Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • Journal article
    Rao A, Dani K, Darzi A, Aylin P, Majeed A, Bottle Ret al., 2019,

    Regional variation in trajectories of long-term readmission rates among patients in England with heart failure

    , BMC Cardiovascular Disorders, Vol: 19, ISSN: 1471-2261

    BackgroundWe aimed to compare the characteristics and types of heart failure (HF) patients termed “high-impact users”, with high long-term readmission rates, in different regions in England. This will allow clinical factors to be identified in areas with potentially poor quality of care.MethodsPatients with a primary diagnosis of heart failure (HF) in the period 2008–2009 were identified using nationally representative primary care data linked to national hospital data and followed up for 5 years. Group-based trajectory models and sequence analysis were applied to their readmissions.ResultsIn each of the 8 NHS England regions, multiple discrete groups were identified. All the regions had high-impact users. The group with an initially high readmission rate followed by a rapid decline in the rate ranged from 2.5 to 11.3% across the regions. The group with constantly high readmission rate compared with other groups ranged from 1.9 to 12.1%. Covariates that were commonly found to have an association with high-impact users among most of the regions were chronic respiratory disease, chronic renal disease, stroke, anaemia, mood disorder, and cardiac arrhythmia. Respiratory tract infection, urinary infection, cardiopulmonary signs and symptoms and exacerbation of heart failure were common causes in the sequences of readmissions among high-impact users in all regions.ConclusionThere is regional variation in England in readmission and mortality rates and in the proportions of HF patients who are high-impact users.

  • Journal article
    Cecil EV, Wilkinson S, Bottle R, Esmail A, Vincent C, Aylin Pet al., 2018,

    A national hospital mortality surveillance system: a descriptive analysis

    , BMJ Quality and Safety, Vol: 27, Pages: 974-981, ISSN: 2044-5415

    Objective To provide a description of the Imperial College Mortality Surveillance System and subsequent investigations by the Care Quality Commission (CQC) in National Health Service (NHS) hospitals receiving mortality alerts.Background The mortality surveillance system has generated monthly mortality alerts since 2007, on 122 individual diagnosis and surgical procedure groups, using routinely collected hospital administrative data for all English acute NHS hospital trusts. The CQC, the English national regulator, is notified of each alert. This study describes the findings of CQC investigations of alerting trusts.Methods We carried out (1) a descriptive analysis of alerts (2007–2016) and (2) an audit of CQC investigations in a subset of alerts (2011–2013).Results Between April 2007 and October 2016, 860 alerts were generated and 76% (654 alerts) were sent to trusts. Alert volumes varied over time (range: 40–101). Septicaemia (except in labour) was the most commonly alerting group (11.5% alerts sent). We reviewed CQC communications in a subset of 204 alerts from 96 trusts. The CQC investigated 75% (154/204) of alerts. In 90% of these pursued alerts, trusts returned evidence of local case note reviews (140/154). These reviews found areas of care that could be improved in 69% (106/154) of alerts. In 25% (38/154) trusts considered that identified failings in care could have impacted on patient outcomes. The CQC investigations resulted in full trust action plans in 77% (118/154) of all pursued alerts.Conclusion The mortality surveillance system has generated a large number of alerts since 2007. Quality of care problems were found in 69% of alerts with CQC investigations, and one in four trusts reported that failings in care may have an impact on patient outcomes. Identifying whether mortality alerts are the most efficient means to highlight areas of substandard care will require further investigation.

  • Journal article
    Rao AM, Bottle A, Bicknell C, Darzi A, Aylin Pet al., 2018,

    Trajectory modelling to assess trends in long-term readmission rate among abdominal aortic aneurysm patients

