Monitoring performance of cardiac surgery: the SCTS governance programme

OBJECTIVES The SCTS have published mortality rates for cardiac surgery by named hospital since 2001 and by named surgeon since 2005. This clinical governance programme has been associated with improved mortality outcomes despite increasing numbers of high-risk patients undergoing surgery. We describe the process of analysing the 2008-11 in-hospital mortality data, including the management, data processing and statistical framework. METHODS All SCTS data from April-2008 to March-2011 were extracted from the central cardiac audit database. The data were cleaned and summaries, including missing data, returned to units for validation. Afterwards, the final extract was cleaned and procedures classed as emergency, salvage, transplantation, trauma or primary-VAD removed. The primary outcome was in-hospital mortality. A contemporary recalibration of the logistic EuroSCORE model was developed for risk-adjustment. Funnel plots were used to detect outlier units with 95% and 99% two-sided confidence limits. One-sided 95% confidence limits corrected for multiple comparisons and multiplicative adjustment for overdispersion are examined. RESULTS A total of 106,982 records were included from 40 hospitals and 301 consultants. The mean mortality was 2.7% in all cardiac surgery. The recalibrated model was well calibrated (Hosmer-Lemeshow test P=0.56) and had good discrimination (AUC=0.78). Funnel plots were generated for each procedure group comparing 1) hospitals and 2) consultants. CONCLUSIONS The detection of ‘outlier’ healthcare providers is a challenging exercise and requires careful planning and analysis. By combing clinical and statistical expertise with robust methodology, we can reduce the chances of falsely classifying a unit as an ‘outlier’

Pregnancy rates and outcomes amongst women with cystic fibrosis in the UK : comparisons with the general population before and after the introduction of disease modifying treatment, 2003-17

Acknowledgements We thank the UK CF Registry team and the UK CF centres and clinics for submitting data to the Registry. Special thanks to the people with cystic fibrosis and their families who have agreed for their UK CF Registry data to be used for research. Funding The study was funded by a Welsh Government Research for Patient and Public Benefit grant. The funder was not involved in the study design, data collection, data analysis, data interpretation or the writing of the report. DT-R is funded by the MRC on a Clinician Scientist Fellowship (MR/P008577/1).Peer reviewedPublisher PD

Exploring the nature of perceived treatment burden: a study to compare treatment burden measures in adults with cystic fibrosis

Background: Despite the importance of reducing treatment burden for people with cystic fibrosis (CF), it has not been fully understood as a concept. This study aims to quantify the treatment burden perceived by CF adults and explore the association between different validated treatment burden measures. Methods: This is a cross-sectional observational study of CF adults attending a single large UK adult center. Participants completed an online survey that contained three different treatment burden scales; CF Questionnaire-Revised (CFQ-R) subscale, CF Quality of Life (CFQoL) subscale, and the generic multimorbidity treatment burden questionnaire (MTBQ). Results: Among 101 participants, the median reported treatment burden by the CFQ-R subscale was 55.5 (IQR 33.3 – 66.6), the CFQoL subscale was 66.6 (IQR 46.6 – 86.6), and the MTBQ reversed global score was 84.6 (IQR 73.1 – 92.3). No correlation was found between respondents’ demographic or clinical variables and treatment burden measured via any of the three measures. All treatment burden measures showed correlations against each other. More treatments were associated with high treatment burden as measured by the CFQ-R, CFQoL subscales, and the MTBQ. However, longer treatment time and more complex treatment plans were correlated with high treatment burden as measured by the CFQ-R and CFQoL subscales, but not with the MTBQ. Conclusions: Treatment burden is a substantial issue in CF. Currently, the only available way to evaluate it is with the CF-specific quality of life measure treatment burden subscales (CFQ-R and CFQoL); both indicated that treatment burden increases with more treatments, longer treatment time, and more complex treatments

Exploring the nature of perceived treatment burden: a study to compare treatment burden measures in adults with cystic fibrosis [version 1; peer review: 2 approved]

Background: Despite the importance of reducing treatment burden for people with cystic fibrosis (CF), it has not been fully understood as a concept. This study aims to quantify the treatment burden perceived by CF adults and explore the association between different validated treatment burden measures. Methods: This is a cross-sectional observational study of CF adults attending a single large UK adult center. Participants completed an online survey that contained three different treatment burden scales; CF Questionnaire-Revised (CFQ-R) subscale, CF Quality of Life (CFQoL) subscale, and the generic multimorbidity treatment burden questionnaire (MTBQ). Results: Among 101 participants, the median reported treatment burden by the CFQ-R subscale was 55.5 (IQR 33.3 – 66.6), the CFQoL subscale was 66.6 (IQR 46.6 – 86.6), and the MTBQ reversed global score was 84.6 (IQR 73.1 – 92.3). No correlation was found between respondents’ demographic or clinical variables and treatment burden measured via any of the three measures. All treatment burden measures showed correlations against each other. More treatments were associated with high treatment burden as measured by the CFQ-R, CFQoL subscales, and the MTBQ. However, longer treatment time and more complex treatment plans were correlated with high treatment burden as measured by the CFQ-R and CFQoL subscales, but not with the MTBQ. Conclusions: Treatment burden is a substantial issue in CF. Currently, the only available way to evaluate it is with the CF-specific quality of life measure treatment burden subscales (CFQ-R and CFQoL); both indicated that treatment burden increases with more treatments, longer treatment time, and more complex treatments

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p

Getting our ducks in a row: The need for data utility comparisons of healthcare systems data for clinical trials

Background: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. “Data Utility Comparison Studies” (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS. // Methods-and-Results: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at “patient-level” or “trial-level”, depending on the item of interest and trial status. // Discussion: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them

Getting our ducks in a row:The need for data utility comparisons of healthcare systems data for clinical trials

BACKGROUND: Better use of healthcare systems data, collected as part of interactions between patients and the healthcare system, could transform planning and conduct of randomised controlled trials. Multiple challenges to widespread use include whether healthcare systems data captures sufficiently well the data traditionally captured on case report forms. "Data Utility Comparison Studies" (DUCkS) assess the utility of healthcare systems data for RCTs by comparison to data collected by the trial. Despite their importance, there are few published UK examples of DUCkS.METHODS-AND-RESULTS: Building from ongoing and selected recent examples of UK-led DUCkS in the literature, we set out experience-based considerations for the conduct of future DUCkS. Developed through informal iterative discussions in many forums, considerations are offered for planning, protocol development, data, analysis and reporting, with comparisons at "patient-level" or "trial-level", depending on the item of interest and trial status.DISCUSSION: DUCkS could be a valuable tool in assessing where healthcare systems data can be used for trials and in which trial teams can play a leading role. There is a pressing need for trials to be more efficient in their delivery and research waste must be reduced. Trials have been making inconsistent use of healthcare systems data, not least because of an absence of evidence of utility. DUCkS can also help to identify challenges in using healthcare systems data, such as linkage (access and timing) and data quality. We encourage trial teams to incorporate and report DUCkS in trials and funders and data providers to support them.</p