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Background Most children now survive cardiac surgery, and the focus of quality improvement initiatives has shifted toward more complex outcome measures. The aim of this investigation was to study the impact of early postoperative morbidities on parent-reported patient quality of life and parental anxiety or depression over 6 months. Methods This prospective case-matched cohort study was conducted in 5 UK children’s cardiac centers. Measures of impact for patient categories of “single morbidity,” “multiple morbidities,” and “extracorporeal life support (ECLS)” were compared with “no morbidity.” The measures used were the Pediatric Quality of Life Inventory (PedsQL) and the 4-item Patient Health Questionnaire (PHQ-4) at 6 weeks and 6 months postoperatively. The study modeled the outcomes using mixed effects regression, adjusting for case mix and clustering within centers. Results The study included 666 patients who underwent operation at a median age of 81 days (interquartile range, 10 to 325 days). At 6-week follow-up, significant adjusted differences to the reference group with no morbidity were found for total PedsQL scores, which were lower in patients with ECLS (P = .01), multiple morbidities (P < .001), and a single morbidity (P = .04), as well as the proportion of parents with anxiety and depression, which were higher in the group with multiple morbidities (P = .04 and P = .01, respectively). At 6 months, measures had improved in all morbidity groups. The only significant adjusted difference in the reference group was for physical PedsQL scores in ECLS (P = .04) and multiple morbidities (P < .01). Conclusions Patient and parent well-being are strongly influenced by postoperative morbidities early after surgery, with improvement by 6 months. Family psychological support and holistic rehabilitation are vital for children who experience postoperative morbidities.

Short-term survival after paediatric cardiac surgery has improved significantly over the past 20 years and increasing attention is being given to measuring and reducing incidence of morbidities following surgery. How to best use routinely collected data to share morbidity information constitutes a challenge for clinical teams interested in analysing their outcomes for quality improvement. We aimed to develop a tool facilitating this process in the context of monitoring morbidities following paediatric cardiac surgery, as part of a prospective multi-centre research study in the United Kingdom.We developed a prototype software tool to analyse and present data about morbidities associated with cardiac surgery in children. We used an iterative process, involving engagement with potential users, tool design and implementation, and feedback collection. Graphical data displays were based on the use of icons and graphs designed in collaboration with clinicians.Our tool enables automatic creation of graphical summaries, displayed as a Microsoft PowerPoint presentation, from a spreadsheet containing patient-level data about specified cardiac surgery morbidities. Data summaries include numbers/percentages of cases with morbidities reported, co-occurrences of different morbidities, and time series of each complication over a time window.Our work was characterised by a very high level of interaction with potential users of the tool, enabling us to promptly account for feedback and suggestions from clinicians and data managers. The United Kingdom centres involved in the project received the tool positively, and several expressed their interest in using it as part of their routine practice.

BackgroundUpdatable understanding of the onset and progression of individuals COVID-19 trajectories underpins pandemic mitigation efforts. In order to identify and characterize individual trajectories, we defined and validated ten COVID-19 phenotypes from linked electronic health records (EHR) on a nationwide scale using an extensible framework.MethodsCohort study of 56.6 million people in England alive on 23/01/2020, followed until 31/05/2021, using eight linked national datasets spanning COVID-19 testing, vaccination, primary & secondary care and death registrations data. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity using a combination of international clinical terminologies (e.g. SNOMED-CT, ICD-10) and bespoke data fields; positive test, primary care diagnosis, hospitalisation, critical care (four phenotypes), and death (three phenotypes). Using these phenotypes, we constructed patient trajectories illustrating the transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.FindingsWe identified 3,469,528 infected individuals (6.1%) with 8,825,738 recorded COVID-19 phenotypes. Of these, 364,260 (11%) were hospitalised and 140,908 (4%) died. Of those hospitalised, 38,072 (10%) were admitted to intensive care (ICU), 54,026 (15%) received non-invasive ventilation and 21,404 (6%) invasive ventilation. Amongst hospitalised patients, first wave mortality (30%) was higher than the second (23%) in non-ICU settings, but remained unchanged for ICU patients. The highest mortality was for patients receiving critical care outside of ICU in wave 1 (51%). 13,083 (9%) COVID-19 related deaths occurred without diagnoses on the death certificate, but within 30 days of a positive test while 10,403 (7%) of cases were identified from mortality data alone with no prior phenotypes recorded. We observed longer patient trajectories in the second pandemic wave compared to the first.InterpretationOur analyses illustrate the wide spectrum of severity that COVID-19 displays and significant differences in incidence, survival and pathways across pandemic waves. We provide an adaptable framework to answer questions of clinical and policy relevance; new variant impact, booster dose efficacy and a way of maximising existing data to understand individuals progression through disease states.Research in ContextEvidence before the studyWe searched PubMed on October 14, 2021, for publications with the terms “COVID-19” or “SARS-CoV-2”, “severity”, and “electronic health records” or “EHR” without date or language restrictions. Multiple studies explore factors associated with severity of COVID-19 infection, and model predictions of outcome for hospitalised patients. However, most work to date focused on isolated facets of the healthcare system, such as primary or secondary care only, was conducted in subpopulations (e.g. hospitalised patients) of limited sample size, and often utilized dichotomised outcomes (e.g. mortality or hospitalisation) ignoring the full spectrum of disease. We identified no studies which comprehensively detailed severity of infections while describing disease severity across pandemic waves, vaccination status, and patient trajectories.Added value of this studyTo our knowledge, this is the first study providing a comprehensive view of COVID-19 across pandemic waves using national data and focusing on severity, vaccination, and patient trajectories. Drawing on linked electronic health record (EHR) data on a national scale (56.6 million people alive and registered with GP in England), we describe key demographic factors, frequency of comorbidities, impact of the two main waves in England, and effect of full vaccination on COVID-19 severities. Additionally, we identify and describe patient trajectory networks which illustrate the main transition pathways of COVID-19 patients in the healthcare system. Finally, we provide reproducible COVID-19 phenotyping algorithms reflecting clinically relevant stages of disease severity i.e. positive tests, primary care diagnoses, hospitalisation, critical care treatments (e.g. ventilatory support) and mortality.Implications of all the available evidenceThe COVID-19 phenotypes and trajectory analysis framework outlined produce a reproducible, extensible and repurposable means to generate national-scale data to support critical policy decision making. By modelling patient trajectories as a series of interactions with healthcare systems, and linking these to demographic and outcome data, we provide a means to identify and prioritise care pathways associated with adverse outcomes and highlight healthcare system ‘touch points’ which may act as tangible targets for intervention.

Objective To offer a quantitative risk–benefit analysis of two doses of SARS-CoV-2 vaccination among adolescents in England. Setting England. Design Following the risk–benefit analysis methodology carried out by the US Centers for Disease Control, we calculated historical rates of hospital admission, Intensive Care Unit admission and death for ascertained SARS-CoV-2 cases in children aged 12–17 in England. We then used these rates alongside a range of estimates for incidence of long COVID, vaccine efficacy and vaccine-induced myocarditis, to estimate hospital and Intensive Care Unit admissions, deaths and cases of long COVID over a period of 16 weeks under assumptions of high and low case incidence. Participants All 12–17 year olds with a record of confirmed SARS-CoV-2 infection in England between 1 July 2020 and 31 March 2021 using national linked electronic health records, accessed through the British Heart Foundation Data Science Centre. Main outcome measures Hospitalisations, Intensive Care Unit admissions, deaths and cases of long COVID averted by vaccinating all 12–17 year olds in England over a 16-week period under different estimates of future case incidence. Results At high future case incidence of 1000/100,000 population/week over 16 weeks, vaccination could avert 4430 hospital admissions and 36 deaths over 16 weeks. At the low incidence of 50/100,000/week, vaccination could avert 70 hospital admissions and two deaths over 16 weeks. The benefit of vaccination in terms of hospitalisations in adolescents outweighs risks unless case rates are sustainably very low (below 30/100,000 teenagers/week). Benefit of vaccination exists at any case rate for the outcomes of death and long COVID, since neither have been associated with vaccination to date. Conclusions Given the current (as at 15 September 2021) high case rates (680/100,000 population/week in 10–19 year olds) in England, our findings support vaccination of adolescents against SARS-CoV2.

