GLOSARIO PARA ARMAS BLANCAS

This section provides a detailed overview of the process of identifying and selecting the data which will support the final decision tree model used in this analysis (Figure 6.6, Section 6.2.7). This includes data on costs and effects of ALS and prehospital care for OHCA in the prehospital care phase as well as in-hospital and post-discharge costs and effects. The result of this process will be summarised in Table 6.4, Section 6.2.8).

6.2.6.1 Prehospital treatment costs – Advanced Life Support

Cost data were obtained for the corresponding section of Trust Alpha, for the financial year 2015/16. The Excel spreadsheet provided by the Trust Alpha finance department included pay for clinical and non-clinical staff as well as costs of dispatch infrastructure, ambulance stations, vehicles, fuel, medical supplies, communications equipment, community first responders and third-party costs. The amount of funding spent on ALS care for OHCA was calculated as the number of OHCA cases multiplied by the number of core resources (paramedic-staffed vehicles), divided by the product of the number of cases of any aetiology and the number of core resources attending these. This was calculated from a one-year summary of the corresponding section of Trust Alpha (April 2016 to March 2017). Calculating

the proportion of resources spent on OHCA in this fashion distributes the costs of waiting time of resources in between emergency proportionally between OHCA cases and those of other aetiology (Lerner et al., 2012).

6.2.6.2 Prehospital treatment costs – prehospital critical care

Cost data were obtained for PCC-1, for the financial year 2015/16 in an excel spreadsheet. This included costs of the helicopter, hangar and staff area, pay for physicians and spending on training and equipment. Additional costs of two rapid response vehicles and pay for critical care paramedics was provided by Trust Alpha and calculated from the Trust Alpha data source. The proportion of PCC-1 funding spent on prehospital critical care was calculated as the number of OHCA cases attended by the critical care service, divided by number of all cases attended by PCC-1. As the vast majority of patients attended by PCC-1 are critically ill and require similar amounts of PCC-1 resources, no further adjustments between OHCA cases and non-OHCA cases was made. This was calculated from the PCC-1 clinical database for the year 2016.

6.2.6.3 Prehospital outcomes

Outcomes of prehospital resuscitation in the ALS and ALS plus prehospital critical care group were simulated for this chapter. I ran the decision analysis model with different effect sizes of prehospital critical care when compared to ALS, with absolute differences in survival following OHCA ranging from 0% to 6%. The rate of survival to hospital arrival for the ALS group is based on preliminary data and a recent publication from the Out-of-Hospital Cardiac Arrest Outcomes (OHCAO) Registry (Hawkes et al., 2017) and is set at 25.0%. Once the results of the observational research, presented in Chapter 7, are available, I will revisit the decision analysis model and calculate a further estimate of cost-effectiveness based on the best available data.

6.2.6.4 Hospital treatment costs

The costs of in-hospital treatment were based on a combination and synthesis of relevant publications, identified by a focused systematic search of the recent literature. PubMed was searched using the search string (((cost) OR economics) OR expenditure) AND "cardiac

arrest", with search results limited to the last 10 years. Inclusion criteria were cost analysis

of any intervention in adult, non-traumatic OHCA in the UK. See Figure 6.3 for a flow chart of the search results.

Figure 6.3 Flow chart of selection of publications, search results from PubMed on 22 November 2017

Twelve of the publications for which abstracts were reviewed were excluded as they were undertaken outside the UK and were therefore likely to have significantly different cost and clinical parameters. The other five excluded studies did not include relevant costs.

Of the three full-text publications reviewed, the studies by Marti et al. (2017) and Gates et

al. (2017) related to the same randomised controlled trial of a mechanical chest compression

device. Hospital treatment cost estimates were based on the length of stay of patients on intensive care units (ICUs) and on regular hospital wards as well as Emergency Department (ED) and outpatient costs. In contrast, Petrie et al. (2015a) included significant additional costs of cardiac interventions, such as percutaneous coronary intervention (PCI), pacemaker insertion or coronary artery bypass graft surgery (CABG), resulting in a higher estimate of hospital costs for OHCA that is more likely to be accurate. See Figure 6.4 for an overview of patient characteristics, cardiac interventions and ICU support in Petrie et al. (2015a).

Figure 6.4 Overview of characteristics and interventions for patients admitted to ICU after OHCA in the economic analysis by Petrie et al. (2015a), with permission from BMJ Open (license number 4252320390390)

I used the data from Petrie et al. (2015a), including the study’s open access source data (Petrie et al., 2015b) in combination with the Department of Health NHS Reference Costs

2015-16 (Department of Health, 2016a) to estimate the costs of in-hospital treatment. Table

Table 6.2 Summary of in-hospital treatment costs following out-of-hospital cardiac arrest

Cost group Source used Value in £ Variability Financial year ED costs per patient Department of

Health (2016a) 372 270-446 (IQR) 2015-16 ICU costs per day

(survivors to hospital discharge)

Petrie et al.

