Supplemental Digital Content
Costs and cost-utility of critical care and subsequent health care - multicenter prospective study
Sakari Jukarainen, MD, PhD,
1*Henriikka Mildh, MD,
1*Ville Pettilä, MD, PhD,
1Unto
Häkkinen, PhD,
2Mikko Peltola PhD,
2Tero Ala-Kokko, MD, PhD,
3Matti Reinikainen,
MD, PhD,
4Suvi T. Vaara, MD, PhD
1,5Table of Contents
Finnish Health Care System and its Funding ... 2
Study Population and Ethics Approval ... 2
eFigure 1. Flow Chart With Number of Patients at Each Stage of Follow-up. ... 3
Evaluation of Costs ... 4
eTable 1. Health Care Sector Costs and Other Expenditure Included in the Analyses ... 5
eTable 2. Major Health Care-related Expenses Not Included in the Analyses ... 5
Evaluation of Health-related Quality-of-life and Multiple Imputation ... 6
eTable 3. Differences Between Patients with Observed vs. Missing Follow-up EQ-5D Data. ... 7
eTable 4. Variables Included in the Multiple Imputation Model ... 8
Comparison of Observed and Imputed Values ... 8
eFigure 2. Kernel Density Plot of EQ-5D Index Values at Follow-up, Observed vs. Imputed ... 9
eFigure 3. Cost-utility estimates for different assumed absolute risk reduction values ... 10
Supplemental Statistical Methods ... 10
Supplemental references ... 11
Supplemental Tables and Figures ... 13
eTable 5. Main Diagnosis Groups at Index Admission According to Acute Physiology and Chronic Health Evaluation II. ... 13
eTable 6. Index Hospitalization Resource Use and Costs for All Patients ... 13
eTable 7. Mean Total Costs in Patient Subgroups ... 14
eTable 8. Types Health Care-related Costs During the Three-year Follow-up from the Social Insurance Institution. ... 16
eTable 9. QALYs and Cost-utilities for Index Hospitalization Episode Costs ... 17
eTable 10. QALYs and Cost-utilities for Patients With SAPS II Score Over 33 ... 18
eFigure 4. Resource Use of Previously Healthy Subjects Under 50 Years at Enrollment and Matched Hospital Controls ... 19
eFigure 5. Univariate Sensitivity Analyses of Cost-utility Estimates (3-year QALYs Divided by Three-year Costs) ... 20
eFigure 6. Univariate Sensitivity Analyses of Cost-utility Estimates (Predicted Lifetime QALYs Divided by Three-year Costs) ... 21
Finnish Health Care System and its Funding
The Finnish health care system is a three-level system, where around four fifths of the funding comes from public expenditure (1). The Constitution of Finland declares that public authorities must guarantee adequate social, medical, and health services for everyone, and promote the health of the population. Primary care is organized by local authorities of each municipality, or joint municipalities together, and funded by the municipality, mainly through tax revenue and government subsidies. Some primary care centers have wards which provide inpatient care and rehabilitation services for patients not needing more specialized care.
Secondary care offers more specialized medical care in hospitals. Mainland Finland has 20 hospital districts and each municipality belongs to a hospital district. In addition, each hospital district belongs to one of the five university hospital catchment areas. These are funded by municipalities with government subsides. Tertiary care is provided by the five university hospitals and 15 central hospitals. All of the central hospitals have intensive care units (ICUs) capable of providing state-of-the-art critical care for most of the patients. Complex cardiac surgery is unavailable at central hospitals, thus patients requiring extracorporeal oxygenation are transferred to a university hospital. Most neurosurgical procedures are concentrated in university hospitals, and Helsinki University Hospital provides all organ transplant surgery. Seven of the FINNAKI study ICUs were affiliated to university hospitals, and 10 were central hospitals.
In addition to publicly funded health care, there is an increasing number of private sector providers, such as enterprises, non-governmental organizations, and foundations, which sell their services to local authorities, joint municipal authorities, or directly to consumers. The private sector accounts for approximately a quarter of all Finnish social care and health care costs, and provides services primarily in assisted living services, skilled nursing facilities, occupational health care, and dental care.
All Finnish residents are covered by a statutory national health insurance, coordinated by the Social Insurance Institution of Finland (SII). The SII pays reimbursements for private medical fees, outpatient medications, occupational health care, and travel expenses related to health care or rehabilitation, and thus collects data on the costs of all these reimbursed services. The Social Insurance Institution also provides sickness allowances, rehabilitation subsidies, and disability pensions for persons without working history.
Study Population and Ethics Approval
Altogether 5853 admissions to FINNAKI study ICUs were screened for eligibility (2). Patients who 1) were intermediate care patients, 2) had end-stage renal disease and were on chronic dialysis, 3) were readmitted and had received RRT during their previous admission, 4) were not permanent Finnish residents, 5) were organ donors, 6) were transferred from another ICU and had already been included for the study data collection periods of five days, or 7) lacked sufficient language skills for providing informed consent were excluded. From the 2901 patients enrolled in the FINNAKI study (2), we excluded those with missing social security code (n=32) necessary for completing the registry follow-up of resource use. Figure 1 presents the patient flow. The sample size for FINNAKI study was computed for the main outcomes (incidence of AKI and 90-day mortality).
