Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study.

Background: The frequency at which patients should have their vital signs (e.g. blood pressure, pulse, oxygen saturation) measured on hospital wards is currently unknown. Current National Health Service monitoring protocols are based on expert opinion but supported by little empirical evidence. The challenge is finding the balance between insufficient monitoring (risking missing early signs of deterioration and delays in treatment) and over-observation of stable patients (wasting resources needed in other aspects of care). Objective: Provide an evidence-based approach to creating monitoring protocols based on a patient's risk of deterioration and link these to nursing workload and economic impact. Design: Our study consisted of two parts: (1) an observational study of nursing staff to ascertain the time to perform vital sign observations; and (2) a retrospective study of historic data on patient admissions exploring the relationships between National Early Warning Score and risk of outcome over time. These were underpinned by opinions and experiences from stakeholders. Setting and participants: Observational study: observed nursing staff on 16 randomly selected adult general wards at four acute National Health Service hospitals. Retrospective study: extracted, linked and analysed routinely collected data from two large National Health Service acute trusts; data from over 400,000 patient admissions and 9,000,000 vital sign observations. Results: Observational study found a variety of practices, with two hospitals having registered nurses take the majority of vital sign observations and two favouring healthcare assistants or student nurses. However, whoever took the observations spent roughly the same length of time. The average was 5:01 minutes per observation over a 'round', including time to locate and prepare the equipment and travel to the patient area. Retrospective study created survival models predicting the risk of outcomes over time since the patient was last observed. For low-risk patients, there was little difference in risk between 4 hours and 24 hours post observation. Conclusions: We explored several different scenarios with our stakeholders (clinicians and patients), based on how 'risk' could be managed in different ways. Vital sign observations are often done more frequently than necessary from a bald assessment of the patient's risk, and we show that a maximum threshold of risk could theoretically be achieved with less resource. Existing resources could therefore be redeployed within a changed protocol to achieve better outcomes for some patients without compromising the safety of the rest. Our work supports the approach of the current monitoring protocol, whereby patients' National Early Warning Score 2 guides observation frequency. Existing practice is to observe higher-risk patients more frequently and our findings have shown that this is objectively justified. It is worth noting that important nurse-patient interactions take place during vital sign monitoring and should not be eliminated under new monitoring processes. Our study contributes to the existing evidence on how vital sign observations should be scheduled. However, ultimately, it is for the relevant professionals to decide how our work should be used. Study registration: This study is registered as ISRCTN10863045. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/05/03) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 6. See the NIHR Funding and Awards website for further award information.

Patient recovery in hospital is tracked by measuring heart rate, blood pressure and other 'vital signs' and converting them into a score. These are 'observed' regularly by nursing staff so that deterioration can be spotted early. However, taking observations can disturb patients, and taking them too often causes extra work for staff. More frequent monitoring is recommended for higher scores, but evidence is lacking. To work out how often patients should be monitored, we needed to know how likely it is for patients to become more unwell between observations. We analysed over 400,000 patient records from two hospitals to understand how scores change with time. We looked at three of the most serious risks for patients in hospital. These risks are dying, needing intensive care or having a cardiac arrest. We also looked at the risk that a patient's condition would deteriorate significantly before their measurements were taken again. We identified early signs of deterioration and how changes in vital signs affected the risk of a patient's condition becoming worse. From this we calculated a maximum risk of deterioration. We then calculated different monitoring schedules that keep individual patients below this risk level. Some of those would consume less staff time than current National Health Service guidelines suggest. We also watched staff record patients' vital signs. We learnt it takes about 5 minutes to take these measurements from each patient. This information helped us calculate how costs would change if patients' vital signs were taken more or less often. We found that patients with a low overall score could have their vital signs monitored less often without being in danger of serious harm. This frees up nursing time so that patients with a higher score can be monitored more often. Importantly, this can be achieved without employing more staff.

Outlier detection of vital sign trajectories from COVID-19 patients.

