Development of an enhanced scoring system to predict ICU readmission or in-hospital death within 24 hours using routine patient data from two NHS Foundation Trusts.

RATIONALE: Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration. OBJECTIVES: We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU. DESIGN: A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a 'static' score of the patient's condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient's risk of deterioration throughout their hospital stay. SETTING: Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model. PARTICIPANTS: A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort. RESULTS: Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653). CONCLUSIONS: We showed that a scoring system incorporating data from a patient's stay in the ICU has better performance than commonly used EWS systems based on vital signs alone. TRIAL REGISTRATION NUMBER: ISRCTN32008295.

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.

Using a novel ambulatory monitoring system to support patient safety on an acute infectious disease ward during an unfolding pandemic.

AIM: To gain staff feedback on the implementation and impact of a novel ambulatory monitoring system to support coronavirus patient management on an isolation ward. DESIGN: Qualitative service evaluation. METHODS: Semi-structured interviews were conducted with 15 multidisciplinary isolation ward staff in the United Kingdom between July 2020 and May 2021. Interviews were audio-recorded, transcribed and analysed using thematic analysis. FINDINGS: Adopting Innovation to Assist Patient Safety was identified as the overriding theme. Three interlinked sub-themes represent facets of how the system supported patient safety. Patient Selection was developed throughout the pandemic, as clinical staff became more confident in choosing which patients would benefit most. Trust In the System described how nurses coped with discrepancies between the ambulatory system and ward observation machines. Finally, Resource Management examined how, once trust was built, staff perceived the ambulatory system assisted with caseload management. This supported efficient personal protective equipment resource use by reducing the number of isolation room entries. Despite these reported benefits, face-to-face contact was still highly valued, despite the risk of coronavirus exposure. CONCLUSION: Hospital wards should consider using ambulatory monitoring systems to support caseload management and patient safety. Patients in isolation rooms or at high risk of deterioration may particularly benefit from this additional monitoring. However, these systems should be seen as an adjunct to nursing care, not a replacement. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Nurses valued ambulatory monitoring as a means of ensuring the safety of patients at risk of deterioration and prioritizing their workload. IMPACT: The findings of this research will be useful to all those developing or considering implementation of ambulatory monitoring systems in hospital wards. REPORTING METHOD: This manuscript follows the Consolidated criteria for Reporting Qualitative Research (COREQ) guidelines with inclusion of relevant SQUIRE guidelines for reporting quality improvement. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Nebulized Recombinant Tissue Plasminogen Activator (rt-PA) for Acute COVID-19-Induced Respiratory Failure: An Exploratory Proof-of-Concept Trial.

Acute lung injury in COVID-19 results in diffuse alveolar damage with disruption of the alveolar-capillary barrier, coagulation activation, alveolar fibrin deposition and pulmonary capillary thrombi. Nebulized recombinant tissue plasminogen activator (rt-PA) has the potential to facilitate localized thrombolysis in the alveolar compartment and improve oxygenation. In this proof-of-concept safety study, adults with COVID-19-induced respiratory failure and a <300 mmHg PaO2/FiO2 (P/F) ratio requiring invasive mechanical ventilation (IMV) or non-invasive respiratory support (NIRS) received nebulized rt-PA in two cohorts (C1 and C2), alongside standard of care, between 23 April-30 July 2020 and 21 January-19 February 2021, respectively. Matched historical controls (MHC; n = 18) were used in C1 to explore efficacy. Safety co-primary endpoints were treatment-related bleeds and <1.0-1.5 g/L fibrinogen reduction. A variable dosing strategy with clinical efficacy endpoint and minimal safety concerns was determined in C1 for use in C2; patients were stratified by ventilation type to receive 40-60 mg rt-PA daily for ≤14 days. Nine patients in C1 (IMV, 6/9; NIRS, 3/9) and 26 in C2 (IMV, 12/26; NIRS, 14/26) received nebulized rt-PA for a mean (SD) of 6.7 (4.6) and 9.1(4.6) days, respectively. Four bleeds (one severe, three mild) in three patients were considered treatment related. There were no significant fibrinogen reductions. Greater improvements in mean P/F ratio from baseline to study end were observed in C1 compared with MHC (C1; 154 to 299 vs. MHC; 154 to 212). In C2, there was no difference in the baseline P/F ratio of NIRS and IMV patients. However, a larger improvement in the P/F ratio occurred in NIRS patients (NIRS; 126 to 240 vs. IMV; 120 to 188) and fewer treatment days were required (NIRS; 7.86 vs. IMV; 10.5). Nebulized rt-PA appears to be well-tolerated, with a trend towards improved oxygenation, particularly in the NIRS group. Randomized clinical trials are required to demonstrate the clinical effect significance and magnitude.