With high levels of the Delta variant of COVID-19 circulating in England during September 2021, schools are set to reopen with few school-based non-pharmaceutical interventions (NPIs). In this paper, we present simulation results obtained from the individual-based model, JUNE, for English school opening after a prior vaccination campaign using an optimistic set of assumptions about vaccine efficacy and the likelihood of prior-reinfection. We take a scenario-based approach to modelling potential interventions to assess relative changes rather than real-world forecasts. Specifically, we assess the effects of vaccinating those aged 16-17, those aged 12-17, and not vaccinating children at all relative to only vaccinating the adult population, addressing what might have happened had the UK began teenage vaccinations earlier. Vaccinating children in the 12-15 age group would have had a significant impact on the course of the epidemic, saving thousands of lives overall in these simulations. In the absence of such a vaccination campaign our simulations show there could still be a significant positive impact on the epidemic (fewer cases, fewer deaths) by continuing NPI strategies in schools. Our analysis suggests that the best results in terms of lives saved are likely derived from a combination of the now planned vaccination campaign and NPIs in schools.

ObjectivesGlobally, healthcare systems have been stretched to the limit by the COVID-19 pandemic. Significant changes have had to be made to the way in which non-COVID-19-related care has been delivered. Our objective was to understand, from the perspective of patients with a chronic, life-long condition (congenital heart disease, CHD) and their parents/carers, the impact of COVID-19 on the delivery of care, how changes were communicated and whether healthcare providers should do anything differently in a subsequent wave of COVID-19 infections.Design and settingQualitative study involving a series of asynchronous discussion forums set up and moderated by three patient charities via their Facebook pages.ParticipantsPatients with CHD and parents/carers of patients with CHD.Main outcome measuresQualitative responses to questions posted on the discussion forums.ResultsThe forums ran over a 6-week period and involved 109 participants. Following thematic analysis, we identified three themes and 10 subthemes related to individual condition-related factors, patient-related factors and health professional/centre factors that may have influenced how patients and parents/carers experienced changes to service delivery as a result of COVID-19. Specifically, respondents reported high levels of disruption to the delivery of care, inconsistent advice and messaging and variable communication from health professionals, with examples of both excellent and very poor experiences of care reported. Uncertainty about follow-up and factors related to the complexity and stability of their condition contributed to anxiety and stress.ConclusionsThe importance of clear, consistent communication cannot be over-estimated. Our findings, while collected in relation to patients with CHD, are not necessarily specific to this population and we believe that they reflect the experiences of many thousands of people with life-long conditions in the UK. Recommendations related to communication, service delivery and support during the pandemic may improve patients’ experience of care and, potentially, their outcomes.

BackgroundWe can improve healthcare services by better understanding current provision. One way to understand this is by linking data sets from clinical and national audits, national registries and other National Health Service (NHS) encounter data. However, getting to the point of having linked national data sets is challenging.ObjectiveWe describe our experience of the data application and linkage process for our study ‘LAUNCHES QI’, and the time, processes and resource requirements involved. To help others planning similar projects, we highlight challenges encountered and advice for applications in the current system as well as suggestions for system improvements.FindingsThe study set up for LAUNCHES QI began in March 2018, and the process through to data acquisition took 2.5 years. Several challenges were encountered, including the amount of information required (often duplicate information in different formats across applications), lack of clarity on processes, resource constraints that limit an audit’s capacity to fulfil requests and the unexpected amount of time required from the study team. It is incredibly difficult to estimate the resources needed ahead of time, and yet necessary to do so as early on as funding applications. Early decisions can have a significant impact during latter stages and be hard to change, yet it is difficult to get specific information at the beginning of the process.ConclusionsThe current system is incredibly complex, arduous and slow, stifling innovation and delaying scientific progress. NHS data can inform and improve health services and we believe there is an ethical responsibility to use it to do so. Streamlining the number of applications required for accessing data for health services research and providing clarity to data controllers could facilitate the maintenance of stringent governance, while accelerating scientific studies and progress, leading to swifter application of findings and improvements in healthcare.

Background Centralisation of paediatric intensive care units (PICUs) has the increased the need for specialist paediatric critical care transport teams (PCCT) to transport critically ill children to PICU. We investigated the impact of care provided by PCCTs for children on mortality and other clinically important outcomes. Methods We analysed linked national data from the Paediatric Intensive Care Audit Network (PICANet) from children admitted to PICUs in England and Wales (2014–2016) to assess the impact of who led the child’s transport, whether prolonged stabilisation by the PCCT was detrimental and the impact of critical incidents during transport on patient outcome. We used logistic regression models to estimate the adjusted odds and probability of mortality within 30 days of admission to PICU (primary outcome) and negative binomial models to investigate length of stay (LOS) and length of invasive ventilation (LOV). Results The study included 9112 children transported to PICU. The most common diagnosis was respiratory problems; junior doctors led the PCCT in just over half of all transports; and the 30-day mortality was 7.1%. Transports led by Advanced Nurse Practitioners and Junior Doctors had similar outcomes (adjusted mortality ANP: 0.035 versus Junior Doctor: 0.038). Prolonged stabilisation by the PCCT was possibly associated with increased mortality (0.059, 95% CI: 0.040 to 0.079 versus short stabilisation 0.044, 95% CI: 0.039 to 0.048). Critical incidents involving the child increased the adjusted odds of mortality within 30 days (odds ratio: 3.07). Conclusions Variations in team composition between PCCTs appear to have little effect on patient outcomes. We believe differences in stabilisation approaches are due to residual confounding. Our finding that critical incidents were associated with worse outcomes indicates that safety during critical care transport is an important area for future quality improvement work.

[First paragraph] Following centralisation of paediatric intensive care in England and Wales, nearly 5000 critically ill children are transported from local hospitals to paediatric intensive care units (PICUs) annually. The majority of transports are performed by ten regionally based paediatric critical care transport teams (PCCTs) [1]. As arrival of the PCCT represents the first interaction these children have with specialist paediatric critical care, a key standard set by the national Paediatric Intensive Care Society (PICS) [2] is that a PCCT should arrive at the child’s bedside within three hours of agreeing they require PICU admission (referred to as ‘time-to-bedside’).

ObjectiveGiven the paucity of long-term outcome data for complex congenital heart disease (CHD), we aimed to describe the treatment pathways and survival for patients who started interventions for functionally univentricular heart (FUH) conditions, excluding hypoplastic left heart syndrome.MethodsWe performed a retrospective cohort study using all procedure records from the National Congenital Heart Diseases Audit for children born in 2000–2018. The primary outcome was mortality, ascertained from the Office for National Statistics in 2020.ResultsOf 53 615 patients, 1557 had FUH: 55.9% were boys and 67.4% were of White ethnic groups. The largest diagnostic categories were tricuspid atresia (28.9%), double inlet left ventricle (21.0%) and unbalanced atrioventricular septal defect (AVSD) (15.2%). The ages at staged surgery were: initial palliation 11.5 (IQR 5.5–43.5) days, cavopulmonary shunt 9.2 (IQR 6.0–17.1) months and Fontan 56.2 (IQR 45.5–70.3) months. The median follow-up time was 10.8 (IQR 7.0–14.9) years and the 1, 5 and 10-year survival rates after initial palliation were 83.6% (95% CI 81.7% to 85.4%), 79.4% (95% CI 77.3% to 81.4%) and 77.2% (95% CI 75.0% to 79.2%), respectively. Higher hazards were present for unbalanced AVSD HR 2.75 (95% CI 1.82 to 4.17), atrial isomerism HR 1.75 (95% CI 1.14 to 2.70) and low weight HR 1.65 (95% CI 1.13 to 2.41), critical illness HR 2.30 (95% CI 1.67 to 3.18) or acquired comorbidities HR 2.71 (95% CI 1.82 to 4.04) at initial palliation.ConclusionAlthough treatment pathways for FUH are complex and variable, nearly 8 out of 10 children survived to 10 years. Longer-term analyses of outcome based on diagnosis (rather than procedure) can inform parents, patients and clinicians, driving practice improvements for complex CHD.

Background The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain. Methods A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). Results One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy ( Conclusion Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.

