Risk assessment for vte
This presentation is the property of its rightful owner.
Sponsored Links
1 / 25

Risk assessment for VTE PowerPoint PPT Presentation


  • 102 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Risk assessment for VTE

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Risk assessment for vte

Risk assessment for VTE

Dr Roopen Arya

King’s College Hospital


Risk assessment for vte

Prevention of VTE in hospitalised patients:

V

Documented mandatory risk assessment for all hospitalised patients


Why the need for risk assessment for vte

Why the need for risk assessment for VTE?

Identifying at-risk patient

Counselling at-risk patient

Prescribe

thromboprophylaxis


Risk assessment

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

Risk assessment models

  • Group-specific (‘opt-out’)

  • Individualized (‘opt-in’)

    • Risk stratification

    • Risk scores

  • Linked to ACTION of thromboprophylaxis


Risk assessment for vte

VTE risk assessment in medical patients


Risk assessment for vte

VTE risk assessment

in surgical patients


Risk assessment for vte

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

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


Risk assessment for vte

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


Study objective

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


Risk assessment for vte

UK multi-centre observational VTE registry of

clinical management practices & patient outcomes


Features of verity

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

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 development1

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

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 performance1

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 χ2 test for trend


Results study populations

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 characteristics assessment development and validation cohorts

Results – baseline characteristicsAssessment, development and validation cohorts


Results risk factor findings in multiple logistic regression model

Results – risk factor findings in multiple logistic regression model


Pair wise interactions for vte risk 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

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 with vte vs risk score

Proportion of patients 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 roc curves for risk score prediction of vte

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)


Conclusions

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


  • Login