The Ethics Committee of the Department of Surgery at Helsinki University Hospital gave approval for the study protocol and the use of deferred consent with written informed consent obtained from the patient or proxy as soon as possible (reference number 18/13/03/02/2010). The Finnish National Institute of Health and Welfare (the THL) approved collection of data of deceased patients from medical records if an informed consent could not be obtained and the use of their data of follow-up resource use and costing. Additionally, the THL provided consent for using matched hospital controls. The SII approved use of their data of costs for the patient and societal costs.
eFigure 1. Flow Chart With Number of Patients at Each Stage of Follow-up.
Evaluation of Costs
All costs were converted to 2016 euros (€) using the price index of public expenditure for the health care sector from Statistics Finland (3). 2016 euros were converted to US dollars ($) using the purchasing power parity of 2016 from OECD (€ cost multiplied by 1.10589) (4). eTable 1 provides an overview of the different health care sector costs included and eTable 2 lists the costs not covered in this analysis.
Critical Care Costs
Costs for the index ICU episode were estimated using the therapeutic intervention scoring system (TISS) by multiplying the total TISS score of each episode with local ICU-specific costs per TISS score estimates. The costs per TISS estimates were previously formed for each ICU by dividing the total costs by the amount of total TISS points in a time period. Operating costs based on budgeting and invoicing data, from personnel, materials, medical equipment, fluids, drugs, blood products, laboratory tests, and radiology were included. Indirect fixed cost sources such as maintaining the hospital buildings or general administrative costs were not included.
Primary and Home Care Costs
Complete data on primary care outpatient visits and home care were obtained from the nationwide Register of Primary Health Care Visits (AvoHILMO) maintained by the National Institute for Health and Welfare (THL) (5). The register covers publicly provided doctor's visits, nurse's visits, occupational health care provided by municipal health centers, physical therapy, occupational therapy, home care (with incomplete information in some municipalities), and other services. Costs were estimated by linking unit cost estimates of 2011 published by the THL (6) to patient-level information from the Register of Primary Health Care Visits. Costs due to privately provided care were obtained directly through the reimbursement data of the SII. Dental care was excluded from the analyses.
Occupational health care provided by the private sector was not included in any of the data, but as it accounts for only around 3.4% of total health care spending in Finland (1) (and is mainly used for the primary care of most of the employed), omitting those services is unlikely to have a significant impact on the precision of our estimates of costs.
Secondary and Tertiary Care Costs
We obtained complete data on all secondary and tertiary care outpatient visits and inpatient episodes (and primary care ward episodes) from the nationwide Care Register for Health Care (HILMO) maintained by the THL (7, 8). The register contains information on all secondary and tertiary care outpatient visits, inpatient ward treatment episodes, and surgical procedures in Finland. Episodes related to obstetrics were excluded.
Costing for primary care, secondary care, and tertiary care ward episodes was performed by first running a diagnosis related group (DRG) classifier (NordDRG 2011) on the data. Published day-specific costs (6) for each DRG group were linked to the data by DRG classes and multiplied by the length of episodes in days. For episodes not classifiable into any DRG, national unit cost estimates based on episode type and specialty (6) were used. Although primary care ward episodes are technically a part of primary care in Finland, we analyze their costs together with secondary and tertiary care ward episodes, since they provide comparable services.
Services and Reimbursements Covered by the Social Insurance Institution of Finland
SII provided data on reimbursed medical- and sickness-related costs. These include medicine expenses of reimbursed outpatient medications (covering the majority of all medically indicated medicine expenses), private doctor visit costs, private laboratory and imaging costs, and travel costs related to medical care and rehabilitation. Information on sickness allowances, rehabilitation allowances, and disability pensions (for those without previous work history) were also provided. The data contained both the part reimbursed by the SII and the part paid by the patient. In the analyses, we included both the reimbursed costs and costs paid by patients together to reflect the total costs regardless of the payer. The SII provided their data stratified according to different pre-specified groups (the groups in Table 3 in the main manuscript). As we lacked individual patient-level data, measures of deviation could not be estimated for the reimbursement data. When reporting SDs, medians, IQRs, and 95% confidence intervals (CI) of aggregated mean costs, including SII costs (Table 2 and 3), first the SDs, medians, IQRs, and 95% CI of the aggregated costs excluding SII costs were calculated. The medians, IQRs, and 95% CIs were adjusted by adding the mean SII cost to them (assuming that the costs were evenly distributed within groups). The SD or 95% CI of costs not including the SII costs was reported and not adjusted. This is unlikely to meaningfully affect the estimates, since the reimbursements accounted for around a tenth of total costs.
eTable 1. Health Care Sector Costs and Other Expenditure Included in the Analyses
Cost type Data source Costing method
Primary care Doctor's visits Nurse's visits
Outpatient rehabilitation Home care
Register of Primary Health Care Visits (AvoHILMO)
Service type specific unit costs (6)
Secondary/tertiary care Hospital ward episodes Primary care ward episodes Outpatient visits
Surgery Consultations
Inpatient rehabilitation
Care Register for
Health Care (HILMO) DRG-specific unit costs, if DRG not applicable, service-type-specific unit costs (6)
Critical care FINNAKI study data Provider-specific TISS-based costs from the Finnish Intensive Care Consortium
Reimbursed costs Outpatient medication Private medical fees
Health care -related travel costs New disability pensions for persons without working history Rehabilitation subsidies Sickness allowances
Social Insurance Institution of Finland data
Direct costs from the data
All costs contain both out-of-pocket costs and costs paid by third parties.