Recovery from the pandemic will be as challenging as the pandemic itself, argue Christina Pagel and Edward Palmer

ObjectivesGlobally, health care systems have been stretched to the limit by the COVID-19 pandemic. Significant changes have had to be made to the way in which non-COVID-19 related care has been delivered. Our objective was to understand, from the perspective of patients with a chronic, life-long condition (congenital heart disease, CHD) and their parents/carers, the impact of COVID-19 on the delivery of care, how changes were communicated and whether health care providers should do anything differently in a subsequent wave of COVID-19 infections.Design and settingA series of asynchronous discussion forums set up and moderated by three patient charities via their Facebook pages.ParticipantsPatients with CHD and parents/carers of patients with CHD.Main outcome measuresQualitative responses to questions posted on the discussion forums.ResultsThe forums ran over a 6-week period and involved 111 participants. Following thematic analysis of the transcripts, we identified three themes and ten subthemes related to individual condition-related factors, patient-related factors and health professional/centre factors that may have influenced how patients and parents/carers experienced changes to service delivery as a result of COVID-19.ConclusionsOur findings, whilst collected in relation to patients with CHD, are not necessarily specific to this population and we believe reflect the experiences of many thousands of people with life-long conditions in the UK. Drawing on what participants told us in the discussion forums, we have developed recommendations related to communication, service delivery and support during the pandemic that would, we think, improve patients’ experience of care and, potentially, their outcomes. Although the data were collected specifically in relation to COVID-19, a number of these recommendations are relevant to the wider delivery of care to patients with chronic underlying health conditions and reflect principles of good communication and service delivery.

Objectives To determine if there is an association between survival rates in intensive care units (ICU) and occupancy of the unit on the day of admission. Design National retrospective observational cohort study spanning the first wave of the England’s COVID-19 pandemic. Setting 114 hospital trusts (groups of hospitals functioning as single operational units). Participants 4,032 adults admitted to an ICU in England between 2nd April and 1st June, 2020, with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. Interventions N/A Main Outcomes and Measures A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible) bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). Results 79,793 patient-days were observed, with a mortality rate of 19.4 per 1,000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (>85% occupancy versus the baseline of 45 to 85%) [OR 1.19 (95% posterior credible interval (PCI): 1.00 to 1.44)]. In contrast, mortality was decreased for admissions during periods of low occupancy ( Conclusion and Relevance Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Public health interventions (such as expeditious vaccination programmes and non-pharmaceutical interventions) to control both incidence and prevalence of COVID-19, and therefore keep ICU occupancy low in the context of the pandemic, are necessary to mitigate the impact of this type of resource saturation. Trial Registration N/A Summary Box What is already known on this topic Pre-pandemic, higher occupancy of intensive care units was shown to be associated with increased mortality risk. However, there is limited data on the extent to which occupancy levels impacted patient outcomes during the first wave of COVID-19, especially in light of the mobilisation of significant additional resources. A recent study from Belgium reported a 42% higher mortality during periods of ICU surge capacity deployment, although in the analysis surge capacity was evaluated only as a binary variable. Although, this contradicts earlier results from smaller studies in Australia and Wales, where no association between ICU occupancy and mortality was identified. What this study adds The results of this study suggest that survival rates for patients with COVID-19 in intensive care settings appears to deteriorate as the occupancy of (surge capacity) beds compatible with mechanical ventilation (a proxy for operational pressure), increases. Moreover, this risk doesn’t occur above a specific threshold, but rather appears linear; whereby going from 0% occupancy to 100% occupancy increases risk of mortality by 92% (after adjusting for relevant individual-level factors). Furthermore, risk of mortality based on occupancy on the date of recorded outcome is even higher; OR 4.74 (95% posterior credible interval: 3.54 – 6.34). As such, this national-level cohort study of England provides compelling evidence for a relationship between occupancy and critical care mortality, and highlights the needs for decisive action to control the incidence and prevalence of COVID-19.

IntroductionMedication errors (MEs), which occur commonly in the perioperative period, have the potential to cause patient harm or death. Many published recommendations exist for preventing perioperative MEs; however, many of these recommendations conflict and are often not applicable to middle-income and low-income countries. The goal of this study is to develop and disseminate consensus-based recommendations for perioperative medication safety that are tailored to country income level.Methods and analysisThe primary site of this mixed-methods study is Massachusetts General Hospital/Harvard Medical School. Participants include a minimum of 108 international medication safety experts, 27 from each of the World Bank’s four country income groups (high, upper-middle, lower-middle and low-income). Using the Delphi method, participants will rate the appropriateness of candidate medication safety recommendations by completing online surveys using RedCAP. We will use Condorcet ranking methods to prioritise the final recommendations for each country income group. We will execute a comprehensive dissemination strategy for the recommendations across each country income group. Finally, we will conduct semistructured interviews with our participants to evaluate the initial adoption and implementation of the recommendations in each country income group.Ethics and disseminationThis study was approved by the Human Research Committee/Institutional Review Board at Partners Healthcare (2019P003567). Findings will be published in peer-reviewed journals and presented at local and international conferences.Trial registration numberNCT04240301.

ObjectiveEarly mortality rates for paediatric cardiac surgery have fallen due to advancements in care. Alternative indicators of care quality are needed. Postoperative morbidities are of particular interest. However, while health impacts have been reported, associated costs are unknown. Our objective was to calculate the costs of postoperative morbidities following paediatric cardiac surgery.DesignTwo methods of data collection were integrated into the main study: (1) case-matched cohort study of children with and without predetermined morbidities; (2) incidence rates of morbidity, measured prospectively.SettingFive specialist paediatric cardiac surgery centres, accounting for half of UK patients.PatientsCohort study included 666 children (340 with morbidities). Incidence rates were measured in 3090 consecutive procedures.MethodsRisk-adjusted regression modelling to determine marginal effects of morbidities on per-patient costs. Calculation of costs for hospital providers according to incidence rates. Extrapolation using mandatory audit data to report annual financial burden for the health service.Outcome measuresImpact of postoperative morbidities on per-patient costs, hospital costs and UK health service costs.ResultsSeven of the 10 morbidity categories resulted in significant costs, with mean (95% CI) additional costs ranging from £7483 (£3–£17 289) to £66 784 (£40 609–£103 539) per patient. On average all morbidities combined increased hospital costs by 22.3%. Total burden to the UK health service exceeded £21 million each year.ConclusionPostoperative morbidities are associated with a significant financial burden. Our findings could aid clinical teams and hospital providers to account for costs and contextualise quality improvement initiatives.

Summary 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.

BackgroundThe medical, health service, societal and economic impact of the COVID-19 emergency 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 (to date at least) have underlying conditions. Models have not incorporated information on high risk conditions or their longer term background (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence rates and differing mortality impacts.MethodsUsing population based linked primary and secondary care electronic health records in England (HDR UK - CALIBER), we report the prevalence of underlying conditions defined by UK Public Health England COVID-19 guidelines (16 March 2020) in 3,862,012 individuals aged ≥30 years from 1997-2017. We used previously validated phenotypes, openly available (https://caliberresearch.org/portal), for each condition using ICD-10 diagnosis, Read, procedure and medication codes. We estimated the 1-year mortality in each condition, and developed simple models of excess COVID-19-related deaths assuming relative risk (RR) of the impact of the emergency (compared to background mortality) of 1.2, 1.5 and 2.0.Findings20.0% of the population are at risk according to current PHE guidelines, of which; 13.7% were age>70 years and 6.3% aged ≤70 years with ≥1 underlying condition (cardiovascular disease (2.3%), diabetes (2.2%), steroid therapy (1.9%), severe obesity (0.9%), chronic kidney disease (0.6%) and chronic obstructive pulmonary disease, COPD (0.5%). Multimorbidity (co-occurrence of ≥2 conditions in an individual) was common (10.1%). The 1-year mortality in the at-risk population was 4.46%, and age and underlying conditions combine to influence background risk, varying markedly across conditions (5.9% in age>70 years, 8.6% for COPD and 13.1% in those with ≥3 or more conditions). In a suppression scenario (at SARS CoV2 rates of 0.001% of the UK population), there would be minimal excess deaths (3 and 7 excess deaths at relative risk, RR, 1.5 and 2.0 respectively). At SARS CoV2 rates of 10% of the UK population (mitigation) the model estimates the numbers of excess deaths as: 13791, 34479 and 68957 (at RR 1.2, 1.5 and 2.0 respectively). At SARS CoV2 rates of 80% in the UK population (“do-nothing”), the model estimates the number of excess deaths as 110332, 275,830 and 551,659 (at RR 1.2, 1.5 and 2.0) respectively.InterpretationWe provide the public, researchers and policy makers a simple model to estimate the excess mortality over 1 year from COVID-19, based on underlying conditions at different ages. If the relative mortality impact of COVID-19 were to be about 20% (similar magnitude as the established winter vs summer mortality excess), then the excess deaths would be 0 when 1 in 100 000 (suppression), 13791 when 1 in 10 (mitigation) and 110332 when 8 in 10 are infected (“do nothing”) scenario. However, the relative impact of COVID-19 is unknown. If the emergency were to double the mortality risk, then we estimate 7, 68957 and 551,659 excess deaths in the same scenarios. These results may inform the need for more stringent suppression measures as well as efforts to target those at highest risk for a range of preventive interventions.

