Actividad 1. Identificación de puntos críticos en el proceso de los trámites de

functions for plausible probabilistic sensitivity analysis configurations

Categorisation of surgical units

Based on the heterogeneity in surgical units adhering to IPG196 and the analyses varying the

assumption of whether or not the P96 group (patients born after 1996) could be infectious from birth, six categories of surgical units were defined (denoted S1 to S6). These were:

l S1 – a unit adheres to IPG196 and guidance on keeping instruments moist. The P96 group are

infectious from birth.

l S2 – a unit does not adhere to IPG196 but adheres to guidance on keeping instruments moist.

The P96 group are infectious from birth.

l S3 – a unit does not adhere to IPG196 nor does it adhere to guidance on keeping instruments

moist. The P96 group are infectious from birth.

l S4 – a unit adheres to IPG196 and guidance on keeping instruments moist. The P96 group are not

infectious from birth.

l S5 – a unit does not adhere to IPG196 but adheres to guidance on keeping instruments moist.

The P96 group are not infectious from birth.

l S6 – a unit does not adhere to IPG196 nor does it adhere to guidance on keeping instruments

moist. The P96 group are not infectious from birth.

Based on the opinion of members of the NICE committee it was assumed that, independent of whether or not the P96 group was assumed to be infectious, 10% of units adhered to IPG196 and guidance on keeping instruments moist, 30% of units adhered only to keeping instruments moist and 60% of units neither followed IPG196 nor kept instruments moist. These probabilities were altered in a scenario analysis.

Employing a heuristic to rule out probabilistic sensitivity analysis configurations that would produce implausible results

Owing to the time required for each run [approximately 12 seconds per ‘plausible’ (defined later) PSA configuration] and the number of PSA configurations, random number (RN) streams, scenarios and PSA configurations that would not be compatible with the observed data, heuristics were used to generate the cost-effectiveness results. At all stages, a cautious approach was employed to ensure that potentially appropriate configurations were not prohibited. Appendix 7 describes the methodology using formal mathematical notation, with a lay description provided in the main text.

The initial step was to develop a metric to exclude PSA draws that would clearly be discrepant to the observed data (known cases of CJD that could potentially be attributed to surgical transmission), without having to run these configurations.

Here, a factor to efficiently maximise the likelihood (FML) was established and any PSA configuration with a value greater than the FML value was discarded.

The FML was derived using a combination of parameters related to the infectious titre after a decontamination cycle, the mass transferred to a patient and the prevalence of prion in tissue in asymptomatic patients:

FML = 10A× B × C, (2)

in which

l A = mean infectious titre (in log-terms) × log-reduction in infectivity associated with the first

autoclaving cycle × log-reduction associated with detergent on the first cycle

l B = residual mass on an instrument × (1 – the proportion of residual mass transferred to the patient) l C = the proportion of asymptomatic individuals with CJD prions in their tissue.

In order to generate the FML threshold value, 2000 PSA configurations were drawn from the

appropriate distributions and run using 12 RN streams for each of the following scenarios: S1, S2 and S3. Having assessed the likelihood of each of the 2000 PSA configurations producing results consistent with the observed data, it was decided that any draw with a FML value of > e12_{would effectively have}

zero weight and could be discarded without affecting the results. Any draw with a value ≤ e12_could

potentially be consistent with the observed data.

Running further analyses to remove probabilistic sensitivity analysis configurations that are potentially consistent with the observed data but generate an implausible number of transmissions when run through the model

In total, 2000 PSA configurations with a FML value of ≤ e12_{were sampled. For each configuration, the}

first RN stream was run, assuming a S3 surgical unit and determining whether or not there was a

violation of the permissible limit (VPL) of clinical transmissions for patients aged ≤ 60 years. It was noted that the clinical experts had stated it was implausible that the correct detection rate of CJD was below 50% in this age group and that the assumed maximum number of clinically apparent cases potentially transmitted via surgery, across all ages, was 15. If there was a VPL, the PSA configuration was deemed to be inconsistent with the observed data and the PSA run was discarded. If there was not a VPL, the next RN stream was run with this process repeated until a maximum of 27 RN streams had been run. The VPL threshold was dynamic and changed as the number of RN streams increased. A large VPL threshold was chosen to reduce the possibility of rejecting viable PSA configurations, while acknowledging that there was also the probability that clinical transmissions had occurred in older patients. The initial threshold for VPL was 36 transmissions, which was constant for the cumulative total across the first six RN streams. From RN streams 7 to 13, the VPL threshold was increased to 40; from RN streams 14 to 17, the VPL threshold was increased to 45; from RN streams 18 to 23, the VPL threshold was increased to 55; and for RN streams 24 to 27 the VPL threshold was increased to 66. This resulted in 509 out of the 2000 PSA runs that all had an FML ≤ e12_{being potentially consistent}

with the observed data. These are denoted ‘plausible’ PSA configurations.