TISS; Therapeutic Intensity Scoring System
eTable 2. Major Health Care-related Expenses Not Included in the Analyses
Cost type Estimated % of costs out of total health care
sector costs Assisted living services and nursing home care 14.1
Dental care 5.9
Privately provided occupational health care 3.4 Outpatient prescription medications without
reimbursement 0.6
Based on 2015 total costs of the health care sector in Finland (1)
Assisted living services and care at nursing home are provided by municipalities or private sector, and we do not have information on costs of these services. However, in Europe, such care that is provided in skilled nursing homes in the US is more frequently provided in long-term hospitals (9, 10) that were included in our data. Moreover, of Finnish patients aged 80 or over admitted to critical care, about 11% did not live at home, and of those surviving one year, 84% had returned to home (11). Thus, although costs related to assisted living and nursing home care
constitute 14% of total health care sector costs in Finland, patients requiring these services are rarely admitted to ICUs thus resulting in minor underestimation of total costs.
Evaluation of Health-related Quality-of-life and Multiple Imputation
Health-related Quality-of-life
Health-related quality-of-life (HRQoL) was measured with the EQ-5D-3L questionnaire (12), which is a generic measure of HRQoL. The EQ-5D-3L questionnaire contains five dimensions: mobility, self-care, usual activities, pain/comfort, and anxiety/depression that are evaluated on a three-level scale ("no problems," "some problems," or
"severe problems"). Assessment of HRQoL after critical care episodes is a routine measurement in all member ICUs of the Finnish Intensive Care Consortium and ICU nurses are trained to perform the questionnaires.
The follow-up EQ-5D-3L questionnaire was presented by phone or letter to survivors approximately at 7.5 months (median 227 and IQR 212-252 days) after the index ICU admission. Immediate family members responded on some of the patients' behalf (10%). The answers to the EQ-5D-3L questionnaire were converted to EQ-5D index values by weighing the different dimensions according to health state preferences derived from the Finnish general population (13). The index values range from 0 (death) to 1 (perfect health), with 0.155 corresponding to lowest HRQoL in alive patients. We assumed that all patients had the lowest possible HRQoL value at the start of the index episode (0.155), since they were admitted into an ICU (similarly as in the CESAR trial (14)).
Three-year Quality-Adjusted Life-Years
We used the EQ-5D index values to calculate 3-year Quality-Adjusted Life Years (QALYs) at the 3-year follow-up according to the following methods:
i) Patients who died prior to six months of follow-up:
The EQ-5D index value was assumed to be 0.155 (minimum for an alive patient) at baseline. QALYs for this group were calculated by multiplying the EQ-5D index value of 0.155 with the survival time of each patient in years. The mean survival in this group was 26 days, therefore different methods for calculating the QALYs will have negligible effects on the results.
ii) Patients who died after the follow-up EQ-5D-3L, but before the end of the 3-year follow-up:
The EQ-5D index value was assumed to be 0.155 at baseline. The QALYs were calculated as the sum of 1) the area under curve of the baseline EQ-5D index value (0.155) linearly interpolated to the value of the follow-up EQ-5D index value and 2) the area under curve from the follow-up EQ-5D index value until the day of death (assuming constant EQ-5D index values), discounting 3% from every year after the first year (15).
iii) Patients who were alive at the 3-year follow-up:
The EQ-5D index value was assumed to be 0.155 at baseline. The QALYs were calculated as the sum of 1) the area under curve of the EQ-5D index value at baseline (0.155) linearly interpolated to the value of the follow-up EQ-5D index, and 2) the area under curve from the follow-up EQ-5D index value to 3 years (assuming constant EQ-5D index values), discounting 3% from every year after the first year (15).
Predicted Lifetime Quality-Adjusted Life-Years and Cost-utility
We additionally calculated the predicted lifetime QALYs similarly as above, except that for the patients that survived past the 3-year follow-up, we calculated the area under curve from the follow-up EQ-5D index value to the predicted time of death by multiplying the predicted lifetime with the follow-up EQ-5D index value, discounting 3% per year.
Apart from discounting, the HRQoL was thus assumed to be constant after the measurement (16). The time of death was predicted using national Finnish age- and sex-specific statistics on life-expectancy (conditional on the patient having survived to a certain age) provided by Statistics Finland (17). Thus, the life-expectancy in patients surviving at least 3 years was assumed to correspond to that of the general population. This might result in some overestimation of the predicted lifetime QALYs, since the patients were selected conditional on having been admitted to an ICU.
However, the mortality rate in Finnish critical care patients has been shown to be comparable to that of the general population after two years from the ICU admission (18), which suggests that our predicted lifetime QALYs are probably not significantly biased due to possible excess mortality in the ICU patient population after the 3-year follow- up.
The three-year and predicted lifetime cost-utilities were calculated by dividing the sum of total costs with either the sum of observed three-year QALYs or the sum of predicted lifetime QALYs. We obtained 95% confidence intervals
for the cost-utility estimates by bootstrapping the estimates (19) with 10000 bootstrap samples in each multiply imputed dataset to produce estimates of standard errors, which were combined across the imputations (20).
Differences Between Individuals With Observed and Missing EQ-5D-3L Responses.
The follow-up EQ-5D-3L data was missing from 711 patients out of 2142 alive (33.2%) at 6 months of follow-up.