(2015b) 1,668 267 (SD) 2011-12

ICU costs per day (non-survivors)

Petrie et al.

(2015b) 1,690 307 (SD) 2011-12

Non-ICU costs (per survivor to hospital discharge)

Petrie et al.

(2015b) 12,257 8,033 (SD) 2011-12

Non-ICU costs (per non- survivor)

Petrie et al.

(2015b) 3,666 2,410 (SD) 2011-12

ED: Emergency department, ICU: Intensive care unit

6.2.6.5 Hospital outcomes

The observational data which will be presented in Chapter 7 are limited to the outcome of survival to hospital discharge or in-hospital death. This is reflected in the initial decision tree model in Figure 6.2. However, in-hospital costs of OHCA treatment vary significantly depending on the length of stay and patient destinations within the hospital, as described in the bullet points below. The decision tree model was therefore updated to accurately reflect these different clinical pathways and their associated costs and effects; see Figure 6.6 for the final decision tree model used for analysis.

 Death in the ED - This is a significant pathway, as it does not incur any further costs beyond the ED, particularly ICU costs, which comprise approximately 65-75% of all in-hospital costs (Petrie et al., 2015a). In a randomised controlled pilot study of 615 patients with OHCA undertaken in the UK setting in 2015, death in the ED occurred in 67 out of 190 (35%) of patients who had ROSC on arrival at hospital (Benger et al., 2016).

 ICU admission with in-hospital death - In 2014, this occurred in 2,687 of 4,517 patients (60.3%) who were admitted to ICU following OHCA, according to data from the Intensive Care National Audit and Research Centre (ICNARC) Case Mix Programme Database analysed by Nolan et al. (2016). The median length of ICU stay in this group was 2.0 days (IQR 0.7 to 4.5). Using the calculations proposed by Wan

et al. (2014), the estimated mean and standard deviation of ICU length of stay are

2.4 days (2.8).

 ICU admission followed by survival to hospital discharge - In the 2014 ICNARC cohort, this occurred in 1,830 of 4,517 patients, with a median length of stay on ICU of 4.7 days (2.3 to 10.0), corresponding to a mean and standard deviation of 5.7 days (5.7) (Nolan et al., 2016; Wan et al., 2014).

I ran the decision analysis model with different effect sizes of prehospital critical care when compared to ALS, with absolute differences in survival following OHCA ranging from 0% to 6%. The rate of survival to hospital discharge for the ALS group was based on preliminary data and set at 9.0%. Once the results of the observational research, presented in Chapter 7, are available, I will revisit the decision analysis model and calculate a revised estimate of cost- effectiveness based on the best available data.

6.2.6.6 Post-discharge costs

In their economic analysis of mechanical chest compression during OHCA, Gates et al. (2017) provide a detailed assessment of post hospital discharge costs from a healthcare provider (NHS) perspective. This includes costs of hospital outpatient appointments, ED or GP visits, various rehabilitation and support services and nursing/residential home costs. Importantly, they found a significant difference between the costs incurred by OHCA survivors with good neurological function (Cerebral Performance Category, CPC 1 and 2) and those with poor neurological function (CPC 3 and 4), largely due to the requirement for nursing/residential home care in CPC 3-4 survivors. The estimated annual cost was £3,315 and £43,146 for CPC 1-2 and CPC 3-4 survivors, respectively. See Box 6.2 for a description of the CPC categories, which are frequently used to measure outcomes following OHCA (Elliott, Rodgers and Brett, 2011). It is common practice to combine CPC 1 and CPC 2 into a survival group with good neurological outcome and CPC 3 and CPC 4 into a group with poor neurological outcome (Martinell et al., 2017; Yasunaga et al., 2010). To reflect the significant difference in post- discharge healthcare costs between survivors with CPC1-2 and CPC3-4, the decision tree was further refined to include these different health states; see Figure 6.6.

Box 6.2 Cerebral performance categories used to measure neurologic recovery after out-of- hospital cardiac arrest and other conditions

CPC 1 - Good cerebral performance: conscious, alert, able to work, might have mild neurologic or psychologic deficit.

CPC 2 - Moderate cerebral disability: conscious, sufficient cerebral function for independent activities of daily life. Able to work in sheltered environment. CPC 3 - Severe cerebral disability: conscious, dependent on others for daily support because of impaired brain function. Ranges from ambulatory state to severe dementia or paralysis.

CPC 4 - Coma or vegetative state: any degree of coma without the presence of all brain death criteria. Unawareness, even if appears awake (vegetative state) without interaction with environment; may have spontaneous eye opening and sleep/awake cycles. Cerebral unresponsiveness.

CPC 5 - Brain death: apnea, areflexia, EEG silence, etc.