Background In the UK, the childhood immunisation programme is given in the first 5 years of life and protects against 12 vaccine-preventable diseases. Recently, this programme has undergone changes with addition of vaccination against Meningitis B from September 2015 and the removal of the primary dose of protection against Meningitis C from July 2016. These hanges have direct impact on the associated diseases but in addition may induce indirect effects on the vaccines that are given simultaneously or later in the programme. In this work, we developed a novel formal method to evaluate the impact of vaccination changes to one aspect of the programme across an entire vaccine programme. Methods Firstly, we combined transmission modelling (for four diseases) and historic data synthesis (for eight diseases) to project, for each disease, the disease burden at different levels of effective coverage against the associated disease. Secondly, we used a simulation model to determine the vector of effective coverage against each disease under three variations of the current childhood schedule. Combining these, we calculated the vector of disease burden across the programme under different scenarios, and assessed the direct and indirect effects of the schedule changes. Results Through illustrative application of our novel framework to three scenarios of the current childhood immunisation programme in the UK, we demonstrated the feasibility of this unifying approach. For each disease in the programme, we successfully quantified the residual disease burden due to the change. For some diseases, the change was indirectly beneficial and reduced the burden, whereas for others the effect was adverse and the change increased the disease burden. Conclusions Our results demonstrate the potential benefit of considering the programme-wide impact of changes to an immunisation schedule, and our framework is an important step in the development of a means for systematically doing so.

IntroductionThe objective of this study was to prospectively validate the “Brief Developmental Assessment”, which is a new early recognition tool for neurodevelopmental abnormalities in children with heart disease that was developed for use by cardiac teams.MethodsThis was a prospective validation study among a representative sample of 960 pre-school children with heart disease from three United Kingdom tertiary cardiac centres who were analysed grouped into five separate age bands.ResultsThe “Brief Developmental Assessment” was successfully validated in the older four age bands, but not in the youngest representing infants under the age of 4 months, as pre-set validation thresholds were met – lower 95% confidence limit for the correlation coefficient above 0.75 – in terms of agreement of scores between two raters and with an external measure the “Mullen Scales of Early Learning”. On the basis of American Association of Pediatrics Guidelines, which state that the sensitivity and specificity of a developmental screening tool should fall between 70 and 80%, “Brief Developmental Assessment” outcome of Red meets this threshold for detection of Mullen scores >2 standard deviations below the mean.ConclusionThe “Brief Developmental Assessment” may be used to improve the quality of assessment of children with heart disease. This will require a training package for users and a guide to action for abnormal results. Further research is needed to determine how best to deploy the “Brief Developmental Assessment” at different time points in children with heart disease and to determine the management strategy in infants younger than 4 months old.

Winter surge management in intensive care is hampered by the annual variability in the winter surge. We aimed to develop a real-time monitoring system that could promptly identify the start, and accurately predict the end, of the winter surge in a paediatric intensive care (PIC) setting. We adapted a statistical process control method from the stock market called “Bollinger bands” that compares current levels of demand for PIC services to thresholds based on the medium term average demand. Algorithms to identify the start and end of the surge were developed for a specific PIC service: the North Thames Children's Acute Transport Service (CATS) using eight winters of data (2005–12) to tune the algorithms and one winter to test the final method (2013/14). The optimal Bollinger band thresholds were 1.2 and 1 standard deviations above and below a 41-day moving average of demand respectively. A simple linear model was found to predict the end of the surge and overall demand volume as soon as the start had been identified. Applying the method to the validation winter of 2013/14 showed excellent performance, with the surge identified from 18th November 2013 to 4th January 2014. An Excel tool running the algorithms has been in use within CATS since September 2014. There were three factors which facilitated the successful implementation of this tool: the perceived problem was pressing and identified by the clinical team; there was close clinical engagement throughout and substantial effort was made to develop an easy-to-use Excel tool for sustainable use.

ObjectiveTo describe the long-term outcomes, treatment pathways and risk factors for patients diagnosed with hypoplastic left heart syndrome (HLHS) in England and Wales.MethodsThe UK’s national audit database captures every procedure undertaken for congenital heart disease and updated life status for resident patients in England and Wales. Patients with HLHS born between 2000 and 2015 were identified using codes from the International Paediatric and Congenital Cardiac Code.ResultsThere were 976 patients with HLHS. Of these, 9.6% had a prepathway intervention, 89.5% underwent a traditional pathway of staged palliation and 6.4% of infants underwent a hybrid pathway. Patients undergoing prepathway procedures or the hybrid pathway were more complex, exhibiting higher rates of prematurity and acquired comorbidity. Prepathway intervention was associated with the highest in-hospital mortality (34.0%).44.6% of patients had an off-pathway procedure after their primary procedure, most frequently stenting or dilation of residual or recoarctation and most commonly occurring between stage 1 and stage 2.The survival rate at 1 year and 5 years was 60.7% (95% CI 57.5 to 63.7) and 56.3% (95% CI 53.0 to 59.5), respectively. Patients with an antenatal diagnosis (multivariable HR (MHR) 1.63 (95% CI 1.12 to 2.38)), low weight (ConclusionTreatment pathways among patients with HLHS are complex and variable. It is essential that the long-term outcomes of conditions like HLHS that require serial interventions are studied to provide a fuller picture and to inform quality assurance and improvement.

The Future of Health Care Reform What does a solutions-oriented conversation about U.S. health care look like at the state level, how do we get there, and what does a shift toward state action mean for the future of reform? State legislators in Colorado and Kansas have provided some provocative answers.

Objectives: To derive a relationship between the Spo(2)/Fio(2) ratio and Pao(2)/Fio(2) ratio across the entire range of Spo(2) values (0-100%) and to evaluate whether mortality prediction using the Pediatric Index of Mortality-3 can be improved by the use of Pao(2)/Fio(2) values derived from Spo(2)/Fio(2).Design: Retrospective analysis of prospectively collected data.Setting: A regional PICU transport service.Patients: Children transported to a PICU.Interventions: None.Measurements and Main Results: The relationship between Spo(2)/Fio(2) and Pao(2)/Fio(2) across the entire range of Spo(2) values was first studied using several mathematical models in a derivation cohort (n = 1,235) and then validated in a separate cohort (n = 306). The best Spo(2)/Fio(2) Pao(2)/Fio(2) relationship was chosen according to the ability to detect respiratory failure (Pao(2)/Fio(2) = 200). The discrimination of the original Pediatric Index of Mortality-3 score and a derived Pediatric Index of Mortality-3 score (where Spo(2)/Fio(2)-derived Pao(2)/Fio(2) values were used inplace of missing Pao(2)/Fio(2) values) were compared in a different cohort (n = 1,205). The best Spo(2)/Fio(2)-Pao(2)/Fio(2) relationship in 1,703 Spo(2)/Fio(2)-to-Pao(2)/Fio(2) data pairs was a linear regression equation of ln[PF] regressed on ln[SF]. This equation identified children with a Pao(2)/Fio(2) less than or equal to 200 with a specificity of 73% and sensitivity of 61% in children with Spo(2) less than 97% (92% and 33%, respectively, when Spo(2) = 97%) in the validation cohort. Pao(2)/Fio(2) derived from Spo(2)/Fio(2) (derived Pao(2)/Fio(2)) was better at predicting PICU mortality (area under receiver operating characteristic curve, 0.64; 95% CI, 0.55-0.73) compared with the original Pao(2)/Fio(2) (area under receiver operating characteristic curve, 0.54; 95% CI, 0.49-0.59; p = 0.02). However, there was no difference in the original and derived Pediatric Index of Mortality-3 scores and their discriminatory ability for mortality.Conclusions: Spo(2)-based metrics perform no worse than arterial blood gas-based metrics in mortality prediction models. Future Pediatric Index of Mortality score versions may be improved by the inclusion of risk factors based on oxygen saturation values, especially in settings where Pao(2) values are missing in a significant proportion of cases.