Sex and age effects on risk of non-traumatic subarachnoid hemorrhage: Retrospective cohort study of 124,234 cases using electronic health records.

OBJECTIVES: The epidemiology of non-traumatic subarachnoid hemorrhage (SAH) is unclear. This study describes the antecedent characteristics of SAH patients, compares the risk of SAH between women and men, and explores if this changes with age. MATERIALS AND METHODS: Retrospective cohort study using an electronic health records network based in the USA (TriNetX). All patients aged 18-90y with at least one healthcare visit were included. Antecedent characteristics of SAH patients (ICD-10 code I60) were measured. The incidence proportion and the relative risk between women and men, were estimated overall, in the 55-90y age group, and in five-year age categories. RESULTS: Of 58.9 million eligible patients, with 190.8 million person-years of observations, 124,234 (0.21%; 63,467 female, 60,671 male) had a first SAH, with a mean age of 56.8 (S.D. 16.8) y (women: 58.2 [16.2] y, men 55.3 [17.2] y). 9,758 SAH cases (7.8%) occurred in people aged 18-30y. Prior to the SAH, an intracranial aneurysm had been diagnosed in 4.1% (women: 5.8% men: 2.5%), hypertension in 25.1% and nicotine dependence in 9.1%. Overall, women had a lower risk of SAH compared to men (RR 0.83, 95% CI 0.83-0.84), with a progressive increase in risk ratio across age groups: from RR 0.36 (0.35-0.37) in people aged 18-24y, to RR 1.07 (1.01-1.13) aged 85-90y. CONCLUSIONS: Men are at greater risk of SAH than women overall, driven by younger adult age groups. Women are at greater risk than men only in the over 75-year age groups. The excess of SAH in young men merits investigation.

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.

Building a Covid-19 secure intensive care unit: A human-centred design approach.

Background: The Covid-19 pandemic has highlighted weaknesses in the National Health Service critical care provision including both capacity and infrastructure. Traditionally, healthcare workspaces have failed to fully incorporate Human-Centred Design principles resulting in environments that negatively affect the efficacy of task completion, patient safety and staff wellbeing. In the summer of 2020, we received funds for the urgent construction of a Covid-19 secure critical care facility. The aim of this project was to design a pandemic resilient facility centred around both staff and patient requirements and safety, within the available footprint. Methods: We developed a simulation exercise, underpinned by Human-Centred Design principles, to evaluate intensive care designs through Build Mapping, Tasks Analysis and Qualitative data. Build Mapping involved taping out sections of the design and mocking up with equipment. Task Analysis and qualitative data were collected following task completion. Results: 56 participants completed the build simulation exercise generating 141 design suggestions (69 task related, 56 patient and relative related, 16 staff related). Suggestions translated to 18 multilevel design improvements; five significant structural changes (Macro level) including wall moves and lift size change. Minor improvements were made at a Meso and Micro design level. Critical care design drivers identified included functional drivers (visibility, Covid-19 secure environment, workflow, and task efficiency) and behavioural drivers (learning and development, light, humanising intensive care and design consistency). Conclusion: Success of clinical tasks, infection control, patient safety and staff/patient wellbeing are highly dependent on clinical environments. Primarily, we have improved clinical design by focusing on user requirements. Secondly, we developed a replicable approach to exploring healthcare build plans revealing significant design changes, that may have only been identified once built.

The quality of vital signs measurements and value preferences in electronic medical records varies by hospital, specialty, and patient demographics.