In this work, we present a novel trajectory comparison algorithm to identify abnormal vital sign trends, with the aim of improving recognition of deteriorating health.There is growing interest in continuous wearable vital sign sensors for monitoring patients remotely at home. These monitors are usually coupled to an alerting system, which is triggered when vital sign measurements fall outside a predefined normal range. Trends in vital signs, such as increasing heart rate, are often indicative of deteriorating health, but are rarely incorporated into alerting systems.We introduce a dynamic time warp distance-based measure to compare time series trajectories. We split each multi-variable sign time series into 180 minute, non-overlapping epochs. We then calculate the distance between all pairs of epochs. Each epoch is characterized by its mean pairwise distance (average link distance) to all other epochs, with clusters forming with nearby epochs.We demonstrate in synthetically generated data that this method can identify abnormal epochs and cluster epochs with similar trajectories. We then apply this method to a real-world data set of vital signs from 8 patients who had recently been discharged from hospital after contracting COVID-19. We show how outlier epochs correspond well with the abnormal vital signs and identify patients who were subsequently readmitted to hospital.

Inequities in hypertension management: observational cross-sectional study in North East London using electronic health records.

BACKGROUND: Hypertension is a key modifiable risk factor for cardiovascular disease - the leading cause of death in the UK. Good blood pressure (BP) control reduces mortality. However, health inequities may lead to variability in hypertension monitoring and control. AIM: To investigate health inequities related to ethnicity, sex, age, and socioeconomic status in the monitoring, treatment, and control of BP in a large cohort of adult patients with hypertension. DESIGN AND SETTING: A cross-sectional cohort study of adults with hypertension registered with general practices in North East London on 1 April 2019. METHOD: Multivariable logistic regression was used to estimate associations of demographics and treatment intensity for the following hypertension management indicators: a) BP recording in past 12 months; b) BP on age- adjusted target; and c) BP on age-adjusted target and BP recorded in past 12 months. RESULTS: In total, 156 296 adults were included. The Black ethnicity group was less likely to have controlled BP than the White ethnicity group (odds ratio [OR] 0.87, 95% [confidence interval] CI = 0.84 to 0.91). The Asian ethnicity group was more likely to have controlled BP (OR 1.28, 95% CI = 1.23 to 1.32). Ethnicity differences in control could not be explained by the likelihood of having a recent BP recording, nor by treatment intensity differences. Older adults (aged ≥50 years) were more likely to have controlled hypertension than younger patients. CONCLUSION: Individuals of Black ethnicity and younger people are less likely to have controlled hypertension and may warrant targeted interventions. Possible explanations for these findings are presented but further research is needed about reasons for ethnic differences.

Multivariable prediction models for atrial fibrillation after cardiac surgery: a systematic review protocol.

INTRODUCTION: Dozens of multivariable prediction models for atrial fibrillation after cardiac surgery (AFACS) have been published, but none have been incorporated into regular clinical practice. One of the reasons for this lack of adoption is poor model performance due to methodological weaknesses in model development. In addition, there has been little external validation of these existing models to evaluate their reproducibility and transportability. The aim of this systematic review is to critically appraise the methodology and risk of bias of papers presenting the development and/or validation of models for AFACS. METHODS: We will identify studies that present the development and/or validation of a multivariable prediction model for AFACS through searches of PubMed, Embase and Web of Science from inception to 31 December 2021. Pairs of reviewers will independently extract model performance measures, assess methodological quality and assess risk of bias of included studies using extraction forms adapted from a combination of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and the Prediction Model Risk of Bias Assessment Tool. Extracted information will be reported by narrative synthesis and descriptive statistics. ETHICS AND DISSEMINATION: This systemic review will only include published aggregate data, so no protected health information will be used. Study findings will be disseminated through peer-reviewed publications and scientific conference presentations. Further, this review will identify weaknesses in past AFACS prediction model development and validation methodology so that subsequent studies can improve upon prior practices and produce a clinically useful risk estimation tool. PROSPERO REGISTRATION NUMBER: CRD42019127329.

Protocol describing a systematic review and mixed methods consensus process to define the deteriorated ward patient.

