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Mapping EQ5D3L from the Knee Injury and Osteoarthritis Outcome Score (KOOS)
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Introduction
With the growing emphasis on the patients’ involvement in clinical decisionmaking, patientreported outcome measures (PROMs) are increasingly used in the clinical settings to assess the effects of diseases and their treatments from the patient perspective [
1]. In addition, generic preferencebased PROMs, such as EQ5D, have an important role in the assessment of healthrelated quality of life (HRQoL) and in calculating qualityadjusted life years (QALYs) for use in health economic evaluations [
2]. QALYs combine HRQoL and survival into a single metric and is a common outcome measure applied in costutility analyses. However, clinical studies mostly use conditionspecific PROMs which cannot be used to estimate QALYs [
3]. In these situations, it is common to use statistical techniques (known as “mapping” or “cross walking”) to convert the responses on a conditionspecific PROM to a generic preferencebased PROM using datasets of patients that have responded to both measures simultaneously [
2,
3]. While mapping studies are criticized for underestimating uncertainty and overprediction of poor health states, these are to some extent a sign of an inappropriate mapping model or inappropriate use and not a feature inherent in mapping [
3]. While data on preferencebased PROMs are preferable, mapping is a viable alternative when these data are not available [
4].
The Knee Injury and Osteoarthritis Outcome Score (KOOS) is a commonly used kneespecific PROM intended for use in people across the lifespan with knee injury including anterior cruciate ligament (ACL) injury that can result in posttraumatic osteoarthritis [
5]. The KOOS contains 42 items covering five subscales: pain, other symptoms, function in daily living (ADL), Function in sport and recreation (Sport/Rec) and kneerelated quality of life (QoL) [
5]. All items have five possible answer options ranged from 0 (no problems) to 4 (extreme problems). A normalized score (100 indicating no symptoms and 0 indicating extreme symptoms) is calculated for each subscale. To our best knowledge, there is no mapping model to estimate EQ5D3L values from the KOOS. To address this knowledge gap, we aimed to develop a mapping model to derive EQ5D3L values from the KOOS for use in costutility analyses among adult patients with ACL injury.
Methods and materials
Data
We used the data from the Swedish National ACL Register (
www.aclregister.nu). This register was initiated in January 2005 comprising patients undergoing ACL reconstruction and ACL revision [
6,
7]. The register coverage is estimated to exceed 90% of all surgical ACL procedures performed annually in Sweden [
6]. The register uses a webbased protocol and the patients respond to the Swedish version of both EQ5D3L and KOOS before the ACL surgery and at 1, 2, 5 and 10 years after the operation. About 70% of patients respond to PROMs prior to operation and this number declines to 50% and 40% at 2 and 5 years follow up, respectively. (
https://aclregister.nu/info/rapport2016en.pdf).
Patients
We obtained the data on 52,584 observations for 25,169 patients operated between January 2005 and December 2014 from the Swedish ACL register. After exclusion of 12,125 observations (3678 missing responses to EQ5D3L, 3236 missing responses to the KOOS, 42 missing responses to both questionnaires and 5169 younger than 18 years when responded to the PROMs), a total of 40,459 observations (12,582 preoperation, and 27,877 postoperation) from 21,854 patients were used for the analysis. We excluded those younger than 18 years since the UK utility weights were obtained from the adult population which may not reflect the preferences of children and adolescents and also dimensions of health relevant to children and adolescents may be different from adults [
8].
Statistical analysis
The conceptual overlap between the two measures used in mapping is important for acceptable performance of mapping algorithms [
2]. Previous studies reported sufficient overlap between EQ5D3L and KOOS [
9,
10]. The only dimension of the EQ5D3L that is not covered directly by the KOOS is anxiety/depression. We assessed the degree of overlap between the two instruments by calculating Spearman’s rank correlation coefficients between EQ5D3L index score and five KOOS subscales scores.
While linear regression is by far the most commonly used method to develop mapping models [
4], it fails to account for some wellknown characteristics of the EQ5D3L distribution such as the right and left bounding, a mass of observations at full health, a large gap between full health and the next feasible EQ5D3L value (e.g. no value between 1 and 0.883 in the UK value set) and multimodality of the distribution [
11]. Therefore, response mapping and mixture models have gained popularity in developing mapping models [
4]. In the current study, we used response mapping and mixture model in addition to linear regression.
For the response mapping, we used the generalized ordered probit model. The standard ordered models (probits or logits) assume the same coefficients for the explanatory variables across the different categories of dependent variable (parallel line assumption) and this has led to multinomial logit models being commonly used for the response mapping [
12]. However, these models ignore the ordered nature of EQ5D3L data. The generalized ordered probit model relaxes the parallel line (proportional odds) assumption while accounting for the ordered nature of the EQ5D3L responses [
12]. This allows the effects of the explanatory variables to vary with the point at which the categories of the dependent variable are dichotomized. In this study, we relaxed parallel line assumption for all explanatory variables. A separate model was estimated for each of five EQ5D3L dimensions and the probability of being at each of three levels (“no problems”, “some problems” and “extreme problems”) was calculated. Then based on these probabilities and the UK EQ5D3L tariff, the expected EQ5D3L value was computed mathematically [
