Strengths and limitations of early warning scores: A systematic review and narrative synthesis

The afferent limb of a rapid response system is responsible for monitoring the patient, detecting deterioration and triggering a clinical response. This chapter describes the observations, early warning systems and calling criteria used to identify the sick or deteriorating patient. Failures in the individual components of the afferent limb are common and frequently result in potentially avoidable, adverse clinical outcomes. We describe the current shortfalls of the afferent limb and consider the actions required to improve its functioning including: improving regular patient monitoring and assessment; ensuring the accurate measurement and recording of vital signs; and enabling the timely calling of a rapid response team. The chapter also considers the role of technology in improving the functioning of the afferent limb.

An overview of the afferent limb

Abnormal respiratory rate (RR) is known to be one of the most clinically effective predictors of catastrophic decline. Despite this, RR is often the least monitored and most inaccurately measured vital sign. This is primarily because of the lack of a non-invasive, robust, automated method for estimating RR. It has previously been shown that the amplitude modulation (AM), frequency modulation (FM), and baseline wander (BW) of both the electrocardiogram (ECG) and photoplethysmogram (PPG) contain respiratory waveform information. However, these respiratory modulations are driven by the physiologic interrelationship between the cardiovascular and respiratory systems and may or may not be present based on a patient’s characteristics and condition. Despite this, current methods for RR estimation from ECG and PPG do not account for this physiologic variability. The investigations in this thesis describe the development and evaluation of respiratory quality indices (RQIs), a novel method for evaluating the presence or absence of physiologically important respiratory information from the AM, FM, and BW extracted from the ECG and PPG. This work is conducted in three unique data sets, CapnoBase, MIMIC-III, and Dialysis III, all of which represent important, different patient populations. Five initial RQIs are described based on five signal processing techniques: fast Fourier transform (FFT), autocorrelation, cosine correlation, autoregression, and Hjorth parameters. Of these, the individual RQIs based on the FFT, autocorrelation, and autoregression are deemed to be good predictors of the presence of respiratory waveform data. The three individual RQIs are used to derive three fusion RQIs based on two supervised learning algorithms: linear regression and support vector regression (SVR) and one unsupervised learning algorithm: principal component analysis (PCA). Both the linear regression and PCA fusion RQIs are accurate and robust. The linear regression fusion RQI is used in the development of an RR estimation algorithm, termed RQIFusion, which achieves highly accurate and more complete RR estimates than existing methods. In the Dialysis III data set, implementation of RQIFusion improved RR estimation error by between 1.35 to 2.29 breaths per minute (brpm) to achieve RR estimation errors between 2.18 to 3.46 brpm, depending on the RR estimation algorithm employed. These results represent a marked improvement in RR estimation and indicate the importance of conducting respiratory quality analysis using RQIs on respiratory modulations extracted from ECG and PPG prior to RR estimation. Related Publications Journals and Conference Proceedings D.A. Birrenkott, M.A.F. Pimentel, P.J. Watkinson, and D.A. Clifton, “A robust fusion model for estimating respiratory rate from photoplethysmography and electrocardiography,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 9, pp. 2033-2041, 2018. P. Charlton, D.A. Birrenkott, T. Bonnici, M.A.F. Pimentel, A.E.W. Johnson, J. Alastruey, L. Tarassenko, P.J. Watkinson, R. Beale, and D.A. Clifton, “Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review,” IEEE Reviews in Biomedical Engineering, vol. 11, pp. 2-20, 2017. M.A.F. Pimentel, A.E.W. Johnson, P. Charlton, D.A. Birrenkott, P.J. Watkinson, L. Tarassenko, and D.A. Clifton, “Towards a robust estimation of respiratory rate from pulse oximeters,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1914-1923, 2017. D.A. Birrenkott, M.A.F. Pimentel, P.J. Watkinson, and D.A. Clifton, “Robust estimation of respiratory rate via ECGand PPG-derived respiratory quality indices,” in the Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 676-679, 2016.