We aimed to assess the frequency of value preferences in recording of vital signs in electronic healthcare records (EHRs) and associated patient and hospital factors. We used EHR data from Oxford University Hospitals, UK, between 01-January-2016 and 30-June-2019 and a maximum likelihood estimator to determine the prevalence of value preferences in measurements of systolic and diastolic blood pressure (SBP/DBP), heart rate (HR) (readings ending in zero), respiratory rate (multiples of 2 or 4), and temperature (readings of 36.0 °C). We used multivariable logistic regression to investigate associations between value preferences and patient age, sex, ethnicity, deprivation, comorbidities, calendar time, hour of day, days into admission, hospital, day of week and speciality. In 4,375,654 records from 135,173 patients, there was an excess of temperature readings of 36.0 °C above that expected from the underlying distribution that affected 11.3% (95% CI 10.6-12.1%) of measurements, i.e. these observations were likely inappropriately recorded as 36.0 °C instead of the true value. SBP, DBP and HR were rounded to the nearest 10 in 2.2% (1.4-2.8%) and 2.0% (1.3-5.1%) and 2.4% (1.7-3.1%) of measurements. RR was also more commonly recorded as multiples of 2. BP digit preference and an excess of temperature recordings of 36.0 °C were more common in older and male patients, as length of stay increased, following a previous normal set of vital signs and typically more common in medical vs. surgical specialities. Differences were seen between hospitals, however, digit preference reduced over calendar time. Vital signs may not always be accurately documented, and this may vary by patient groups and hospital settings. Allowances and adjustments may be needed in delivering care to patients and in observational analyses and predictive tools using these factors as outcomes or exposures.

Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure.

There is a growing emphasis being placed on the potential for cuffless blood pressure (BP) estimation through modelling of morphological features from the photoplethysmogram (PPG) and electrocardiogram (ECG). However, the appropriate features and models to use remain unclear. We investigated the best features available from the PPG and ECG for BP estimation using both linear and non-linear machine learning models. We conducted a clinical study in which changes in BP ([Formula: see text]BP) were induced by an infusion of phenylephrine in 30 healthy volunteers (53.8% female, 28.0 (9.0) years old). We extracted a large and diverse set of features from both the PPG and the ECG and assessed their individual importance for estimating [Formula: see text]BP through Shapley additive explanation values and a ranking coefficient. We trained, tuned, and evaluated linear (ordinary least squares, OLS) and non-linear (random forest, RF) machine learning models to estimate [Formula: see text]BP in a nested leave-one-subject-out cross-validation framework. We reported the results as correlation coefficient ([Formula: see text]), root mean squared error (RMSE), and mean absolute error (MAE). The non-linear RF model significantly ([Formula: see text]) outperformed the linear OLS model using both the PPG and the ECG signals across all performance metrics. Estimating [Formula: see text]SBP using the PPG alone ([Formula: see text] = 0.86 (0.23), RMSE = 5.66 (4.76) mmHg, MAE = 4.86 (4.29) mmHg) performed significantly better than using the ECG alone ([Formula: see text] = 0.69 (0.45), RMSE = 6.79 (4.76) mmHg, MAE = 5.28 (4.57) mmHg), all [Formula: see text]. The highest ranking features from the PPG largely modelled increasing reflected wave interference driven by changes in arterial stiffness. This finding was supported by changes observed in the PPG waveform in response to the phenylephrine infusion. However, a large number of features were required for accurate BP estimation, highlighting the high complexity of the problem. We conclude that the PPG alone may be further explored as a potential single source, cuffless, blood pressure estimator. The use of the ECG alone is not justified. Non-linear models may perform better as they are able to incorporate interactions between feature values and demographics. However, demographics may not adequately account for the unique and individualised relationship between the extracted features and BP.

Design and validation of a new Healthcare Systems Usability Scale (HSUS) for clinical decision support systems: a mixed-methods approach.

OBJECTIVE: To develop and validate a questionnaire to assess the usability of clinical decision support systems (CDSS) and to assist in the early identification of usability issues that may impact patient safety and quality of care. DESIGN: Mixed research methods were used to develop and validate the questionnaire. The qualitative study involved scale item development, content and face validity. Pilot testing established construct validity using factor analysis and facilitated estimates for reliability and internal consistency using the Cronbach's alpha coefficient. SETTING: Two hospitals within a single National Health Service Trust. PARTICIPANTS: We recruited a panel of 7 experts in usability and questionnaire writing for health purposes to test content validity; 10 participants to assess face validity and 78 participants for the pilot testing. To be eligible for this last phase, participants needed to be health professionals with at least 3 months experience using the local hospital electronic patient record system. RESULTS: Feedback from the face and content validity phases contributed to the development and improvement of scale items. The final Healthcare Systems Usability Scale (HSUS) proved quick to complete, easy to understand and was mostly worded by potential users. Exploratory analysis revealed four factors related to patient safety, task execution, alerts or recommendations accuracy, the effects of the system on workflow and ease of system use. These separate into four subscales: patient safety and decision effectiveness (seven items), workflow integration (six items), work effectiveness (five items) and user control (four items). These factors affect the quality of care and clinician's ability to make informed and timely decisions when using CDSS. The HSUS has a very good reliability with global Cronbach's alpha 0.914 and between 0.702 and 0.926 for the four subscales. CONCLUSION: The HSUS is a valid and reliable tool for usability testing of CDSS and early identification of usability issues that may cause medical adverse events.

