ORCID Profile
0000-0001-9978-2011
Current Organisation
Imperial College London Faculty of Medicine
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Publisher: BMJ
Date: 03-2022
DOI: 10.1136/OPENHRT-2021-001888
Abstract: To estimate the long-term survival of two cohorts of people diagnosed with heart failure 10 years apart and to assess differences in patient characteristics, clinical guideline compliance and survival by diagnosis setting. Data for patients aged 18 and over with a new diagnosis of heart failure in the Clinical Practice Research Datalink in 2001–2002 (5966 patients in 156 practices) and 2011–2012 (12 827 patients in 331 practices). Survival rates since diagnosis were described using Kaplan-Meier plots. Compliance with national guidelines was summarised. 2011/2012 patients were older than those diagnosed a decade before, with lower blood pressure and cholesterol but more comorbidity and healthcare contacts. For those diagnosed in 2001/2002, the 5-year survival was 40.0% (40.2% in the 2011/2012 cohort), 10-year survival was 20.8%, and 15-year survival 11.1%. Improvement in survival between the two time periods was seen only in those diagnosed in primary care (5-year survival 46.0% vs 57.4%, compared with 33.9% and 32.6% for hospital-diagnosed patients). Beta-blocker use rose from 24.3% to 39.1% renin–angiotensin system blockers rose from 31.8% to 54.3% (both p .001). There was little change for loop diuretics and none for thiazide diuretics. For the 9963 patients with symptoms recorded by their general practitioner before diagnosis, brain natriuretic peptide (BNP) testing was low, but echocardiogram use rose from 8.3% to 19.3%, and specialist referral rose from 7.2% to 24.6% (all p .001). The 10 years saw some long-term survival gains but only modest improvement in national clinical guideline compliance, from a low baseline, despite the introduction of national initiatives.
Publisher: BMJ
Date: 05-2021
DOI: 10.1136/OPENHRT-2021-001658
Abstract: Implant rates of mechanical circulatory supports such as left ventricular assist devices (LVAD) have steadily increased in the last decade. We assessed the utility of administrative data to provide information on hospital use and outcomes. Using 2 years of national hospital administrative data for England linked to the death register, we identified all patients with an LVAD and extracted hospital activity for 5 years before and after the LVAD implantation date. In the two index years April 2011 to March 2013, 157 patients had an LVAD implanted. The mean age was 50.9 (SD 15.4), and 78.3% were men. After 5 years, 92 (58.6%) had died the recorded cause of death was noncardiovascular in 67.4%. 42 (26.8%) patients received a heart±lung transplantation. Compared with the 12 months before implantation, the 12 months after but not including the month of implantation saw falls in total inpatient and day case admissions, a fall in admissions for heart failure (HF), a rise in non-HF admissions, a fall in emergency department visits not ending in admission and a rise in outpatient appointments (all per patient at risk). Postimplantation complications were common in the subsequent 5 years: 26.1% had a stroke, 23.6% had a device infection and 13.4% had a new LVAD implanted. Despite patients’ young age, their mortality is high and their hospital use and complications are common in the 5 years following LVAD implantation. Administrative data provide important information on resource use in this patient group.
Publisher: BMJ
Date: 07-07-2021
DOI: 10.1136/BMJQS-2021-012990
Abstract: A report suggesting large between-hospital variations in mortality after admission for COVID-19 in England attracted much media attention but used crude rates. We aimed to quantify these variations between hospitals and over time during England’s first wave (March to July 2020) and assess available patient-level and hospital-level predictors to explain those variations. We used administrative data for England, augmented by hospital-level information. Admissions were extracted with COVID-19 codes. In-hospital death was the primary outcome. Risk-adjusted mortality ratios (standardised mortality ratios) and interhospital variation were calculated using multilevel logistic regression. Early-wave (March to April) and late-wave (May to July) periods were compared. 74 781 admissions had a primary diagnosis of COVID-19, with 21 984 in-hospital deaths (29.4%) the 30-day total mortality rate was 28.8%. The crude in-hospital death rate fell in all ages and overall from 32.9% in March to 13.4% in July. Patient-level predictors included age, male gender, non-white ethnic group (early period only) and several comorbidities (obesity early period only). The only significant hospital-level predictor was daily COVID-19 admissions in the late period we did not find a relation with staff absences for COVID-19, mechanical ventilation bed occupancies, total bed occupancies or bed occupancies for COVID-19 admissions in either period. Just 4 (3%) and 2 (2%) hospitals were high, and 5 (4%) and 0 hospitals were low funnel plot mortality outliers at 3 SD for early and late periods, respectively, after risk adjustment. We found no strong correlation between early and late hospital-level mortality (r=0.17, p=0.06). There was modest variation in mortality following admission for COVID-19 between English hospitals after adjustment for risk and random variation, in marked contrast to early media reports. Early-period mortality did not predict late-period mortality.