12,
13].
There has been an increasing popularity in the use of mixture models for mapping in recent years mainly due to their flexibility and the ability to capture multimodality of EQ5D3L data. The main concept in mixture modelling is that an underlying observed distribution can be represented by a mixture of distinct simpler distributions (components) with potential heterogeneity of covariates and their effects for each of these components [
14]. The probability of being in each component is estimated using a multinomial logit model. In this study, we used a betamixture model which has recently been introduced by Gray et al. [
15,
16] based on the truncated inflated beta regression model [
17]. This is a twopart model including a multinomial logit model and a betamixture model. The multinomial logit model deals with the data at the boundaries and a mass of observations at full health and the mixture of beta distributions capture multimodality of the EQ5D3L data [
15].
The KOOS was included in three alternative forms: individual KOOS subscales scores, the KOOS
_{5} score (the average of the five KOOS subscales scores, ranged from 0 to 100 in our sample) and the KOOS
_{4} score (the average of the KOOS subscales scores excluding the ADL subscale, as previously used in ACL injured populations [
18], ranged from 1.25 to 100 in our sample). We also used the KOOS individual items but it caused convergence problem in betamixture model and we decided to not include them in our final analysis to ensure the models were comparable. For each of these alternatives, we applied a series of model specifications based on main terms, and main terms plus squared and square root terms (likelihood ratio test was used for exclusion of squared and square root terms). The models estimated for linear regression and response mapping are presented in Supplementary Tables 1 and 2. For betamixture model, we estimated different specifications with different numbers of components (starting with a onecomponent model equivalent to a beta regression model), with and without inclusion of the gap between full health and the next feasible value (UK EQ5D3L = 0.883), and with and without probability masses at full health and truncation point of the EQ5D3L distribution. An example of models estimated for a single specification is presented in Supplementary Table 3.
Assessment of model performance
We used the Bayesian information criterion (BIC) to assess the goodness of fit of these specifications within each class of models, where a smaller BIC indicates a better model fit. The predictive ability of models was assessed using mean error (ME), mean absolute error (MAE) and root mean squared error (RMSE). The MAE is the mean of absolute differences between the observed and predicted EQ5D3L index scores, whilst the RMSE is defined as the squared root of the mean of squared differences between the observed and predicted EQ5D3L index scores. For each alternative form of KOOS (individual KOOS subscales scores, the KOOS
_{5} and the KOOS
_{4} scores) and each class of models (linear, response mapping, betamixture), we selected one model with the smallest BIC, and the lowest ME, MAE and RMSE in the whole sample and across the distribution of disease severity measured by the KOOS
_{5}/KOOS
_{4} scores as
preferred model (Supplementary Tables 4–12). Then, we selected one model as the
optimal model for each class of models. In our decision to select the preferred models, we gave higher priority to the models with smallest BIC, while in selecting the optimal models higher priority was given to models with better predictive ability (in our study optimal models had both lower BIC and better predictive ability compared to other models).
An important application of mapping models is estimating EQ5D3L values in individual simulationbased costeffectiveness models where many hypothetical individual patients with varying characteristics are simulated over a long time period or in trial based economic evaluations [
11]. As a further assessment of model performance, we simulated data using the estimated models as the data generating process based on 100 replications for each observation in the sample (a total of 4,045,900 simulated EQ5D data points) [
12,
13]. The distribution of these simulated data was compared with the distribution of the observed EQ5D3L data. A model that correctly fits the EQ5D3L data should produce a distribution that resembles the distribution of the actual EQ5D3L data [
2,
12,
13]. All analyses were performed in STATA v.15. We used the “goprobit” command [
19] for response mapping and the “betamix” command [
15] for betamixture model. Standard errors were adjusted for repeated observations from individual patients (using the “cluster” option). We used the “predict” postestimation command for obtaining predicted values for linear and betamixture models. We did not transform the predictions outside the possible EQ5D3L range.
Results
The patient sample had a mean (standard deviation) age of 29.1 (10.0) years and 42.3% were women at the date of ACL operation. The proportion of responses with some/extreme problems on EQ5D3L dimensions ranged from 1.6% in selfcare to 68.2% in pain (Table
1). Across KOOS subscales the worst and best scores were reported for KOOSQoL and KOOSADL, respectively. A total of 145 out of 243 possible EQ5D3L health states were observed. The full health (health state “11111”) was the most frequent health state (27.3%, Fig.
1) followed by health states “11121” (25.1%) and “11122” (11.0%). The Spearman rank correlation between EQ5D3L values and KOOS subscales ranged from 0.45 (Symptoms) to 0.56 (ADL) for preoperation and from 0.66 (Symptoms) to 0.75 (QoL) for postoperation observations.
Table 1
Characteristics of the study sample, stratified by sex
Preoperation