Diagnosing hypertension in primary care: a retrospective cohort study to investigate the importance of night-time blood pressure assessment.

BACKGROUND: Ambulatory blood-pressure monitoring (ABPM) has become less frequent in primary care since the COVID-19 pandemic, with home blood-pressure monitoring (HBPM) often the preferred alternative; however, HBPM cannot measure night-time blood pressure (BP), and patients whose night-time BP does not dip, or rises (reverse dipping), have poorer cardiovascular outcomes. AIM: To investigate the importance of measuring night-time BP when assessing individuals for hypertension. DESIGN AND SETTING: Retrospective cohort study of two patient populations - namely, hospital patients admitted to four UK acute hospitals located in Oxfordshire, and participants of the BP in different ethnic groups (BP-Eth) study, who were recruited from 28 UK general practices in the West Midlands. METHOD: Using BP data collected for the two cohorts, three systolic BP phenotypes (dipper, non-dipper, and reverse dipper) were studied. RESULTS: Among the hospital cohort, 48.9% (n = 10 610/21 716) patients were 'reverse dippers', with an average day-night systolic BP difference of +8.0 mmHg. Among the community (BP-Eth) cohort, 10.8% (n = 63/585) of patients were reverse dippers, with an average day-night systolic BP difference of +8.5 mmHg. Non-dipper and reverse-dipper phenotypes both had lower daytime systolic BP and higher night-time systolic BP than the dipper phenotype. Average daytime systolic BP was lowest in the reverse-dipping phenotype (this was 6.5 mmHg and 6.8 mmHg lower than for the dipper phenotype in the hospital and community cohorts, respectively), thereby placing them at risk of undiagnosed, or masked, hypertension. CONCLUSION: Not measuring night-time BP puts all groups, other than dippers, at risk of failure to identify hypertension. As a result of this study, it is recommended that GPs should offer ABPM to all patients aged ≥60 years as a minimum when assessing for hypertension.

Prognostic model for atrial fibrillation after cardiac surgery: a UK cohort study.

OBJECTIVE: To develop a validated clinical prognostic model to determine the risk of atrial fibrillation after cardiac surgery as part of the PARADISE project (NIHR131227). METHODS: Prospective cohort study with linked electronic health records from a cohort of 5.6 million people in the United Kingdom Clinical Practice Research Datalink from 1998 to 2016. For model development, we considered a priori candidate predictors including demographics, medical history, medications, and clinical biomarkers. We evaluated associations between covariates and the AF incidence at the end of follow-up using logistic regression with the least absolute shrinkage and selection operator. The model was validated internally with the bootstrap method; subsequent performance was examined by discrimination quantified with the c-statistic and calibration assessed by calibration plots. The study follows TRIPOD guidelines. RESULTS: Between 1998 and 2016, 33,464 patients received cardiac surgery among the 5,601,803 eligible individuals. The final model included 13-predictors at baseline: age, year of index surgery, elevated CHA2DS2-VASc score, congestive heart failure, hypertension, acute coronary syndromes, mitral valve disease, ventricular tachycardia, valve surgery, receiving two combined procedures (e.g., valve replacement + coronary artery bypass grafting), or three combined procedures in the index procedure, statin use, and ethnicity other than white or black (statins and ethnicity were protective). This model had an optimism-corrected C-statistic of 0.68 both for the derivation and validation cohort. Calibration was good. CONCLUSIONS: We developed a model to identify a group of individuals at high risk of AF and adverse outcomes who could benefit from long-term arrhythmia monitoring, risk factor management, rhythm control and/or thromboprophylaxis.

Regional citrate anticoagulation versus systemic heparin anticoagulation for continuous kidney replacement therapy in intensive care.