CPC: Cerebral Performance Category, EEG: Electroencephalogram

6.2.6.7 Post-discharge outcomes

As the data from my observational research were limited to the primary outcome of survival to hospital discharge, both quality and length of life had to be estimated from previously published work. I undertook a focused literature search in PubMed, using the search string

(((quality of life[MeSH Terms]) OR utility) OR life expectancy[MeSH Terms]) AND cardiac arrest[MeSH Terms], with search results limited to the last 10 years. Inclusion criteria were

adult non-traumatic OHCA and outcomes of either length or quality of life measured in utility. Where available, publications using data from the UK were included; where no UK data were available, data from North America or Australia were deemed sufficient. See Figure 6.4 for a flow chart of the literature review.

Figure 6.4 Focused literature search for data on length and quality of life following out-of- hospital cardiac arrest

The two questions required for the decision tree and Markov model were:

 What is the proportion of CPC 1-2 and CPC 3-4 amongst OHCA patients discharged from hospital alive?

 What is the quality and length of life of survivors of OHCA with CPC 1-2 and CPC 3-4, respectively?

Proportions of CPC 1-2 and CPC 3-4 amongst survivors to hospital discharge

Gates et al. (2017) provide the most current and relevant information in their randomised controlled trial and embedded economic analysis of mechanical CPR during OHCA in the UK. CPC was measured at 3 months after discharge from hospital, with 245 of 272 (90%) of patients achieving a CPC 1-2. This is somewhat higher than the 78% and 85% of CPC 1-2 survivors reported by Petrie et al. (2015a) and (Phelps et al., 2013) in a UK and Australian patient population, respectively. However both of these studies measured CPC at hospital discharge, rather than at 3 months after discharge. Figure 6.5 shows that patients discharged from hospital after OHCA with CPC 3-4 have a significantly higher mortality in the first months post-discharge when compared to those discharged with CPC 1-2. Additional data from Phelps et al. (2013) at 12 months post-discharge demonstrate that, of the 804 patients who survived to 1 year post-discharge, 90% were discharged from hospital with CPC 1-2. I therefore decided that 85% was a robust estimate for the percentage of OHCA survivors discharged with CPC 1-2.

Figure 6.5 Kaplan Meier survival curve of patients discharged from hospital after out-of- hospital cardiac arrest, according to Cerebral Performance Category (CPC) Phelps et al. (2013), with permission from Critical Care (license number 4252320818003)

Length of life of survivors with CPC 1-2 and CPC 3-4

Phelps et al. (2013) also provide the most detailed description of long-term survival following OHCA, according to CPC at discharge from hospital, including person-years for the first year and the following 4 years after hospital discharge. See Table 6.3 for a summary of survival rates and person-years.

Table 6.3 Survival rates and person-years according to Phelps et al. (2013)

Year 1 Year 1-5 Survival (95% CI) Person-years (per 100 discharges*) Survival (95% CI)** Person-years (per 100 discharges per year*) CPC 1-2 87.4% (85.0% - 89.5%) 78.5 84.6% (81.8% - 87.1%) 241.1 CPC 3-4 51.7% (43.7% - 59.6%) 9.39 79.0% 68.5% - 86.6%) 24.7

*Assuming 85% of survivors to discharge have CPC 1-2 and 15% have CPC 3-4 at hospital discharge. ** Percentage of survivors at 5 years per survivors at 1 year.

CPC: Cerebral Performance Category

The information in Table 6.3 was used to model mortality during the first 5 years after discharge from hospital following OHCA (Phelps et al., 2013). Survival rates after 5 years

following discharge from hospital after OHCA have been shown to be the same as those of the standard population (Andrew et al., 2017; Phelps et al., 2013). Mortality rates were therefore obtained from the National Life Tables for England (Office for National Statistics, 2016) and applied to the patient cohort from 5 years onwards. This resulted in an overall survival rate of 65.0% and 50.0% (CPC1-2) and 36.0% and 27.8% (CPC3-4) at 10 and 15 years post-discharge, respectively.

Quality of life

In their RCT which recruited a total of 4,471 patients with OHCA between 2010 and 2013 in four UK ambulance services, Gates et al. (2017) reported health-related quality of life (HRQL) using the EuroQol – five-dimension descriptive system (EQ-5D). The EQ-5D has been validated for measuring HRQL and for use in economic evaluations in a variety of conditions, including cardiac arrest (Payakachat, Ali and Tilford, 2015). Gates et al. (2017) converted their findings to health-state utilities using the UK tariff. Using a combination of decision tree and Markov model similar to the one used in this research, they calculated a utility of 0.75 and 0.47 for the CPC 1-2 and CPC 3-4 group, respectively. Use of EQ-5D for the calculation of utility is recommended by NICE (National Institute for Health and Care Excellence, 2013).