Integrated rapid-cycle comparative effectiveness trials using flexible point of care randomisation in electronic health record systems.

Whilst the Randomised Controlled Trial remains the gold standard for deriving robust causal estimates of treatment efficacy, too often a traditional design proves prohibitively expensive or cumbersome when it comes to assessing questions regarding the comparative effectiveness of routinely used treatments. As a result, patients experience variation in practice as clinicians lack the evidence needed to personalise treatments effectively. This variation may be classified as unwarranted, where existing evidence is ignored, or legitimate where in the absence of evidence, clinicians rely on experience, expert opinion, and inferred principles from basic science to make decisions. We argue that within the right ethical and technological framework, legitimate variation can be transformed into a mechanism for evidence generation and learning. Learning Health Systems which harness existing variation in practice, represent a novel approach for generating evidence from everyday clinical practice. The development of these systems has gained traction due to the increased availability of modern Electronic Health Record Systems. However, despite their promise, overcoming hurdles to successfully integrating clinical trials within Learning Health Systems has proven challenging. This article describes the origins of integrated clinical trials and explores two main barriers to their further implementation - how best to obtain informed consent from patients to participate in routine comparative effectiveness research, and how to automate and integrate randomisation into a clinical workflow. Having described these barriers, we present a potential solution in the form of a research pipeline using a novel form of flexible point-of-care randomisation to allow clinicians and patients to participate in studies where there is clinical equipoise.

Nudging within learning health systems: next generation decision support to improve cardiovascular care.

The increasing volume and richness of healthcare data collected during routine clinical practice have not yet translated into significant numbers of actionable insights that have systematically improved patient outcomes. An evidence-practice gap continues to exist in healthcare. We contest that this gap can be reduced by assessing the use of nudge theory as part of clinical decision support systems (CDSS). Deploying nudges to modify clinician behaviour and improve adherence to guideline-directed therapy represents an underused tool in bridging the evidence-practice gap. In conjunction with electronic health records (EHRs) and newer devices including artificial intelligence algorithms that are increasingly integrated within learning health systems, nudges such as CDSS alerts should be iteratively tested for all stakeholders involved in health decision-making: clinicians, researchers, and patients alike. Not only could they improve the implementation of known evidence, but the true value of nudging could lie in areas where traditional randomized controlled trials are lacking, and where clinical equipoise and variation dominate. The opportunity to test CDSS nudge alerts and their ability to standardize behaviour in the face of uncertainty may generate novel insights and improve patient outcomes in areas of clinical practice currently without a robust evidence base.

Combining faecal immunochemical testing with blood test results for colorectal cancer risk stratification: a consecutive cohort of 16,604 patients presenting to primary care.

BACKGROUND: Faecal immunochemical tests (FITs) are used to triage primary care patients with symptoms that could be caused by colorectal cancer for referral to colonoscopy. The aim of this study was to determine whether combining FIT with routine blood test results could improve the performance of FIT in the primary care setting. METHODS: Results of all consecutive FITs requested by primary care providers between March 2017 and December 2020 were retrieved from the Oxford University Hospitals NHS Foundation Trust. Demographic factors (age, sex), reason for referral, and results of blood tests within 90 days were also retrieved. Patients were followed up for incident colorectal cancer in linked hospital records. The sensitivity, specificity, positive and negative predictive values of FIT alone, FIT paired with blood test results, and several multivariable FIT models, were compared. RESULTS: One hundred thirty-nine colorectal cancers were diagnosed (0.8%). Sensitivity and specificity of FIT alone at a threshold of 10 µg Hb/g were 92.1 and 91.5% respectively. Compared to FIT alone, blood test results did not improve the performance of FIT. Pairing blood test results with FIT increased specificity but decreased sensitivity. Multivariable models including blood tests performed similarly to FIT alone. CONCLUSIONS: FIT is a highly sensitive tool for identifying higher risk individuals presenting to primary care with lower risk symptoms. Combining blood test results with FIT does not appear to lead to better discrimination for colorectal cancer than using FIT alone.

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

BACKGROUND: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. METHODS: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. FINDINGS: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. INTERPRETATION: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. FUNDING: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.

Descriptors of Sepsis Using the Sepsis-3 Criteria: A Cohort Study in Critical Care Units Within the U.K. National Institute for Health Research Critical Care Health Informatics Collaborative.

