracy of 98.8% for patient detection, a mean intersection-over-union (IOU) score of 88.6% for skin segmentation and an accuracy of 94.5% for clinical intervention detection using two-fold cross validation. Our deep learning models produced accurate results and were robust to different skin tones, changes in light conditions, pose variations and different clinical interventions by medical staff and family visitors., Significance: Our approach allows cardio-respiratory signals to be continuously derived from the patient's skin during which the patient is present and no clinical intervention is undertaken.

Background: Data smoothing of vital signs has been reported in the anesthesia literature, suggesting that clinical staff are biased toward measurements of normal physiology. However, these findings may be partially explained by clinicians interpolating spurious values from noisy signals and by the undersampling of physiological changes by infrequent manual observations. We explored the phenomenon of data smoothing using a method robust to these effects in a large postoperative dataset including respiratory rate, heart rate, and oxygen saturation (SpO2). We also assessed whether the presence of the vital sign taker creates an arousal effect., Methods: Study data came from a UK upper gastrointestinal postoperative ward (May 2009 to December 2013). We compared manually recorded vital sign data with contemporaneous continuous data recorded from monitoring equipment. We proposed that data smoothing increases differences between vital sign sources as vital sign abnormality increases. The primary assessment method was a mixed-effects model relating continuous-manual differences to vital sign values, adjusting for repeated measurements. We tested the regression slope significance and predicted the continuous-manual difference at clinically important vital sign values. We calculated limits of agreement (LoA) between vital sign sources using the Bland-Altman method, adjusting for repeated measures. Similarly, we assessed whether the vital sign taker affected vital signs, comparing continuous data before and during manual recording., Results: From 407 study patients, 271 had contemporaneous continuous and manual recordings, allowing 3740 respiratory rate, 3844 heart rate, and 3896 SpO2 paired measurements for analysis. For the model relating continuous-manual differences to continuous-manual average vital sign values, the regression slope (95% confidence interval) was 0.04 (-0.01 to 0.10; P = .11) for respiratory rate, 0.04 (-0.01 to 0.09; P = .11) for heart rate, and 0.10 (0.07-0.14; P < .001) for SpO2. For SpO2 measurements of 91%, the model predicted a continuous-manual difference (95% confidence interval) of -0.88% (-1.17% to -0.60%). The bias (LoA) between measurement sources was -0.74 (-7.80 to 6.32) breaths/min for respiratory rate, -1.13 (-17.4 to 15.1) beats/min for heart rate, and -0.25% (-3.35% to 2.84%) for SpO2. The bias (LoA) between continuous data before and during manual observation was -0.57 (-5.63 to 4.48) breaths/min for respiratory rate, -0.71 (-10.2 to 8.73) beats/min for heart rate, and -0.07% (-2.67% to 2.54%) for SpO2., Conclusions: We found no evidence of data smoothing for heart rate and respiratory rate measurements. Although an effect exists for SpO2 measurements, it was not clinically significant. The wide LoAs between continuous and manually recorded vital signs would commonly result in different early warning scores, impacting clinical care. There was no evidence of an arousal effect caused by the vital sign taker.

Volume-clamp technology (e.g. Finometer) has become a popular method of collecting continuous, non-invasive, hemodynamic information during hemodialysis. There is minimal data validating the technique in this patient group. A gold standard cardiac output measurement can be obtained using ultrasound dilution in patients with arterio-venous fistulae. Continuous cardiac output was measured in 124 hemodialysis sessions in 27 patients using a volume-clamp device (Finometer PRO). Ultrasound dilution measurement was first taken at baseline (Transonic HD03), then used to calibrate the Finometer. Ultrasound dilution measurement was repeated 2 h into hemodialysis to assess drift following calibration. Pearson's correlation and Bland-Altman statistics, modified for repeated measures, were used to assess agreement between methods. Linear mixed models were constructed to identify factors that could explain session-level and patient-level variation in agreement. For baseline cardiac output before calibration, agreement between volume-clamp and ultrasound dilution measurements was poor, at 25 ± 75% (correlation 0.26, P < 0.001). There was significant variation in agreement between patients, with age, peripheral vascular disease and hemodialysis vintage contributing to poorer agreement. For cardiac output 2 h after calibration, agreement was -5.2 ± 57.5% (correlation 0.6, P < 0.001). Dynamic changes in blood pressure and fluid balance during hemodialysis resulted in greater drift over time after calibration. There was a large error, both random and systematic, in volume-clamp estimates of absolute, pre-calibration cardiac output in this prevalent hemodialysis population. There was minimal bias and reasonable correlation for cardiac output 2 h post-calibration, but limits of agreement remained too wide to meet current clinical standards., (© 2018 International Society for Apheresis, Japanese Society for Apheresis, and Japanese Society for Dialysis Therapy.)