Postoperation

Total



Number of patients

12,582

16,983

21,854

Number of observations

12,582

27,877

40,459

Mean (SD) age at operation, years

30.0 (9.4)

29.5 (10.3)

29.1 (10.0)

EQ5D mobility


No problems (%)

66.8

88.5

81.7

Some problems (%)

32.8

11.5

18.1

Extreme problems (%)

0.4

0.0

0.2

EQ5D selfcare


No problems (%)

97.0

99.0

98.4

Some problems (%)

2.4

0.7

1.2

Extreme problems (%)

0.6

0.3

0.4

EQ5D usual activities


No problems (%)

54.1

82.5

73.6

Some problems (%)

35.9

16.6

22.6

Extreme problems (%)

10.0

0.9

3.8

EQ5D pain


No problems (%)

15.5

39.2

31.9

Some problems (%)

79.1

58.1

64.6

Extreme problems (%)

5.4

2.7

3.5

EQ5D anxiety/depression


No problems (%)

50.6

71.8

65.2

Some problems (%)

43.9

25.8

31.4

Extreme problems (%)

5.5

2.4

3.4

Proportion in full health (EQ5D3L = 1), %

9.0

35.5

27.3

Proportion reporting EQ5D3L < 0

1.8

0.9

1.2

Mean (SD) EQ5D3L index

0.66 (0.24)

0.81 (0.20)

0.77 (0.22)

Mean (SD) KOOS
_{5}

58.9 (16.5)

75.7 (17.8)

70.5 (19.1)

Mean (SD) KOOS
_{4}

53.1 (16.9)

71.9 (19.5)

66.1 (20.6)

Mean (SD) KOOSpain

73.4 (17.8)

84.6 (15.9)

81.1 (17.3)

Mean (SD) KOOSsymptoms

68.7 (18.3)

77.9 (18.1)

75.1 (18.7)

Mean (SD) KOOSactivity of daily living

82.1 (17.6)

91.1 (13.5)

88.3 (15.5)

Mean (SD) KOOSsports/recreation

38.3 (26.7)

64.5 (27.7)

56.3 (30.0)

Mean (SD) KOOSquality of life

32.0 (17.3)

60.6 (24.0)

51.7 (25.8)