The patients with missing EQ-5D-3L values differed in several characteristics from those with observed values (eTable 3). At follow-up, patients with missing EQ-5D values were younger (P<.001), were more likely to use psychiatric services in primary care (P=.001) and secondary care (P< .001) during the follow-up period, and had a higher number of comorbidities (P<.001) (eTable 1). Grouped by KDIGO AKI stage, there were no significant differences in the amount missing EQ-5D values (P=.17). Grouped by APACHE II diagnosis group, there were significant differences in missing values (P<.001).
eTable 3. Differences Between Patients with Observed vs. Missing Follow-up EQ-5D Data.
Observed EQ-
5D (n=1431) Missing EQ- 5D (n=711)
Continuous variables Mean (SD) SMD P value
Age (at baseline) 60.6 (15.6) 54.5 (18.2) 0.36 <.001
SAPS II score 35.4 (13.9) 34.6 (14.6) 0.06 .20
Ordered categorical variables P value
No. comorbidities (0 to 7) 1.29 (1.29) 1.05 (1.17) 0.19 <.001
AKI stage (1 to 5)a 0.77 (1.33) 0.65 (1.20) 0.12 .17
Binary variables No. (%) % difference
(95% CI) P value
Female 534 (37.5) 247 (34.8) -2.6 (-7–1.8) .25
Mechanical ventilation 954 (66.9) 452 (63.8) -2.9 (-7.3–1.5) .16 Psychiatric primary care after 8
months 174 (12.3) 123 (17.9) 5.6 (2.2–9.1) .001
Psychiatric secondary care after
8 months 239 (16.8) 184 (26.8) 9.9 (6–13.9) <.001
Home care after 8 months 569 (40.1) 252 (36.7) -3.4 (-7.9–1.1) .15
SMD, standardized mean difference. P values for continuous, ordered categorical, and binary variables are from t-tests, Kruskal- Wallis tests, and χ2 tests respectively.
a 1: No acute kidney injury (AKI), 2: AKI stage 1, 3: AKI stage 2, 4: AKI stage 3 without renal replacement therapy, and 5: AKI stage 3 with renal replacement therapy.
Multiple Imputation of Missing Data
EQ-5D data was assumed to be missing at random (MAR) in the context of examined patient characteristics. Missing data in EQ-5D index values at baseline and follow-up were imputed with multiple imputation using chained equations (MICE) (21). Variables used in multiple imputation are presented in eTable 4. As the MICE algorithm requires that all variables used in imputation are also imputed, variables not used for analysis after imputation were imputed. Fifty imputed datasets were created with 20 iterations. Convergence was monitored through plotting the means and standard deviations of imputed variables in each different imputation sequence set against the iteration number and was achieved during the first few iterations as variance between different imputation sequences was no greater than the variance within each individual sequence. The EQ-5D index values were imputed using predictive mean matching (PMM), which is a general purpose imputation method that imputes missing values with values sampled from the observed values conditional on the covariates used to inform the imputation. The estimates and statistical tests on the imputed data sets were combined using Rubin’s rules (22).
eTable 4. Variables Included in the Multiple Imputation Model
Variable Variable type Imputation
method n missing
(%)
Follow-up EQ-5D index Continuous PMM 711 (33.2*)
Age Continuous Not imputed 0
Simplified Acute Physiology II
score Continuous Not imputed 0
Study site Categorical (17 levels) Not imputed 0
AKI stage Ordered categorical (6 levels) Not imputed 0
Survival time (1 to 12 months in months, or more than 12 months)
Ordered categorical (13
levels) Not imputed 0
No. comorbidities Ordered categorical (8 levels) Not imputed 0
APACHE II diagnosis group Categorical (19 levels) PMM 1
Sex Binary Not imputed 0
Mechanical ventilation Binary Not imputed 0
Psychiatric primary care after 3
months Binary Logistic regression 677**
Psychiatric secondary care
after 3 months Binary Logistic regression 677**
Home care after 3 months Binary Logistic regression 677**
AKI; Acute kidney injury; APACHE; Acute Physiology and Chronic Health Evaluation; PMM, predictive mean matching; *Percentage does not include those who died before six months of follow-up. **Missing values are all from patients that died earlier than 3 months from admission.
Variables were selected for use in the imputation model if they were expected to either: 1) correlate with EQ-5D index values, or 2) correlate with probability of missing values in the EQ-5D-3L questionnaires, as recommended in White et al. (23) Whether the patient utilized psychiatric primary or secondary services after 3 months after primary admission was included because psychiatric illnesses are associated with lower HRQoL and higher probability of missing values at follow-up. Whether the patient utilized home care services after 3 months after primary admission was included as a measure of functional capacity and thus assumed to be associated with HRQoL and probability of missing values at follow-up. These last three variables were set as missing for individuals who did not survive past 3 months from primary admission. This was done to avoid classifying those patients, who did not utilize the services due to dying early and thus having no chance to utilize them, with patients who did not utilize the services despite having the chance. Three months was chosen because higher initial mortality plateaued in the patients at approximately that point (Figure 1).
Comparison of Observed and Imputed Values
Imputed values for the EQ-5D index at follow-up had a similar distribution to the observed values (eFigure 1). There were no significant differences between means of observed and imputed follow-up EQ-5D index values (P=.99) with mean (SD) in observed 0.799 (0.197) and in imputed 0.808 (0.202).
eFigure 2. Kernel Density Plot of EQ-5D Index Values at Follow-up, Observed vs. Imputed
Lines depict kernel density estimates for the distributions. Blue lines are observed values. Red lines are multiply imputed values with each line corresponding to a different imputed dataset (n=50).