    , Surgery Research and Practice, Vol: 2018, ISSN: 2356-7759

    Introduction. The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (GBTM) was performed on the patient cohort (2006-2009) identified through national administrative data from all NHS English hospitals. Proc Traj software was used in SAS program to conduct GBTM, which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair. Based on the trends of readmission rates, patients were classified into low- and high-impact users. The high-impact group had a higher annual readmission rate throughout 5-year follow-up. Short-term high-impact users had initial high readmission rate followed by rapid decline, whereas chronic high-impact users continued to have high readmission rate. Results. Based on the trends in readmission rates, GBTM classified elective AAA repair () patients into 2 groups: low impact (82.0%) and high impact (18.0%). High-impact users were significantly associated with female sex () undergoing other vascular procedures (), poor socioeconomic status index (), older age (), and higher comorbidity score (). The AUC for c-statistics was 0.84. Patients with ruptured AAA repair () had 3 groups: low impact (82.7%), short-term high impact (7.2%), and chronic high impact (10.1%). Chronic high impact users were significantly associated with renal failure (), heart failure (P = 0.01), peripheral vascular disease (), female sex (P = 0.02), open repair (), and undergoing other related procedures (). The AUC for c-statistics was 0.71. Conclusion. Patients with persistent high readmission rates exist among AAA population; however, their readmissions and mortality are not related to AAA repair. They may benefit from optimization of their medical management of comorbiditie

  • Journal article
    Balinskaite V, Bottle A, Shaw LJ, Majeed A, Aylin Pet al., 2018,

    Reorganisation of stroke care and impact on mortality in patients admitted during weekends: a national descriptive study based on administrative data

    , BMJ Quality and Safety, Vol: 27, Pages: 611-618, ISSN: 2044-5415

    OBJECTIVE: To evaluate mortality differences between weekend and weekday emergency stroke admissions in England over time, and in particular, whether a reconfiguration of stroke services in Greater London was associated with a change in this mortality difference. DESIGN, SETTING AND PARTICIPANTS: Risk-adjusted difference-in-difference time trend analysis using hospital administrative data. All emergency patients with stroke admitted to English hospitals from 1 January 2008 to 31 December 2014 were included. MAIN OUTCOMES: Mortality difference between weekend and weekday emergency stroke admissions. RESULTS: We identified 507 169 emergency stroke admissions: 26% of these occurred during the weekend. The 7-day in-hospital mortality difference between weekend and weekday admissions declined across England throughout the study period. In Greater London, where the reorganisation of stroke services took place, an adjusted 28% (relative risk (RR)=1.28, 95% CI 1.09 to 1.47) higher weekend/weekday 7-day mortality ratio in 2008 declined to a non-significant 9% higher risk (RR=1.09, 95% CI 0.91 to 1.32) in 2014. For the rest of England, a 15% (RR=1.15, 95% CI 1.09 to 1.22) higher weekend/weekday 7-day mortality ratio in 2008 declined to a non-significant 3% higher risk (RR=1.03, 95% CI 0.97 to 1.10) in 2014. During the same period, in Greater London an adjusted 12% (RR=1.12, 95% CI 1.00 to 1.26) weekend/weekday 30-day mortality ratio in 2008 slightly increased to 14% (RR=1.14, 95% CI 1.00 to 1.30); however, it was not significant. In the rest of England, an 11% (RR=1.11, 95% CI 1.07 to 1.15) higher weekend/weekday 30-day mortality ratio declined to a non-significant 4% higher risk (RR=1.04, 95% CI 0.99 to 1.09) in 2014. We found no statistically significant association between decreases in the weekend/weekday admissions difference in mortality and the centralisation of stroke services in Greater London. CONCLUSIONS: There was a steady reduction in weekend/weekday differences i

  • Journal article
    Bottle A, Honeyford K, Chowdhury F, Bell D, Aylin Pet al., 2018,

    Factors associated with hospital emergency readmission and mortality rates in patients with heart failure or chronic obstructive pulmonary disease: a national observational study

    , Health and Social Care Delivery Research, Vol: 6, Pages: 1-84, ISSN: 2755-0060

    Background: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) lead to unplannedhospital activity, but our understanding of what drives this is incomplete.Objectives: To model patient, primary care and hospital factors associated with readmission and mortalityfor patients with HF and COPD, to assess the statistical performance of post-discharge emergencydepartment (ED) attendance compared with readmission metrics and to compare all the results for thetwo conditions.Design: Observational study.Setting: English NHS.Participants: All patients admitted to acute non-specialist hospitals as an emergency for HF or COPD.Interventions: None.Main outcome measures: One-year mortality and 30-day emergency readmission following the patient’sfirst unplanned admission (‘index admission’) for HF or COPD.Data sources: Patient-level data from Hospital Episodes Statistics were combined with publicly availablepractice- and hospital-level data on performance, patient and staff experience and rehabilitationprogramme website information.Results: One-year mortality rates were 39.6% for HF and 24.1% for COPD and 30-day readmission rates were19.8% for HF and 16.5% for COPD. Most patients were elderly with multiple comorbidities. Patient factorspredicting mortality included older age, male sex, white ethnicity, prior missed outpatient appointments, (long)index length of hospital stay (LOS) and several comorbidities. Older age, missed appointments, (short) LOS andcomorbidities also predicted readmission. Of the practice and hospital factors we considered, only moredoctors per 10 beds [odds ratio (OR) 0.95 per doctor; p < 0.001] was significant for both cohorts for mortality,with staff recommending to friends and family (OR 0.80 per unit increase; p < 0.001) and number of general practitioners (GPs) per 1000 patients (OR 0.89 per extra GP; p = 0.004) important for COPD. For readmission,only hospital size [OR per 100 beds = 2.16, 95% confidence interval (