Background: Use of automated systems to aid identification of patient deterioration in routine hospital practice is limited and their impact on patient outcomes remains unclear. This study was designed to evaluate the feasibility of implementing an electronic observation chart with automated early warning score (EWS) calculation in the high-acuity area of an emergency department., Methods: This study enrolled 3219 participants before and 3352 after implementation of an automated system, using bedside vital-sign entry on networked mobile devices. The primary outcome measure was the percentage of participants for whom an EWS was accurately recorded at each stage., Results: Of the participants, 52.7% before and 92.9% after implementation of the electronic system had an accurate EWS recorded on charts available to the study team. Participant groups were well balanced for baseline characteristics and acuity., Conclusion: In this study, the feasibility and limitations of implementing an electronic observation chart in the ED were demonstrated. Accurate EWS documentation was more frequent after implementation of the electronic observation chart. Retrospective analysis suggests that the use of an electronic observation system may lead to a greater percentage of observations being taken from those patients with a higher EWS.

Background: Chronic obstructive pulmonary disease (COPD) is a progressive, chronic respiratory disease with a significant socioeconomic burden. Exacerbations, the sudden and sustained worsening of symptoms, can lead to hospitalization and reduce quality of life. Major limitations of previous telemonitoring interventions for COPD include low compliance, lack of consensus on what constitutes an exacerbation, limited numbers of patients, and short monitoring periods. We developed a telemonitoring system based on a digital health platform that was used to collect data from the 1-year EDGE (Self Management and Support Programme) COPD clinical trial aiming at daily monitoring in a heterogeneous group of patients with moderate to severe COPD., Objective: The objectives of the study were as follows: first, to develop a systematic and reproducible approach to exacerbation identification and to track the progression of patient condition during remote monitoring; and second, to develop a robust algorithm able to predict COPD exacerbation, based on vital signs acquired from a pulse oximeter., Methods: We used data from 110 patients, with a combined monitoring period of more than 35,000 days. We propose a finite-state machine-based approach for modeling COPD exacerbation to gain a deeper insight into COPD patient condition during home monitoring to take account of the time course of symptoms. A robust algorithm based on short-period trend analysis and logistic regression using vital signs derived from a pulse oximeter is also developed to predict exacerbations., Results: On the basis of 27,260 sessions recorded during the clinical trial (average usage of 5.3 times per week for 12 months), there were 361 exacerbation events. There was considerable variation in the length of exacerbation events, with a mean length of 8.8 days. The mean value of oxygen saturation was lower, and both the pulse rate and respiratory rate were higher before an impending exacerbation episode, compared with stable periods. On the basis of the classifier developed in this work, prediction of COPD exacerbation episodes with 60%-80% sensitivity will result in 68%-36% specificity., Conclusions: All 3 vital signs acquired from a pulse oximeter (pulse rate, oxygen saturation, and respiratory rate) are predictive of COPD exacerbation events, with oxygen saturation being the most predictive, followed by respiratory rate and pulse rate. Combination of these vital signs with a robust algorithm based on machine learning leads to further improvement in positive predictive accuracy., Trial Registration: International Standard Randomized Controlled Trial Number (ISRCTN): 40367841; http://www.isrctn.com/ISRCTN40367841 (Archived by WebCite at http://www.webcitation.org/6olpMWNpc)., (©Syed Ahmar Shah, Carmelo Velardo, Andrew Farmer, Lionel Tarassenko. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.03.2017.)

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e., the provision of healthcare services to individuals anytime anywhere, has become a major focus for the research community. In this paper, we map out the current state of pervasive healthcare research by presenting an overview of three emerging areas in personalized health monitoring, namely: 1) mobile phone sensing via in-built or external sensors, 2) self-reporting for manually captured health information, such as symptoms and behaviors, and 3) social sharing of health information within the individual's community. Systems deployed in a real-life setting as well as proofs-of-concept for achieving pervasive health are presented, in order to identify shortcomings and increase our understanding of the requirements for the next generation of pervasive healthcare systems addressing these three areas.