BackgroundFollowing paediatric cardiac surgery, quality of life may be significantly impacted by morbidities associated with cardiac surgery. Parental understanding of the potential for postoperative morbidity is important for informed decision making. As part of a broader research study, we aimed to elicit parental understanding and experience of the communication of morbidities following their child’s cardiac surgery, using traditional focus groups together with an online forum.MethodsThe Children’s Heart Federation set up and moderated a closed, anonymous online discussion group via their Facebook page, focusing on complications, information needs and methods of providing families with information. Additionally, we ran three focus groups with parents/carers, moderated by an experienced independent professional. Focus groups were recorded and transcribed and a single transcript was generated from the online forum. All transcripts were thematically analysed.ResultsAll data were collected in 2014. The forum ran over 3 months in 2014 and involved 72 participants. Focus groups involved 13 participants. Three broad themes were identified: (1) clinicians’ use of language, (2) feeling unprepared for complications and (3) information needs of families.ConclusionsClinicians’ language is often misunderstood, with wide variability in the way morbidities are described, and between differing teams looking after the same child. Information may not be easily absorbed or retained by families, who often felt unprepared for morbidities that arose after their child’s heart surgery. Here, we propose key principles of good communication tailored to the individual receiving it.

Variability in demand for staffed beds from existing patients and new referrals in intensive care units presents a substantial problem to managers. Short term fluctuations in the number of patients requiring a bed can result in demand for beds exceeding capacity, or alternatively, seemingly inefficient use of an expensive resource. While operational research methods can help in capacity planning, there are many barriers to implementing such methods in practice. In this paper we describe an entire operational research project cycle. This included: deriving exact expressions for the probability distribution for the time-varying bed demand on an intensive care unit taking account of occupancy at the point of forecast and future planned and emergency admissions; applying these expressions to a specific hospital’s intensive care unit using historical data; building software that the hospital staff can use daily to produce forecasts of short term bed demand; implementing the software within the hospital; and an evaluation of this implementation from both a technical and non-technical perspective. The main contribution of this paper is in describing the process of implementing an abstract mathematical model in a busy intensive care unit and the independent qualitative evaluation of the work about how potential barriers to implementation were addressed as part of a “modellers in residence” programme that led to us building a software tool that is still being used by the hospital more than 4 years after initial implementation. In particular, we draw together lessons from our work that we think will benefit other operational researchers wanting to work effectively with health care organisations on similar problems.

ObjectiveTo describe neurodevelopment and follow-up services in preschool children with heart disease (HD).DesignSecondary analysis of a prospectively collected multicentre dataset.SettingThree London tertiary cardiac centres.PatientsPreschool childrenMethodsWe analysed results of Mullen Scales of Early Learning (MSEL) and parental report of follow-up services in a representative convenience sample evaluated between January 2014 and July 2015 within a previous study.ResultsOf 971 preschool children: 577 (59.4%) had ≥1 heart operation, 236 (24.3%) had a known diagnosis linked to developmental delay (DD) (‘known group’) and 130 (13.4%) had history of clinical event linked to DD. On MSEL assessment, 643 (66.2%) had normal development, 181 (18.6%) had borderline scores and 147 (15.1%) had scores indicative of DD. Of 971 children, 609 (62.7%) were not receiving follow-up linked to child development and were more likely to be under these services with a known group diagnosis, history of clinical event linked to DD and DD (defined by MSEL). Of 236 in known group, parents of 77 (32.6%) and of 48 children not in a known group but with DD 29 (60.4%), reported no child development related follow-up. DD defined by MSEL assessment was more likely with a known group and older age at assessment.ConclusionsOur findings indicate that a ‘structured neurodevelopmental follow-up pathway’ in preschool children with HD should be considered for development and evaluation as children get older, with particular focus on those at higher risk.

Background Despite awareness of neurodevelopmental abnormalities in children with Congenital Heart Disease (CHD), there is no routine developmental monitoring of children with CHD in the United Kingdom. Method An early recognition tool (Brief Developmental Assessment – BDA) was therefore developed using quality improvement methodology involving several iterations and rounds of pilot testing. An expert group of community paediatricians were then engaged to refine the tool and a scoring system developed which enabled categorisation as green (appropriate for age), red (delayed) or amber (equivocal). A convenience sample of 960 pre-school children with CHD from each of five age bands (0–4 months, 5–8 months, 9–14 months, 15 months-2.9 years, 3–4.9 years) were recruited from three UK tertiary cardiac centres as part of an NIHR-funded mixed-methods study. The BDA was prospectively validated using Mullen Scales of Early Learning (MSEL). Construct validity was based on BDA detection of children with known neurodevelopmental abnormalities, and sensitivity and specificity of BDA was evaluated against MSEL. The BDA was successfully validated in the older four age bands (not those 0.75) between two raters and with MSEL. Based on American Association of Paediatrics Guidelines, which state that the sensitivity and specificity of a developmental screening tool should fall between 70%–80%, the BDA outcome of ‘red’ met threshold for detection of MSEL scores>2 SD below the mean. A Delphi survey detailing two scenarios (a child identified with red BDA and a child identified with amber BDA) was sent to 80 professionals and stakeholders and agreement was obtained about a clear referral pathway. Conclusion The BDA, along with a user-training package and an action guide, may be used to improve the quality of neurodevelopmental assessment of children with CHD, facilitating early detection and increased access to services.

Unplanned reintervention (uRE) is used as an indicator of patient morbidity and quality of care in pediatric cardiac surgery. We investigated associated factors and early mortality after uREs.Morbidity data were prospectively collected in 5 UK centers between 2015 and 2017; uRE included surgical cardiac, interventional transcatheter cardiac, permanent pacemaker, and diaphragm plication procedures. Mortality (30-day and 6-month) in uRE/no-uRE patients was reported before and after matching. Predicted 30-day mortality was calculated using the Partial Risk Adjustment in Surgery score.A total of 3090 procedures (2861 patients) were included (median age, 228 days). There were 146 uREs, resulting in an uRE rate of 4.7%. Partial Risk Adjustment in Surgery score, 30-day mortality and 6-month mortality in uRE and no-uRE groups were 2.4% versus 1.3%, 8.9% versus 1%, and 17.1% versus 2.4%, respectively. After matching, mortality at 6 months remained higher in uRE compared with no-uRE (12.2% vs 1.4%; P = .02; 74 pairs). In the uRE group, 21 out of 25 deaths at 6 months occurred when at least 1 additional postoperative complication was present. In multivariable analysis, neonatal age (P = .002), low weight (P = .009), univentricular heart (P .001), and arterial shunt (P .001) were associated with increased risk of uRE, but Partial Risk Adjustment in Surgery score was not (only in univariable analysis).uREs are a relatively frequent complication after pediatric cardiac surgery and are associated with some patient characteristics, but not the Partial Risk Adjustment in Surgery risk score. Early mortality was higher after uRE, independent of preoperative factors, but linked to other postoperative complications.

