Costs and expected gain in lifetime health from intensive care versus general ward care of 30,712 individual patients: a distribution-weighted cost-effectiveness analysis

Clinicians, hospital managers, policy makers, and researchers are concerned about high costs, increased demand, and variation in priorities in intensive care [1‐7]. Population-level studies find that, on average, admission of critically ill patients to intensive care units (ICUs) versus non-intensive care is cost-effective [8‐10]. It does not necessarily follow that every individual ICU admission represents efficient resource use; the critically ill are a diverse group of patients [11]. However, certain characteristics among the patients may justify higher resource use. Society may accept higher costs per unit of health gain among severe cases compared to less severe cases [12‐14]. A better understanding of individual variation in terms of severity of disease, cost, and effects of ICUs is therefore needed.

Analysis of the extra costs and health gains associated with ICU admission versus some next-best treatment option for the critically ill could inform ICU priorities [15, 16]. Robust estimates of the real health benefits and extra costs of ICU admission are unavailable because the alternative to ICU admission—the appropriate comparator—is hard to quantify since randomised controlled trials (RCTs) cannot be performed due to ethical reasons [9, 15, 17]. Previous cost-effectiveness analyses (CEAs) of intensive care have used average cost-effectiveness ratios or assumed that patients incur no costs and die if not admitted to the ICU [8, 18‐20]. Two cost-effectiveness analyses from Europe compared ICU admission to non-intensive care [9, 10]. Acknowledging methodological constraints, the authors concluded that ICU admission represents good value for money and can compare with other essential health care services. Few cost-effectiveness studies of ICU admission take into account important individual variability in terms of the patients’ death risks, age, treatment needs and resource use, and potential survival benefit of admission [11, 21‐24].

Rationing intensive care is inevitable and should follow explicit criteria such as cost-effectiveness [25, 26]. Cost-effectiveness must be balanced against other concerns that may conflict with or are not always captured by the use of CEAs—e.g. “rule of rescue”—the imperative to save patients from imminent death, respect for autonomy, or disease-related severity [14, 27, 28]. Expected lifetime health can be used to compare severity across diseases and individuals and is discussed as an explicit criterion for priority setting in Norway and endorsed by the World Health Organization under the name of priority to the worse off [13, 29]. A high risk of imminent death, profound pain, functional deterioration or reduced health-related quality of life, early rather than late onset of illness in life, and long duration of illness or disability will each, or together, contribute to a high degree of severity of disease. The ethical rationale is that patients with higher severity of disease deserve extra priority; improving their situation may reduce inequalities in expected lifetime health between patient groups with different conditions [30, 31]. White et al. proposed that a similar lifecycle argument could guide priorities in life support during a public emergency [32]. An explicit concern for severity of disease is rarely integrated into CEAs [33].

A substantial increase in ICU capacity may be necessary in the coming decades because of increased demand due to an ageing population and changing disease patterns [7]. Since CEA is increasingly used to judge the value of different types of health care (predominantly new interventions), we sought to evaluate the opportunity cost of ICU admission compared with some next-best alternative. It is impossible to remove confounding from such comparisons since we do not have good data from clinical trials. This modelling exercise necessarily requires a number of general assumptions about the differences in short- and long-term mortality, health-related quality of life, and resource use between those admitted or hypothetically not admitted to the ICU.

The aims of this paper are to describe the extra costs and expected health gains associated with admission to the ICU versus the general ward for 30,712 patients, and the variation in cost-effectiveness estimates among subgroups and individuals, and to perform a distribution-weighted economic evaluation incorporating extra weighting to patients with high severity of disease.

The main finding of this modelling study is that the average health gain per patient from ICU admission was 1.6 QALYs at an incremental cost of €18,700 compared with treating the same patients in general wards. The resulting mean ICER of ICU admission was €11,600/QALY, and would be considered cost-effective over common cost-effectiveness thresholds, including the Norwegian setting at a threshold range of €22,000–64,000/QALY. The probability of ICU admission being cost-effective was 95% at a threshold of €22,000/QALY. By contrast, the disaggregated results demonstrate a much greater uncertainty given by the range in probability of cost-effectiveness due to individual variability and uncertainty around input parameters. There was a 50% probability that ICU admission was cost-effective for 85% of the patients at a threshold of €64,000/QALY. At this level of uncertainty, about one sixth of the individual patient admissions were estimated to be not cost-effective. Half of these, and consequently 92% of all individual admissions, had an acceptable relationship between resource use and health gains in the distributional analysis giving extra weight to health gains for those with higher severity of disease (probability = 50%).

Our study contributes in three main areas. First, it is the first study from a Nordic country that estimated the difference in costs and QALYs between ICU admission and ward care. Second, we account for the heterogeneity and distribution of incremental costs and health gains in a nationwide ICU population. We modelled individual admissions using real data from NIR to describe variability across admissions; the variation in the cost-effectiveness across individuals is due to observed differences in their profile of length of stay, short-term risk of death and age, and effect size. Third, in the explorative distribution-weighted evaluation we adjusted the ICER by severity of disease in terms of lifetime QALYs. ICU admission was more cost-effective among high-severity patients (lifetime QALYs <50). Our study is among the first to incorporate a concern for severity of disease into a standard cost-effectiveness model by giving higher weight to health gains for the critically ill with fewer lifetime QALYs if not admitted [58, 62]. This approach is relevant to health care systems where priority setting aims at the twin goals of maximizing healthy life years for all and reducing inequalities in lifetime health across individuals or patient groups [13].

