How to collect and report outcomes of heart valve surgery

1. How to collect and report outcomes of heart valve surgery Hanneke Takkenberg Dept. of Cardio-Thoracic Surgery Erasmus University Medical Center, Rotterdam The Netherlands East European Heart Valve Postgraduate Course Sep 2007

2. Survival after mechanical AVR relative to general population

3. Components of mortality after valve procedures • Operative mortality • Valve-related events (Edmunds 1996 Guidelines/new Guidelines coming soon!!!): • Structural valve deterioration • Non-structural valve deterioration • Endocarditis • Thrombo-embolism • Bleeding • Valve thrombosis And their consequences: death, reop, invalidation • Excess mortality yet unexplained

5. Study designs • Randomized trial • Highest level of evidence • Usually difficult to accomplish • Selection bias • Cohort study • Prospective • Retrospective • Case-control study • Useful when the outcome is rare

6. Study designs • Randomized trial • Highest level of evidence • Usually difficult to accomplish • Selection bias • Cohort study • Prospective • Retrospective • Case-control study • Useful when the outcome is rare

7. How to collect heart valve surgery data? • Make a plan (and create a budget) first!

8. How to collect heart valve surgery data? • Make a plan (and create a budget) first! • Define your variables carefully and document this • Follow the Reporting guidelines (new ones coming up!!!) • Build a database (for example MS Access) • Obtain approval from your Institutional Review Board • Prospective rather than retrospective (recall bias, missing information) • Set up your annual prospective follow-up (using queries in MS Access) • Check your database periodically for quality and completeness of data (10% of entered data contains errors)

10. How to report outcome after heart valve operations • Descriptives: • Describe the patient and procedure characteristics • Number of deaths/events (early and late) • Incidence rates of late events (number/year, Weibull,…) • Describe modes of failure, clinical status, last echo • Logistic regression: • To assess factors that may influence early outcome, OR is calculated • Univariate versus multivariate • Kaplan- Meier analysis (time-to event model in the presence of censored cases): • To describe freedom from death/events • Comparison of KM-estimates: log rank test • Cox regression: • To assess factors that may influence outcome over time, HR is calculated • Univariate versus multivariate

12. Standard methods of outcome assessment after heart valve operations • Descriptives: • Describe the patient and procedure characteristics • Number of deaths/events (early and late) • Incidence rates of late events (number/year, Weibull,…) • Describe modes of failure, clinical status, last echo • Logistic regression: • To assess factors that may influence early outcome, OR is calculated • Univariate versus multivariate • Kaplan- Meier analysis (time-to event model in the presence of censored cases): • To describe freedom from death/events • Comparison of KM-estimates: log rank test • Cox regression: • To assess factors that may influence outcome over time, HR is calculated • Univariate versus multivariate

13. Continuous variables: Mean (Median) Standard deviation Range Discrete variables: Proportion Number In your methods section provide clear definitions of your parameters

14. Do provide counts but also explain!

15. Description of the 6 CABG complications:

16. Description of the causes of operative mortality:

17. Early versus late complications • Early complications (<30 days postop or during hospitalization): • Describe using proportions (%) and numbers • Late complications (>30 days postop): • Describe using numbers, incidence rates, or other functions (Weibull, Gompertz, 2-period risk) • Incidence rate = number of complications / number of patient years • Example:

18. Incidence rates are also used by the FDA for measuring the OPCs of valves OPC = Objective performance criteria

19. Example of a Weibull function for SVD

20. Standard methods of outcome assessment after heart valve operations • Descriptives: • Describe the patient and procedure characteristics • Number of deaths/events (early and late) • Incidence rates of late events (number/year, Weibull,…) • Describe modes of failure, clinical status, last echo • Logistic regression: • To assess factors that may influence early outcome, OR is calculated • Univariate versus multivariate • Kaplan- Meier analysis (time-to event model in the presence of censored cases): • To describe freedom from death/events • Comparison of KM-estimates: log rank test • Cox regression: • To assess factors that may influence outcome over time, HR is calculated • Univariate versus multivariate