INTRODUCTION: Most patients admitted to hospital recover with treatments that can be administered on the general ward. A small but important group deteriorate however and require augmented organ support in areas with increased nursing to patient ratios. In observational studies evaluating this cohort, proxy outcomes such as unplanned intensive care unit admission, cardiac arrest and death are used. These outcome measures introduce subjectivity and variability, which in turn hinders the development and accuracy of the increasing numbers of electronic medical record (EMR) linked digital tools designed to predict clinical deterioration. Here, we describe a protocol for developing a new outcome measure using mixed methods to address these limitations. METHODS AND ANALYSIS: We will undertake firstly, a systematic literature review to identify existing generic, syndrome-specific and organ-specific definitions for clinically deteriorated, hospitalised adult patients. Secondly, an international modified Delphi study to generate a short list of candidate definitions. Thirdly, a nominal group technique (NGT) (using a trained facilitator) will take a diverse group of stakeholders through a structured process to generate a consensus definition. The NGT process will be informed by the data generated from the first two stages. The definition(s) for the deteriorated ward patient will be readily extractable from the EMR. ETHICS AND DISSEMINATION: This study has ethics approval (reference 16399) from the Central Adelaide Local Health Network Human Research Ethics Committee. Results generated from this study will be disseminated through publication and presentation at national and international scientific meetings.

New-onset atrial fibrillation in intensive care: epidemiology and outcomes.

AIMS: New-onset atrial fibrillation (NOAF) is common in patients treated on an intensive care unit (ICU), but the long-term impacts on patient outcomes are unclear. We compared national hospital and long-term outcomes of patients who developed NOAF in ICU with those who did not, before and after adjusting for comorbidities and ICU admission factors. METHODS AND RESULTS: Using the RISK-II database (Case Mix Programme national clinical audit of adult intensive care linked with Hospital Episode Statistics and mortality data), we conducted a retrospective cohort study of 4615 patients with NOAF and 27 690 matched controls admitted to 248 adult ICUs in England, from April 2009 to March 2016. We examined in-hospital mortality; hospital readmission with atrial fibrillation (AF), heart failure, and stroke up to 6 years post discharge; and mortality up to 8 years post discharge. Compared with controls, patients who developed NOAF in the ICU were at a higher risk of in-hospital mortality [unadjusted odds ratio (OR) 3.22, 95% confidence interval (CI) 3.02-3.44], only partially explained by patient demographics, comorbidities, and ICU admission factors (adjusted OR 1.50, 95% CI 1.38-1.63). They were also at a higher risk of subsequent hospitalization with AF [adjusted cause-specific hazard ratio (aCHR) 5.86, 95% CI 5.33-6.44], stroke (aCHR 1.47, 95% CI 1.12-1.93), and heart failure (aCHR 1.28, 95% CI 1.14-1.44) independent of pre-existing comorbidities. CONCLUSION: Patients who develop NOAF during an ICU admission are at a higher risk of in-hospital death and readmissions to hospital with AF, heart failure, and stroke than those who do not.

Artificial intelligence and clinical deterioration.

PURPOSE OF REVIEW: To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI). RECENT FINDINGS: There are five leading AI driven systems in this field: the Advanced Alert Monitor (AAM), the electronic Cardiac Arrest Risk Triage (eCART) score, Hospital wide Alert Via Electronic Noticeboard, the Mayo Clinic Early Warning Score, and the Rothman Index (RI). Each uses Electronic Patient Record (EPR) data and machine learning to predict adverse events. Less mature but relevant evolutions are occurring in the fields of Natural Language Processing, Time and Motion Studies, AI Sepsis and COVID-19 algorithms. SUMMARY: Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging.

Evaluation of a digital system to predict unplanned admissions to the intensive care unit: A mixed-methods approach.

BACKGROUND: We have developed the Hospital Alerting Via Electronic Noticeboard (HAVEN) which aims to identify hospitalised patients most at risk of reversible deterioration. HAVEN combines patients' vital-sign measurements with laboratory results, demographics and comorbidities using a machine learnt algorithm. OBJECTIVES: The aim of this study was to identify variables or concepts that could improve HAVEN predictive performance. METHODS: This was an embedded, mixed methods study. Eligible patients with the five highest HAVEN scores in the hospital (i.e., 'HAVEN Top 5') had their medical identification details recorded. We conducted a structured medical note review on these patients 48 hours post their identifiers being recorded. Methods of constant comparison were used during data collection and to analyse patient data. RESULTS: The 129 patients not admitted to ICU then underwent constant comparison review, which produced three main groups. Group 1 were patients referred to specialist services (n = 37). Group 2 responded to ward-based treatment, (n = 38). Group 3 were frail and had documented treatment limitations (n = 47). CONCLUSIONS: Digital-only validation methods code the cohort not admitted to ICU as 'falsely positive' in sensitivity analyses however this approach limits the evaluation of model performance. Our study suggested that coding for patients referred to other specialist teams, those with treatment limitations in place, along with those who are deteriorating but then respond to ward-based therapies, would give a more accurate measure of the value of the scores, especially in relation to cost-effectiveness of resource utilisation.