  • Journal article
    Rao A, Bicknell C, Bottle R, Darzi A, Aylin PPet al., 2018,

    Common sequences of emergency readmissions among high-impact users following AAA repair

    , Surgery Research and Practice, Vol: 2018, ISSN: 2356-7759

    IntroductionThe aim of the study was to examine common sequences of causes of readmissions among those patients with multiple hospital admissions, high-impact users, after abdominal aortic aneurysm (AAA) repair and to focus on strategies to reduce long-term readmission rate. MethodsThe patient cohort (2006-2009) included patients from Hospital Episodes Statistics, the national administrative data of all NHS English hospitals, and followed up for 5 years. Group-based trajectory modelling and sequence analysis were performed on the data. ResultsFrom a total of 16,973 elective AAA repair patients, 18% (n=3055) were high-impact users. The high-impact users among rAAA repair constituted 17.3% of the patient population (n=4144). There were 2 subtypes of high-impact users, short-term (7.2%) with initial high readmission rate following by rapid decline and chronic high-impact (10.1%) with persistently high readmission rate. Common causes of readmissions following elective AAA repair were respiratory tract infection (7.3%), aortic graft complications (6.0%), unspecified chest pain (5.8%), and gastro-intestinal haemorrhage (4.8%). However, high-impact users included significantly increased number of patients with multiple readmissions and distinct sequences of readmissions mainly consisting of COPD (4.7%), respiratory tract infection (4.7%) and ischaemic heart disease (3.3%).ConclusionA significant number of patients were high-impact users after AAA repair. They had a common and distinct sequence of causes of readmissions following AAA repair, mainly consisting of cardiopulmonary conditions and aortic graft complications. The common causes of long-term mortality were not related to AAA repair. The quality of care can be improved by identifying these patients early and focusing on prevention of cardiopulmonary diseases in the community.

  • Journal article
    Cecil E, Bottle A, Esmail A, Wilkinson S, Vincent C, Aylin PPet al., 2018,

    Investigating the association of alerts from a national mortality surveillance system with subsequent hospital mortality in England: an interrupted time series analysis

    , BMJ Quality and Safety, Vol: 27, Pages: 965-973, ISSN: 2044-5415

    OBJECTIVE: To investigate the association between alerts from a national hospital mortality surveillance system and subsequent trends in relative risk of mortality. BACKGROUND: There is increasing interest in performance monitoring in the NHS. Since 2007, Imperial College London has generated monthly mortality alerts, based on statistical process control charts and using routinely collected hospital administrative data, for all English acute NHS hospital trusts. The impact of this system has not yet been studied. METHODS: We investigated alerts sent to Acute National Health Service hospital trusts in England in 2011-2013. We examined risk-adjusted mortality (relative risk) for all monitored diagnosis and procedure groups at a hospital trust level for 12 months prior to an alert and 23 months post alert. We used an interrupted time series design with a 9-month lag to estimate a trend prior to a mortality alert and the change in trend after, using generalised estimating equations. RESULTS: On average there was a 5% monthly increase in relative risk of mortality during the 12 months prior to an alert (95% CI 4% to 5%). Mortality risk fell, on average by 61% (95% CI 56% to 65%), during the 9-month period immediately following an alert, then levelled to a slow decline, reaching on average the level of expected mortality within 18 months of the alert. CONCLUSIONS: Our results suggest an association between an alert notification and a reduction in the risk of mortality, although with less lag time than expected. It is difficult to determine any causal association. A proportion of alerts may be triggered by random variation alone and subsequent falls could simply reflect regression to the mean. Findings could also indicate that some hospitals are monitoring their own mortality statistics or other performance information, taking action prior to alert notification.

