Using Scottish Administrative Data to Predict Mortality after Coronary Artery Bypass Graft Surgery

John Quinn, David N Clark, Adam RedpathHeart Disease & Stroke Programme, ISD Scotland

Saturday 19th September 2009Exploiting Existing Data for Health ResearchSt Andrews University

CABG Mortality Modelling ISD CHD & Stroke Programme Coronary Artery Bypass Graft (CABG) Motivation Scottish Medical Record Linkage

System Dataset Design

Comparison with Published English Data (Aylin)

Optimal Multivariate Model Summary & Further Work

ISD CHD & Stroke Programme

Aims & Objectives• The Programme aims to provide a "patient-centred" data

and information service to support the drive for improvements in care and services for patients with coronary heart disease or stroke.

– To lead the work with NHS QIS and national advisory groups in developing information systems to monitor and improve quality and performance of clinical services.

– To support the implementation of the new e-cardiology and e-stroke record.

– To provide a comprehensive, accessible and definitive information service to the SG, NHS HBs, MCNs and clinicians and the public.

– To develop research capability using routine data. – Diversify into new areas- e.g. person level primary care data and

community services for stroke.

CABG

Background/Motivation

Background/Motivation (cont’d) Research published by Aylin et al (BMJ

2007) based on English administrative data

Concluded hospital episode statistics provide similar predictive discrimination to detailed clinical datasets

Can Scottish data produce similar robust models?

Can these be used to adjust outcome measures for case-mix?

Scottish Linked Acute DatasetAll available records back to 1981

Inpatients and day cases Discharges from non-obstetric specialtiesCancer registrationsGRO death records

Database contentPatient identifiableDemographic & socioeconomicEpisode managementClinical

Clinical support informationDiagnoses (ICD-9 / ICD-10) – 6 diagnostic positionsOperations (OPCS-3 / OPCS-4) - 2/4 pair codes

Hospital Discharges SMR1Psychiatric Inpatients SMR4

Scottish Cancer Register SMR6GRO Death Records

Analysis Dataset Design 10,171 CABG main operation admissions in Scottish NHS

hospitals (2003-2007)

Excludes admissions with valve operations, PCI rescues, and other complicating procedures.

Demographics: Age, Sex, Area of Residence, Deprivation, Urban-Rural Classification

Clinical/Episode Management: Admission Type, Day of Admission, Revision CABG, Number of Arteries Replaced

Analysis Dataset Design (cont’d)

Prior Admission (within past 90 days, including index): # for AMI

Prior Admissions (within past year): # for IHD

Prior Admissions (within past 5 years): # Emergencies, Total Bed Days, Charlson Comorbidities (specific diseases & index score )

Prior Operations (since 1989): CABG, Heart Operations

Outcome: Death on discharge and/or within 30 days of admission

Logistic Regression was implemented to model deaths occurring within 30 days or on discharge from hospital.

Comparison of Models

8 year: 1996/97 – 2003/04

152,523 CABGs 3,247 (2.13%) Deaths

5 year: 2003 – 2007 10,171 CABGs 208 (2.05%) Deaths

Aylin StudyISD Study

Simple Model (Year, Age, Sex)

Intermediate Model (Admission type, Deprivation category, Charlson comorbidity score, Previous emergency admission)

Complex Model (Revision Procedures, Previous IHD Admission, AMI Admissions in the past 5 years, Previous Heart Op and Number of

arteries replaced)

Comparison of ResultsThe results of the study were very similar to those produced by Aylin.

The models were compared against one another using the receiver operating characteristic (ROC) curve scores (c statistic).

Simple Model c statistic = 0.67 Hosmer-Lemeshow p=0.75 Intermediate Model c statistic = 0.75 Hosmer-Lemeshow p=0.63 Complex Model c statistic = 0.77 Hosmer-Lemeshow p=0.60

Aylin StudyISD Study Simple Model c statistic = 0.69 Hosmer-Lemeshow p=0.55 Intermediate Model c statistic = 0.72 Hosmer-Lemeshow p=0.01 Complex Model c statistic = 0.77 Hosmer-Lemeshow p=0.13

UK national adult cardiac surgical database: c statistic = 0.78

Intermediate model: Odds Ratios

Table 1 ISD Study Aylin Study

 Exp(B)

95% C.I.for EXP(B)

Exp(B)95% C.I.for EXP(B)

Intermediate Model Lower Upper Lower UpperAge:            

>=85 5.38 1.12 25.94 20.10 12.50 32.4280-84 3.06 0.85 11.00 9.97 6.90 14.4175-79 2.98 0.91 9.72 5.40 3.89 7.7770-74 1.91 0.59 6.20 3.43 2.44 4.8465-69 1.32 0.40 4.36 2.42 1.72 3.4260-64 1.10 0.33 3.71 1.72 1.22 2.4455-59 0.84 0.24 2.97 1.22 0.85 1.7550-54 0.58 0.14 2.37 1.00 0.68 1.4745-49 0.67 0.15 3.04 1.11 0.73 1.68<=44 1.00     1.00    

