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Can STOP-Bang and Pulse Oximetry Detect and Exclude Obstructive Sleep Apnea?

Christensson, Eva, MD, DESA*,†; Franklin, Karl A., MD, PhD; Sahlin, Carin, PhD§; Palm, Andreas, MD‖,¶; Ulfberg, Jan, MD, PhD#; Eriksson, Lars I., MD, PhD, FRCA*,†; Lindberg, Eva, MD, PhD; Hagel, Eva, MSSc**; Jonsson Fagerlund, Malin, MD, PhD, DESA*,†

doi: 10.1213/ANE.0000000000003607
Respiration and Sleep Medicine: Original Clinical Research Report
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SDC

BACKGROUND: Obstructive sleep apnea (OSA) is related to postoperative complications and is a common disorder. Most patients with sleep apnea are, however, undiagnosed, and there is a need for simple screening tools. We aimed to investigate whether STOP-Bang and oxygen desaturation index can identify subjects with OSA.

METHODS: In this prospective, observational multicenter trial, 449 adult patients referred to a sleep clinic for evaluation of OSA were investigated with ambulatory polygraphy, including pulse oximetry and the STOP-Bang questionnaire in 4 Swedish centers. The STOP-Bang score is the sum of 8 positive answers to Snoring, Tiredness, Observed apnea, high blood Pressure, Body mass index >35 kg/m2, Age >50 years, Neck circumference >40 cm, and male Gender.

RESULTS: The optimal STOP-Bang cutoff score was 6 for moderate and severe sleep apnea, defined as apnea-hypopnea index (AHI) ≥15, and the sensitivity and specificity for this score were 63% (95% CI, 0.55–0.70) and 69% (95% CI, 0.64–0.75), respectively. A STOP-Bang score of <2 had a probability of 95% (95% CI, 0.92–0.98) to exclude an AHI >15 and a STOP-Bang score of ≥6 had a specificity of 91% (95% CI, 0.87–0.94) for an AHI >15. The items contributing most to the STOP-Bang were the Bang items. There was a positive correlation between AHI versus STOP-Bang and between AHI versus oxygen desaturation index, Spearman ρ 0.50 (95% CI, 0.43–0.58) and 0.96 (95% CI, 0.94–0.97), respectively.

CONCLUSIONS: STOP-Bang and pulse oximetry can be used to screen for sleep apnea. A STOP-Bang score of <2 almost excludes moderate and severe OSA, whereas nearly all the patients with a STOP-Bang score ≥6 have OSA. We suggest the addition of nightly pulse oximetry in patients with a STOP-Bang score of 2–5 when there is a need for screening for sleep apnea (ie, before surgery).

From the *Function Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden

Department of Physiology and Pharmacology, Section for Anesthesiology and Intensive Care Medicine, Karolinska Institutet, Stockholm, Sweden

Departments of Surgical and Perioperative Sciences, Surgery

§Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden

Department of Medical Sciences, Respiratory, Allergy and Sleep Research, Uppsala University, Uppsala, Sweden

#Sleep Apnea Clinic, Capio Läkargruppen, Örebro, Sweden

**Medical Statistics Unit, Department of Learning, Informatics, Managements and Ethics, Karolinska Institutet, Stockholm, Sweden.

Published ahead of print June 28, 2018.

Accepted for publication May 25, 2018.

Funding: This study was supported by the departments and institutions involved and by grants from the Stockholm County Council; the Swedish Society of Medicine; Tore Nilsons Funds; Jaensens Funds; and Karolinska Institutet funds (M.J.F.), Stockholm, Sweden.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website.

This work has been presented in part as an abstract at the annual meeting for Swedish Society of Anesthesia and Intensive Care, September 22, 2015, Stockholm, Sweden and Society for Anesthesia and Sleep Medicine, October 23, 2015, San Diego, CA.

Clinical trial number: #NCT02293421.

Reprints will not be available from the authors.