Sensitivity Analyses
As the covariates include various measures of disease severity and baseline patient characteristics, the imputed values are on average probably close to the true values (missing at random). We performed additional univariate sensitivity analyses. First, under the assumption that EQ-5D index data was missing not at random (MNAR), where we assumed that the imputed missing EQ-5D index values were 20% lower or higher than those observed (24). The imputed EQ- 5D values were multiplied by 0.8 or 1.2 at each imputation step and constrained not to be lower than 0.155 or higher than 1. Additionally, we examined a similar scenario where EQ-5D index values for all subjects were 20% lower or higher. For the third scenario, we varied the QALY discount rate from 0 to 5%. For the fourth scenario, we assumed that the predicted time of death used to calculate predicted lifetime QALYs was 10% earlier or later from the end of follow-up. The effect of these univariate sensitivity analyses on the 3-year and lifetime cost-utilities are presented in eFigure 5 and 6 as tornado plots.
Secondary Sensitivity Analysis of Overall Cost-utility
We performed an additional analysis of overall cost-utility of critical care based on same methodology as in Ridley et al. 2007 (25). This was done to assess how the estimate of overall cost-utility from our approach using the 100%
mortality assumption compares to that of an alternative modeling-based approach without a 100% mortality assumption. To replicate the analyses of Ridley et al. (25) we performed the following calculations:
1. We estimated the number of incremental QALYs gained from ICU admission compared to counterfactual non-ICU care, assuming an absolute risk reduction (ARR) for mortality of 0.174, by multiplying the mean discounted predicted QALYs of hospital survivors with the assumed ARR due to ICU admission: 9.82 QALYS ´ 0.174, resulting in 1.71 incremental QALYs gained.
2. Next, we estimated incremental hospital costs due to ICU admission by calculating the incremental mean cost of ICU care per day by subtracting the mean cost of a non ICU ward day in our data ($765) from the mean cost of an ICU day ($3524), and multiplying the incremental cost of ICU care ($2760) by the mean length of stay in the ICU (4.27 days), resulting in an average incremental cost of ICU admission of $11 782 per episode.
3. Also, the long-term health care costs of the extra survivors were estimated by multiplying the life years gained per patient treated (2.54 years) by average annual costs of the general population ($1153), arriving at $2932 incremental costs per patient treated due to health care costs of the extra survivors.
4. Finally, we estimated the incremental cost effectiveness ratio of ICU admission by dividing the sum of incremental hospital costs due to ICU admission and the long-term health care costs of extra survivors by the incremental QALYs gained to arrive at an estimate of the overall cost-utility of ICU admission: ($11 782 +
$2932) / (1.71 QALYs) = 8610 $/QALY. See (eFigure 3) below for similar $/QALY calculations for ARRs ranging from 5 to 30%.
Using the same methodology and assumptions as in Ridley et al. 2007 (25), the estimate of the cost-utility of ICU admission in all patients (8610 $/QALY) happens to be really close to our primary estimate using the 100% mortality assumption (8460 $/QALY). This method, however, relies on some strong assumptions. Especially, the ARR of hospital mortality due to ICU admission is hard to estimate, since observational studies on admitted and rejected patients inevitably suffer from bias. Moreover, this method does not allow for a subgroup analysis, since the estimates of ARR of hospital mortality due ICU admission are even more difficult to estimate for patient subgroups.
eFigure 3. Cost-utility estimates for different assumed absolute risk reduction values
Supplemental Statistical Methods
Analyses were carried out using R version 3.3.0 (26) with R studio version 1.0.153 (27) and SPSS version 22 and 24.
Following R packages were used: dplyr 0.7.4 (28) and tidyr 0.6.3 (29) for data transformation. ggplot2 2.2.1, (30) lattice 0.20-33, (31) and survminer 0.4.1 (32) for plotting. mice 2.30 (33) for multiple imputation. boot 1.3-18 (34) for bootstrapping. Hypothesis tests were 2-sided, with α=.05. The presence of non-overlapping 95% confidence intervals of means between two estimates was interpreted as a statistically significant difference.
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Supplemental Tables and Figures
eTable 5. Main Diagnosis Groups at Index Admission According to Acute Physiology and Chronic Health Evaluation II.