PURPOSE: Many intensive care units (ICUs) have transitioned from systemic heparin anticoagulation (SHA) to regional citrate anticoagulation (RCA) for continuous kidney replacement therapy (CKRT). We evaluated the clinical and health economic impacts of ICU transition to RCA. MATERIALS AND METHODS: We surveyed all adult general ICUs in England and Wales to identify transition dates and conducted a micro-costing study in eight ICUs. We then conducted an interrupted time-series analysis of linked, routinely collected health records. RESULTS: In 69,001 patients who received CKRT (8585 RCA, 60,416 SHA) in 181 ICUs between 2009 and 2017, transition to RCA was not associated with a change in 90-day mortality (adjusted odds ratio 0.98, 95% CI 0.89-1.08) but was associated with step-increases in duration of kidney support (0.53 days, 95% CI 0.28-0.79), advanced cardiovascular support (0.23 days, 95% CI 0.09-0.38) and ICU length of stay (0.86 days, 95% CI 0.24-1.49). The estimated one-year incremental net monetary benefit per patient was £ - 2376 (95% CI £ - 3841-£ - 911), with an estimated likelihood of cost-effectiveness of <0.1%. CONCLUSIONS: Transition to RCA was associated with significant increases in healthcare resource use, without corresponding clinical benefit, and is highly unlikely to be cost-effective over a one-year time horizon.

Preexisting Neuropsychiatric Conditions and Associated Risk of Severe COVID-19 Infection and Other Acute Respiratory Infections.

Importance: Evidence indicates that preexisting neuropsychiatric conditions confer increased risks of severe outcomes from COVID-19 infection. It is unclear how this increased risk compares with risks associated with other severe acute respiratory infections (SARIs). Objective: To determine whether preexisting diagnosis of and/or treatment for a neuropsychiatric condition is associated with severe outcomes from COVID-19 infection and other SARIs and whether any observed association is similar between the 2 outcomes. Design, Setting, and Participants: Prepandemic (2015-2020) and contemporary (2020-2021) longitudinal cohorts were derived from the QResearch database of English primary care records. Adjusted hazard ratios (HRs) with 99% CIs were estimated in April 2022 using flexible parametric survival models clustered by primary care clinic. This study included a population-based sample, including all adults in the database who had been registered with a primary care clinic for at least 1 year. Analysis of routinely collected primary care electronic medical records was performed. Exposures: Diagnosis of and/or medication for anxiety, mood, or psychotic disorders and diagnosis of dementia, depression, schizophrenia, or bipolar disorder. Main Outcomes and Measures: COVID-19-related mortality, or hospital or intensive care unit admission; SARI-related mortality, or hospital or intensive care unit admission. Results: The prepandemic cohort comprised 11 134 789 adults (223 569 SARI cases [2.0%]) with a median (IQR) age of 42 (29-58) years, of which 5 644 525 (50.7%) were female. The contemporary cohort comprised 8 388 956 adults (58 203 severe COVID-19 cases [0.7%]) with a median (IQR) age of 48 (34-63) years, of which 4 207 192 were male (50.2%). Diagnosis and/or treatment for neuropsychiatric conditions other than dementia was associated with an increased likelihood of a severe outcome from SARI (anxiety diagnosis: HR, 1.16; 99% CI, 1.13-1.18; psychotic disorder diagnosis and treatment: HR, 2.56; 99% CI, 2.40-2.72) and COVID-19 (anxiety diagnosis: HR, 1.16; 99% CI, 1.12-1.20; psychotic disorder treatment: HR, 2.37; 99% CI, 2.20-2.55). The effect estimate for severe outcome with dementia was higher for those with COVID-19 than SARI (HR, 2.85; 99% CI, 2.71-3.00 vs HR, 2.13; 99% CI, 2.07-2.19). Conclusions and Relevance: In this longitudinal cohort study, UK patients with preexisting neuropsychiatric conditions and treatments were associated with similarly increased risks of severe outcome from COVID-19 infection and SARIs, except for dementia.

Full Blood Count Trends for Colorectal Cancer Detection in Primary Care: Development and Validation of a Dynamic Prediction Model.