Address correspondence to Eva Christensson, MD, DESA, Function Perioperative Medicine and Intensive Care, Karolinska University Hospital and Karolinska Institutet, SE-171 76 Stockholm, Sweden. Address e-mail to eva.christensson@sll.se.

KEY POINTS

  • Question: Can the STOP-Bang screening questionnaire and oxygen desaturation index identify subjects with obstructive sleep apnea (OSA)?
  • Findings: A STOP-Bang score <2 almost excludes moderate and severe OSA, whereas nearly all the patients with a STOP-Bang score ≥6 have OSA.
  • Meaning: The addition of pulse oximetry in patients with a STOP-Bang score of 2–5 is useful for patients in need of screening for sleep apnea (ie, before surgery).

Obstructive sleep apnea (OSA) is associated with an increased risk for postoperative pulmonary complications and unplanned admittance to the intensive care unit,1,2 and it is increasingly common within the surgical patient population worldwide, affecting >20% of adults undergoing general non–upper airway surgery.3 On the basis of this, countries have adopted screening recommendations to identify patients with suspected OSA before surgery, allowing the appropriate measurement to minimize the risk for perioperative complications.4,5 While more than 300 million inpatient surgical procedures are being performed worldwide annually,6 the majority of surgical patients with OSA still go undiagnosed.3,7 A common reason behind this is failure to implement routine screening and lack of resources to investigate at-risk patients before surgery, even those with a known risk for OSA, such as the elderly and those with obesity. In addition, there is a lack of knowledge about how to diagnose sleep apnea preoperatively without delaying surgery.

OSA is associated with intermittent upper airway obstructions followed by hypoxia and microarousals, a disorder now well characterized phenotypically.8 On the basis of this, the STOP-Bang questionnaire was constructed to detect at-risk OSA patients before surgery.9 This bedside scoring method consists of 8 questions related to the typical OSA phenotype. Each question can generate 1 score, and a score of ≥3 has a good sensitivity to detect moderate and severe OSA; however, an important caveat has been a low specificity.10 We hypothesized that simple pulse oximetry could increase the diagnostic value in STOP-Bang scores with low specificity.

We therefore undertook an in-depth analysis of the STOP-Bang scoring method and oxygen desaturation index (ODI) to find an appropriate OSA screening program that could be put forth as a clinical tool for detection of moderate or severe OSA (ie, subjects in need of therapy). A secondary objective was to assess the independent contribution of variables included in STOP-Bang with a special focus on gender.

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METHODS

Ethics

This study was conducted according to the Declaration of Helsinki and was approved by the Regional Ethics Committee on Human Research at the Karolinska Institutet, Stockholm, Sweden (Dnr 2014/1323–31 and 2015/611–32). Oral and written consent was obtained from all patients taking part in the study, and the trial was conducted according to good clinical practice. The study was registered before patient enrollment at clinicaltrials.gov #NCT02293421 2014-11-13 (principal investigator: Malin Jonsson Fagerlund; date of registration: November 18, 2014).

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Study Subjects

Adult patients (≥18 years of age) referred to a sleep clinic for evaluation of sleep apnea were consecutively enrolled during the first visit or part of the first visit, in this prospective observational multicenter study in 3 hospital departments of respiratory medicine (Gävle, Umeå, and Uppsala) and a private sleep clinic (Örebro). The study was conducted between November 2014 and January 2016.

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Sleep Study

All patients had an in-home overnight diagnostic sleep test (Embletta, Embla, Reykjavik, Iceland or NOX T3, Nox Medical, Reykjavik, Iceland) providing continuous recordings of thoracic and abdominal movements, nasal airflow via a nasal cannula connected to a pressure transducer, peripheral oxygen saturation, heart rate, and body position. Two sleep clinics used only Embletta as the diagnostic tool, 1 center used only NOX T3, and 1 clinic used both. Sleep time was estimated by use of each subject’s diary in conjunction with visual assessment of the overnight tracing.