OBJECTIVES: To describe the epidemiology of sepsis in critical care by applying the Sepsis-3 criteria to electronic health records. DESIGN: Retrospective cohort study using electronic health records. SETTING: Ten ICUs from four U.K. National Health Service hospital trusts contributing to the National Institute for Health Research Critical Care Health Informatics Collaborative. PATIENTS: A total of 28,456 critical care admissions (14,332 emergency medical, 4,585 emergency surgical, and 9,539 elective surgical). MEASUREMENTS AND MAIN RESULTS: Twenty-nine thousand three hundred forty-three episodes of clinical deterioration were identified with a rise in Sequential Organ Failure Assessment score of at least 2 points, of which 14,869 (50.7%) were associated with antibiotic escalation and thereby met the Sepsis-3 criteria for sepsis. A total of 4,100 episodes of sepsis (27.6%) were associated with vasopressor use and lactate greater than 2.0 mmol/L, and therefore met the Sepsis-3 criteria for septic shock. ICU mortality by source of sepsis was highest for ICU-acquired sepsis (23.7%; 95% CI, 21.9-25.6%), followed by hospital-acquired sepsis (18.6%; 95% CI, 17.5-19.9%), and community-acquired sepsis (12.9%; 95% CI, 12.1-13.6%) (p for comparison less than 0.0001). CONCLUSIONS: We successfully operationalized the Sepsis-3 criteria to an electronic health record dataset to describe the characteristics of critical care patients with sepsis. This may facilitate sepsis research using electronic health record data at scale without relying on human coding.

An informatics consult approach for generating clinical evidence for treatment decisions.

BACKGROUND: An Informatics Consult has been proposed in which clinicians request novel evidence from large scale health data resources, tailored to the treatment of a specific patient. However, the availability of such consultations is lacking. We seek to provide an Informatics Consult for a situation where a treatment indication and contraindication coexist in the same patient, i.e., anti-coagulation use for stroke prevention in a patient with both atrial fibrillation (AF) and liver cirrhosis. METHODS: We examined four sources of evidence for the effect of warfarin on stroke risk or all-cause mortality from: (1) randomised controlled trials (RCTs), (2) meta-analysis of prior observational studies, (3) trial emulation (using population electronic health records (N = 3,854,710) and (4) genetic evidence (Mendelian randomisation). We developed prototype forms to request an Informatics Consult and return of results in electronic health record systems. RESULTS: We found 0 RCT reports and 0 trials recruiting for patients with AF and cirrhosis. We found broad concordance across the three new sources of evidence we generated. Meta-analysis of prior observational studies showed that warfarin use was associated with lower stroke risk (hazard ratio [HR] = 0.71, CI 0.39-1.29). In a target trial emulation, warfarin was associated with lower all-cause mortality (HR = 0.61, CI 0.49-0.76) and ischaemic stroke (HR = 0.27, CI 0.08-0.91). Mendelian randomisation served as a drug target validation where we found that lower levels of vitamin K1 (warfarin is a vitamin K1 antagonist) are associated with lower stroke risk. A pilot survey with an independent sample of 34 clinicians revealed that 85% of clinicians found information on prognosis useful and that 79% thought that they should have access to the Informatics Consult as a service within their healthcare systems. We identified candidate steps for automation to scale evidence generation and to accelerate the return of results. CONCLUSION: We performed a proof-of-concept Informatics Consult for evidence generation, which may inform treatment decisions in situations where there is dearth of randomised trials. Patients are surprised to know that their clinicians are currently not able to learn in clinic from data on 'patients like me'. We identify the key challenges in offering such an Informatics Consult as a service.

Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study.

BACKGROUND: Preoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality. METHODS AND FINDINGS: We conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment. CONCLUSIONS: In this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making.

The complete costs of genome sequencing: a microcosting study in cancer and rare diseases from a single center in the United Kingdom.

PURPOSE: The translation of genome sequencing into routine health care has been slow, partly because of concerns about affordability. The aspirational cost of sequencing a genome is $1000, but there is little evidence to support this estimate. We estimate the cost of using genome sequencing in routine clinical care in patients with cancer or rare diseases. METHODS: We performed a microcosting study of Illumina-based genome sequencing in a UK National Health Service laboratory processing 399 samples/year. Cost data were collected for all steps in the sequencing pathway, including bioinformatics analysis and reporting of results. Sensitivity analysis identified key cost drivers. RESULTS: Genome sequencing costs £6841 per cancer case (comprising matched tumor and germline samples) and £7050 per rare disease case (three samples). The consumables used during sequencing are the most expensive component of testing (68-72% of the total cost). Equipment costs are higher for rare disease cases, whereas consumable and staff costs are slightly higher for cancer cases. CONCLUSION: The cost of genome sequencing is underestimated if only sequencing costs are considered, and likely surpasses $1000/genome in a single laboratory. This aspirational sequencing cost will likely only be achieved if consumable costs are considerably reduced and sequencing is performed at scale.

