Bridgewater, B

Grant, SW

Hickey, GL

English

The University of Manchester - Institutional Repository

The University of Manchester ResearchUnlocking a national adult cardiac surgery audit registrywith RLink to publication record in Manchester Research ExplorerCitation for published version (APA):Hickey, GL., Grant, SW., & Bridgewater, B. (2013). Unlocking a national adult cardiac surgery audit registry with R.use-R 2013, Albacete, Spain.Citing this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. 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Oct. 2025Unlocking a national adult cardiac surgery audit registry with GL Hickey1,2,3, SW Grant2,3 & B Bridgewater1,2,3  1Northwest Institute of BioHealth Informatics, University of Manchester 2University Hospital of South Manchester 3National Institute of Cardiovascular Outcomes Research, UCL The R User Conference 2013 University of Castilla-La Mancha, Albacete, Spain BACKGROUND Bristol Inquiry Contributory factors that led to the failings included:  1. Inadequate collection of data 2. Inadequate monitoring of data    National Adult Cardiac Surgery Audit registry • Up to 166 clinical variables collected on each patient: administrative, demographics, comorbidities, operative factors, outcomes • 15 years of data • 465,000 records • 44 hospitals + >400 consultant surgeons    Flow of data NICOR NIBHI HOSPITALS DATABASE CLEANING ANALYSES AUDIT & GOVERNANCE TOOLS CLINICAL RESEARCHERS NATIONAL DEATH REGISTER* * Ability to link with many other national registries RESEARCH UNLOCKING THE REGISTRY MESSY DATA Cleaning the registry in  DATA EXTRACT VARIABLE 1 VARIABLE 2 VARIABLE 3 ………… EXCLUDE RECORDS ADD VALUE CLEANED DATA Scripts to add: • Risk scores • Combined variables • ‘Resolve’ conflicting variables • Script per each variable • Some dependencies E.g. duplicates Rapidly reproducible > with(SCTS, table(X4.04.Discharge.Destination, X4.05.Status.at.Discharge))                                               X4.05.Status.at.Discharge X4.04.Discharge.Destination                           0. Alive 1. Dead                                                   828    48296    2453   . Another dept within the trust                   0       57       0   0                                                 1        1       0   0. Not applicable - patient deceased              0        0       1   1 Home                                            0     4104       0   1. Home                                         674   370763     374   2 Convalescence                                   0       63       0   2. Convalescence                                  8     7347       4   2. Convalescence (Non acute Hospital)             2     2164       0   3 Other hospital                                  0        1       0   3 Other Hospital                                  0      151       0   3 Other Hospital  -  wd 6                         0        1       0   3 Other Hospital wd 2                             0        1       0   3 Other ward                                      0        1       0   3. Other Acute hospital                           1     7680       1   3. Other hospital                               115    22935      37   4 Patient deceased                                0        0     173   4. Not applicable - patient deceased             51      412   13286   4. Patient Deceased                               0        0      19   5                                                 0        7       0   5. Transferred to different Consultant - NGH      0       42       0   7                                                 0        2       0   8                                                 0       38       4   9                                               114     3820     518   Second op                                         0        2       6 Illegal options Transcriptional discrepancies Missing data Conflicts • Errors are difficult to find and not all can be resolved • Excluding all imperfect data not an option • Balance between a ‘research ready’ dataset and robust audit capability • Needs to be reproducible  • It is locked to clinicians & researchers without being cleaned    Cleaning the registry in  Warning: cleaning clinical registries without experts is dangerous* * Applies to analysing healthcare data also + = DATA UNLOCKING THE REGISTRY MONITORING ● ●Crude RAMR0%5%10%15%0 200 400 600 800 0 200 400 600 800Number of proceduresMortality rateHealthcareprovider●23867802503756316611432077763226274328689834511803631845444563844732044683551● ●Crude RAMR0%5%10%15%0 200 400 600 800Number of proceduresMortality rateHealthcareprovider●23867802503756316611432077763226274328689834511803631845444563844732044683551Publication of named healthcare provider outcomes http://www.scts.org/patients/ Publication of named healthcare provider outcomes FILTER DATA  subset RISK ADJUSTMENT  glm, glmer {lme4}, mfp {mfp}, predict, auc {pROC},  CLASSIFICATION & PRESENTATION  ggplot {ggplot2}, write.csv AGGREGATION  summaryBy {doBy}, merge, arrange {plyr} Exploratory analyses http://www.scts.org/DynamicCharts/ summaryBy {doBy} + gvisMotionChart {googleVis} Monitoring medical devices • Currently does not happen in UK • Data: 200 valve types entered 13,000 ways (free text) • But R is good with regular expressions UNLOCKING THE REGISTRY RESEARCH 0.00.20.40.60.81.00 1 2 3 4 5 6 7 8 9 10Time from procedure (years)Survival probabilityNo. at risk 1415 991 779 559 398 276 180 114 64 23 6All octogenarians having MV surgeryEvidence based medicine Octogenarians having Mitral Valve Surgery ± CABG ± TV repair over 10-year window    survfit + Surv {survival}  kmplot {by Tatsuki Koyama} Mean 4 patients per unit / year Contemporary statistical methodology for retrospective data Unmatched3 2 1 0 1 20.00.20.30.50.60.80.9Mechanical BiologicalPropensity scoreMatched3 2 1 0 1 2 30.00.20.30.50.60.80.9Mechanical BiologicalPropensity scorematchit {MatchIt} Probability of receiving a mechanical valve Mechanical valve Biological valve Mechanical valve Biological valve Risk prediction: status quo 2002 2004 2006 2008 20100.020.040.060.080.10TimeMortality proportionObservedExpectedActualOverall averageTrendMortality proportion Ratio = 0.37 Ratio = 0.73 2% 4% 6% 8% 10% Mortality Date of surgery Risk prediction: with R + Intercept−6.00−5.75−5.50−5.252002 2004 2006 2008 2010TimeCoefficientEstimate 95% CINo updateRolling 24−month window (12−months)Rolling 24−month window (1−month)Piecewise recalibration (12−months)Piecewise recalibration (24−months)Dynamic logistic regressionlogistic.dma {dma} CONCLUSIONS Conclusions • We need to unlock healthcare registries to:  Monitor quality & avoid a repeat of Bristol  Revalidation of professional credentials  Facilitate patient choice  Develop & validate evidence based medicine  Increase in demand  • We can do it all in R!  Comments & suggestions • Funded by Heart Research UK [Grant Number RG2583]  • Dr Norman Stein, North West e-Health  Acknowledgements graeme.hickey@manchester.ac.uk 

Unlocking a national adult cardiac surgery audit registry with R

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Unlocking a national adult cardiac surgery audit registry with R

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