Diagnosis group n (%) Length of stay in ICU, days,
median (IQR) Mortality during index hospitalization, n (%)
Cardiovascular, postoperative 436 (15.2) 2.88 (1.89–4.98) 49 (11.2) Cardiovascular, non-operative 384 (13.4) 2.73 (1.25–4.21) 154 (40.1)
Respiratory 352 (12.3) 3.51 (1.72–6.52) 98 (27.8)
Metabolic 256 (8.9) 1.19 (0.72–2.18) 16 (6.3)
Gastrointestinal, postoperative 252 (8.8) 2.73 (1.31–5.60) 61 (24.2)
Neurology 251 (8.8) 2.70 (1.11–5.76) 59 (23.5)
Gastrointestinal, non–operative 185 (6.5) 2.75 (1.10–5.59) 59 (31.9)
Sepsis 181 (6.3) 2.91 (1.35–5.86) 64 (35.4)
Neurology, postoperative 135 (4.7) 3.08 (1.42–6.72) 35 (25.9) Trauma, non-operative 127 (4.4) 1.89 (0.84–6.62) 16 (12.6) Respiratory, postoperative 70 (2.4) 2.78 (1.31–4.83) 7 (10.0) Kidney disease, non-operative 68 (2.4) 2.19 (1.35–5.26) 18 (26.5) Trauma, postoperative 58 (2.0) 3.16 (1.23–8.79) 10 (17.2)
Other non-operative 53 (1.8) 1.50 (0.83–3.14) 4 (7.5)
Orthopedic, postoperative 22 (0.8) 1.45 (0.85–2.60) 5 (22.7) Gynecology, postoperative 20 (0.7) 1.11 (0.78–1.88) 0 (0) Kidney disease, postoperative 11 (0.4) 2.65 (1.78–7.26) 1 (9.1)
Hematology 7 (0.2) 1.51 (0.99–1.62) 1 (14.3)
ICU; intensive care unit
eTable 6. Index Hospitalization Resource Use and Costs for All Patients
Cost source Costs
($M) Costs per patient ($) Length of stay (d)
Critical care 43.17 15 000 ± 17 300, 9650 (5290–17 500) 4.27 ± 5.26, 2.58 (1.20–5.00) Ward treatment 63.18 22 000 ± 39 200, 11 100 (3710–23 800) 29.1 ± 49.7, 12 (5–30) Other services 1.22 424 ± 585, 318 (0–618)
Whole index episode
107.57 37 500 ± 46 900, 23 900 (13 000–43 000)
33.4 ± 51.1, 16 (7.94–35.7)
Numbers are mean ± SD, median (IQR).
eTable 7. Mean Total Costs in Patient Subgroups
Group n
n of 3-year survivors (%)
Index episode
Year 1 after index episode
Year 1 total Year 2 Year 3 Whole follow-up Age
≤50 691 585 (84.7%)
35400 ± 56300, 16400 (8130–
37500)
16300 ± 21100, 8620 (5640–
16800)
50500 ± 63400, 28200 (16100–
57300)
13900 ± 29900, 6680 (4680–
12800)
11500 ± 19900, 6040 (4390–
10700)
72500 ± 89800, 43400 (27200–
84400)
51–64 815 553 (67.9%)
39000 ± 44900, 24000 (14200–
45900)
18900 ± 25200, 9850 (6400–
20500)
55500 ± 51600, 37700 (24400–
67200)
14000 ± 18700, 7020 (5030–
14900)
11700 ± 16000, 5810 (4470–
10300)
74800 ± 62000, 53000 (34300–
94800)
65–73 646 383 (59.3%)
39100 ± 41400, 26500 (15700–
45700)
15700 ± 21100, 7440 (4180–
17200)
51700 ± 47900, 38100 (22400–
62500)
13200 ± 20500, 5620 (3460–
13600)
12300 ± 16400, 5500 (3760–
12400)
68400 ± 60600, 49600 (30300–
81300)
≥74 717 318 (44.4%)
36400 ± 43800, 26500 (16000–
42200)
15500 ± 19800, 7200 (3520–
20300)
47900 ± 47300, 37200 (22600–
59300)
16000 ± 21600, 7180 (3910–
18700)
14300 ± 18100, 6250 (3700–
16900)
64100 ± 58600, 48800 (27600–
80900) No.
comorbidities
0 944 747 (79.1%)
38300 ± 50100, 22200 (10400–
45400)
14400 ± 18900, 7150 (4480–
15500)
51500 ± 56000, 32600 (17900–
62700)
11500 ± 25700, 5030 (3480–
10800)
8820 ± 12100, 4530 (3260–
8450)
68300 ± 73400, 44200 (27000–
83500)
1–2 1404 850 (60.5%)
38100 ± 45400, 24700 (14100–
42900)
17000 ± 23100, 8640 (5240–
18400)
51900 ± 51500, 36100 (22300–
61500)
14200 ± 21400, 6800 (4640–
13800)
13100 ± 19500, 6150 (4390–
12800)
70100 ± 66700, 49400 (30800–
85400)
≥3 521 242 (46.4%)
34300 ± 45000, 24400 (14500–
41300)
21300 ± 24900, 10700 (6340–
28400)
50700 ± 50900, 38300 (22400–
63600)
21100 ± 21800, 12000 (6450–
28900)
19100 ± 23000, 9510 (6200–
22400)
73500 ± 64900, 56800 (33300–
91400) Chronic kidney
disease
No CKD 2681 1756 (65.5%)
37500 ± 45500, 23700 (12900–
43400)
16300 ± 21500, 8320 (4940–
17700)
51200 ± 51500, 35300 (20700–
62500)
13400 ± 22500, 6260 (4150–
13500)
11400 ± 15300, 5510 (3940–
10900)
68900 ± 65900, 48300 (29600–
84600)