Comparative effectiveness of common treatments for new-onset atrial fibrillation within the ICU: Accounting for physiological status.

BACKGROUND: New-onset atrial fibrillation (NOAF) is common in patients on an intensive care unit (ICU). Evidence guiding treatments is limited, though recent reports suggest beta blocker (BB) therapy is associated with reduced mortality. METHODS: We conducted a multicentre cohort study of adult patients admitted to 3 ICUs in the UK and 5 ICUs in the USA. We analysed the haemodynamic changes associated with NOAF. We analysed rate control, rhythm control, and hospital mortality associated with common NOAF treatments. We balanced admission and post-NOAF, pre-treatment covariates across treatment groups. RESULTS: NOAF was followed by a systolic blood pressure reduction of 5 mmHg (p < 0.001). After adjustment, digoxin therapy was associated with inferior rate control versus amiodarone (adjusted hazard ratio (aHR) 0.56, [95% CI 0.34-0.92]). Calcium channel blocker (CCB) therapy was associated with inferior rhythm control versus amiodarone (aHR 0.59 (0.37-0.92). No difference was detected between BBs and amiodarone in rate control (aHR 1.15 [0.91-1.46]), rhythm control (aHR 0.85, [0.69-1.05]), or hospital mortality (aHR 1.03 [0.53-2.03]). CONCLUSIONS: NOAF in ICU patients is followed by decreases in blood pressure. BBs and amiodarone are associated with similar cardiovascular control and appear superior to digoxin and CCBs. Accounting for key confounders removes previously reported mortality benefits associated with BB treatment.

Circadian variation in new-onset atrial fibrillation in patients in ICUs.

New-onset atrial fibrillation (NOAF) is common in patients treated on an intensive care unit (ICU). Onset of certain arrhythmias exhibit circadian variation. Whether NOAF follows a circadian rhythm in patients in ICU is unknown. We undertook a retrospective observational study of two ICU databases to explore the timing of NOAF onset. We identified 2017 patients who developed NOAF during their ICU stay. NOAF onset exhibited a bimodal distribution with peaks at 8 am and 8 pm, consistent with the onset of paroxysmal AF in patients in the community. Future studies in ICUs should record time of AF onset, as understanding high risk periods may inform timing of preventative interventions.

How long do nursing staff take to measure and record patients' vital signs observations in hospital? A time-and-motion study.

INTRODUCTION: Monitoring vital signs in hospital is an important part of safe patient care. However, there are no robust estimates of the workload it generates for nursing staff. This makes it difficult to plan adequate staffing to ensure current monitoring protocols can be delivered. OBJECTIVE: To estimate the time taken to measure and record one set of patient's vital signs; and to identify factors associated with the time required to measure and record one set of patient's vital signs. METHODS: We undertook a time-and-motion study of 16 acute medical or surgical wards across four hospitals in England. Two trained observers followed a standard operating procedure to record the time taken to measure and record vital signs. We used mixed-effects models to estimate the mean time using whole vital signs rounds, which included equipment preparation, time spent taking vital signs at the bedside, vital signs documentation, and equipment storing. We tested whether our estimates were influenced by nurse, ward and hospital factors. RESULTS: After excluding non-vital signs related interruptions, dividing the length of a vital signs round by the number of vital signs assessments in that round yielded an estimated time per vital signs set of 5 min and 1 second (95% Confidence Interval (CI) = 4:39-5:24). If interruptions within the round were included, the estimated time was 6:26 (95% CI = 6:01-6:50). If only time taking each patient's vital signs at the bedside was considered, after excluding non-vital signs related interruptions, the estimated time was 3:45 (95% CI = 3:32-3:58). We found no substantial differences by hospital, ward or nurse characteristics, despite different systems for recording vital signs being used across the hospitals. DISCUSSION: The time taken to observe and record a patient's vital signs is considerable, so changes to recommended assessment frequency could have major workload implications. Variation in estimates derived from previous studies may, in part, arise from a lack of clarity about what was included in the reported times. We found no evidence that nurses save time when using electronic vital signs recording, or that the grade of staff measuring the vital signs influenced the time taken. CONCLUSIONS: Measuring and recording vital signs is time consuming and the impact of interruptions and preparation away from the bedside is considerable. When considering the nursing workload around vital signs assessment, no assumption of relative efficiency should be made if different technologies or staff groups are deployed.

Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System.

Rationale: Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score (EWS) systems and electronic health records, deterioration still goes unrecognized. Objectives: To develop and externally validate a Hospital- wide Alerting via Electronic Noticeboard (HAVEN) system to identify hospitalized patients at risk of reversible deterioration. Methods: This was a retrospective cohort study of patients 16 years of age or above admitted to four UK hospitals. The primary outcome was cardiac arrest or unplanned admission to the ICU. We used patient data (vital signs, laboratory tests, comorbidities, and frailty) from one hospital to train a machine-learning model (gradient boosting trees). We internally and externally validated the model and compared its performance with existing scoring systems (including the National EWS, laboratory-based acute physiology score, and electronic cardiac arrest risk triage score). Measurements and Main Results: We developed the HAVEN model using 230,415 patient admissions to a single hospital. We validated HAVEN on 266,295 admissions to four hospitals. HAVEN showed substantially higher discrimination (c-statistic, 0.901 [95% confidence interval, 0.898-0.903]) for the primary outcome within 24 hours of each measurement than other published scoring systems (which range from 0.700 [0.696-0.704] to 0.863 [0.860-0.865]). With a precision of 10%, HAVEN was able to identify 42% of cardiac arrests or unplanned ICU admissions with a lead time of up to 48 hours in advance, compared with 22% by the next best system. Conclusions: The HAVEN machine-learning algorithm for early identification of in-hospital deterioration significantly outperforms other published scores such as the National EWS.

Trajectories of vital signs in patients with COVID-19.

BACKGROUND: The global pandemic of coronavirus disease 2019 (COVID-19) has placed a huge strain on UK hospitals. Early studies suggest that patients can deteriorate quickly after admission to hospital. The aim of this study was to model changes in vital signs for patients hospitalised with COVID-19. METHODS: This was a retrospective observational study of adult patients with COVID-19 admitted to one acute hospital trust in the UK (CV) and a cohort of patients admitted to the same hospital between 2013-2017 with viral pneumonia (VI). The primary outcome was the start of continuous positive airway pressure/non-invasive positive pressure ventilation, ICU admission or death in hospital. We used non-linear mixed-effects models to compare changes in vital sign observations prior to the primary outcome. Using observations and FiO2 measured at discharge in the VI cohort as the model of normality, we also combined individual vital signs into a single novelty score. RESULTS: There were 497 cases of COVID-19, of whom 373 had been discharged from hospital. 135 (36.2%) of patients experienced the primary outcome, of whom 99 died in hospital. In-hospital mortality was over 4-times higher in the CV than the VI cohort (26.5% vs 6%). For those patients who experienced the primary outcome, CV patients became increasingly hypoxaemic, with a median estimated FiO2 (0.75) higher than that of the VI cohort (estimated FiO2 of 0.35). Prior to the primary outcome, blood pressure remained within normal range, and there was only a small rise in heart rate. The novelty score showed that patients with COVID-19 deteriorated more rapidly that patients with viral pneumonia. CONCLUSIONS: Patients with COVID-19 who deteriorate in hospital experience rapidly-worsening respiratory failure, with low SpO2 and high FiO2, but only minor abnormalities in other vital signs. This has potential implications for the ability of early warning scores to identify deteriorating patients.

Night work for hospital nurses and sickness absence: a retrospective study using electronic rostering systems.