  • Report
    Aylin P, Benn J, Bottle A, Burnett S, Vincent C, Esmail A, Cecil E, Charles K, D'Lima Det al., 2018,

    Evaluation of a national surveillance system for mortality alerts: a mixed-methods study. Health Serv Deliv Res 2018;6(7)

    , Evaluation of a national surveillance system for mortality alerts: a mixed-methods study

    BackgroundSince 2007, Imperial College London has generated monthly mortality alerts, based on statistical process control charts and using routinely collected hospital administrative data, for all English acute NHS hospital trusts. The impact of this system has not yet been studied.ObjectivesTo improve understanding of mortality alerts and evaluate their impact as an intervention to reduce mortality.DesignMixed methods.SettingEnglish NHS acute hospital trusts.ParticipantsEleven trusts were included in the case study. The survey involved 78 alerting trusts.Main outcome measuresRelative risk of mortality and perceived efficacy of the alerting system.Data sourcesHospital Episodes Statistics, published indicators on quality and safety, Care Quality Commission (CQC) reports, interviews and documentary evidence from case studies, and a national evaluative survey.MethodsDescriptive analysis of alerts; association with other measures of quality; associated change in mortality using an interrupted time series approach; in-depth qualitative case studies of institutional response to alerts; and a national cross-sectional evaluative survey administered to describe the organisational structure for mortality governance and perceptions of efficacy of alerts.ResultsA total of 690 mortality alerts generated between April 2007 and December 2014. CQC pursued 75% (154/206) of alerts sent between 2011 and 2013. Patient care was cited as a factor in 70% of all investigations and in 89% of sepsis alerts. Alerts were associated with indicators on bed occupancy, hospital mortality, staffing, financial status, and patient and trainee satisfaction. On average, the risk of death fell by 58% during the 9-month lag following an alert, levelling afterwards and reaching an expected risk within 18 months of the alert. Acute myocardial infarction (AMI) and sepsis alerts instigated institutional responses across all the case study sites, although most sites were undertaking some parallel activities

  • Journal article
    Furnivall D, Bottle R, Aylin P, 2018,

    Retrospective analysis of the national impact of industrial action by English junior doctors in 2016

    , BMJ Open, Vol: 8, ISSN: 2044-6055

    Objectives: To examine the impacts of the four episodes of industrial action by English junior doctors in early 2016.Design: Descriptive retrospective study of admitted patient care, accident and emergency (A&E) and outpatient activity in English hospitals.Setting: All hospitals across England.Participants: All patients who attended A&E or outpatient appointments, or those who were admitted to hospital during the three week period surrounding each of the four strikes (January 12th, February 10th, March 9th-10th and April 26th-27th, excluding weekends.)Main outcome measures: Raw numbers and percentage changes of outpatient appointments and cancellations, A&E visits, admitted patients and all in-hospital mortality on strike days compared with patient activity on the same weekday in the weeks before and after the strikes.Results: There were 3.4 million admissions, 27 million outpatient appointments and 3.4 million A&E attendances over the four 3-week periods analysed. Across the four strike days, there were 31,651 fewer admissions (-9.1%), 23,895 fewer A&E attendances (-6.8%) and 173,462 fewer outpatient appointments (-6.0%) than expected. Additionally, 101,109 more outpatient appointments were cancelled by hospitals than expected (+52%). The April 26th-27th strike, where emergency services were also affected, showed the largest impacts on regular service. Mortality did not measurably increase on strike days. Regional analysis showed that services in the Yorkshire and the Humber region were disproportionately more affected by the industrial action. Conclusions: Industrial action by junior doctors during early 2016 caused a significant impact on the provision of healthcare provided by English hospitals. We also observed regional variations in how these strikes affected providers.

  • Journal article
    Ali AM, Loeffler MD, Aylin P, Bottle Aet al., 2017,

    Correction: Factors Associated With 30-Day Readmission After Primary Total Hip Athroplasty: Analysis of 514455 Procedures in the UK National Health Service

    , JAMA Surgery, Vol: 152, Pages: 1184-1184, ISSN: 2168-6254

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://www.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=866&limit=10&page=3&respub-action=search.html Current Millis: 1652711358277 Current Time: Mon May 16 15:29:18 BST 2022