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Derivation and Validation of a Prediction Tool for Venous Thromboembolism (VTE): A VERITY Registry Study Roopen Arya, Shankaranarayana Paneesha, Aidan.

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Presentation on theme: "Derivation and Validation of a Prediction Tool for Venous Thromboembolism (VTE): A VERITY Registry Study Roopen Arya, Shankaranarayana Paneesha, Aidan."— Presentation transcript:

1 Derivation and Validation of a Prediction Tool for Venous Thromboembolism (VTE): A VERITY Registry Study Roopen Arya, Shankaranarayana Paneesha, Aidan McManus, Nick Parsons, Nicholas Scriven, Tim Farren, Tim Nokes, Denise O'Shaughnessy & Peter Rose for the VERITY Investigators

2 Disclosures for Dr. Roopen Arya Presentation includes discussion of the following off-label use of a drug or medical device: N/A Research Support/P.I.Sanofi-aventis EmployeeNo relevant conflicts of interest to declare ConsultantNo relevant conflicts of interest to declare Major StockholderNo relevant conflicts of interest to declare Speakers BureauNo relevant conflicts of interest to declare HonorariaSanofi-aventis Scientific Advisory BoardBoehringer Ingelheim In compliance with ACCME policy, ASH requires the following disclosures to the session audience: 49 th ASH Annual Meeting ♦ Atlanta, Georgia

3 V Prevention of VTE in hospitalised patients: the UK experience Documented mandatory risk assessment for all hospitalised patients

4 Why the need for risk assessment for VTE? Identifying at-risk patient Counselling at-risk patient Prescribethromboprophylaxis

5 Risk Assessment The highest ranking safety practice was the appropriate use of prophylaxis to prevent VTE in patients at risk. AHRQ “Making Health Safer: A Critical Analysis of Patient Safety Practices” 2001 We recommend that every hospital develop a formal strategy that addresses the prevention of thromboembolic complications. This should generally be in the form of a written thromboprophylaxis policy especially for high risk groups. ACCP guidelines “ Prevention of VTE” 2004

6 Risk assessment models Group-specific (‘opt-out’) Individualized (‘opt-in’) –Risk stratification –Risk scores Linked to ACTION of thromboprophylaxis

7 Kucher, N. et al. N Engl J Med 2005;352:969-977 Clinical FeatureScore Active cancer (treatment ongoing or within 6 months or palliative)3 Personal history of VTE3 Thrombophilia3 Recent major surgery2 Advanced age (≥ 75 years)1 Obesity (BMI >29)1 Bed rest (medical inpatient/immobilized >3d in last 4 wks/paralysis)1 Hormonal therapy (OCP/HRT)1 Risk scoring for VTE: Kucher risk score

8 Primary end point: Freedom from VTE Intervention Control Number at risk 1255977900853 1251976893839 Intervention Control Time (days) 0306090 Freedom from DVT or PE (%) 90 92 94 96 98 100 P < 0.001 Kucher, N. et al. N Engl J Med 2005;352:969-977

9 Study objective to develop a multiple regression model for VTE risk, based on Kucher, and validate its performance to employ the extensive VTE risk factor data recorded in a UK VTE treatment registry (VERITY) –VERITY enrolls patients presenting to hospital with suspected VTE

10 UK multi-centre observational VTE registry of clinical management practices & patient outcomes

11 Features of VERITY National registry – outpatient VTE treatment Full spectrum of VTE – DVT and PE Records information on patients presenting with suspected and confirmed VTE Expanded data on demographics, presentation, management & outcomes Extensive risk factor data

12 Statistical plan – model development As a preliminary to a formal multiple regression analysis, the effects of the 8 risk Kucher factors on VTE risk were investigated individually by univariate analysis Initial findings: univariate analysis (n=5928; 32.4% with diagnosis of VTE) suggested VTE risk was not accounted for by the 8 Kucher risk factors An additional 3 risk factors were added (leg paralysis, smoking, IV drug use) and also patient sex, and the model was created with these 12 factors

13 Statistical plan – model development The multiple logistic regression model was developed using backward stepwise regression The open source statistical package ‘R’ was employed to conduct the regression analysis

14 Statistical plan – model performance We tested the accuracy of the Kucher score and the new logistic regression model to classify patients by receiver operating characteristic (ROC) curve analysis, plotted as 1-specificity versus sensitivity for VTE diagnosis –The c statistic (area under the curve), representing the ability of the model to correctly classify patients, was estimated using the nonparametric method of Hanley and McNeil We validated the model using a risk factor database of patients enrolled at an outpatient DVT clinic at King’s College Hospital

15 Statistical plan – model performance We interpreted the predicted probabilities from the logistic regression model as a risk score –each tenth of predicted risk was scored as 1 i.e. lower tenth of risk = risk score of 1; upper tenth of risk = risk score of 10 We assessed the degree of agreement between the observed rate and the predicted rate of VTE by plotting the risk score vs. observed VTE rate –Differences in the rates of VTE vs. increasing risk score were assessed using the χ 2 test for trend

16 Results - study populations VERITY n=55996 Assessment cohort (n=5938) 8 risk factors known VTE status known Development cohort (n=5241) 12 risk factors known VTE status known Validation Cohort (n=915) 12 risk factors known VTE status known DVT O/P KCL n=928 Univariate regression analysis Multiple regression analysis

17 Results – baseline characteristics Assessment, development and validation cohorts

18 Results – risk factor findings in multiple logistic regression model

19 Pair-wise interactions for VTE risk in multiple logistic regression model

20 Receiver operating characteristic (ROC) curves for risk score prediction of VTE Kucher (––) c statistic 0.617 95% CI 0.599–0.634 VERITY (- - -) c statistic 0.720 95% CI 0.705–0.735 VERITY significantly better than Kucher (p<0.001)

21 Proportion of patients with VTE vs. risk score VERITY risk scoreKucher risk score Strong positive correlation between an increasing risk score and the percentage of VTE-positive cases in the development cohort (P<0.001 by χ 2 test for trend).

22 Validation cohort: ROC curves for risk score prediction of VTE Kucher (––) c statistic 0.587 95% CI 0.542–0.632 VERITY (- - -) c statistic 0.678 95% CI 0.635–0.721 VERITY c statistic no different from development cohort (p=NS)

23 Conclusions The c statistic for this VERITY risk model (0.72) indicates a good test for likelihood of VTE diagnosis This VERITY risk model was superior to Kucher for predicting the likelihood of a diagnosis of VTE in a cohort in whom the diagnosis was suspected This risk model was validated in an independent VTE database A prospective study is required to determine clinical value as a risk prediction tool for VTE at the time of hospital admission to assist in assessing prophylaxis needs


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