Scoring was manually performed in accordance with validated guidelines8 and blinded for the STOP-Bang questionnaire and clinical information of the patient. Apnea was defined as ≥90% reduction in airflow from baseline for ≥10 seconds. Hypopnea was defined as ≥30% reduction in airflow from baseline for a minimum of 10 seconds combined with a reduction of oxygen saturation of ≥3%. An apnea-hypopnea index (AHI) was then determined based on estimated sleep time as the average number of apneas or hypopneas per hour of sleep. Sleep apnea was defined as an AHI ≥5. Moderate sleep apnea was defined as an AHI of 15–30, and severe sleep apnea was defined as an AHI ≥30. ODI was defined as the number of arterial oxygen desaturations of ≥3% per hour.8

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STOP-Bang Questionnaire

The STOP-Bang questionnaire consists of 8 acronyms (yes/no questions): S, Snoring; T, Tiredness; O, Observed apnea; P, high blood Pressure; B, Body mass index (BMI) >35 kg/m2; A, Age >50 years; N, Neck circumference >40 cm; and G, male Gender.10 The patients answered the first 4 questions (STOP questions), and the sleep clinic staff measured patient weight, height, blood pressure, neck circumference and then answered the last 4 questions (Bang questions), noting the type of portable sleep monitor. Besides answering the STOP questions, all patients also filled in a short medical questionnaire.

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Statistical Analysis

For our primary aim, to evaluate the correlation between AHI versus STOP-Bang, AHI versus ODI, and ODI versus STOP-Bang, a Spearman correlation coefficient was used because AHI and ODI are skewed and STOP-Bang is ordinal. Multiple logistic regression models were used for both primary and secondary aims to assess the individual contribution of each explanatory variable of the STOP-Bang items on an AHI ≥15 and an AHI ≥5. In addition, for both primary and secondary aims, receiver operating characteristic curves were created to assess the performance of the STOP-Bang questionnaire and gender for a predefined AHI of ≥15 and ≥5. Optimal STOP-Bang cutoff scores were identified using the Youden index, which maximizes the effectiveness of the test by maximizing the sum of the sensitivity and specificity, hence finding the cutpoint that generates the highest proportion of overall correct classifications. False positives and false negatives were given equal weight in the optimization.

Nominal values (STOP-Bang items and comorbid diseases) are presented as frequencies and percentages (Table 1). Normally distributed continuous variables (age and BMI) are presented as mean and standard deviation. Ordinal data (STOP-Bang score) and continuous variables with skewed distribution (AHI and ODI) are presented as median and quartiles. The Mantel-Haenszel test was used to look trends in STOP-Bang items and comorbid diseases in relation to AHI categories, and the Spearman rank order correlation was used for STOP-Bang score, AHI, and ODI (Table 1).

Table 1

Table 1

Graphs were made using SPSS software (version 23, IBM, Armonk, NY), and statistics were calculated using SPSS or R (Vienna, Austria). A P value of <.05 was considered statistically significant.

The sample size was calculated to test a STOP-BANG score ≥6 with an AHI ≥15. A sample size of n = 286 was calculated to be sufficient to detect a sensitivity for an AHI ≥15 at 0.5 (95% CI, 0.40–0.60) and a specificity at 0.9 (95% CI, 0.85–0.95) if the prevalence of an AHI >15 was 40%. The expected CI width was indicating and justifying the number of patients based on the width being precise enough. To capture generalizable data from 4 centers, we decided on a sample size of 450 patients, and each center received a predefined number of patients to include.

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RESULTS

Patient Characteristics

Of the 460 patients included in the study, 11 had missing STOP-Bang or AHI data, allowing the remaining 449 patients to complete the full study protocol (Gävle, n = 99; Örebro, n = 98; Umeå, n = 149; and Uppsala, n = 103). A total of 333 study subjects had Embletta, and 116 used NOX T3 recordings.

