Influencia de la diferencia de diámetros en el salto térmico

An additional threshold analysis was carried out (for the base-case comparison, GPiT vs.

best practice) to determine the robustness of the model to an increase in the rate of major extracranial bleeding in the TIA mimic population. This was intended to test the uncertainty surrounding the harm of inappropriate treatment in a population with misdiagnosed TIA in Primary Care. An analysis was performed in Excel by holding the other parameters within the model constant, and varying the daily transition probability TIA-major extracranial haemorrhage such that a maximum ceiling ratio of £20,000 per QALY was attained.¹⁰ This provides detail of the maximum acceptable rate of major haemorrhage at which GPiT remained cost-effective.

Sensitivity analysis for poor prognosis in carotid surgeries foregone

An additional sensitivity analysis was carried out to adjust the model results for carotid surgeries foregone in the alternative GPiT strategies involving no referral for specialist assessment. This was to examine the impact of medical management of patients with significant stenosis. This adjustment applies to the GPiT alternative strategy with no subsequent referral and the alternative strategy with partial referral by ABCD2 risk score.

In the latter case, carotid surgeries are only foregone in patients in the low risk group. No adjustment was made to the current practice scenario as the average treatment effect with

10 Formally this was done by using the EXCEL add-in ‘goal seek’.

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current practice is likely to already reflect poor prognosis as a result of delayed carotid surgeries.

Wardlaw et al. (2007) identify the cumulative risk of recurrent stroke as 29% at 90 days in the affected sub-group with carotid stenosis (defined as 70% occlusion according to ECST criteria). The equivalent rate in the period of 90 days to three years was 48%. The above figures suggest that in a cohort of 1000 suspected TIA cases, of which 500 are true positive, and 25 (5% of 500) are candidates for surgery. If no surgeries were offered, this would suggest that there would be approximately 7 recurrent events at 90 days and a further 5 events at 3 years, i.e. 12 recurrent events. These results do not indicate the excess risk of stenosis over and above patients without significant stenosis but the absolute risk.

However evidence presented to date indicates that the risk is low in those without significant stenosis and on optimal medical management. There is an issue of which data source to use to control/adjust for a population without stenosis as there are problems with data reporting stroke rates stratified by degree of artery occlusion. Wardlaw et al. pool results for patients with no stenosis with those of complete artery occlusion (Wardlaw et al., 2006). The excess risk up to 90 days was therefore assumed to be the stroke rate from the best performing stroke clinics, and equivalent to the aggregate risk estimated using survival methods. i.e. about 0.2%. To be conservative, a sensitivity analysis was carried out on the short and long-term model predictions when the proportion of the cohort with stroke was assumed to increase by 7 (at 90 days) and 12 (for the lifetime horizon). The corresponding increases for the GPiT strategy with referral of the high risk cases only assumed that the excess risk of medically treated stenosis were 1.4 at 90 days and 1 at lifetime.

132 5.13. Technical summary: Stroke free survival

Stroke free survival rates (proportion of the original cohort who are alive) at t=2, t=7 and t=90 (i.e. from the figures above 0.93, 0.90 and 0.83). In addition, it can be assumed that the entire cohort is alive at t=0 (i.e. where survival is at a maximum i.e. 1)

To calculate the proportion of the cohort free from stroke between the intervals of 2 and 7 days you need the stroke free survival rates (proportion of the original cohort event free) at t=2, and t=7 (i.e. from the figures above, using notation S(t₂)=0.93 and S(t₇)=0.90).

In order to estimate the proportion of the cohort event free at other points in time (ti) within the interval for which we have data (i.e. ti=2,3,…≤90 etc.) one option is to calculate and apply survival methods.

An exponential function was used to implement time dependency in the model. It would be possible to use other functional forms here but the rationale for using this function was suggested by the clinical evidence on recurrence. To calculate this, the natural log of the ratio of the above survival rates is used to calculate the hazard or (instantaneous event rate) for any timepoint within the interval. In this case, t evaluates to 5 (the interval, in days, corresponding to the follow-up period relating to the data)

ht= -ln(0.90/0.93)/(5) = -0.0066.

(Note this evaluates to a constant hazard rate for all time points within the above interval) i. The above four steps were repeated (substituting in for S(t₉₀) and S(t₇)) to

estimate the hazard rate between 7 and 90 days.

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ii. Therefore the relation between the hazard rate and the survival function can be used to calculate continuous (stroke free) survival rates for the TIA population for any S_i within the interval for which data is required by the model, i.e. for days 2-90.

iii. To estimate points earlier in time, the above steps (i-ii) were repeated for the interval 0 and 2 where the stroke free survival rates were S0=100% and S2=0.93%. (Note that t=0 is taken to be the point at which the patient presents to the GP).

iv. This provides the modelling method for the non-urgently treated TIA.

v. This exercise was repeated for specialist study clinics initiating optimal secondary prevention agents urgently to derive the hazard rates under optimum service delivery.

vi. The two curves were used to implement treatment status. Patients faced the non-urgently treated TIA curve until treatment is initiated. At this point, the hazard rate from urgently treated TIA is applied.

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Table 33: Cumulative event free survival: by strategy (first 9 days)

Time (days) GPiT

(corresponds to treatment on day 1)

Best practice (corresponds to treatment on day 2)

Current practice (corresponds to treatment on day

0 1 1 1

1 0.984481 0.984481 0.984481

2 0.983943 0.969203 0.969203

3 0.983406 0.968674 0.954162

4 0.982869 0.968145 0.939354

5 0.982333 0.967616 0.924777

6 0.981796 0.967088 0.910425

7 0.98126 0.96656 0.896296

8 0.981131 0.966432 0.896178

9 0.981001 0.966305 0.89606

Discussion

This chapter has outlined the methods for the identification and application of data to build and structure the GPiT strategy model. In one area, GP diagnostic accuracy, the need for a more structured literature review was necessitated. This is the focus of the next chapter.

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CHAPTER 6: NARRATIVE REVIEW OF STUDIES ASSESSING THE