Risk assessment for VTE Dr Roopen Arya King’s College Hospital

V Prevention of VTE in hospitalised patients: Documented mandatory risk assessment for all hospitalised patients

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

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

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

VTE risk assessment in medical patients

VTE risk assessment in surgical patients

Kucher, N. et al. N Engl J Med 2005;352: 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

Primary end point: Freedom from VTE Intervention Control Number at risk Intervention Control Time (days) Freedom from DVT or PE (%) P < Kucher, N. et al. N Engl J Med 2005;352:

Derivation and Validation of a Prediction Tool for Venous Thromboembolism (VTE): a VERITY Registry Study

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

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

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

Statistical plan – model development As a preliminary to a formal multiple regression analysis, the effects of the 8 Kucher risk 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

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

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

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

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 KCH n=928 Univariate regression analysis Multiple regression analysis

Results – baseline characteristics Assessment, development and validation cohorts

Results – risk factor findings in multiple logistic regression model

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

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

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).

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

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