Patient median AHI was 9.6 (3.3–23.9). The mean age was 53.5 ± 13.8 years, BMI was 30.3 ± 5.7 kg/m2, and 61% were male. Thirty-nine percent of the patients had moderate or severe OSA (AHI ≥15), and their median AHI was 30.5 (21.8–49.0). Age, BMI, neck circumference, male gender, observed apneas, hypertension, renal failure, history of previous myocardial infarction, and former smoking were significantly different between the 3 groups: none, mild, or at least moderate OSA. Demographic data are presented in Table 1.

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STOP-Bang Characteristics of the Patients

The median STOP-Bang was 4 (3–5) in the total population, whereas it was 5 (4–6) in patients with moderate or severe OSA, 4 (3–5) in patients with mild OSA, and 3 (3–4) in the non-OSA population (P < .0001; Table 1). A STOP-Bang score of ≥3 was obtained by 89% of the patients. There was a moderate positive correlation between STOP-Bang score and AHI with a Spearman ρ of 0.50 (95% CI, 0.43–0.58; Figure 1). The distribution of the STOP-Bang score in all patients is presented in Supplemental Digital Content 1, Appendix 1, http://links.lww.com/AA/C485.

Figure 1

Figure 1

A score of 6 for an AHI ≥15 and 5 for an AHI ≥5 maximized the trade-off between sensitivity and specificity for STOP-Bang according to receiver operating characteristic curves for STOP-Bang scores (Figure 2).

Figure 2

Figure 2

Table 2

Table 2

A STOP-Bang score of <2 had a probability of 95% (95% CI, 0.92–0.98) to exclude an AHI ≥15 and a STOP-Bang score of ≥6 had a specificity of 91% (95% CI, 0.87–0.94) for an AHI ≥15. The diagnostic accuracy for STOP-Bang is displayed as sensitivity and specificity; positive predictable values and negative predictable values are presented in Table 2, and probability values are presented in Supplemental Digital Content 2, Appendix 2, http://links.lww.com/AA/C486.

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Exploration of the Individual Items of the STOP-Bang Questionnaire

Table 3

Table 3

A summary of different STOP-Bang items related to the degree of OSA is presented in Table 1. Using a multiple logistic analysis in which all STOP-Bang items were treated as binary explanatory variables, we found that all items positively affected the odds ratio for an AHI ≥15, but all were not significant when adjusted against each other (Table 3). Those items that significantly contributed to increase the odds ratio of an AHI ≥15 was P (high blood pressure), B (BMI), A (age), and N (neck circumference), with age (A) being the strongest contributing item.

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Effect of Gender on Polygraphy Data and STOP-Bang

There was a difference in the optimal cutoff for STOP-Bang at an AHI ≥15 in men and women, 6 (4–7) vs 4 (3–5) (P < .0001). AHI was higher in men compared to women, at 10.6 (4.0–28.9) vs 7.5 (2.4–19.7) (P = .05). The median STOP-Bang score was 5 (4–6) in men and 3 (3–4) in women (P < .0001). The items S (snoring), O (observed apnea), and N (neck circumference) were scored more frequently by men compared to women, while items T (tiredness), B (BMI), and A (age) were scored more frequently in women compared to men. While 62% of the men had a positive scoring on item N (neck circumference), only 16% of the females scored on that item. However, 52% of men and 57% of women who scored positive on neck circumference also had an AHI ≥15.

For an AHI ≥15, the item that had the lowest proportion was snoring (45%) for men and tiredness (32%) for women, while the item that had the highest proportion was BMI (67%) for men and neck circumference (57%) for women.

Multiple logistic analysis exploring the STOP-Bang items for men and women respectively, showed that all items positively affected the odds ratio, for an AHI ≥15 except for female tiredness. The items that significantly contributed to an increased odds ratio of an AHI ≥15 was P (high blood pressure), B (BMI), and A (age) for both men and women, but also N (neck circumference) for men (Supplemental Digital Content 3, Appendix 3, http://links.lww.com/AA/C487).

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Correlation Between ODI and AHI

AHI and ODI were highly correlated (Spearman ρ of 0.96 [95% CI, 0.94–0.97]; Figure 3).