Publisher: Wiley
Date: 30-11-2022
DOI: 10.1002/EHF2.14250
Abstract: Many risk prediction models have been proposed for heart failure (HF), but few studies have used only information available to general practitioners (GPs) in primary care electronic health records (EHRs). We describe the predictors and performance of models built from GP‐based EHRs in two cohorts of patients 10 years apart. Linked primary and secondary care data for incident HF cases in England were extracted from the Clinical Practice Research Datalink for 2001–02 and 2011–12. Time‐to‐event models for all‐cause mortality were developed using a long list of potential baseline predictors. Discrimination and calibration were calculated. A total of 5966 patients in 156 general practices were diagnosed in 2001–02, and 12 827 patients in 331 practices were diagnosed in 2011–12. The 5‐year survival rate was 40.0% in 2001–02 and 40.2% in 2011–12, though the latter population were older, frailer, and more comorbid for 2001–02, the 10‐year survival was 20.8% and 15‐year survival 11.1%. Consistent predictors included age, male sex, systolic blood pressure, body mass index, GP domiciliary visits before diagnosis, and some comorbidities. Model performance for both time windows was modest ( c = 0.70), but calibration was generally excellent in both time periods. Information routinely available to UK GPs at the time of diagnosis of HF gives only modest predictive accuracy of all‐cause mortality, making it hard to decide on the type, place, and urgency of follow‐up. More consistent recording of data relevant to HF (such as echocardiography and natriuretic peptide results) in GP EHRs is needed to support accurate prediction of healthcare needs in in iduals with HF.
Publisher: American Medical Association (AMA)
Date: 03-2012
DOI: 10.1001/ARCHSURG.2011.311
Abstract: To quantify the occurrence of significant medical complications following elective colorectal resection and investigate potential differences in medical morbidity following open and minimal access colorectal surgery. Retrospective analysis of Hospital Episode Statistics, which is a prospectively maintained national database. All patients undergoing colorectal resection in National Health Service trusts in England. Adult patients undergoing elective or planned surgery between April 2001 and March 2008. Colorectal resection for benign and malignant diagnoses. Mortality and morbidity at 30 days and 1 year following elective colorectal resection. One hundred thirty-eight thousand seven hundred thirty-five elective colorectal resections were identified between the study dates. Thirty-day in-hospital mortality was 3.4% and 1.7% following conventional and laparoscopic surgery, respectively (P < .001). Overall, the 30-day postoperative medical morbidity rate was 14.6%. Use of the minimal access approach demonstrated a significant reduction in total morbidity risk at 30 days (odds ratio, 0.79 P < .001) and 365 days (odds ratio, 0.81 P < .001) following case-mix adjustment. Multiple regression analyses demonstrated that cardiorespiratory complications and venous thromboembolism occurred less frequently during the index admission and up to 1 year following minimal access surgery when compared with the conventional approach (P < .049). In this population-based study, patients selected for laparoscopic colorectal resection were associated with lower risk of mortality as well as reduced cardiorespiratory and venous thromboembolic risk than those undergoing open surgery.
Publisher: BMJ
Date: 06-2022
DOI: 10.1136/BMJOPEN-2021-060251
Abstract: To assess patient-level and hospital-level predictors of death and variation in death rates following admission for COVID-19 in England’s first two waves after accounting for random variation. To quantify the correlation between hospitals’ first and second wave death rates. Observational study using administrative data. Acute non-specialist hospitals in England. All patients admitted with a primary diagnosis of COVID-19. In-hospital death. Hospital Episode Statistics (HES) data were extracted for all acute hospitals in England for COVID-19 admissions from March 2020 to March 2021. In wave 1 (March to July 2020), there were 74 484 admissions and 21 883 deaths (crude rate 29.4%) in wave 2 (August 2020 to March 2021), there were 165 642 admissions and 36 040 deaths (21.8%). Wave 2 patients were younger, with more hypertension and obesity but lower rates of other comorbidities. Mortality improved for all ages in wave 2, it peaked in December 2020 at 24.2% (lower than wave 1’s peak) but halved by March 2021. In multiple multilevel modelling combining HES with hospital-level data from Situational Reports, wave 2 and wave 1 variables significantly associated with death were mostly the same. The median odds ratio for wave 1 was just 1.05 and for wave 2 was 1.07. At 99.8% control limits, 3% of hospitals were high and 7% were low funnel plot outliers in wave 1 these figures were 9% and 12% for wave 2. Four hospitals were (low) outliers in both waves. The correlation between hospitals’ adjusted mortality rates between waves was 0.45 (p .0001). Length of stay was similar in each wave. England’s first two COVID-19 waves were similar regarding predictors and moderate interhospital variation. Despite the challenges, variation in death rates and length of stay between hospitals was modest and might be accounted for by unobserved patient factors.
Publisher: Springer Science and Business Media LLC
Date: 10-05-2011
DOI: 10.1007/S10729-011-9159-6
Abstract: We have previously described a system for monitoring a number of healthcare outcomes using case-mix adjustment models. It is desirable to automate the model fitting process in such a system if monitoring covers a large number of outcome measures or subgroup analyses. Our aim was to compare the performance of three different variable selection strategies: "manual", "automated" backward elimination and re-categorisation, and including all variables at once, irrespective of their apparent importance, with automated re-categorisation. Logistic regression models for predicting in-hospital mortality and emergency readmission within 28 days were fitted to an administrative database for 78 diagnosis groups and 126 procedures from 1996 to 2006 for National Health Services hospital trusts in England. The performance of models was assessed with Receiver Operating Characteristic (ROC) c statistics, (measuring discrimination) and Brier score (assessing the average of the predictive accuracy). Overall, discrimination was similar for diagnoses and procedures and consistently better for mortality than for emergency readmission. Brier scores were generally low overall (showing higher accuracy) and were lower for procedures than diagnoses, with a few exceptions for emergency readmission within 28 days. Among the three variable selection strategies, the automated procedure had similar performance to the manual method in almost all cases except low-risk groups with few outcome events. For the rapid generation of multiple case-mix models we suggest applying automated modelling to reduce the time required, in particular when examining different outcomes of large numbers of procedures and diseases in routinely collected administrative health data.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Alex Bottle.