×
The estimates and full variance–covariance matrix of the preferred models based on individual subscales, KOOS
_{5} and KOOS
_{4} for three classes of models (linear regression, response mapping and betamixture) are reported in Supplementary Tables 4–12. In all three classes of models, the optimal models were those based on individual KOOS subscales.
In the optimal linear regression model (Supplementary Table 6), improvement in KOOSPain, Symptoms, ADL and QoL subscales scores (indicating better function) were associated with increase in EQ5D3L index score, even though for Symptoms the increase was at a lesser rate. The EQ5D3L improved up to a KOOS Sport/Rec score of 15, then it declined up to 50, and improved again once KOOS Sport/Rec exceeded 50. Older age and being female were associated with better EQ5D3L scores.
In the generalized ordered probit model, the inclusion of specific explanatory variables for each EQ5D3L dimension showed a better performance than the inclusion of the same explanatory variables for all EQ5D3L dimensions. The interpretation of the coefficients from the generalized ordered probit model is not straightforward and the inclusion of the squared and square root terms complicates this even further. In the optimal generalized ordered probit model (Supplementary Table 9), for the EQ5D3L mobility, selfcare and usual activities, being female increased the probability of being at level 1 (“no problems”) and decreased the probability of being at level 3 (“severe problems”), all else equal. The opposite was true for the EQ5D3L pain dimension.
Our optimal betamixture model (Supplementary Table 12) was a threecomponent model including the gap between full health and the next feasible EQ5D3L value with a probability mass at full health (convergence was a problem with a fourcomponent model). The three components centred on EQ5D3L values of 0.75, 0.29 and 0.71 with component membership probability of 0.88, 0.09 and 0.03, respectively. Different explanatory variables were included in predicting the components mean, probability of component membership and probability of being in full health. The excel calculator in Supplement can be used to estimate EQ5D3L values using the optimal linear, betamixture and generalized ordered probit models.
In the full sample, while the linear regression provided the closest estimate to the observed mean (including a constant in a linear regression ensures this is the case), it had larger MAE and RMSE than the response mapping and mixture models (Table
2). In addition, across the range of disease severity measured by the KOOS
_{5} scores, the response mapping and mixture model outperformed linear regression in terms of all summary measures and importantly this was more profound (the highest proportional improvements in the MAE and RMSE) at the extremes of the distribution of disease severity. Compared with the response mapping model, the betamixture model estimated closer mean to the observed mean in overall and across the range of the KOOS
_{5} score except those < 25 (most severe). For all models the magnitude of MAE and RMSE rose with the severity of the disease. The results were generally similar when we measured disease severity by the KOOS
_{4} scores (Table
3).
Table 2
Prediction performance of optimal models in full sample and across the range of disease severity (measured by KOOS
_{5} scores)
Sample and summary statistics

Linear regression
^{a}

Response mapping
^{b}

Betamixture model
^{c}


Full sample (
n = 40,459)


ME

1.65 × 10
^{−17}

0.0026

− 0.0003

MAE

0.1037

0.0996

0.0988

RMSE

0.1505

0.1489

0.1490

KOOS
_{5} 0 to < 25 (
n = 543)


ME

− 0.0359

−
0.0005

− 0.0145

MAE

0.2305

0.2140

0.2177

RMSE

0.2776

0.2671

0.2691

KOOS
_{5} 25 to < 50 (
n = 6069)


ME

0.0067

0.0078

− 0.0001

MAE

0.1948

0.1897

0.1877

RMSE

0.2358

0.2340

0.2340

KOOS
_{5} 50 to < 70 (
n = 11,370)


ME

0.0030

0.0007

0.0004

MAE

0.0951

0.0923

0.0922

RMSE

0.1504

0.1493

0.1495

KOOS
_{5} 70 to < 85 (
n = 11,535)


ME

− 0.0107

0.0012

0.0007

MAE

0.0874

0.0831

0.0830

RMSE

0.1242

0.1229

0.1229

KOOS
_{5} 85 to ≤ 100 (
n = 10,942)


ME

0.0062

0.0032

− 0.0015

MAE

0.0729

0.0693

0.0671

RMSE

0.0962

0.0947

0.0946

Table 3
Prediction performance of optimal models across the range of disease severity (measured by KOOS
_{4} scores)
Sample and summary statistics

Linear regression
^{a}

Response mapping
^{b}

Betamixture model
^{c}


KOOS
_{4} 0 to < 25 (
n = 1136)


ME

− 0.0246

0.0121

−
0.0017

MAE

0.2472

0.2364

0.2390

RMSE

0.2821

0.2760

0.2768

KOOS
_{4} 25 to < 50 (
n = 8528)


ME

0.0088

0.0035

−
0.0017

MAE

0.1633

0.1582

0.1565

RMSE

0.2114

0.2098

0.2098

KOOS
_{4} 50 to < 70 (
n = 11,892)


