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.

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.

Predicting protein function from sequence and structure.

While the number of sequenced genomes continues to grow, experimentally verified functional annotation of whole genomes remains patchy. Structural genomics projects are yielding many protein structures that have unknown function. Nevertheless, subsequent experimental investigation is costly and time-consuming, which makes computational methods for predicting protein function very attractive. There is an increasing number of noteworthy methods for predicting protein function from sequence and structural data alone, many of which are readily available to cell biologists who are aware of the strengths and pitfalls of each available technique.

Are long nursing shifts on hospital wards associated with sickness absence? A longitudinal retrospective observational study.

OBJECTIVE: To investigate whether working 12 hr shifts is associated with increased sickness absence among registered nurses and health care assistants. BACKGROUND: Previous studies reported negative impacts on nurses' 12 hr shifts; however, these studies used cross-sectional techniques and subjective nurse-reported data. METHODS: A retrospective longitudinal study using routinely collected data across 32 general inpatient wards at an acute hospital in England. We used generalized linear mixed models to explore the association between shift patterns and the subsequent occurrence of short (<7 days) or long-term (≥7 days) sickness absence. RESULTS: We analysed 601,282 shifts and 8,090 distinct episodes of sickness absence. When more than 75% of shifts worked in the past 7 days were 12 hr in length, the odds of both a short-term (adjusted odds ratio = 1.28; 95% confidence index: 1.18-1.39) and long-term sickness episode (adjusted odds ratio = 1.22; 95% confidence index: 1.08-1.37) were increased compared with working none. CONCLUSION: Working long shifts on hospital wards is associated with a higher risk of sickness absence for registered nurses and health care assistants. IMPLICATIONS FOR NURSING MANAGEMENT: The higher sickness absence rates associated with long shifts could result in additional costs or loss of productivity for hospitals. The routine implementation of long shifts should be avoided.

A Comparison of the Quick Sequential (Sepsis-Related) Organ Failure Assessment Score and the National Early Warning Score in Non-ICU Patients With/Without Infection.

OBJECTIVES: The Sepsis-3 task force recommended the quick Sequential (Sepsis-Related) Organ Failure Assessment score for identifying patients with suspected infection who are at greater risk of poor outcomes, but many hospitals already use the National Early Warning Score to identify high-risk patients, irrespective of diagnosis. We sought to compare the performance of quick Sequential (Sepsis-Related) Organ Failure Assessment and National Early Warning Score in hospitalized, non-ICU patients with and without an infection. DESIGN: Retrospective cohort study. SETTING: Large U.K. General Hospital. PATIENTS: Adults hospitalized between January 1, 2010, and February 1, 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied the quick Sequential (Sepsis-Related) Organ Failure Assessment score and National Early Warning Score to 5,435,344 vital signs sets (241,996 hospital admissions). Patients were categorized as having no infection, primary infection, or secondary infection using International Classification of Diseases, 10th Edition codes. National Early Warning Score was significantly better at discriminating in-hospital mortality, irrespective of infection status (no infection, National Early Warning Score 0.831 [0.825-0.838] vs quick Sequential [Sepsis-Related] Organ Failure Assessment 0.688 [0.680-0.695]; primary infection, National Early Warning Score 0.805 [0.799-0.812] vs quick Sequential [Sepsis-Related] Organ Failure Assessment 0.677 [0.670-0.685]). Similarly, National Early Warning Score performed significantly better in all patient groups (all admissions, emergency medicine admissions, and emergency surgery admissions) for all outcomes studied. Overall, quick Sequential (Sepsis-Related) Organ Failure Assessment performed no better, and often worse, in admissions with infection than without. CONCLUSIONS: The National Early Warning Score outperforms the quick Sequential (Sepsis-Related) Organ Failure Assessment score, irrespective of infection status. These findings suggest that quick Sequential (Sepsis-Related) Organ Failure Assessment should be reevaluated as the system of choice for identifying non-ICU patients with suspected infection who are at greater risk of poor outcome.

What impact does nursing care left undone have on patient outcomes? Review of the literature.