Figure 3

Figure 3

There was a positive correlation between STOP-Bang score and ODI (Supplemental Digital Content 4, Appendix 4, http://links.lww.com/AA/C488), with a Spearman ρ of 0.50 (95% CI, 0.43–0.58), equal to that of STOP-Bang score and AHI.

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DISCUSSION

We found that the optimal cutoff score for the STOP-Bang questionnaire was 6 to identify patients with moderate to severe OSA. A STOP-Bang score of <2 rules out patients with OSA, with a probability of 95% (95% CI, 0.92–0.98). Ninety-one percent (95% CI, 0.87–0.94) of patients with a STOP-Bang Score of ≥6 had moderate and severe sleep apnea. A STOP-Bang score of 2–5 had a low specificity to rule in and rule out patients with OSA. There was a strong correlation (Spearman ρ of 96%, 95% CI, 0.94–0.97) between ODI and AHI. Our main focus has been on patients with moderate and severe sleep apnea (ie, subjects in need of therapy).

The STOP-Bang questionnaire was originally designed to rule out OSA in a general surgical patient population in which STOP-Bang scores of ≥3 were established as a cutoff for increased risk of OSA, yet this approach unfortunately includes many false positives. On the basis of this, a more detailed analysis of the STOP-Bang questionnaire has recently shown that scores of 0–2 are being considered low risk, scores of 3–4 as intermediate risk, and scores of 5–8 as a high risk of at least moderate OSA.11 In the present study, we demonstrate a moderate positive correlation between STOP-Bang score and AHI, which is in line with previous studies showing that a higher STOP-Bang score predicts more severe OSA.12,13

Our study of the STOP-Bang questionnaire shows a slightly higher sensitivity and negative predictive value and a lower specificity and positive predicted value at a cutoff STOP-Bang value of 3 compared to the meta-analysis by Nagappa et al.14 However, our findings regarding sensitivity, specificity, and positive and negative predicted values for both AHI ≥5 and AHI ≥15 are in line with previous reports in sleep clinic patients.15–17

In our population, we found that the Bang items contributed most to the overall STOP-Bang score in patients with OSA. In other studies, O (observed apnea) has been an item with a large impact on the data. We speculate that observed apnea in this population of patients actively consulting at a sleep clinic is common. Of the Bang items, A (age) was the item with the largest impact, which is also in contrast to the original paper by Chung et al.10 The population in the present study shares similar demographics as in the study by Cowan et al,15 but compared to other American sleep clinic studies, our study included more women, and study subjects had a slightly higher age.

We confirm previous observations that OSA is more common in the elderly and in those with increased BMI.18 However, although several studies have reported an increase in the prevalence of laboratory-defined OSA by age, OSA seem to have a less significant impact on elderly populations because the combination of OSA and clinical symptoms is highest among the middle aged.19

Sleep apnea has been considered a male disorder, and male gender is also included in the STOP-Bang questionnaire (ie, men by default will receive one more point in the STOP-Bang score than women. However, about one-third of our study population with at least moderate sleep apnea were women, and it was recently reported that 50% of women 20–70 years age have sleep apnea with an AHI >5.18 It is therefore of importance to acknowledge that OSA is not only a male disease, and that the inclusion of the male gender in the STOP-Bang questionnaire introduces a risk of discriminating women from further investigation of sleep apnea before surgery.

The correlation between AHI and ODI was as high as 0.96 (95% CI, 0.94–0.97), which supports previous observations to suggest that ODI can be used instead of AHI.20,21 ODI has also been shown to have a strong correlation with breathing results from portable polygraphy in the surgical population.22 It may be argued that a diagnostic test such as ODI obtained with only peripheral pulse oximetry might give the patient a better quality of sleep compared to a standard portable home monitor and provide a lower threshold for testing patients where resources are limited and waiting lists for more comprehensive sleep studies are long. The obvious limitation is that no breathing pattern information is obtained, and it is thus not possible to estimate sleep time or to differentiate between obstructive and central apneas.