There is conflicting evidence on the effect of night work on sickness absence. Most previous studies used self-reporting to identify shift patterns and measure levels of sickness absence. In contrast, this study used objective data from electronic rosters to explore the association of nurses' patterns of night work and sickness absence. This was a retrospective longitudinal study of nurse roster data from 32 general medical and surgical wards in a large acute hospital in England. We used data from 3 years and included both registered nurses and unregistered nursing assistants. We used generalized linear-mixed models to explore the association between night work and the subsequent occurrence of sickness absence. Of 601,282 shifts worked by 1944 nursing staff, 38,051 shifts were lost due to sickness absence. After controlling for potential confounders including proportion of long (≥12 h) shifts worked, proportion of overtime shifts, proportion of shifts worked in the past 7 days, and staff grade, we found that staff working more than 75% of their shifts in the past 7 days as night shifts were more likely to experience sickness absence (aOR = 1.12; 95% CI: 1.03-1.21), compared to staff working on day only schedules. Sub-group analysis found that an association between a high proportion of night shifts worked and long-term sickness (aOR = 1.31; 95% CI: 1.15-1.50), but not short-term sickness. Working high proportions of night shifts, likely representing permanent night work schedules, is associated with a higher risk of long-term sickness absence for nurses working in inpatient adult wards in acute hospitals. The higher sickness absence rates associated with permanent night shifts could result in additional costs or loss of productivity for hospitals. This study challenges the assumption that permanent night schedules maximize circadian adjustment and, therefore, reduce health problems.

Prophylactic use of platelets in critically ill patients with thrombocytopaenia: A retrospective two-centre observational study.

PURPOSE: We report the use and effect of prophylactic platelet transfusions in critically ill thrombocytopaenic patients, comparing patients with or without bone marrow failure as a cause of thrombocytopaenia. METHODS: A retrospective observational study of admissions to three intensive care units (ICU) in the UK. We identified thrombocytopaenic patients who received a platelet transfusion and extracted the platelet count prior and subsequent to platelet transfusion. We grouped patients with or without suspected bone marrow failure, defined by a total white cell count ≤1.0 × 109/L. RESULTS: Of 11,757 admissions, 399 (3.4%) patients received a platelet transfusion for thrombocytopaenia. The median [IQR] platelet count prior to transfusion in patients without bone marrow failure was 42 [28-64] × 109/L versus 14 [7-24] × 109/L (p < .0001) in those with. The median [IQR] increment in platelets following transfusion was lower in patients with marrow failure (12 [-1-23] × 109/L) compared to those without (18 [5-36] × 109/L) (p = .006). CONCLUSIONS: Platelet transfusions were given at a higher median platelet count than suggested by guidelines. Patients with bone marrow failure were transfused at a lower threshold and experienced a smaller increment in platelet count when compared to patients without marrow failure.

Managing new-onset atrial fibrillation in critically ill patients: a systematic narrative review.

OBJECTIVES: The aim of this review is to summarise the latest evidence on efficacy and safety of treatments for new-onset atrial fibrillation (NOAF) in critical illness. PARTICIPANTS: Critically ill adult patients who developed NOAF during admission. PRIMARY AND SECONDARY OUTCOMES: Primary outcomes were efficacy in achieving rate or rhythm control, as defined in each study. Secondary outcomes included mortality, stroke, bleeding and adverse events. METHODS: We searched MEDLINE, EMBASE and Web of Knowledge on 11 March 2019 to identify randomised controlled trials (RCTs) and observational studies reporting treatment efficacy for NOAF in critically ill patients. Data were extracted, and quality assessment was performed using the Cochrane Risk of Bias Tool, and an adapted Newcastle-Ottawa Scale. RESULTS: Of 1406 studies identified, 16 remained after full-text screening including two RCTs. Study quality was generally low due to a lack of randomisation, absence of blinding and small cohorts. Amiodarone was the most commonly studied agent (10 studies), followed by beta-blockers (8), calcium channel blockers (6) and magnesium (3). Rates of successful rhythm control using amiodarone varied from 30.0% to 95.2%, beta-blockers from 31.8% to 92.3%, calcium channel blockers from 30.0% to 87.1% and magnesium from 55.2% to 77.8%. Adverse effects of treatment were rarely reported (five studies). CONCLUSION: The reported efficacy of beta-blockers, calcium channel blockers, magnesium and amiodarone for achieving rhythm control was highly varied. As there is currently significant variation in how NOAF is managed in critically ill patients, we recommend future research focuses on comparing the efficacy and safety of amiodarone, beta-blockers and magnesium. Further research is needed to inform the decision surrounding anticoagulant use in this patient group.