AIMS AND OBJECTIVES: Systematic review of the impact of missed nursing care on outcomes in adults, on acute hospital wards and in nursing homes. BACKGROUND: A considerable body of evidence supports the hypothesis that lower levels of registered nurses on duty increase the likelihood of patients dying on hospital wards, and the risk of many aspects of care being either delayed or left undone (missed). However, the direct consequence of missed care remains unclear. DESIGN: Systematic review. METHODS: We searched Medline (via Ovid), CINAHL (EBSCOhost) and Scopus for studies examining the association of missed nursing care and at least one patient outcome. Studies regarding registered nurses, healthcare assistants/support workers/nurses' aides were retained. Only adult settings were included. Because of the nature of the review, qualitative studies, editorials, letters and commentaries were excluded. PRISMA guidelines were followed in reporting the review. RESULTS: Fourteen studies reported associations between missed care and patient outcomes. Some studies were secondary analyses of a large parent study. Most of the studies used nurse or patient reports to capture outcomes, with some using administrative data. Four studies found significantly decreased patient satisfaction associated with missed care. Seven studies reported associations with one or more patient outcomes including medication errors, urinary tract infections, patient falls, pressure ulcers, critical incidents, quality of care and patient readmissions. Three studies investigated whether there was a link between missed care and mortality and from these results no clear associations emerged. CONCLUSIONS: The review shows the modest evidence base of studies exploring missed care and patient outcomes generated mostly from nurse and patient self-reported data. To support the assertion that nurse staffing levels and skill mix are associated with adverse outcomes as a result of missed care, more research that uses objective staffing and outcome measures is required. RELEVANCE TO CLINICAL PRACTICE: Although nurses may exercise judgements in rationing care in the face of pressure, there are nonetheless adverse consequences for patients (ranging from poor experience of care to increased risk of infection, readmissions and complications due to critical incidents from undetected physiological deterioration). Hospitals should pay attention to nurses' reports of missed care and consider routine monitoring as a quality and safety indicator.

Teaching a 'good' ward round.

Ward rounds are a vital part of hospital medicine and junior doctors play a key role in their delivery. Despite the importance of ward rounds to patient care and experience, we believe that junior doctors may lack the training and skills to carry them out most effectively. We designed a simulation-based training session focusing on ward round skills themed to key patient safety issues and have delivered the training to over 100 learners (medical students and foundation year one doctors). Few learners had any prior training in ward rounds. The session was highly valued by all participants and surveys completed both before and after the session showed statistically significant improvements in confidence in leading and documenting ward rounds. In addition, 94% of final year medical students and 93% of doctors felt such training should be included in the undergraduate curriculum. We believe there is a current gap in training around ward round skills and would strongly encourage simulation-based ward round training to be developed for undergraduates. Further sessions following qualification may then consolidate and develop ward round skills adapted to the level of the doctor.

Development of a national maternity early warning score: centile based score development and Delphi informed escalation pathways.