Objective: The aim of the study was to evaluate the ability of a data-fusion patient status index (PSI) to detect patient deterioration in the emergency department (ED) in comparison with track-and-trigger (T&T)., Materials and Methods: A single-centre observational cohort study was conducted in a medium-sized teaching hospital ED. Vital sign data and any documented T&T scores (paper T&T) were collected from adults attending the resuscitation room, majors or observation ward. For each set of vital signs, we retrospectively calculated T&T (eT&T). PSI was calculated retrospectively from the continuous vital sign data using a statistical model of normality. Clinical notes were examined to identify 'escalation' events, and the numbers of these escalations identified by paper T&T, eT&T and PSI were retrospectively calculated., Results: Data from 472 patient episodes were examined. A total of 20 patients had PSI data at the time of an escalation related to vital sign abnormalities that occurred during their ED stay (vs. on arrival). Only four patient events were detected at the time by paper T&T. In all, 17 were detected retrospectively by eT&T and 15 by PSI. PSI had a calculated false-alert rate of 1.13 alerts/bed-day., Conclusion: Electronic data capture offers opportunities for increased detection of deteriorating patients in a busy clinical environment compared with paper charts. Sample size in this study is insufficient to determine which electronic method (eT&T or PSI) offers superior detection of the need for escalation.

Purpose: Real-time symptom monitoring using a mobile phone is potentially advantageous for patients receiving oral chemotherapy. We therefore conducted a pilot study of patient dose adaptation using mobile phone monitoring of specific symptoms to investigate relative dose intensity of capecitabine, level of toxicity and perceived supportive care., Methods: Patients with breast or colorectal cancer receiving capecitabine completed a symptom, temperature and dose diary twice a day using a mobile phone application. This information was encrypted and automatically transmitted in real time to a secure server, with moderate levels of toxicity automatically prompting self-care symptom management messages on the screen of the patient's mobile phone or in severe cases, a call from a specialist nurse to advise on care according to an agreed protocol., Results: Patients (n = 26) completed the mobile phone diary on 92.6 % of occasions. Twelve patients had a maximum toxicity grade of 3 (46.2 %). The average dose intensity for all patients as a percentage of standard dose was 90 %. In eight patients, the dose of capecitabine was reduced, and in eight patients, the dose of capecitabine was increased. Patients and healthcare professionals involved felt reassured by the novel monitoring system, in particular, during out of hours., Conclusion: It is possible to optimise the individual dose of oral chemotherapy safely including dose increase and to manage chemotherapy side effects effectively using real-time mobile phone monitoring of toxicity parameters entered by the patient.

Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient's face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter.

We consider an integrated patient monitoring system, combining electronic patient records with high-rate acquisition of patient physiological data. There remain many challenges in increasing the robustness of "e-health" applications to a level at which they are clinically useful, particularly in the use of automated algorithms used to detect and cope with artifact in data contained within the electronic patient record, and in analyzing and communicating the resultant data for reporting to clinicians. There is a consequential "plague of pilots," in which engineering prototype systems do not enter into clinical use. This paper describes an approach in which, for the first time, the Emergency Department (ED) of a major research hospital has adopted such systems for use during a large clinical trial. We describe the disadvantages of existing evaluation metrics when applied to such large trials, and propose a solution suitable for large-scale validation. We demonstrate that machine learning technologies embedded within healthcare information systems can provide clinical benefit, with the potential to improve patient outcomes in the busy environment of a major ED and other high-dependence areas of patient care.

The current standard of clinical practice for patient monitoring in most developed nations is connection of patients to vital-sign monitors, combined with frequent manual observation. In some nations, such as the UK, manual early warning score (EWS) systems have been mandated for use, in which scores are assigned to the manual observations, and care escalated if the scores exceed some pre-defined threshold. We argue that this manual system is far from ideal, and can be improved using machine learning techniques. We propose a system based on Gaussian process regression for improving the efficacy of existing EWS systems, and then demonstrate the method using manual observation of vital signs from a large-scale clinical study.

Aim of Study: To develop an early warning score (EWS) system based on the statistical properties of the vital signs in at-risk hospitalised patients., Materials and Methods: A large dataset comprising 64,622 h of vital-sign data, acquired from 863 acutely ill in-hospital patients using bedside monitors, was used to investigate the statistical properties of the four main vital signs. Normalised histograms and cumulative distribution functions were plotted for each of the four variables. A centile-based alerting system was modelled using the aggregated database., Results: The means and standard deviations of our population's vital signs are very similar to those published in previous studies. When compared with EWS systems based on a future outcome, the cut-off values in our system are most different for respiratory rate and systolic blood pressure. With four-hourly observations in a 12-h shift, about 1 in 8 at-risk patients would trigger our alerting system during the shift., Conclusions: A centile-based EWS system will identify patients with abnormal vital signs regardless of their eventual outcome and might therefore be more likely to generate an alert when presented with patients with redeemable morbidity or avoidable mortality. We are about to start a stepped-wedge clinical trial gradually introducing an electronic version of our EWS system on the trauma wards in a teaching hospital., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Background: Novel mobile phone technology linked to a server that communicates patients' symptoms to healthcare professionals has been adapted to register the side- effects of chemotherapy and provide advice on management of toxicity. We report a feasibility study to examine the utility of home monitoring of patients' symptoms via a mobile phone., Methods: Six colon cancer patients receiving adjuvant chemotherapy, entered symptom data onto user friendly screens on a mobile phone twice daily. This 'real time' self assessment of nausea, vomiting, mucositis, diarrhoea and hand-foot syndrome and measurement of temperature was sent via a secured connection to a remote computer. In the event of moderate or severe symptoms (generating amber and red alerts respectively), the nurse was immediately alerted by the computer, via a pager. The nurse then contacted the patient to reinforce the automatic advice sent to the patient on their phone and to assess the patient using clinical algorithms., Results: The patient used the mobile phones during the first two cycles of chemotherapy. The data were successfully analysed by the server software and alerts were generated alerting the study nurses to patients' symptoms at the appropriate time. There were 91 alerts-54 red and 37 amber; 54% (29/54) of the red alerts were data delay and transmission problems which were swiftly rectified. The remaining red alerts were managed appropriately by the study nurses. Both patients and staff felt confident in this approach to symptom management., Conclusions: This study demonstrates that the technology for monitoring patients' symptoms worked well. The patients felt secure in the knowledge that their symptoms were being closely monitored and that they were participating effectively in their own care management.