BackgroundMorbidity is defined as a state of being unhealthy or of experiencing an aspect of health that is “generally bad for you”, and postoperative morbidity linked to paediatric cardiac surgery encompasses a range of conditions that may impact the patient and are potential targets for quality assurance.MethodsAs part of a wider study, a multi-disciplinary group of professionals aimed to define a list of morbidities linked to paediatric cardiac surgery that was prioritised by a panel reflecting the views of both professionals from a range of disciplines and settings as well as parents and patients.ResultsWe present a set of definitions of morbidity for use in routine audit after paediatric cardiac surgery. These morbidities are ranked in priority order as acute neurological event, unplanned re-operation, feeding problems, the need for renal support, major adverse cardiac events or never events, extracorporeal life support, necrotising enterocolitis, surgical site of blood stream infection, and prolonged pleural effusion or chylothorax. It is recognised that more than one such morbidity may arise in the same patient and these are referred to as multiple morbidities, except in the case of extracorporeal life support, which is a stand-alone constellation of morbidity.ConclusionsIt is feasible to define a range of paediatric cardiac surgical morbidities for use in routine audit that reflects the priorities of both professionals and parents. The impact of these morbidities on the patient and family will be explored prospectively as part of a wider ongoing, multi-centre study.

This article evaluates whether providing extra clinical resource during the winter of 2014/15 improved the availability of paediatric intensive care retrieval teams in a North London retrieval service. We explored the potential impact of different staffing patterns in future years to inform service planning. We used retrospective routine data to compare the proportion of referrals refused due to lack of capacity between the winter of 2014/15 and the previous five winters. In the winter of 2014/15, an additional team were on shift for 12% of the time. We compared shifts where the additional team were available in the winter of 2014/15 to similar shifts in previous years. We used mathematical modelling to predict the potential impact of staffing levels in future winters. From 1 November 2014 to 3 January 2015, the service performed 380 emergency retrievals and refused 25 due to no available team (6.2%). The proportion of refusals during shifts with the additional team available in 2014/15 was 4.2% vs 12.4% in similar shifts from 2009/10 to 2013/14 (p = 0.026). Mathematical modelling showed that staffing an additional full-time team could result in 2% of referrals refused compared to 8% without and that much benefit could be obtained by a third team working a 12-hour shift from 10am until 10pm (refusal rate 2.8%).

Among congenital heart diseases, none has undergone a more drastic change in diagnostic approach, management, and outcomes over the last 30 years than hypoplastic left heart syndrome (HLHS), with comfort care replaced as the predominant form of management by a palliative surgical treatment pathway.1 The outcomes of palliative procedures for HLHS have been well documented, but the longer-term picture is less clear. The UK National Congenital Heart Diseases Audit (NCHDA) captures every procedure undertaken for congenital heart disease and updated life status for patients resident in England and Wales from death certifications. From this data set, we identified patients born between 2000 and 2015 who fulfilled the criteria for HLHS: a small left ventricle, stenosis or atresia of the left-sided heart valves, normally aligned great arteries, and no common atrioventricular junction.2 All patients with HLHS require surgical palliation with a traditional Norwood or hybrid pathway or heart transplantation to survive. Hence, we were able to use procedure information in combination with diagnostic information to identify patients with HLHS within the NCHDA data set using the International Paediatric and Congenital Cardiac Codes. Because the NCHDA is a procedure-based data set, patients with HLHS who did not undergo any interventional treatment are not included. Because the hybrid procedure was introduced in the United Kingdom in 2006, we considered 2 eras for …

Given excellent 30-day survival for pediatric cardiac surgery, other outcome measures are important. We aimed to study important early postoperative morbidities selected by stakeholders following a rigorous and evidenced-based process, with a view to identifying potential risk factors.The incidence of selected morbidities was prospectively measured for 3090 consecutive pediatric cardiac surgical admissions in 5 UK centers between October 2015 and June 2017. The relationship between the candidate risk factors and the incidence of morbidities was explored using multiple regressions. Patient survival, a secondary outcome, was checked at 6 months.A total of 675 (21.8%) procedure episodes led to at least 1 of the following: acute neurologic event, unplanned reoperation, feeding problems, renal replacement therapy, major adverse events, extracorporeal life support, necrotizing enterocolitis, surgical infection, or prolonged pleural effusion. The highest adjusted odds ratio of morbidity was in neonates compared with children, 5.26 (95% confidence interval, 3.90-7.06), and complex heart diseases (eg, hypoplastic left heart), 2.14 (95% confidence interval, 1.41-3.24) compared with low complexity (eg, atrial septal defect, P .001 for all). Patients with any selected morbidity had a 6-month survival of 88.2% (95% confidence interval, 85.4-90.6) compared with 99.3% (95% confidence interval, 98.9-99.6) with no defined morbidity (P .001).Evaluation of postoperative morbidity provides important information over and above 30-day survival and should become a focus for audit and quality improvement. Our results have been used to initiate UK-based audit for 5 of these 9 morbidities, co-develop software for local monitoring of these morbidities, and parent information about these morbidities.

BackgroundFollowing centralisation of UK paediatric intensive care units in 1997, specialist paediatric intensive care retrieval teams (PICRTs) were established to transport critically ill children from district general hospitals (DGHs). The current location and catchment area of PICRTs covering England and Wales are based on historical referral patterns. National quality standards specify that PICRTs should reach the patient bedside within 3 hours of accepting a referral.ObjectiveTo determine what proportion of demand for PICRT services in England and Wales can be reached within 3 hours and to explore the potential coverage impact of more stringent ‘time to bedside’ standards.MethodsWe used mathematical location–allocation methods to: (1) determine the optimal allocation of DGHs to current PICRT locations to minimise road journey time and calculated the proportion of demand reachable within 3 hours, 2 hours, 90 min, 75 min and 1 hour and (2) explore the impact of changing the number and location of PICRTs on demand coverage for the different time thresholds.ResultsFor current (and optimal) location of 11 PICRTs, 98% (98%) of demand is reachable within 3 hours; 86% (91%) within 2 hours; 59% (69%) within 90 min; 33% (39%) within 75 min; and 20% (20%) within 1 hour. Five hospitals were not reachable within 3 hours. For the 3-hour standard, eight optimally located PICRT locations had similar coverage as the current 11 locations.ConclusionsIf new evidence supports reduction in the time to bedside standard, many more hospitals will not be adequately covered. Location–allocation optimisation is a powerful technique for supporting evidence-based service configuration.

Background Within the NHS context there is currently no specific additional neurodevelopmental follow up for children with congenital heart disease (CHD). Cardiologists are not trained in neurodevelopment and face many competing pressures, which may result in delayed detection of problems. We aimed to assess whether there is appropriate service provision for children with CHD and abnormal developmental outcome in the UK, and identify risk factors that could trigger early referral and more timely intervention. Methods Usingvalidated scales of developmental/cognitive function with children less than 17 years of age with CHD treated at one of 3 centres in London, we assessed whether those with abnormal scores (defined as >2 standard deviations below the mean) were under follow up by appropriate developmental services. Logistic regression was used to identify potential socio-economic and clinical risk factors for adverse developmental and cognitive outcomes. Results Being under follow up by developmental services was strongly associated (p Conclusion We identified a large number of patients with abnormal development not currently under appropriate care services, highlighting a potential failure in the care pathway for these children. A number of risk factors for adverse neurodevelopment were identified which could provide useful insight for clinicians to ensure high risk patients are appropriately referred, ensuring more timely assessment and intervention to enhance future development and quality of life for these children.

The article discusses the possibility that bipartisan health care reform can be achieved in America as of 2017, and it mentions efforts to repeal and replace the U.S. Patient Protection and Affordable Care Act, as well as information about the health policy-related priorities of Democrat and Republican state legislators. U.S. surveys are examined, along with goals regarding medical care cost reductions and limits on government involvement in health care.

