Annual Report 2003 Power Point Presentation. Mechanics of merging data.

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Presentation transcript:

Annual Report 2003 Power Point Presentation

Mechanics of merging data

Number of entries in the database

The growth of the database (n=2,742)

Number of entries by centre

Period of data collection by centre

VTE diagnosis rate by centre (n= 2,361)

Number of entries by centre (n=2,742)

Age and disease

Age distribution for all cases (n= 2,734)

Age distribution for patients with VTE (n=558)

Age distribution by final diagnosis for patients with VTE (n=558)

Age and gender for non-VTE patients

Age and gender distribution for non- VTE patients (n=1,795)

Age and gender patients with VTE

Age and gender for patients with VTE (n=558)

Number of risk factors and diagnosis

Number of risk factors for patients with VTE (n=430)

VTE and non-VTE final diagnosis by the number of risk factors (n=1,868)

Number of risk factors and history of VTE in patients with current VTE

History of VTE amongst patients with current VTE (n=430)

Number of risk factors and age in patients with VTE

Number of risk factors by age in patients with VTE (n=427)

Number of risk factors and gender in patients with VTE

Number of risk factors by gender in patients with VTE (n=430)

Recent major surgery in patients with VTE

Recent major surgery in patients with VTE (n=522)

Recent major surgery by specialty in patients with VTE

Specialty for VTE patients who have undergone recent major surgery (n=45)

Recent medical inpatient – stay in patients with VTE

Recent medical inpatient- stay in patients with VTE (n=535)

Final diagnosis of VTE in surgical and medical inpatients

Cancer in patients with VTE according to centre

Cancer in patients with VTE (n=545)

Cancer in patients with VTE by age and gender

Cancer in patients with VTE by age and gender (n=542)

Cancer in female patients with VTE by age (n=268)

Cancer in male patients with VTE by age (n=274)

Long-distance travel in patients with VTE

History of long-distance travel by the number of risk factors in patients with VTE (n=430)

D-dimer result and final diagnosis

Final diagnosis by D-dimer result (n=1,795)

DVT pre-test probability and final diagnosis

Final diagnosis by DVT pre-test probability (n=1,314)

DVT pre-test probability and D-dimer result

D-dimer result by DVT pre-test probability (n=993)

D-dimer result, DVT pre-test probability and final diagnosis

Final diagnosis by D-dimer result and DVT pre-test probability (n=912)

PE pre-test probability and final diagnosis of PE

Final diagnosis by PE pre-test probability (n=1,351)

Cancer, D-dimer result and pre-test probability

D-dimer result in the context of cancer

Final diagnosis by D-dimer result and DVT pre-test probability for patients who had cancer (n=83)

Suitability for home treatment

Suitability for home treatment by final diagnosis (n=551)

Use of LMWH

Use of LMWH therapy by final diagnosis

Duration of LMWH therapy in patients with VTE

Duration of LMWH therapy in patients with VTE (n=406)

Time to therapeutic INR

Time to therapeutic INR patients with VTE (n=349)

Time to therapeutic INR in patients with VTE (n=349)

Duration of LMWH therapy and time to therapeutic INR in patients with VTE (n=341)

What do Bayes tables do?

ROC curve for a general Bayesian risk model designed to predict DVT diagnoses (n=2,361)

Calibration plot for the general model (n=2,361)

Risk-adjusted funnel plot on DVT diagnosis rate using the general Bayesian model as the predictor of risk (n=2,361)

Funnel plot on DVT diagnosis rate (n=2,361)

Calibration plot for specific model 1 – low DVT diagnosis rate hospital model (n=981)

Calibration plot for specific model 2 – average diagnosis rate hospital model (n=1,302)

Funnel plot on DVT diagnosis rate (n=2,283)

Risk adjusted funnel plot on DVT diagnosis rate using the specific Bayesian models as the predictors of risk (n = 2,283)

Calculation by computer