ME

− 0.0025

0.0011

0.0012

MAE

0.0826

0.0806

0.0806

RMSE

0.1363

0.1354

0.1355

KOOS
_{4} 70 to < 85 (
n = 10,167)


ME

− 0.0084

0.0011

−
0.0002

MAE

0.0957

0.0912

0.0910

RMSE

0.1231

0.1218

0.1218

KOOS
_{4} 85 to ≤ 100 (
n = 8736)


ME

0.0078

0.0040

−
0.0009

MAE

0.0647

0.0607

0.0580

RMSE

0.0889

0.0870

0.0870

Moving from the lowest (< 25) to the highest (≥ 85) level of KOOS
_{5} score was associated with 0.785 change in EQ5D3L values in the observed data. The corresponding value was 0.743, 0.782 and 0.772 for linear, response mapping and betamixture model, respectively, indicating a difference of 0.038 between models.
The distribution of simulated data showed that linear regression clearly failed to account for main characteristics of the original data (Table
4; Fig.
2). While linear regression generated EQ5D3L values that fall way outside the feasible range (− 0.594 to 1.0), neither the response mapping nor the betamixture model suffer from this limitation by design. In contrast to the betamixture model, the response mapping take into account the discrete nature of the EQ5D3L data. The data generated by the betamixture model more closely resemble the original data.
Table 4
Summary statistics of the observed and simulated data sets generated using the optimal models
Observed

