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Risk assessment for VTEPowerPoint Presentation

Risk assessment for VTE

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Prevention of VTE in hospitalised patients:

V

Documented mandatory risk assessment for all hospitalised patients

Why the need for risk assessment for VTE?

Identifying at-risk patient

Counselling at-risk patient

Prescribe

thromboprophylaxis

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

in surgical patients

Risk scoring for VTE: Kucher risk score

Kucher, N. et al. N Engl J Med 2005;352:969-977

Primary end point: Freedom from VTE

100

98

Intervention

96

Freedom from DVT or PE (%)

94

P < 0.001

Control

92

90

Time (days)

0

30

60

90

Number at risk

Intervention

1255

977

900

853

Control

1251

976

893

839

Kucher, N. et al. N Engl J Med 2005;352:969-977

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

- each tenth of predicted risk was scored as 1
- 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 χ2 test for trend

Results - study populations

VERITY

n=55996

Assessment cohort (n=5938)

8 risk factors known

VTE status known

Univariate regression

analysis

Development cohort (n=5241)

12 risk factors known

VTE status known

Multiple regression

analysis

DVT O/P

KCH

n=928

Validation Cohort (n=915)

12 risk factors known

VTE status known

Results – baseline characteristicsAssessment, 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 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)

Proportion of patients score prediction of VTE with VTE vs. 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 χ2 test for trend).

VERITY risk score

Kucher risk score

Validation cohort: score prediction of VTE 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)

Conclusions score prediction of VTE

- 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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