Colorectal cancer has low survival rates when late-stage, so earlier detection is important. The full blood count (FBC) is a common blood test performed in primary care. Relevant trends in repeated FBCs are related to colorectal cancer presence. We developed and internally validated dynamic prediction models utilising trends for early detection. We performed a cohort study. Sex-stratified multivariate joint models included age at baseline (most recent FBC) and simultaneous trends over historical haemoglobin, mean corpuscular volume (MCV), and platelet measurements up to baseline FBC for two-year risk of diagnosis. Performance measures included the c-statistic and calibration slope. We analysed 250,716 males and 246,695 females in the development cohort and 312,444 males and 462,900 females in the validation cohort, with 0.4% of males and 0.3% of females diagnosed two years after baseline FBC. Compared to average population trends, patient-level declines in haemoglobin and MCV and rise in platelets up to baseline FBC increased risk of diagnosis in two years. C-statistic: 0.751 (males) and 0.763 (females). Calibration slope: 1.06 (males) and 1.05 (females). Our models perform well, with low miscalibration. Utilising trends could bring forward diagnoses to earlier stages and improve survival rates. External validation is now required.

Increased long-term mortality following new-onset atrial fibrillation in the intensive care unit: A systematic review and meta-analysis.

PURPOSE: We performed a systematic review and meta-analysis to investigate the long-term outcomes of patients who develop new-onset atrial fibrillation (NOAF) during an intensive care unit (ICU) admission. METHODS: We searched the MEDLINE and EMBASE databases from 2000 to 2022. We included studies of adults based in general ICUs that evaluated long-term outcomes (at least 30 days after hospital discharge) of NOAF. We excluded studies involving patients with a history of atrial fibrillation (AF). We performed risk of bias assessment of the included studies based on a modified Newcastle Ottawa score (NOS). We extracted summary data for long-term outcomes. Where the outcome was reported in three or more studies we pooled effect sizes. RESULTS: We screened 2206 studies and included 15 studies reporting data from 561,797 patients. Pooled analysis of 4 studies using a random effects model revealed an association between NOAF acquired in an ICU and 90-day mortality (including ICU and hospital mortality) (RR 1.53, 95% CI 1.12-2.08). We also found an association between NOAF and 1-year mortality from 7 studies (RR 1.79, 95% CI 1.65-1.96), which remained when analysing 1-year mortality in hospital survivors (RR 1.72 (95% CI 1.49-1.98). CONCLUSIONS: In patients who develop NOAF in an ICU, both 90-day and 1-year mortality are increased in comparison to those who do not develop NOAF. Current evidence suggests an increased risk of thromboembolic events after hospital discharge in patients who develop NOAF in an ICU.

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.

Ambulatory blood pressure monitoring using telemedicine: proof-of-concept cohort and failure modes and effects analyses.

Background: The COVID-19 pandemic has accelerated adoption of remote consulting in healthcare. Despite opportunities posed by telemedicine, most hypertension services in Europe have suspended ambulatory blood pressure monitoring (ABPM). Methods: We examined the process and performance of remotely delivered ABPM using two methodologies: firstly, a Failure Modes and Effects Analysis (FMEA) and secondly, a quantitative analysis comparing ABPM data from a subgroup of 65 participants of the Screening for Hypertension in the INpatient Environment (SHINE) diagnostic accuracy study. The FMEA was performed over seven sessions from February to March 2021, with a multidisciplinary team comprising a patient representative, a research coordinator with technical expertise and four research clinicians. Results: The FMEA identified a single high-risk step in the remote ABPM process. This was cleaning of monitoring equipment in the context of the COVID-19 pandemic, unrelated to the remote setting. A total of 14 participants were scheduled for face-to-face ABPM appointments, before the UK March 2020 COVID-19 lockdown; 62 were scheduled for remote ABPM appointments since emergence of the COVID-19 pandemic between November 2020 and August 2021. A total of 65 (88%) participants completed ABPMs; all obtained sufficient successful measurements for interpretation. For the 10 participants who completed face-to-face ABPM, there were 402 attempted ABPM measurements and 361 (89%) were successful. For the 55 participants who completed remote ABPM, there were 2516 attempted measurements and 2214 (88%) were successful. There was no significant difference in the mean per-participant error rate between face-to-face (0.100, SD 0.009) and remote (0.143, SD 0.132) cohorts (95% CI for the difference -0.125 to 0.045 and two-tailed P-value 0.353). Conclusions: We have demonstrated that ABPM can be safely and appropriately provided in the community remotely and without face-to-face contact, using video technology for remote fitting appointments, alongside courier services for delivery of equipment to participants.