The main conclusion of the standard CEA is similar to other studies from Europe that sought to estimate the difference in costs and health outcomes between adult patients accepted and rejected to the ICU. The mean extra costs of ICU versus non-ICU care per patient of €18,700 are higher than previous reports. Edbrooke et al. estimated the extra hospital costs to be €4886 in 2005 (multicentre, Europe) [9]. Ridley and Morris included extra hospital costs plus discounted lifetime average health care costs of extra survivors due to ICU support (£8902 in 2003, UK) [10]. NIR lacks data about the long-term economic consequences of critical illness. We therefore applied available data from Scotland on hospital resource use in ICU survivors up to 5 years after the initial admission [48]. ICU admission after planned surgery was less cost-effective than after acute surgery and medical admissions. This can be explained by a lower hospital mortality, lower SAPS II and, therefore, lower expected health benefits (1.1 QALYs) from ICU admission after planned surgery (Tables 2 and 3). Edbrooke et al. found that patients with low, medium, and high predicted short-term risk of death have variable effect from ICU admission, and concluded that ICU admission is more cost-effective with increasing severity as measured by SAPS II [9]. One strength of our study is that we varied the survival benefit according to SAPS II and estimated expected outcomes for individual admissions. The distribution of individualized ICERs shows that behind the average results for the population as a whole there are a number of patients where ICU admission is predicted not to be cost-effective. In the probabilistic analysis, 15% of the patient profiles had a median cost to effect ratio of more than €64,000/QALY. Currently, new health care technologies such as cancer immunotherapies with an ICER above this limit will not be reimbursed. Prioritising interventions with such high ICERs may displace existing health care activities that benefit patients more.

We note some limitations of our analysis. We rely on crude estimates of the incremental costs of ICU versus ward care. We used the perspective of a decision maker allocating resources within the hospital sector. We tried to capture differences in direct costs of care for and health benefits to the critically ill patients, but acknowledge that there might be differences in the quality of end-of-life care and in the burden of critical care and illness felt by patients and family members in the ICU compared with the ward group [27]. The assumptions regarding the hospital length of stay and costs for both treatment options may under- or overestimate the difference in hospital costs. The length of the hospital stay was unavailable in NIR. The probability that ICU admission was cost-effective at a threshold of €22,000/QALY was high in the main analysis and scenario analyses, despite the uncertainty around possible input parameters and the broad ranges used. In scenario analysis, the extra costs of ICU admission increased for medical admissions (€18,400) and after acute surgery (€21,500) when we reduced the differences in daily costs throughout the ICU or ward stay. The extra cost decreased in admissions after planned surgery where ICU LOS was shorter compared with the other types of admission (€15,700; Table 2). A major challenge is the hypothetical nature of the comparator. Since RCT data are not available, all analysis must rely on counterfactual assumptions. The assumptions about the effect of ICU admission were informed by observational studies where patients refused and accepted to the ICU are likely to differ in case mix. We suspect effect estimates from these studies would underestimate the mortality reduction among those who benefit the most from ICU admission in a nationwide ICU population. We sought to compensate for lack of robust effect data by assuming that those with mid- to high-range SAPS II benefit the most, as illustrated in Fig. 1 [21]. Our study does not capture well how hospitals deal with levels of care below high-level ICU (multi-organ support), but above a regular ward. However, the issue is partly taken into account through the uncertainty range of model parameters (see Additional file 2 or effect and costs assumptions). The adjustment for excess long-term mortality and reduced HRQoL in hospital survivors could be stratified by patient categories (acute respiratory distress syndrome (ARDS), post-sepsis, cancer, etc.). We did not have such information to classify the NIR patients, and do not know if making more detailed distinctions among patients would impact on overall results or the distribution of individualized ICERs.

The methodology using a large individual-level (ICU) database to account for sub-group heterogeneity and individual variability of costs and expected outcomes is relevant to any country considering cost-effectiveness information in priority setting to improve health as much as possible. The cost-effectiveness results may at least be generalizable to other Scandinavian countries as the case mix of our ICU populations and ICU and health systems organisation are similar [59]. The study provides useful contextual information about the relationship between extra costs and health gains associated with current ICU care compared to other high-cost health care competing for the same resources [60]. Introducing new high-cost technologies with less favourable cost-effectiveness ratios than ICUs may have opportunity costs in terms of healthy life years forgone. The study does not directly inform a priority-setting task of choosing between specific future policy options since it does not assess a new intervention, such as scaling up ICU care by admitting more or other types of patients from the at-risk population before triage, or modernizing care of the critically ill by introducing intermediate care units [3, 17].

Even if patient groups may be denied access to an intervention because the mean group-level cost-effectiveness ratio of the intervention is above an accepted threshold value, clinicians may identify individual cases who are likely to benefit more and/or cost less than the expected average [61]. In intensive care, our findings suggest that we have the opposite case: overall ICU admission versus ward care is predicted to be cost-effective, but a number of patients are expected to have ICU admissions that are not cost-effective. If we can identify these types of individual ICU patients, should we then deny them treatment on grounds of cost-effectiveness? We believe drawing such a conclusion is premature. Given the difficulty of ICU triage and the uncertainty around selecting those who can benefit the most from admission to a reasonable cost, we may have to accept the great variability in individualized ICERs, and leave judgement to clinicians and clinical guidelines, as long as the average results can be considered cost-effective. Clinicians must be cautious about the assumptions underlying the individualized cost-effectiveness estimates and their use for a particular individual [11].