Causal inference in perioperative medicine observational research: part 2, advanced methods.

Although RCTs represent the gold standard in clinical research, most clinical questions cannot be answered using this technique, because of ethical considerations, time, and cost. The goal of observational research in clinical medicine is to gain insight into the relationship between a clinical exposure and patient outcome, in the absence of evidence from RCTs. Observational research offers additional benefit when compared with data from RCTs: the conclusions are often more generalisable to a heterogenous population, which may be of greater value to everyday clinical practice. In Part 2 of this methods series, we will introduce the reader to several advanced methods for supporting the case for causality between an exposure and outcome, including: mediation analysis, natural experiments, and joint effects methods.

Causal inference in perioperative medicine observational research: part 1, a graphical introduction.

Graphical models have emerged as a tool to map out the interplay between multiple measured and unmeasured variables, and can help strengthen the case for a causal association between exposures and outcomes in observational studies. In Part 1 of this methods series, we will introduce the reader to graphical models for causal inference in perioperative medicine, and set the framework for Part 2 of the series involving advanced methods for causal inference.

The Association between Supraphysiologic Arterial Oxygen Levels and Mortality in Critically Ill Patients. A Multicenter Observational Cohort Study.

Rationale: There is conflicting evidence on harm related to exposure to supraphysiologic PaO2 (hyperoxemia) in critically ill patients.Objectives: To examine the association between longitudinal exposure to hyperoxemia and mortality in patients admitted to ICUs in five United Kingdom university hospitals.Methods: A retrospective cohort of ICU admissions between January 31, 2014, and December 31, 2018, from the National Institute of Health Research Critical Care Health Informatics Collaborative was studied. Multivariable logistic regression modeled death in ICU by exposure to hyperoxemia.Measurements and Main Results: Subsets with oxygen exposure windows of 0 to 1, 0 to 3, 0 to 5, and 0 to 7 days were evaluated, capturing 19,515, 10,525, 6,360, and 4,296 patients, respectively. Hyperoxemia dose was defined as the area between the PaO2 time curve and a boundary of 13.3 kPa (100 mm Hg) divided by the hours of potential exposure (24, 72, 120, or 168 h). An association was found between exposure to hyperoxemia and ICU mortality for exposure windows of 0 to 1 days (odds ratio [OR], 1.15; 95% compatibility interval [CI], 0.95-1.38; P = 0.15), 0 to 3 days (OR 1.35; 95% CI, 1.04-1.74; P = 0.02), 0 to 5 days (OR, 1.5; 95% CI, 1.07-2.13; P = 0.02), and 0 to 7 days (OR, 1.74; 95% CI, 1.11-2.72; P = 0.02). However, a dose-response relationship was not observed. There was no evidence to support a differential effect between hyperoxemia and either a respiratory diagnosis or mechanical ventilation.Conclusions: An association between hyperoxemia and mortality was observed in our large, unselected multicenter cohort. The absence of a dose-response relationship weakens causal interpretation. Further experimental research is warranted to elucidate this important question.

Does Transfer to Intensive Care Units Reduce Mortality? A Comparison of an Instrumental Variables Design to Risk Adjustment.

BACKGROUND: Instrumental variable (IV) analysis can estimate treatment effects in the presence of residual or unmeasured confounding. In settings wherein measures of baseline risk severity are unavailable, IV designs are, therefore, particularly appealing, but, where established measures of risk severity are available, it is unclear whether IV methods are preferable. OBJECTIVE: We compared regression with an IV design to estimate the effect of intensive care unit (ICU) transfer on mortality in a study with well-established measures of risk severity. RESEARCH DESIGN: We use ICU bed availability at the time of assessment for ICU transfer as an instrument. Bed availability increases the chance of ICU admission, contains little information about patient characteristics, and it is unlikely that bed availability has any direct effect on in-hospital mortality. SUBJECTS: We used a cohort study of deteriorating ward patients assessed for critical care unit admission, in 49 UK National Health Service hospitals between November 1, 2010, and December 31, 2011. MEASURES: Detailed demographic, physiological, and comorbidity data were collected for all patients. RESULTS: The risk adjustment methods reported that, after controlling for all measured covariates including measures of risk severity, ICU transfer was associated with higher 28-day mortality, with a risk difference of 7.2% (95% confidence interval=5.3%-9.1%). The IV estimate of ICU transfer was -5.4% (95% confidence interval=-47.1% to 36.3%) and applies to the subsample of patients whose transfer was "encouraged" by bed availability. CONCLUSIONS: IV estimates indicate that ICU care is beneficial but are imprecisely estimated. Risk-adjusted estimates are more precise but, even with a rich set of covariates, report that ICU care is harmful.