Calculating the likelihood of each plausible probabilistic sensitivity analysis configuration being consistent with the observed data

Approximate Bayesian computation methods were used to estimate the likelihood of a PSA configuration being consistent with the observed data. Full details are provided in Appendix 7. A likelihood ranges from 1, where the simulated number of transmissions that are clinically detected are entirely consistent with the number of observed cases, to zero where the simulated number of transmissions that are clinically detected cannot be consistent with the number of observed cases. Within this decision problem, any PSA configuration that produces ≤ 15 transmissions that result in clinical symptoms would have a likelihood of 1, whereas any PSA configuration that produced > 30 transmissions that result in clinical symptoms, in patients than < 60 years of age, would have a likelihood of zero.

The likelihoods for each PSA configuration are shown in Figure 17. These have been ranked in

descending order and have been curtailed at 250 of the 509 PSA configurations. A large proportion of the PSA configurations that were not rejected have likelihoods close to zero, which offers support to the belief that it was unlikely that potentially appropriate PSA configurations were discarded. For information, the lowest likelihood was 10–12_{where the P96 group was assumed to be infectious and}

10–13where the P96 group was assumed not to be infectious.

Generating estimates of the expected numbers of future surgically transmitted Creutzfeldt–Jakob disease, life-years lost and quality-adjusted life-years lost

The likelihoods associated with each PSA sample were multiplied by the results (future stCJD deaths, life-years lost and QALYs lost) produced when using that PSA sample and these were added together and divided by the sum of the likelihood to produce expectations for the combined results.

Exploring the uncertainty in the results produced within the base-case analyses

In order to explore more pessimistic scenarios, the maximum value across all of the 509 PSA configurations of the number of QALYs simulated to be lost multiplied by the likelihood of the PSA was also calculated. These values are necessarily greater than the expectations, which use the average value multiplied by the likelihood of the PSA rather than the maximum value. Generating CIs around the mean of each output was more complex owing to the use of likelihoods, as not all of the 509 scenarios were weighted equally. In order to provide an indication of the width of the CI (which would need to be halved if only looking at increasing or decreasing the value from the mean), an approximation was made, which is detailed in Appendix 7, that involved simulation to translate each PSA likelihood into either zero or 1 and then using statistical techniques to estimate a CI.

Exploring the probability that each type of surgical unit was the most cost-effective

Exploratory analyses were undertaken to provide indicative probabilities that each type of surgical unit (one of S1, S2 and S3, or S4, S5 and S6) or moving to single-use instruments were most cost-effective across a range of cost-per-QALY thresholds. This analysis assumed that a surgical centre was a S3 (S6), meaning that expenditure was required to move to S1 or S2 (S4 or S5). The probabilities were calculated assuming that the weight applied to each of the 509 PSA values would be provided to the surgical unit or single-use instrument scenario that was most cost-effective at a chosen cost-per-QALY threshold. The summated total of weights for each option was divided by the sum of the total weights to provide a probability of being most cost-effective, which summate to 1.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1 10 19 28 37 46 55 64 73 82 91_{100 109 118 127 136 145 154 163 172 181 190 199 208 226 235 244} Likelihood

Ranked PSA (curtailed at 251 of 509)

P96 group not infectious P96 group infectious

217

FIGURE 17 The likelihoods of the PSA configurations being compatible with the observed data (curves are drawn on top of each other).

Exploring the changes in the results produced with alternative assumptions relating to the assumed distribution of surgical units between the assumed decontamination levels

In the base-case analyses, it was assumed that 10% of surgical units would both follow IPG196 and keep instruments moist; 30% would not follow IPG196; and 60% of surgical units neither kept

instruments moist nor followed IPG196. The NICE committee requested that a scenario analysis be run that changed these proportions to 50%; 30%; and 20%, respectively. Thus, in this scenario analysis half of surgical units both followed IPG196 and kept instruments moist.