Sex:            Female 1.27 0.94 1.73 1.39 1.29 1.51

Male 1.00     1.00    

Intermediate model: Odds Ratios contd

Table 1 contd ISD Study Aylin Study

 Exp(B)

95% C.I.for EXP(B)

Exp(B)95% C.I.for EXP(B)

Intermediate Model - contd Lower Upper Lower UpperMethod of Admission:            

Emergency 2.28 1.71 3.03 1.54 1.40 1.69Elective 1.00 1.00

Per Year: 0.94 0.85 1.03 0.93 0.91 0.94Fifth of Deprivation:            

5 (most deprived) 1.82 1.11 2.99 1.17 1.04 1.314 1.49 0.89 2.50 1.04 0.92 1.163 1.38 0.81 2.35 1.02 0.91 1.142 1.73 1.04 2.90 0.96 0.85 1.08

1 (least deprived) 1.00     1.00    Per unit increase in Charlson comorbidity score

1.37 1.24 1.51 1.72 1.66 1.78

Per previous emergency admission

1.05 0.95 1.15 1.20 1.15 1.24

             

Complex model: Odds Ratios

Table 2 ISD Study Aylin Study

 Exp(B)

95% C.I.for EXP(B)

Exp(B)

95% C.I.for EXP(B)

Complex Model Lower Upper Lower UpperRevision 4.04 0.96 16.95 1.70 1.33 2.18Per previous admission for IHD 0.80 0.68 0.94 0.85 0.80 0.91Recent admission for MI 1.22 0.84 1.78 1.34 1.14 1.57Previous Heart Op 1.82 0.64 5.24 0.70 0.31 1.57Number of arteries replaced:            

one 1.00     1.00    two 1.30 0.77 2.20 1.09 0.95 1.26

three 1.90 1.06 3.41 1.30 1.12 1.49four 0.93 0.12 7.11 1.39 1.15 1.68

connection of thoracic artery to coronary artery

1.40 0.87 2.25 0.96 0.84 1.11

other bypass of coronary artery 9.73 0.99 96.23 1.30 0.57 2.97unknown or other specified 1.48 0.39 5.56 3.07 2.42 3.90

             

Significant Predictors of Mortality

Charlson Co-Morbidity Index

Admission Type Age Revision Procedures

AMI Admissions in the past 5 years

Not having a IHD admission within the last year

Multivariate ModelA full forward stepwise model was used to find the

significant variables.

Stepwise optimal model: Odds Ratios

Table 3 ISD Study   

Sig Exp(B)95% C.I.for EXP(B)

Stepwise Model Cases Lower UpperAge:          

>=85 38 0.05 4.73 0.97 23.0480-84 274 0.14 2.66 0.74 9.5875-79 1153 0.10 2.73 0.84 8.9070-74 1901 0.35 1.75 0.54 5.7165-69 2073 0.73 1.23 0.37 4.0660-64 1787 0.96 1.03 0.31 3.4655-59 1420 0.70 0.78 0.22 2.7650-54 811 0.39 0.54 0.13 2.2045-49 485 0.54 0.62 0.14 2.82<=44 229 1.00    

           Revision 2286 0.00 4.15 1.83 9.39Emergency Admission 2275 0.00 2.00 1.44 2.76Per previous admission for IHD 8050 0.03 0.69 0.49 0.97

Stepwise optimal model: Odds Ratios contd

Table 3 contd ISD Study

   Sig Exp(B)

95% C.I.for EXP(B)

Stepwise Model Cases Lower UpperNumber of prior admissions for MI:          

0 7562 1.00     1 2284 0.13 0.76 0.54 1.082 286 0.02 1.94 1.12 3.36

3+ 39 0.20 2.24 0.65 7.77           Charlson Index Score:          

0 3466 1.00    1 3292 0.00 2.08 1.34 3.232 2000 0.01 1.88 1.14 3.083 851 0.00 4.95 3.01 8.134 329 0.01 2.61 1.25 5.485 141 0.00 7.34 3.50 15.40

6+ 92 0.00 10.38 4.82 22.33

Optimal Model:Multivariate Stepwise Model

AreaStd.

ErroraAsymptot

ic Sig.b

Asymptotic 95% Confidence IntervalLower Bound

Upper Bound

.758 .017 .000 .725 .792

Stepwise Modelc statistic = 0.76Hosmer-Lemeshow = 9.899 (0.272)0.758

Conclusion & Future work

The similarities between the two studies suggest we are able to use the Scottish linked hospital admissions and deaths data as an effective tool to adjust hospital outcomes of CABG mortality for case-mix.

Further cross-validation required.

Write up a paper on the methodology and results.

Produce regular case-mix adjusted CABG mortality by hospital.