Systematic review and consensus definitions for the Standardised Endpoints in Perioperative Medicine (StEP) initiative: infection and sepsis.

BACKGROUND: Perioperative infection and sepsis are of fundamental concern to perioperative clinicians. However, standardised endpoints are either poorly defined or not routinely implemented. The Standardised Endpoints in Perioperative Medicine (StEP) initiative was established to derive a set of standardised endpoints for use in perioperative clinical trials. METHODS: We undertook a systematic review to identify measures of infection and sepsis used in the perioperative literature. A multi-round Delphi consensus process that included more than 60 clinician researchers was then used to refine a recommended list of outcome measures. RESULTS: A literature search yielded 1857 titles of which 255 met inclusion criteria for endpoint extraction. A long list of endpoints, with definitions and timescales, was generated and those potentially relevant to infection and sepsis circulated to the theme subgroup and then the wider StEP-COMPAC working group, undergoing a three-stage Delphi process. The response rates for Delphi rounds 1, 3, and 3 were 89% (n=8), 67% (n=62), and 80% (n=8), respectively. A set of 13 endpoints including fever, surgical site, and organ-specific infections as defined by the US Centres for Disease Control and Sepsis-3 are proposed for future use. CONCLUSIONS: We defined a consensus list of standardised endpoints related to infection and sepsis for perioperative trials using an established and rigorous approach. Each endpoint was evaluated with respect to validity, reliability, feasibility, and patient centredness. One or more of these should be considered for inclusion in future perioperative clinical trials assessing infection, sepsis, or both, thereby permitting synthesis and comparison of future results.

Postoperative critical care and high-acuity care provision in the United Kingdom, Australia, and New Zealand.

BACKGROUND: Decisions to admit high-risk postoperative patients to critical care may be affected by resource availability. We aimed to quantify adult ICU/high-dependency unit (ICU/HDU) capacity in hospitals from the UK, Australia, and New Zealand (NZ), and to identify and describe additional 'high-acuity' beds capable of managing high-risk patients outside the ICU/HDU environment. METHODS: We used a modified Delphi consensus method to design a survey that was disseminated via investigator networks in the UK, Australia, and NZ. Hospital- and ward-level data were collected, including bed numbers, tertiary services offered, presence of an emergency department, ward staffing levels, and the availability of critical care facilities. RESULTS: We received responses from 257 UK (response rate: 97.7%), 35 Australian (response rate: 32.7%), and 17 NZ (response rate: 94.4%) hospitals (total 309). Of these hospitals, 91.6% reported on-site ICU or HDU facilities. UK hospitals reported fewer critical care beds per 100 hospital beds (median=2.7) compared with Australia (median=3.7) and NZ (median=3.5). Additionally, 31.1% of hospitals reported having high-acuity beds to which high-risk patients were admitted for postoperative management, in addition to standard ICU/HDU facilities. The estimated numbers of critical care beds per 100 000 population were 9.3, 14.1, and 9.1 in the UK, Australia, and NZ, respectively. The estimated per capita high-acuity bed capacities per 100 000 population were 1.2, 3.8, and 6.4 in the UK, Australia, and NZ, respectively. CONCLUSIONS: Postoperative critical care resources differ in the UK, Australia, and NZ. High-acuity beds may have developed to augment the capacity to deliver postoperative critical care.

The Prevalence of Canine Oral Protozoa and Their Association with Periodontal Disease.

Periodontal disease is one of the most important health concerns for companion animals. Research into canine forms of periodontitis has focused on the identification and characterization of the bacterial communities present. However, other microorganisms are known to inhabit the oral cavity and could also influence the disease process. A novel, broad spectrum 18S PCR was developed and used, in conjunction with next-generation sequencing analyses to target the identification of protists. Trichomonas sp. and Entamoeba sp. were identified from 92 samples of canine plaque. The overall prevalence of trichomonads was 56.52% (52/92) and entamoebae was 4.34% (4/92). Next-generation sequencing of pooled healthy, gingivitis, early-stage periodontitis, and severe periodontitis samples revealed the proportion of trichomonad sequences to be 3.51% (health), 2.84% (gingivitis), 6.07% (early periodontitis), and 35.04% (severe periodontitis), respectively, and entamoebae to be 0.01% (health), 0.01% (gingivitis), 0.80% (early-stage periodontitis), and 7.91% (severe periodontitis) respectively. Both genera of protists were statistically associated with plaque from dogs with periodontal disease. These findings provide the first conclusive evidence for the presence of oral protozoa in dog plaque and suggest a possible role for protozoa in the periodontal disease process.