CKD 188 83 (44.1%)
37100 ± 63800, 25800 (16200–
38900)
24700 ± 29100, 12100 (7210–
33000)
55900 ± 69900, 38600 (26600–
63200)
28600 ± 34000, 15500 (9050–
33100)
29100 ± 42300, 13400 (9220–
37300)
87100 ± 99700, 62000 (39300–
101000) Primary
admission type
Medical 1863 1122 (60.2%)
33500 ± 48500, 20100 (10600–
37900)
17300 ± 22700, 8830 (5290–
18800)
47400 ± 53900, 31600 (18000–
57300)
15300 ± 24800, 7190 (4790–
15500)
12900 ± 16600, 6200 (4530–
12300)
66000 ± 68900, 44800 (27000–
80700)
Operative
(elective) 335 270 (80.6%)
38400 ± 34200, 28100 (21800–
39700)
12800 ± 19300, 6390 (4050–
12400)
50800 ± 40200, 38900 (28100–
57900)
9930 ± 20800, 3760 (2770–
7630)
9100 ± 22700, 3600 (2680–
7630)
67300 ± 63500, 48200 (34600–
77800)
Operative
(emergency) 671 447 (66.6%)
48200 ± 46400, 32100 (18900–
62400)
17700 ± 22100, 8920 (4990–
22100)
63600 ± 54000, 46000 (27100–
83000)
13900 ± 20800, 6990 (4300–
14500)
12700 ± 17400, 6140 (4080–
12400)
82900 ± 69300, 61400 (37100–
105000)
Continued
Group n
n of 3-year survivors (%)
Index episode
Year 1 after index episode
Year 1 total Year 2 Year 3 Whole follow-up SAPS II
score
≤33 1166 963 (82.6%)
32000 ± 37900, 20600 (11500–
35600)
15600 ± 20800, 8070 (5200–
16200)
47200 ± 44700, 33000 (20900–
56600)
12700 ± 19800, 5840 (4090–
12200)
10600 ± 17200, 5110 (3720–
10200)
67700 ± 60700, 47900 (31900–
81600)
34–43 687 439 (63.9%)
42300 ± 49700, 28500 (17200–
51900)
18000 ± 24300, 9030 (5330–
20700)
58500 ± 56000, 42300 (26800–
72200)
15000 ± 23600, 7570 (4870–
15300)
14300 ± 19700, 6750 (4600–
14100)
79900 ± 69800, 59800 (38100–
97700)
44–58 579 312 (53.9%)
44800 ± 60100, 29000 (15100–
52500)
18600 ± 23300, 9390 (4850–
22100)
59400 ± 64600, 41900 (24500–
73600)
17400 ± 32800, 7760 (4580–
18000)
13100 ± 16300, 6390 (4610–
12600)
77800 ± 85200, 56400 (32600–
92700)
≥59 437 125 (28.6%)
34800 ± 42100, 21800 (10800–
39200)
15100 ± 18700, 6780 (3530–
18300)
41700 ± 48300, 24900 (12500–
52700)
14600 ± 17200, 7210 (4570–
15700)
14800 ± 17900, 6670 (4730–
15700)
51100 ± 58400, 29200 (14700–
66200) Severe
sepsis or septic shock
Not
present 2004 1368 (68.3%)
35400 ± 48300, 22200 (11600–
39700)
15900 ± 20600, 7910 (4860–
17100)
49100 ± 53700, 33500 (19500–
59600)
13100 ± 23100, 5930 (4020–
12800)
11400 ± 16800, 5510 (3960–
10700)
67300 ± 70100, 46200 (28800–
81600)
Present 865 471 (54.5%)
42400 ± 43300, 29500 (16400–
51600)
19200 ± 25500, 9960 (5680–
22600)
57100 ± 50600, 41700 (25100–
70100)
17300 ± 24000, 8990 (5470–
18900)
14700 ± 20100, 6640 (4690–
14200)
76800 ± 64900, 56800 (33900–
96500) AKI status
No AKI 1738 1225 (70.5%)
31600 ± 35900, 21500 (11400–
36300)
16100 ± 21200, 7810 (4680–
16900)
46100 ± 43100, 33000 (19900–
56000)
12600 ± 18900, 5930 (3910–
12900)
11300 ± 17100, 5310 (3710–
10800)
64400 ± 57500, 46100 (29700–
78600)
AKI without RRT
840 470 (56.0%)
40900 ± 54600, 26900 (15000–
46300)
16600 ± 23400, 8590 (5190–
19700)
53600 ± 59600, 37400 (21700–
65100)
14500 ± 22600, 7170 (4500–
15400)
12600 ± 17900, 6050 (4300–
11600)
70500 ± 70800, 50200 (29000–
85800)
AKI with
RRT 291 144 (49.5%)
62600 ± 67600, 42700 (20900–
83300)
23000 ± 24200, 12800 (7770–
28200)
78200 ± 73900, 59000 (27700–
104000)
26900 ± 46900, 11500 (8090–
30100)
19300 ± 22200, 9180 (7220–
18200)
103000 ± 106000, 76000 (37800–
131000) Numbers are mean ± SD, median (IQR). Includes costs from all sources.
95% confidence intervals for the cost-utility estimates were calculated from standard error estimates obtained by bootstrapping.
AKI; acute kidney injury RRT; renal replacement therapy SAPS; Simplified Acute Physiology Score QALY; quality-adjusted life year
eTable 8. Types Health Care-related Costs During the Three-year Follow-up from the Social Insurance Institution.