We conducted a randomised controlled trial of mandated five-channel physiological monitoring vs standard care, in acute medical and surgical wards in a single UK teaching hospital. In all, 402 high-risk medical and surgical patients were studied. The primary outcome was the proportion of patients experiencing one or more major adverse events, including urgent staff calls, changes to higher care levels, cardiac arrests or death, in 96 h following randomisation. Secondary outcomes were the proportion of patients requiring acute treatment changes, and the 30-day and hospital mortality. In the 96 h following randomisation, 113 (56%) patients in the monitored arm and 116 (58%) in the control arm (OR 0.94, 95% CI 0.63-1.40, p = 0.76) had a major event. An acute change in treatment was necessary in 107 (53%) monitored patients and 101 (50%) control patients (OR 0.55, 95% CI 0.87-1.29). Thirty-four (17%) monitored patients and 35 (17%) control patients died within 30 days. Thirteen patients in the control group received full five-channel monitoring at the request of the ward staff. We conclude that mandated electronic vital signs monitoring in high risk medical and surgical patients has no effect on adverse events or mortality.

Recently there has been an upsurge of interest in strategies for detecting at-risk patients in order to trigger the timely intervention of a Medical Emergency Team (MET), also known as a Rapid Response Team (RRT). We review a real-time automated system, BioSign, which tracks patient status by combining information from vital signs monitored non-invasively on the general ward. BioSign fuses the vital signs in order to produce a single-parameter representation of patient status, the Patient Status Index. The data fusion method adopted in BioSign is a probabilistic model of normality in five dimensions, previously learnt from the vital sign data acquired from a representative sample of patients. BioSign alerts occur either when a single vital sign deviates by close to +/-3 standard deviations from its normal value or when two or more vital signs depart from normality, but by a smaller amount. In a trial with high-risk elective/emergency surgery or medical patients, BioSign alerts were generated, on average, every 8 hours; 95% of these were classified as 'True' by clinical experts. Retrospective analysis has also shown that the data fusion algorithm in BioSign is capable of detecting critical events in advance of single-channel alerts.

We have been investigating the use of electrical impedance methods for the study of cerebral haemodynamics in the newborn for many years. Of particular interest has been the early detection of intraventricular haemorrhage, which is a major cause of death or handicap in low birthweight infants. Several problems exist in obtaining representative impedance measurements from the newborn, most notably movement artefact, respiratory-based modulation and a blood-flow related pulsatile component. Movement artefact is by far the most significant problem; in order to perform long-term monitoring we have developed an effective algorithm for rejection of corrupted data. To remove the respiratory component, which can be 1-2% of the impedance measurement, we link to the output of a respiratory monitor (standard in intensive care units) as a means of synchronising measurements to a fixed point in the respiratory cycle. Similarly, to remove the blood-flow pulsatile component, measurements are gated from the R-wave of the recorded ECG. Our total system consists of a front-end measurement system that passes image data over a serial link to a host computer. Data storage, image reconstruction and display is performed on the host, which can be any of a wide range of personal computers. The front-end contains the impedance measurement circuit, electrode switching electronics and a microprocessor for control, and is of a sufficiently small size to fit into the incubator next to the baby. Incorporating a microprocessor into the front-end produces a very flexible system that has many benefits, including: asynchronous operation from host, 'intelligent' pre-processing of measurements, command driven operation from host, etc. Software development for the front-end is performed on the host with program down-load for interactive debugging. Details of the front-end electronics and software, system performance, preliminary clinical results and other application areas of the impedance imaging technique to the care of the newborn are presented.