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

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Presentation on theme: "Annual Report 2003 Power Point Presentation. Mechanics of merging data."— Presentation transcript:

1 Annual Report 2003 Power Point Presentation

2 Mechanics of merging data

3 Number of entries in the database

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

5 Number of entries by centre

6 Period of data collection by centre

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

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

9 Age and disease

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

11 Age distribution for patients with VTE (n=558)

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

13 Age and gender for non-VTE patients

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

15 Age and gender patients with VTE

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

17 Number of risk factors and diagnosis

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

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

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

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

22 Number of risk factors and age in patients with VTE

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

24

25 Number of risk factors and gender in patients with VTE

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

27 Recent major surgery in patients with VTE

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

29 Recent major surgery by specialty in patients with VTE

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

31 Recent medical inpatient – stay in patients with VTE

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

33 Final diagnosis of VTE in surgical and medical inpatients

34

35 Cancer in patients with VTE according to centre

36 Cancer in patients with VTE (n=545)

37 Cancer in patients with VTE by age and gender

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

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

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

41 Long-distance travel in patients with VTE

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

43 D-dimer result and final diagnosis

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

45 DVT pre-test probability and final diagnosis

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

47 DVT pre-test probability and D-dimer result

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

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

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

51 PE pre-test probability and final diagnosis of PE

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

53 Cancer, D-dimer result and pre-test probability

54 D-dimer result in the context of cancer

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

56 Suitability for home treatment

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

58 Use of LMWH

59 Use of LMWH therapy by final diagnosis

60 Duration of LMWH therapy in patients with VTE

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

62

63 Time to therapeutic INR

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

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

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

67 What do Bayes tables do?

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

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

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

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

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

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

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

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

76 Calculation by computer


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