Delay to admission to critical care and mortality among deteriorating ward patients in UK hospitals: a multicentre, prospective, observational cohort study.

BACKGROUND: Annually, more than 11 million patients are admitted to hospital overnight in England, but the UK is ranked 24 of 31 European countries with respect to per head provision of intensive care unit (ICU) beds. This lack of beds places strain on the capacity to admit patients from the ward because of high ICU occupancy. Such delay can cause harm, but the effect of such harm is difficult to measure. Prompt admissions are prompt precisely because these patients are severely unwell. Measured severity is unlikely to completely capture the clinical judgment used to allocate early admission, and therefore risk-adjusted outcomes will be biased against the early admission. We aimed to evaluate the effect of delayed admission to critical care without this treatment selection bias. METHODS: We did a prospective cohort study of deteriorating ward patients assessed for critical care admission in National Health Service hospitals in the UK. Early admission was defined as within 4 h of assessment. The primary endpoint was 90-day survival. We used critical care occupancy as an instrumental variable, assuming that a full ICU could only affect outcome of a ward patient by deflecting or delaying admission. FINDINGS: 12 495 patients from 48 hospitals were available for analysis of whom 3797 (30·4%) died within 90 days. 4494 (36·0%) patients were admitted to critical care of whom 2492 (55·5%) were admitted early. The median time to admission was 2 h (IQR 1-3) with a bedside decision to admit, and 12 h otherwise (5-29). 991 patients (7·9%) were assessed when the critical care unit was fully occupied. Compared with those assessed when more than one bed was available, these patients were admitted less often (odds ratio [OR] 0·37, 95% CI 0·28-0·48), experienced greater delays (median increase 2 h, IQR 1-3), and deteriorated further while waiting (1·4 ICNARC physiology points, 95% CI 0·4-2·5). Early admission reduced mortality (OR 0·49, 95% CI 0·27-0·89). When averaged across the full population, absolute mortality fell by 13·9% (95% CI 25-23·0). INTERPRETATION: Our study has shown that the deteriorating ward patient is vulnerable with a high short-term mortality (none of these patients had treatment limitations). Delays to admission were large and common, and arose both from our inability to perfectly triage these patients, and from limits to the capacity of the system. That these delays cause harm is very likely. FUNDING: Wellcome Trust, National Institute for Health Research Service Support Costs, Intensive Care National Audit & Research Centre.

Factors associated with early provision of respiratory support in deteriorating ward patients with chest sepsis.

BACKGROUND: Implementation of the National Early Warning Score in the National Health Service (NHS) has renewed focus on prompt identification and referral of the deteriorating ward patient. A large body of published work suggests that delay in both referral and admission to critical care can be associated with poor outcomes. We sought to explore factors associated with early provision of respiratory support in a cohort of deteriorating ward patients referred to the critical care team. METHODS: SPOT(light) (Sepsis Pathophysiological and Organisational Timing) was a prospective observational study carried out between 2010 and 2011 in acute NHS hospitals in the UK. In a pilot retrospective analysis, we merged data from this study with organ support data from the Critical Care Minimum Data Set. Deteriorating adult ward patients referred to the critical care team with presumed severe chest sepsis and with no treatment limitations in place were eligible for inclusion. We used these data to assess critical care bed availabilty and factors affecting decisions to accept patients to the intensive care unit. FINDINGS: 828 patients at 13 acute hospitals were referred to the critical care team. 7-day mortality was 17% (138 patients); 115 (83%) of these patients had not received inspiratory positive pressure ventilation (IPPV) despite having had no treatment limitations in place. 275 (33%) of the 828 patients were accepted by the critical team after review. A decision to accept was significantly more likely when beds were available than when not available (269/275 [34%] vs 6/275 [15%], p=0·010). Mean time to commencing IPPV was significantly reduced by critical care bed availability (0·8 days [SD 1·3] vs 2·3 [1·4], p=0·001). 130 patients (16%) received IPPV, of whom 93 (72%) proceeded directly to IPPV rather than via non-invasive ventilation (NIV) initially. Patients were more likely to proceed directly to IPPV where the critical care team made a decision to admit (72/93 [77%] vs 21/93 [57%], p=0·018). INTERPRETATION: These pilot data suggest that critical care bed availability and a decision to admit to critical care are associated with both a faster and a more direct provision of IPPV (rather than via NIV initially). In this small sample, a large proportion of the mortality occurred in patients who had not received IPPV despite having had no treatment limitations in place. FUNDING: Wellcome Trust, National Institute for Health Service Support Costs, Intensive Care National Audit & Research Centre.