Cost group Year 0–
1 Year 1–2 Year 2–3 Total
Total costs from SII 8.81 7.39 6.90 23.11
Medication costs (outpatient)
Total costs ($M) 3.51 3.15 2.76 9.42
Costs per patient alive ($) 1457 1513 1409 3903
Sickness allowances
Total costs ($M) 1.01 0.27 0.30 1.58
Costs per patient alive ($) 420 128 155 656
New disability pensions and rehabilitation subsidies
Total costs ($M) 2.20 2.42 2.43 7.05
Costs per patient alive ($) 911 1167 1241 2922
Travel cost reimbursements
Total costs ($M) 1.83 1.35 1.22 4.40
Costs per patient alive ($) 760 648 622 1822
Private medical fees
Total costs ($M) 0.26 0.20 0.19 0.66
Costs per patient alive ($) 107 98 98 272
Includes total costs, not just out-of-pocket costs paid by patients.
eTable 9. QALYs and Cost-utilities for Index Hospitalization Episode Costs
n 3-year QALYs, mean (95% CI)
Predicted lifetime QALYs, mean (95% CI)
Index episode costs per 3-year QALYs (95% CI)
Index episode costs per estimated lifetime QALYs (95% CI) All patients 2869 1.49 (1.46–1.53) 8.23 (7.94–8.51) 25 100 (23 800–26 500) 4560 (4290–4830) Index ICU episode
survivors 2212 1.93 (1.90–1.96) 10.67 (10.36–
10.97) 20 000 (18 800–21 100) 3620 (3390–3850) Survival time
Died in hospital 657 0.01 (0.01–0.01) 3 200 000 (2 540 000–3 860 000)
3 200 000 (2 540 000–3 860 000)
Survival < 1 year 142 0.15 (0.12–0.18) 247 000 (189 000–304 000)
247 000 (189 000–304 000)
Survival 1–3 years 231 1.14 (1.07–1.21) 38 600 (30 500–46 600) 38 600 (30 500–46 600) Survival >3 years 1839 2.17 (2.15–2.19) 12.68 (12.39–
12.96) 17 500 (16 500–18 600) 3010 (2810–3200)
Age
≤50 691 1.90 (1.84–1.96) 16.37 (15.75–
16.98) 18 600 (16 300–21 000) 2170 (1880–2450) 51–64 815 1.59 (1.53–1.66) 8.77 (8.33–9.21) 24 500 (22 200–26 800) 4450 (4020–4880) 65–73 646 1.39 (1.31–1.47) 5.33 (4.97–5.68) 28 100 (25 300–31 000) 7350 (6580–8120) ≥74 717 1.08 (1–1.15) 2.38 (2.18–2.57) 33 800 (30 000–37 600) 15 300 (13 500–17 100)
No. comorbidities
0 944 1.83 (1.77–1.89) 12.72 (12.17–
13.26) 21 000 (19 000–22 900) 3010 (2710–3310) 1–2 1404 1.41 (1.36–1.47) 6.75 (6.40–7.10) 27 100 (25 100–29 100) 5660 (5190–6120) ≥3 521 1.11 (1.03–1.20) 4.07 (3.64–4.50) 30 900 (26 700–35 100) 8460 (7170–9740) Chronic kidney
disease
Not present 2681 1.52 (1.48–1.56) 8.53 (8.23–8.83) 24 700 (23 300–26 000) 4400 (4140–4660) Present 188 1.09 (0.95–1.24) 3.9 (3.17–4.63) 34 200 (24 400–44 000) 9600 (6580–12 600) Primary admission
type
Medical 1863 1.40 (1.35–1.45) 8.29 (7.92–8.67) 23 900 (22 100–25 700) 4040 (3710–4370) Operative
(elective) 335 1.95 (1.86–2.04) 7.89 (7.31–8.47) 19 700 (17 400–22 000) 4880 (4220–5530) Operative
(emergency) 671 1.52 (1.44–1.59) 8.21 (7.63–8.78) 31 800 (28 900–34 600) 5880 (5260–6490)
SAPS II score
≤33 1166 1.93 (1.88–1.97) 11.99 (11.54–
12.45) 16 600 (15 300–17 900) 2670 (2450–2900) 34–43 687 1.5 (1.42–1.57) 6.87 (6.38–7.36) 28 300 (25 300–31 200) 6170 (5460–6880) 44–58 579 1.26 (1.17–1.35) 6.46 (5.87–7.04) 35 600 (31 100–40 200) 6950 (5990–7910) ≥59 437 0.64 (0.55–0.73) 2.65 (2.2–3.1) 54 700 (46 000–63 300) 13 200 (10 800–15 600) Severe sepsis or
septic shock
Not present 2004 1.60 (1.55–1.64) 8.91 (8.57–9.25) 22 100 (20 700–23 600) 3970 (3680–4260) Present 865 1.25 (1.18–1.32) 6.65 (6.15–7.14) 33 900 (30 900–36 900) 6400 (5740–7050)
Acute kidney injury
No AKI 1738 1.65 (1.61–1.70) 9.43 (9.05–9.80) 19 200 (18 000–20 400) 3360 (3120–3600) AKI without RRT 840 1.3 (1.22–1.37) 6.52 (6.04–7.01) 31 500 (28 200–34 900) 6270 (5540–7010) AKI with RRT 291 1.11 (0.98–1.23) 5.97 (5.16–6.77) 56 700 (48 000–65 500) 10 500 (8740–12 300) 95% confidence intervals for the cost-utility estimates were calculated from standard error estimates obtained by bootstrapping.
Includes costs from all sources.
AKI; acute kidney injury RRT; renal replacement therapy SAPS; Simplified Acute Physiology Score QALY; quality-adjusted life year