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Strengths and Weaknesses

The strength of the present study is the prospective multicenter study design with a predefined goal to validate the STOP-Bang tool against a standardized OSA diagnosis tool based on in-home portable polygraphy blindly analyzed by sleep medicine expertise. Compared to previous sleep studies,14 the present study included a large sample size of >400 prospective patients, with a large proportion of women (39%). Furthermore, there were very few dropouts (2%) and strikingly high compliance, with 94% of included patients completing the full study protocol, which allowed us to identify relevant cutoff levels with better precision. This is in line with the original paper by Chung et al.10

Due to inherited study design with multiple sleep clinics, different portable diagnostic devices were applied for home monitoring. Previous studies have shown a good correlation of OSA diagnostics between the portable monitors Embletta and NOX T3 and laboratory nocturnal polysomnography being considered the gold standard of sleep apnea investigations,23–25 and 67% of all diagnostic testing in Europe is based on cardiorespiratory polygraphy.26

In the present study, conducted in 4 different sleep clinics, we found a high prevalence of 89% of OSA (AHI ≥5), which is in line with previous studies of the same study population.14 It has been shown that the prevalence of OSA differs in different study populations,14 and it is generally higher in the sleep clinic population compared to other groups, such as the surgical population in general.

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Clinical Perspective

Based on the data from this and other studies, the STOP-Bang questionnaire has a good correlation to the degree of OSA. However, we found that a score <6 has a low specificity, leading to large number of false-positive tests. It is of value in the preoperative setting to exclude sleep apnea and to identify patients with moderate and severe OSA. We suggest that the STOP-Bang questionnaire is used as a first-line screening tool to exclude patients with low risk of sleep apnea (ie, a STOP-Bang score <2) and to identify patients with a high risk of moderate and severe sleep apnea (ie, a STOP-Bang score of ≥6). In line with previous studies, we found a very high correlation between AHI and ODI, and we therefore suggest that ODI could be used as a surrogate for AHI in a preoperative setting when there is a need for a rapid diagnosis without delaying surgery.22

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CONCLUSIONS

STOP-Bang and pulse oximetry can be used to screen for sleep apnea. A STOP-Bang score of <2 almost excludes moderate and severe OSA, whereas nearly all the patients with a STOP-Bang score ≥6 have OSA. We suggest the addition of nightly pulse oximetry in patients with a STOP-Bang score of 2–5, when there is a need for screening for sleep apnea (ie, before surgery).

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ACKNOWLEDGMENTS

We thank the staff at the sleep clinics involved in patient inclusions and diagnostic testing.

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DISCLOSURES

Name: Eva Christensson, MD, DESA.

Contribution: This author helped design the study, perform the data and statistical analysis, interpret the results of the experiment, and write the manuscript.

Name: Karl A. Franklin, MD, PhD.

Contribution: This author helped design the study, perform the data and statistical analysis, interpret the results of the experiment, and write the manuscript.

Name: Carin Sahlin, PhD.

Contribution: This author helped perform the experiments, interpret the results of the experiment, and write the manuscript.

Name: Andreas Palm, MD.

Contribution: This author helped perform the experiments, interpret the results of the experiment, and write the manuscript.

Name: Jan Ulfberg, MD, PhD.

Contribution: This author helped perform the experiments, interpret the results of the experiment, and write the manuscript.

Name: Lars I. Eriksson, MD, PhD, FRCA.

Contribution: This author helped interpret the results of the experiment and write the manuscript.

Name: Eva Lindberg, MD, PhD.

Contribution: This author helped perform the experiments, interpret the results of the experiment, and write the manuscript.

Name: Eva Hagel, MSSc.

Contribution: This author helped interpret the results of the experiment, conduct statistical analysis, and write the manuscript.

Name: Malin Jonsson Fagerlund, MD, PhD, DESA.

Contribution: This author helped design the study, perform the data and statistical analysis, interpret the results of the experiment, and write the manuscript.

This manuscript was handled by: David Hillman, MD.

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