Linear regression
^{a}

Response mapping
^{b}

Betamixture model
^{c}



Mean

0.766

0.767

0.781

0.767

Variance

0.049

0.069

0.055

0.050

Skewness

− 1.633

− 0.268

− 2.289

−
1.644

Kurtosis

6.478

3.289

10.459

6.571

Minimum

− 0.594

− 1.112

−
0.594

−
0.594

Maximum

1.000

2.002

1.000

1.000

EQ5D3L = 1, %

27.26

0.0

31.81

27.26

EQ5DEL > 1, %

0.0

18.59

0.0

0.0

EQ5DEL < 0, %

1.16

0.47

2.52

1.01

Percentiles


1%

− 0.016

0.097

− 0.239

−
0.002

5%

0.228

0.320

0.293

0.223

10%

0.620

0.429

0.620

0.592

25%

0.725

0.599

0.689

0.711

50%

0.796

0.777

0.796

0.777

75%

1.000

0.946

1.000

1.000

90%

1.000

1.093

1.000

1.000

95%

1.000

1.179

1.000

1.000

99%

1.000

1.337

1.000

1.000

×
Discussion
This is, to our knowledge, the first study to develop mapping models to predict EQ5D3L values from the KOOS. This facilitates the application of KOOS in costutility analyses in ACL studies when the directly collected EQ5D3L data are not available. The overall MAE (0.099 to 0.104) and RMSE (0.149 to 0.151) found in our study were comparable to those generally reported in the mapping literature (from 0.0011 to 0.19 for MAE, and from 0.084 to 0.20 for RMSE) [
3]. Our results also confirmed that linear regression might not be appropriate for mapping. The threecomponent betamixture model fit the data generally better and generated simulated data that more closely resembled the observed data.
Our results showed that regardless of econometric technique, the models based on the individual KOOS subscales had better performance than those based on the average of the subscale scores (i.e. KOOS
_{5} and KOOS
_{4}). However, individual scores are not always available to map from and models using average scores are needed. It should be noted that previous studies suggested that the KOOSADL subscale might have poor content validity for young adults with ACL injury [
20], however its inclusion in our study improved the predictive ability of our mapping models.
In addition to linear regression, we applied two other statistical techniques based on recent advances in modelling EQ5D data: betamixture model, and generalized ordered probit model. Both these techniques outperformed linear regression overall and across the range of disease severity particularly at the extremes of the distribution of disease severity. This is in agreement with recent evidence suggesting that the characteristics of EQ5D3L data make linear regression inappropriate for mapping [
11,
12,
21]. A recent systematic review found that the proportion of mapping studies using solely linear regression declined from 49% in 1997–2011 to 13% in 2014–2016 [
4]. Some studies reported small differences in predictive ability of linear compared to other models [
22–
24] including the response mapping and mixture models [
14]. However, these studies solely relied on the observed mean EQ5D3L value and dismiss the data generating process of these models and its importance for simulationbased costeffectiveness analyses. Furthermore, while due to regress toward mean, linear regression might have better performance in overall, it generally has poor performance compared with other models at the extreme of disease severity [
21]. Moreover, it is important to bear in mind that due to very limited range of EQ5D3L data, small differences in prediction errors should not be overlooked [
25].
For mixture modelling, we used betamixture model which, to our knowledge, has been applied only in one previous mapping study where it marginally outperformed the adjusted limited dependent variable mixture model [
16]. Our preferred betamixture model was a threecomponent model including the gap between full health and the next feasible EQ5D3L value with a probability mass at full health. While adding more components to betamixture model resulted in convergence problem in our study, assessing the performance of models with larger number of components in other data sets is a subject for future research. We have also estimated models with the probability masses at both full health and truncation point, but these had poorer fit compared to our preferred model. This was not unexpected because only 0.6% of the observations were at the EQ5D3L truncation point (0.883). It also should be noted that our betamixture model was estimated using the UK value set reported by Dolan et al. [
26]. Different countries have different value sets and the mapping function given here is not necessarily applicable to all other countries. The response mapping estimates reported here could be used for alternative countries by attaching the corresponding value set in the second step if a better performing mapping function is not available under the additional assumption that the responses to the questionnaire would be similar across different countries.
To our knowledge, the predictive ability of betamixture model and response mapping has not been previously compared. Our results demonstrated that while both techniques are appealing for the purposes of mapping, the betamixture model performed better across the entire range of disease severity except most severe range in this dataset. However, only 1.3% of observations were at this extreme level of disease severity. In line with this, two previous mapping studies reported that while a limited dependent variable mixture model outperformed the response mapping, this was not universal across entire range of disease severity [
12,
25]. Furthermore, the simulated data produced from our preferred betamixture model had very close summary statistics to those in the original data. It should be noted that our models were developed in a sample of young patients with ACL injury and hence their application in other populations (e.g. older age groups, patients with other knee problems) should be taken with caution [
27].
Developing the first models for mapping EQ5D3L from the KOOS, using a large data set covering a wide range of disease severity, and the first comparison of betamixture model and response mapping are the main strengths of the current study. However, several limitations of the study should be acknowledged. First, a very small portion of the observations were at the most severe range (< 25) for the KOOSPain (0.5%), Symptoms (0.7%) and ADL (0.3%) subscales which might influence the generalizability of our models to data sets with a greater portion of patients at this severe range. However, it should be noted that these subscales are generally less affected compared with Sport/Rec and QoL subscales in patients with ACL injury [
28,
29]. For example, in the Multicenter Orthopaedic Outcomes Network cohort [
28], the 25th percentile for Pain, Symptoms and ADL subscales were 64, 57 and 74 which are comparable to our data. Second, we were not able to validate our models on an external data set. While some mapping studies randomly split their data into an “estimation” and a “validation” subsamples, this approach is not universally recommended [
2,
25]. Mapping models are inputs into subsequent analyses and, therefore, validation should take this second step into account. A mapping model could be used to, either predict a conditional mean, or simulate individual level data from the conditional distribution. We present measures in the paper on which to judge the internal validity of models in those two areas. External validity will be dependent on subsequent analyses and cannot be generalized. Third, we used the data from the Swedish ACL register and the high percentage of nonresponses to the PROMs, particularly in follow up, is of concern. Fourth, measurement error in the predictors is a potential problem in mapping models and remains an area of future research.
Conclusions
To facilitate the use of KOOS in costutility analyses, we developed the first set of models to estimate the EQ5D3L values from the KOOS using data from adult patients with ACL injury. Our results confirmed inadequacy of linear regression for mapping and also showed that betamixture model had superior performance compared with response mapping. Further research is warranted to investigate predictive ability of the estimated models in other data sets or other settings, e.g. different age distribution and other knee conditions.
Acknowledgements
Open access funding provided by Lund University. We thank Dr. Forssblad Magnus and Henrik Magnusson from the Swedish National ACL Register for the help with data acquisition.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
Ethical approval
All procedures performed in this study were approved by the Regional Ethical Review Board in Stockholm, Sweden, and were in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments.
Informed consent
Informed consent was obtained from all individual participants included in the study. No written consent is necessary in Sweden for national healthcare registries.
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