BackgroundIn 2011, we developed a risk model for 30-day mortality after children’s heart surgery. The PRAiS (Partial Risk Adjustment in Surgery) model uses data on the procedure performed, diagnosis, age, weight and comorbidity. Our treatment of comorbidity was simplistic because of data quality. Software that implements PRAiS is used by the National Congenital Heart Disease Audit (NCHDA) in its audit work. The use of PRAiS triggered the temporary suspension of surgery at one unit in 2013. The public anger that surrounded this illustrated the need for public resources around outcomes monitoring.Objectives(1) To improve the PRAiS risk model by incorporating more information about comorbidities. (2) To develop online resources for the public to help them to understand published mortality data.DesignObjective 1 The outcome measure was death within 30 days of the start of each surgical episode of care. The analysts worked with an expert panel of clinical and data management representatives. Model development followed an iterative process of clinical discussion of risk factors, development of regression models and assessment of model performance under cross-validation. Performance was measured using the area under the receiving operator characteristic (AUROC) curve and calibration in the cross-validation test sets. The final model was further assessed in a 2014–15 validation data set.Objective 2 We developed draft website material that we iteratively tested through four sets of two workshops (one workshop for parents of children who had undergone heart surgery and one workshop for other interested users). Each workshop recruited new participants. The academic psychologists ran two sets of three experiments to explore further understanding of the web content.DataWe used pseudonymised NCHDA data from April 2009 to April 2014. We later unexpectedly received a further year of data (2014–15), which became a prospective validation set.ResultsObjective 1The cleaned 2009–14 data comprised 21,838 30-day surgical episodes, with 539 deaths. The 2014–15 data contained 4207 episodes, with 97 deaths. The final regression model included four new comorbidity groupings. Under cross-validation, the model had a median AUROC curve of 0.83 (total range 0.82 to 0.83), a median calibration slope of 0.92 (total range 0.64 to 1.25) and a median intercept of –0.23 (range –1.08 to 0.85). In the validation set, the AUROC curve was 0.86 [95% confidence interval (CI) 0.83 to 0.89], and its calibration slope and intercept were 1.01 (95% CI 0.83 to 1.18) and 0.11 (95% CI –0.45 to 0.67), respectively. We recalibrated the final model on 2009–15 data and updated the PRAiS software.Objective 2We coproduced a website (http://childrensheartsurgery.info/) that provides interactive exploration of the data, two animations and background information. It was launched in June 2016 and was very well received.LimitationsWe needed to use discharge status as a proxy for 30-day life status for the 14% of overseas patients without a NHS number. We did not have sufficient time or resources to extensively test the usability and take-up of the website following its launch.ConclusionsThe project successfully achieved its stated aims. A key theme throughout has been the importance of collaboration and coproduction. In particular for aim 2, we generated a great deal of generalisable learning about how to communicate complex clinical and mathematical information.Further workExtending our codevelopment approach to cover many other aspects of quality measurement across congenital heart disease and other specialised NHS services.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

Risk-adjusted survival statistics after children's heart surgery are published annually in the United Kingdom. Interpreting these statistics is difficult, and better resources about how to interpret survival data are needed. Here we describe how a multidisciplinary team of mathematicians, psychologists, and a charity worked with parents of heart surgery children and other users to codevelop online resources to present survival outcomes. Early and ongoing involvement of users was crucial and considerably changed the content, scope, and look of the website, and the formal psychology experiments provided deeper insight. The website http://childrensheartsurgery.info/ was launched in June 2016 to very positive reviews.

Objective With survival following paediatric cardiac surgery improving, the attention of quality assurance and improvement initiatives is shifting to long-term outcomes and early surgical morbidities. We wanted to involve family representatives and a range of clinicians in selecting the morbidities to be measured in a major UK study. Setting Paediatric cardiac surgery services in the UK. Participants We convened a panel comprising family representatives, paediatricians from referring centres, and surgeons and other clinicians from surgical centres. Primary and secondary outcome measures Using the nominal group technique augmented by a robust voting process to identify group preferences, suggestions for candidate morbidities were elicited, discussed, ranked and then shortlisted. The shortlist was passed to a clinical group that provided a view on the feasibility of monitoring each shortlisted morbidity in routine practice. The panel then met again to select a prioritised list of morbidities for further study, with the list finalised by the clinical group and chief investigators. Results At the first panel meeting, 66 initial suggestions were made, with this reduced to a shortlist of 24 after two rounds of discussion, consolidation and voting. At the second meeting, this shortlist was reduced to 10 candidate morbidities. Two were dropped on grounds of feasibility and replaced by another the panel considered important. The final list of nine morbidities included indicators of organ damage, acute events and feeding problems. Family representatives and clinicians from outside tertiary centres brought some issues to greater prominence than if the panel had consisted solely of tertiary clinicians or study investigators. Conclusion The inclusion of patient and family perspectives in identifying metrics for use in monitoring a specialised clinical service is challenging but feasible and can broaden notions of quality and how to measure it.

Background When considering early survival rates after pediatric cardiac surgery it is essential to adjust for risk linked to case complexity. An important but previously less well understood component of case mix complexity is comorbidity. Methods The National Congenital Heart Disease Audit data representing all pediatric cardiac surgery procedures undertaken in the United Kingdom and Ireland between 2009 and 2014 was used to develop and test groupings for comorbidity and additional non–procedure-based risk factors within a risk adjustment model for 30-day mortality. A mixture of expert consensus based opinion and empiric statistical analyses were used to define and test the new comorbidity groups. Results The study dataset consisted of 21,838 pediatric cardiac surgical procedure episodes in 18,834 patients with 539 deaths (raw 30-day mortality rate, 2.5%). In addition to surgical procedure type, primary cardiac diagnosis, univentricular status, age, weight, procedure type (bypass, nonbypass, or hybrid), and era, the new risk factor groups of non-Down congenital anomalies, acquired comorbidities, increased severity of illness indicators (eg, preoperative mechanical ventilation or circulatory support) and additional cardiac risk factors (eg, heart muscle conditions and raised pulmonary arterial pressure) all independently increased the risk of operative mortality. Conclusions In an era of low mortality rates across a wide range of operations, non–procedure-based risk factors form a vital element of risk adjustment and their presence leads to wide variations in the predicted risk of a given operation.

ObjectivesGiven the current excellent early mortality rates for paediatric cardiac surgery, stakeholders believe that this important safety outcome should be supplemented by a wider range of measures. Our objectives were to prospectively measure the incidence of morbidities following paediatric cardiac surgery and to evaluate their clinical and health-economic impact over 6 months.DesignThe design was a prospective, multicentre, multidisciplinary mixed methods study.SettingThe setting was 5 of the 10 paediatric cardiac surgery centres in the UK with 21 months recruitment.ParticipantsIncluded were 3090 paediatric cardiac surgeries, of which 666 patients were recruited to an impact substudy.ResultsFamilies and clinicians prioritised:Acute neurological event, unplanned re-intervention, feeding problems, renal replacement therapy, major adverse events, extracorporeal life support, necrotising enterocolitis, postsurgical infection and prolonged pleural effusion or chylothorax.Among 3090 consecutive surgeries, there were 675 (21.8%) with at least one of these morbidities. Independent risk factors for morbidity included neonatal age, complex heart disease and prolonged cardiopulmonary bypass (pConclusionsEvaluation of postoperative morbidity is more complicated than measuring early mortality. However, tracking morbidity after paediatric cardiac surgery over 6 months offers stakeholders important data that are of value to parents and will be useful in driving future quality improvement.

ObjectiveTo explore communication between clinicians and families of children undergoing heart surgery.DesignThis study was part of a larger study to select, define and measure the incidence of postoperative complications in children undergoing heart surgery. Parents of children recruited to a substudy between October 2015 and December 2017 were asked to complete a questionnaire about communication during their child’s inpatient stay. We explored all responses and then disaggregated by the following patient characteristics: presence of a complication, length of stay, hospital site, ethnicity and child’s age. This was a descriptive study only.SettingFour UK specialist hospitals.ResultsWe recruited 585 children to the substudy with 385 responses (response rate 66%).81% of parents reported that new members of staff always introduced themselves (18% sometimes, 1% no). Almost all parents said they were encouraged to be involved in decision-making, but often only to some extent (59% ‘yes, definitely’; 37% ‘to some extent’). Almost two-thirds of parents said they were told different things by different people which left them feeling confused (10% ‘a lot’; 53% ‘sometimes’). Two-thirds (66%) reported that staff were definitely aware of their child’s medical history (31% ‘to some extent’). 90% said the operation was definitely explained to them (9% ‘to some extent’) and 79% that they were definitely told what to do if they were worried after discharge (17% ‘to some extent’).Parents of children with a complication tended to give less positive responses for involvement in decision-making, consistent communication and staff awareness of their child’s medical history. Parents whose children had longer stays in hospital tended to report lower levels of consistent communication and involvement in decision-making.ConclusionsOur results emphasise the need for consistent communication with families, particularly where complications arise or for children who have longer stays in the hospital.