21. Logistic regression • Is used to study factors that may influence a bivariate outcome that is not time-dependent • Outcome measure is Odds Ratio: OR • First perform univariate logistic regression analysis: • One factor at once into the model • Repeat this for all factors that you think may affect outcome • Preferably use continuous measures of factors instead of categories • Then perform multivariate logistic regression analysis: • All factors that were significant in the univariate model (p<0.05 or 0.10) • Note: do not put too many factors in (1 per 7-10 outcomes) • Avoid factors that represent approximately the same (example ECC time and aortic cross clamp time)

22. Univariate model OR (95% CI) P-value Multivariate model OR (95% CI) P-value Patient age (yrs) 1.07 (1.03-1.12) 0.001 1.07 (1.01-1.12) 0.016 Creatinin (mol/L) 1.005 (1.00-1.01) 0.004 1.005 (1.00-1.01) 0.002 Perfusion time (min) 1.007 (1.00-1.01) 0.004 1.004 (1.00-1.01) NS Procedure-related CABG 9.85 (1.65-38.77) 0.01 13.14 (1.27-136.21) 0.03 NYHA class 1.67 (1.06-2.60) 0.03 1.46 (0.89-2.40) NS Gender 2.52 (0.88-7.22) 0.09 -- -- Active endocarditis 2.46 (0.80-7.53) 0.12 -- -- Urgency (within 24 hrs) 2.16 (0.57-8.13) NS -- -- Concomitant procedures 2.25 (0.75-6.75) NS -- -- Circulatory arrest 0.53 (0.07-4.15) NS -- -- Prior operations 1.41 (0.47-4.28) NS -- -- SC vs ARR technique 1.06 (0.35-3.19) NS -- -- Ventilatory support 2.70 (0.56-13.17) NS -- -- Left ventricular function 0.79 (0.34-1.84) NS -- -- Example of logistic regression analysis Independent risk factors

23. Standard methods of outcome assessment after heart valve operations • Descriptives: • Describe the patient and procedure characteristics • Number of deaths/events (early and late) • Incidence rates of late events (number/year, Weibull,…) • Describe modes of failure, clinical status, last echo • Logistic regression: • To assess factors that may influence early outcome, OR is calculated • Univariate versus multivariate • Kaplan- Meier analysis (time-to event model in the presence of censored cases): • To describe freedom from death/events • Comparison of KM-estimates: log rank test • Cox regression: • To assess factors that may influence outcome over time, HR is calculated • Univariate versus multivariate

24. Example of a KM cumulative survival graph Log-rank test: p<0.01 Always mention number of patients at risk over time! If <10% is still at risk estimates are no longer valid

25. Standard methods of outcome assessment after heart valve operations • Descriptives: • Describe the patient and procedure characteristics • Number of deaths/events (early and late) • Incidence rates of late events (number/year, Weibull,…) • Describe modes of failure, clinical status, last echo • Logistic regression: • To assess factors that may influence early outcome, OR is calculated • Univariate versus multivariate • Kaplan- Meier analysis (time-to event model in the presence of censored cases): • To describe freedom from death/events • Comparison of KM-estimates: log rank test • Cox regression: • To assess factors that may influence outcome over time, HR is calculated • Univariate versus multivariate

26. Cox regression analysis • Is simply logistic regression that has time as a covariable • Therefore it allows study of factors that may influence the occurrence of complications (death/valve-related events) over time • Outcome measure = hazard ratio (HR)

27. Newer statistical methods • Actual versus actuarial (KM) method: • The Kaplan Meier method is in general very useful for describing outcome over time • However, when a non-fatal event is described by means of the KM method there is an overestimate of the risk that a patient may experience this event. • Why? Because the patient may die before the event occurs • The actual method corrects for the competing risk of death • Important: The actual risk can be misused to make valve performance look better • Actuarial method: describes valve outcome • Actual method : describes patient outcome (and not valve performance!) • Simulation methods • Longitudinal data analysis (for example echo data)

28. Beware: Too much information!

29. More information or a copy of this presentation: E-mail: j.j.m.takkenberg@erasmusmc.nl Or download this presentation at: www.cardiothoracicresearch.nl (first register)