Objective: To derive a new maternity early warning score (MEWS) from prospectively collected data on maternity vital signs and to design clinical response pathways with a Delphi consensus exercise. Design: Centile based score development and Delphi informed escalation pathways. Setting: Pregnancy Physiology Pattern Prediction (4P) prospective UK cohort study, 1 August 2012 to 28 December 2016. Participants: Pregnant people from the 4P study, recruited before 20 weeks' gestation at three UK maternity centres (Oxford, Newcastle, and London). 841, 998, and 889 women provided data in the early antenatal, antenatal, and postnatal periods. Main outcome measures: Development of a new national MEWS, assigning numerical weights to measurements in the lower and upper extremes of distributions of individual vital signs from the 4P prospective cohort study. Comparison of escalation rates of the new national MEWS with the Scottish and Irish MEWS systems from 18 to 40 weeks' gestation. Delphi consensus exercise to agree clinical responses to raised scores. Results: A new national MEWS was developed by assigning numerical weights to measurements in the lower and upper extremes (5%, 1%) of distributions of vital signs, except for oxygen saturation where lower centiles (10%, 2%) were used. For the new national MEWS, in a healthy population, 56% of observation sets resulted in a total score of 0 points, 26% a score of 1 point, 12% a score of 2 points, and 18% a score of ≥2 points (escalation of care is triggered at a total score of ≥2 points). Corresponding values for the Irish MEWS were 37%, 25%, 22%, and 38%, respectively; and for the Scottish MEWS, 50%, 18%, 21%, and 32%, respectively. All three MEWS were similar at the beginning of pregnancy, averaging 0.7-0.9 points. The new national MEWS had a lower mean score for the rest of pregnancy, with the mean score broadly constant (0.6-0.8 points). The new national MEWS had an even distribution of healthy population alerts across the antenatal period. In the postnatal period, heart rate threshold values were adjusted to align with postnatal changes. The centile based score derivation approach meant that each vital sign component in the new national MEWS had a similar alert rate. Suggested clinical responses to different MEWS values were agreed by consensus of an independent expert panel. Conclusions: The centile based MEWS alerted escalation of care evenly across the antenatal period in a healthy population, while reducing alerts in healthy women compared with other MEWS systems. How well the tool predicted adverse outcomes, however, was not assessed and therefore external validation studies in large datasets are needed. Unlike other MEWS systems, the new national MEWS was developed with prospectively collected data on vital signs and used a systematic, expert informed process to design an associated escalation protocol.

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.

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.

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.

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.

Gene3D: merging structure and function for a Thousand genomes.

Over the last 2 years the Gene3D resource has been significantly improved, and is now more accurate and with a much richer interactive display via the Gene3D website (http://gene3d.biochem.ucl.ac.uk/). Gene3D provides accurate structural domain family assignments for over 1100 genomes and nearly 10,000,000 proteins. A hidden Markov model library, constructed from the manually curated CATH structural domain hierarchy, is used to search UniProt, RefSeq and Ensembl protein sequences. The resulting matches are refined into simple multi-domain architectures using a recently developed in-house algorithm, DomainFinder 3 (available at: ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/DomainFinder3/). The domain assignments are integrated with multiple external protein function descriptions (e.g. Gene Ontology and KEGG), structural annotations (e.g. coiled coils, disordered regions and sequence polymorphisms) and family resources (e.g. Pfam and eggNog) and displayed on the Gene3D website. The website allows users to view descriptions for both single proteins and genes and large protein sets, such as superfamilies or genomes. Subsets can then be selected for detailed investigation or associated functions and interactions can be used to expand explorations to new proteins. Gene3D also provides a set of services, including an interactive genome coverage graph visualizer, DAS annotation resources, sequence search facilities and SOAP services.

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.

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.

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.

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.

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.

A fast and automated solution for accurately resolving protein domain architectures.

MOTIVATION: Accurate prediction of the domain content and arrangement in multi-domain proteins (which make up >65% of the large-scale protein databases) provides a valuable tool for function prediction, comparative genomics and studies of molecular evolution. However, scanning a multi-domain protein against a database of domain sequence profiles can often produce conflicting and overlapping matches. We have developed a novel method that employs heaviest weighted clique-finding (HCF), which we show significantly outperforms standard published approaches based on successively assigning the best non-overlapping match (Best Match Cascade, BMC). RESULTS: We created benchmark data set of structural domain assignments in the CATH database and a corresponding set of Hidden Markov Model-based domain predictions. Using these, we demonstrate that by considering all possible combinations of matches using the HCF approach, we achieve much higher prediction accuracy than the standard BMC method. We also show that it is essential to allow overlapping domain matches to a query in order to identify correct domain assignments. Furthermore, we introduce a straightforward and effective protocol for resolving any overlapping assignments, and producing a single set of non-overlapping predicted domains. AVAILABILITY AND IMPLEMENTATION: The new approach will be used to determine MDAs for UniProt and Ensembl, and made available via the Gene3D website: http://gene3d.biochem.ucl.ac.uk/Gene3D/. The software has been implemented in C++ and compiled for Linux: source code and binaries can be found at: ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/DomainFinder3/ CONTACT: yeats@biochem.ucl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.