Objective The study objective was to develop a risk model incorporating diagnostic information to adjust for case-mix severity during routine monitoring of outcomes for pediatric cardiac surgery. Methods Data from the Central Cardiac Audit Database for all pediatric cardiac surgery procedures performed in the United Kingdom between 2000 and 2010 were included: 70% for model development and 30% for validation. Units of analysis were 30-day episodes after the first surgical procedure. We used logistic regression for 30-day mortality. Risk factors considered included procedural information based on Central Cardiac Audit Database "specific procedures," diagnostic information defined by 24 "primary" cardiac diagnoses and "univentricular" status, and other patient characteristics. Results Of the 27,140 30-day episodes in the development set, 25,613 were survivals, 834 were deaths, and 693 were of unknown status (mortality, 3.2%). The risk model includes procedure, cardiac diagnosis, univentricular status, age band (neonate, infant, child), continuous age, continuous weight, presence of non–Down syndrome comorbidity, bypass, and year of operation 2007 or later (because of decreasing mortality). A risk score was calculated for 95% of cases in the validation set (weight missing in 5%). The model discriminated well; the C-index for validation set was 0.77 (0.81 for post-2007 data). Removal of all but procedural information gave a reduced C-index of 0.72. The model performed well across the spectrum of predicted risk, but there was evidence of underestimation of mortality risk in neonates undergoing operation from 2007. Conclusions The risk model performs well. Diagnostic information added useful discriminatory power. A future application is risk adjustment during routine monitoring of outcomes in the United Kingdom to assist quality assurance.

Objective To implement routine in-house monitoring of risk-adjusted 30-day mortality following paediatric cardiac surgery. Design Collaborative monitoring software development and implementation in three specialist centres. Patients and methods Analyses incorporated 2 years of data routinely audited by the National Institute of Cardiac Outcomes Research (NICOR). Exclusion criteria were patients over 16 or undergoing non-cardiac or only catheter procedures. We applied the partial risk adjustment in surgery (PRAiS) risk model for death within 30 days following surgery and generated variable life-adjusted display (VLAD) charts for each centre. These were shared with each clinical team and feedback was sought. Results Participating centres were Great Ormond Street Hospital, Evelina Children9s Hospital and The Royal Hospital for Sick Children in Glasgow. Data captured all procedures performed between 1 January 2010 and 31 December 2011. This incorporated 2490 30-day episodes of care, 66 of which were associated with a death within 30 days.The VLAD charts generated for each centre displayed trends in outcomes benchmarked to recent national outcomes. All centres ended the 2-year period within four deaths from what would be expected. The VLAD charts were shared in multidisciplinary meetings and clinical teams reported that they were a useful addition to existing quality assurance initiatives. Each centre is continuing to use the prototype software to monitor their in-house surgical outcomes. Conclusions Timely and routine monitoring of risk-adjusted mortality following paediatric cardiac surgery is feasible. Close liaison with hospital data managers as well as clinicians was crucial to the success of the project.

ObjectiveTo compare the predictive performance of 18 paediatric early warning systems (PEWS) in predicting critical deterioration.DesignRetrospective case-controlled study. PEWS values were calculated from existing clinical data, and the area under the receiver operator characteristic curve (AUROC) compared.SettingUK tertiary referral children's hospital.PatientsPatients without a ‘do not attempt resuscitation’ order admitted between 1 January 2011 and 31 December 2012. All patients on paediatric wards who suffered a critical deterioration event were designated ‘cases’ and matched with a control closest in age who was present on the same ward at the same time.Main outcome measuresRespiratory and/or cardiac arrest, unplanned transfer to paediatric intensive care and/or unexpected death.Results12 ‘scoring’ and 6 ‘trigger’ systems were suitable for comparative analysis. 297 case events in 224 patients were available for analysis. 244 control patients were identified for the 311 events. Three PEWS demonstrated better overall predictive performance with an AUROC of 0.87 or greater. Comparing each system with the highest performing PEWS with Bonferroni's correction for multiple comparisons resulted in statistically significant differences for 13 systems. Trigger systems performed worse than scoring systems, occupying the six lowest places in the AUROC rankings.ConclusionsThere is considerable variation in the performance of published PEWS, and as such the choice of PEWS has the potential to be clinically important. Trigger-based systems performed poorly overall, but it remains unclear what factors determine optimum performance. More complex systems did not necessarily demonstrate improved performance.

Improvements in health-care quality can contribute to healthier populations. However, many global and national health strategies are not sufficiently considering the issues of measuring and improving health-care quality in low-resource settings. (1) The barriers to delivering high-quality care are often similar across different health systems. However, the extent and mechanisms through which these barriers affect quality improvement interventions may be different in resource-poor settings. (2) Investments in health systems strengthening without continuous quality improvement is thought to be a useless effort. (3) Conversely, only focusing on quality improvement in a resource-poor context without engaging the broader health system for support is of limited value. Hence, both areas must be improved simultaneously. Here, we call for renewed focus on quality improvement of health-system delivery by policy-makers, managers and health-care providers, working at all levels of health-care systems in resource-poor settings. To maximize the potential of quality improvements, we propose an approach focusing on five elements: (i) systems thinking; (ii) stakeholders' participation; (iii) accountability; (iv) evidence-based interventions; and (v) innovative evaluation (Box 1). Some of the elements are well supported by peer-reviewed literature, while other elements are lacking good evidence. We base our ideas on our experience in diverse countries and settings. We hope that bringing all these elements together into a unified approach will stimulate debate, highlight important research gaps and support policy-makers, healthcare providers and patient and community representatives working in this field. The first element, systems thinking, views health-care systems in a holistic manner and is often described as operating at micro- (clinical team), meso- (health facilities) and macro-level (health-care system). Systems thinking offers a useful framework for addressing the interdependency of these different levels that influence health-care delivery and health outcomes. (4) Interventions targeted to improve quality of care are unlikely to succeed or be sustained if designed without an adequate understanding of relevant contextual factors at these different levels. (5) Researchers and implementers are now recognizing the importance of these factors and their dynamic interaction in delivering safer, more effective care. (6) Failure to address these interdependencies may be particularly damaging in resource-poor settings, where constrained resources, lack of infrastructure and weak governance can exacerbate the difficulties of implementing an intervention. Examples range from unused expensive equipment due to lack of trained staff or electricity to operate it, to scarce health workers migrating to national and international organizations leading to understaffed primary, maternal and child care. Cultural and behavioural factors also affect these interdependencies and can contribute to problems during implementation. Interventions in resource-poor settings have to consider improvement across the different levels of the health system when trying to improve care at the clinical microsystems level. (7) Box 1 Elements to consider when improving health-care quality in resource-poor settings Systems thinking Health systems are dynamic complex adaptive systems, where all parts need to be considered. These parts are (I) the Inter-relationships between the patient, clinical and noncllnlcal workers In the health system; (II) the different levels of the health system ranging from the community to tertiary referral system; and (III) the required human and material resources and training, supervision and management structures. Participatory approach Participatory, grounded and bottom-up approaches Involving health-care professionals, patients and communities as well as researchers-in-resldence are Important to understand health systems. …

A mathematical model has been developed for the purpose of evaluating vaccination against pneumococcus as a countermeasure against pandemic influenza. As the characteristics of a future pandemic cannot be known in advance, three distinct pandemic scenarios were considered, corresponding to a 1918-like pandemic, a 1957/1968-like pandemic and a 2009-like pandemic. Model estimates for each of these pandemic scenarios are presented for two options of vaccination programme; universal vaccination of the entire UK population and vaccination only of those people considered to be at heightened risk of developing influenza complications. We find that the benefits of each option (in terms of estimated number of deaths and hospital admissions avoided and the courses of antibiotics saved) are high in a 1918-like pandemic and very small in a 2009-like pandemic. Given that the decision regarding deployment of the counter measure would occur prior to knowledge of the flu-strain characteristics being available, we also present the weighted average of the outcomes from the three pandemic scenarios. Based on the historical occurrence of pandemics over the last 100 years, the weighted average of outcomes is an estimated 1400 deaths prevented by the universal vaccination option and 400 deaths saved by the targeted vaccination option (at a cost of approximately 400 million and 50 million courses of vaccine respectively). Finally, the longer term implications of using PPV as a countermeasure against pandemic influenza have been considered by estimating the expected number of courses of vaccine bought and the expected number of deaths and hospital admissions prevented over time under each policy.