Respiratory quality indices for automated monitoring of respiration from sensor data

Thousands of in-hospital deaths each year in the UK are potentially preventable, being often preceded by physiological deterioration. The current standard of clinical practice for patient monitoring on general wards is the periodic observation of vital signs by nursing staff. The use of early warning score (EWS) systems should enable a more timely response to, and assessment of, acutely ill patients. The investigations described in this thesis seek to apply principled approaches based on machine learning to the analysis of vital-sign data from patients who are recovering from major surgery. A dataset comprising observational vital-sign data from 407 post-operative patients taking part in a two-phase clinical trial in the Oxford Cancer Centre is introduced. A second independent dataset collected from clinical data obtained from 24,212 patients admitted to the Medical Assessment Unit of a different hospital is used for validation purposes. When applied to post-operative patients, currently-used EWS systems achieve values of Area Under the Receiver-Operating Characteristic curve (AUROC) that range from 0.717 to 0.841 for predicting a composite outcome of death, emergency admission to the Intensive Care Unit, and cardiac arrest within 24 hours. We also demonstrate that the method of recording vital signs on the ward plays a fundamental role in the design and performance of EWS systems. Using the same set of physiological variables, kernel density estimators and support vector machines give equivalent results to those of EWS systems which have been carefully optimised by trial and error. A method for describing the physiological trajectories of post-operative patients is developed using machine learning techniques. We further introduce the concept of variability of vital signs over a 24-hour period, and propose a strategy for incorporating this information into the machine learning models studied. The resulting model leads to an improvement in performance (AUROC = 0.856). An approach based on Gaussian processes is then discussed for exploring and representing patterns of vital-sign time-series data. The approach allows different types of normal physiological trends to be identified in patients recovering from surgery. Knowledge of different patterns among hospitalised patients and their incorporation in monitoring systems improves early-warning scoring systems for the identification of physiological deterioration in specific patient groups.

Modelling of vital-sign data from post-operative patients

Objective To validate whether a wearable remote vital signs monitor could accurately measure heart rate (HR), respiratory rate (RR) and temperature in a postsurgical patient population at high risk of complications. Design Manually recorded vital signs data were paired with vital signs data derived from the remote monitor set in patients participating in the Trial of Remote versus Continuous INtermittent monitoring (TRaCINg) study: a trial of continuous remote vital signs monitoring. Setting St James’s University Hospital, UK. Participants 51 patients who had undergone major elective general surgery. Interventions The intervention was the SensiumVitals monitoring system. This is a wireless patch worn on the patient’s chest that measures HR, RR and temperature continuously. The reference standard was nurse-measured manually recorded vital signs. Primary and secondary outcome measures The primary outcomes were the 95% limits of agreement between manually recorded and wearable patch vital sign recordings of HR, RR and temperature. The secondary outcomes were the percentage completeness of vital sign patch data for each vital sign. Results 1135 nurse observations were available for analysis. There was no clinically meaningful bias in HR (1.85 bpm), but precision was poor (95% limits of agreement −23.92 to 20.22 bpm). Agreement was poor for RR (bias 2.93 breaths per minute, 95% limits of agreement −8.19 to 14.05 breaths per minute) and temperature (bias 0.82°C, 95% limits of agreement −1.13°C to 2.78°C). Vital sign patch data completeness was 72.8% for temperature, 59.2% for HR and 34.1% for RR. Distributions of RR in manually recorded measurements were clinically implausible. Conclusions The continuous monitoring system did not reliably provide HR consistent with nurse measurements. The accuracy of RR and temperature was outside of acceptable limits. Limitations of the system could potentially be overcome through better signal processing. While acknowledging the time pressures placed on nursing staff, inaccuracies in the manually recorded data present an opportunity to increase awareness about the importance of manual observations, particularly with regard to methods of manual HR and RR measurements.

https://bmjopen.bmj.com/content/bmjopen/9/8/e031150.full.pdf

Reliability of a wearable wireless patch for continuous remote monitoring of vital signs in patients recovering from major surgery: a clinical validation study from the TRaCINg trial.

The charting of physiological variables in hospital inpatients allows for recognition and treatment of deteriorating patients. The use of electronic records to capture patients' vital signs is still in its infancy in the United Kingdom. The main objective of this article was to describe the adoption of an electronic observation charting function integrated into an established bedside e-prescribing record system on acute wards in a large English university hospital. This new function also has the capability of contacting Critical Care Outreach and clinical staff when patients deteriorate. Data captured over a 4-month period from the pilot wards showed that 80% of observation sets were completed sufficiently to produce early warning scores over the time period. A daily average of 419 Standardized Early Warning Score produced 74 alerts to clinical staff, and two critical alarms per day were e-mailed to the Outreach team. The wards showed different levels of completeness of observations (from 69% to 92%). Although a good overall rate of completeness of physiological data was found, traditional gaps in observation recording documented in the literature (eg, recording of respiratory rate) were still apparent. This system can be used for audit for targeted staff education and to evaluate the Critical Care Outreach service.

Adoption of an electronic observation chart with an integrated early warning scoring system on pilot wards: a descriptive report.

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Adoption of an electronic observation chart with an integrated early warning scoring system on pilot wards: a descriptive report.