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First Evidence of a Polygenic Susceptibility to Pain in a Pediatric Cohort

Mamie, Chantal MD*; Rebsamen, Michela C. PhD; Morris, Michael A. DPhil; Morabia, Alfredo MD, PhD§

doi: 10.1213/ANE.0b013e31826f0637
Pediatric Anesthesiology: Research Reports
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BACKGROUND: There is currently no evidence about the genetic bases of postoperative pain variability in children.

METHODS: We prospectively followed a cohort of 168 children after orthopedic or abdominal surgery, who were under morphine patient-controlled analgesia. The children and their parents were genotyped for 6 candidate-gene polymorphisms (single-nucleotide polymorphisms [SNPs]) implicated in nociception and opiate metabolism: ABCB1C3435T, COMTVal158Met, NTRK1His40Tyr, OPRMA118G, POMCArg236Gln, and a haplotype of CYP2D6. Postoperative pain was assessed using the Faces Pain Scale (FPS), at rest and during mobilization, 11 times during the first 24 postoperative hours.

RESULTS: At rest, and to a lesser extent, at mobilization, having at least 4 pain peaks of FPS score >6 in 24 hours was more frequent in children with ABCB1_CC than in children with ABCB1_CT and ABCB1_TT (adjusted risk ratio = 4.5; 95% confidence interval [CI],1.5–13.4; corrected CI for multiple comparisons, 0.98–20.55) and was more frequent in children with OPRM_GA than those with OPRM_AA (adjusted risk ratio = 3.5; 95% CI, 1.1–11.2; corrected CI, 0.70–17.30). After adjusting for parental mating type and correcting for multiple comparisons, mean FPS scores across the 24 postoperative hours were higher for OPRM_GA than for OPRM_AA at rest (P < 0.0002), higher for NTRK1_ CT or NTRK1_ TT than NTRK1_ CC during mobilization (P = 0.002), and lower for COMT_GG than COMT_AA and COMT_GA, during mobilization (P = 0.005).

CONCLUSIONS: ABCB1 and OPRM genotypes are associated with clinically meaningful pain variability, whereas NTRK1 and COMT are linked to subclinical effects. This first but small cohort study provides clues to further explore the genetic foundations of pediatric pain.

Published ahead of print December 7, 2012 Supplemental Digital Content is available in the text.

From the *Unit of Pediatric Anesthesiology, Division of Anesthesiology, Department of Anesthesiology, Pharmacology and Intensive Care, Geneva University Hospitals; Laboratory of Toxicogenetics, Service of Laboratory Medicine, Geneva University Hospitals; Molecular Diagnostic Laboratory, Service of Genetic Medicine, Geneva University Hospitals; and §Division of Clinical Epidemiology, Department of Community Medicine, Geneva University Hospitals, Geneva, Switzerland.

Accepted for publication August 13, 2012.

Published ahead of print December 7, 2012

A.M. is currently at Mailman School of Public Health, Columbia University, New York; and Center for the Biology of Natural Systems, Queens College of the City of New York, Queens, NY.

Supported by grant 32473B-111910 from the Swiss National Science Foundation, Bern, Switzerland.

The authors declare no conflicts of interest.

Reprints will not be available from the authors.

Address correspondence to Chantal Mamie, MD, Unit of Pediatric Anesthesiology, Division of Anesthesiology, Department of Anesthesiology, Pharmacology and Intensive Care, Geneva University Hospitals, Geneva, Switzerland. Address e-mail to chantal.mamie@hcuge.ch

There is a wealth of research on the genetic bases of pain in animals and human adults,1 but there are no reports of genetic epidemiology studies of pain in pediatric settings.2,3

We conducted a cohort study to explore whether polymorphisms from several candidate genes are associated with the postoperative pain of children undergoing major surgery. The patients were genotyped for 6 candidate genes, which were involved in relevant pain-mediating pathways and in the pharmacokinetics of opioids, and for some of them, already investigated in adult clinical studies.4 The following candidate genes were investigated:

1. ABCB1 codes for the membrane-bounded P-glycoprotein (Pgp), which is the most important efflux transporter from the central nervous system of exogenous opioids.4–7Reduced activity of Pgp can result in more effective analgesia by increasing the half-life and/or concentration of analgesics in the brain.5Although both alleles of the single-nucleotide polymorphism (SNP) C3435T are synonymously coding for isoleucine, they seem to be functionally different, as a result of conformational changes.8

2. OPRM1 codes for the µ-opioid receptor, which is less efficient in the presence of the nonsynonymous SNP A118G, leading to the substitution of asparagine for aspartate.9 Patients harboring the G allele appear to require slightly more opiates after hysterectomy or total knee arthroplasty.10,11

3. COMT codes catechol-O-methyltransferase, which inactivates the neurotransmitters dopamine, epinephrine, and norepinephrine, which are implicated in the modulation of pain. Research about the SNP COMTVal158Met has yielded conflicting results in adults, increased pain being associated with the variant Met/Met genotype,12 or with the wild-type Val/Val genotype,13,14 or unrelated to a genotype.15

4.The NTRK1 codes for the neurotrophic tyrosine kinase receptor type 1, essential for the development of nociceptors. Because 3 mutations of NTRK1 lead to insensitivity to pain with anhydrosis,16 we postulate that this gene could play a role in the variability of pain intensity and investigate the nonsynonymous SNP NTRK1H604Y, with substitution of tyrosine for histidine.

5. CYP2D6 codes for the cytochrome P450, which acts at critical steps of endogenous morphine biosynthesis. More effective endogenous pain inhibition is described in ultrarapid metabolizers.17We have opted to analyze a haplotype of CYP2D6.

6. POMC, which codes for proopiomelanocortin, plays a key role in β-endorphin synthesis. We genotyped the nonsynonymous SNP POMC Arg236 Gln.18

All the selected SNPs are described in Table 1, and the connections and pathways are presented in Figure 1.

Table 1

Table 1

Figure 1

Figure 1

Population stratification could be an important bias of gene–disease association studies, and could be one cause of lack of replication. Disease prevalence (pain in the present study) could differ between population subgroups. Allelic frequencies for a genotype could also differ by chance between these subgroups, with these differences being independent. However, this could lead to false associations, or mask or reverse an association. Geneva is an international city and has a great diversity of nationalities among its residents. Massive migrations occurred successively during the last 50 years (first from Italy, then from Spain, later from Portugal, and recently from Kosovo). Adjusting for parental mating type is a means to control for population stratification, by assessing associations within categories of parental combination of genotypes and then averaging across these categories. It is an optimal way to adjust for determinants of pain related to the studied allele when it is more common in some ethnic groups than in others.19 Thus, we also genotyped the parents of the studied children, determined their mating types (e.g., heterozygous × heterozygous for the rare variant), and performed mating-type-adjusted analyses.

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METHODS

After IRB approval and parents’ written consent, 201 children, aged 4 to 16 years, were enrolled in this prospective observational study. We scheduled children younger than 6 years for patient–parents–nurses-controlled analgesia (PPNCA), and children 6 years and older for patient-controlled analgesia (PCA). All underwent orthopedic or abdominal surgery at the Children’s Hospital of Geneva, Switzerland, from September 2006 to September 2009.

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Data Collection

A nurse specialized in pediatric postoperative pain management longitudinally assessed the intensity of post operative pain at rest and, if feasible, during mobilization (or cough) using the revised FACES Pain Scale (FPS)20 at 0.5, 1, 1.5, 2, 4, 6, 8, 12, 16, 20, and 24 postoperative hours, granted that the child was awake.

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Anesthesia

A balanced anesthetic technique was used for all children, maintained with sevoflurane (1 minimum alveolar concentration) and air/O2 50/50. Fentanyl was titrated to keep heart rate and mean arterial blood pressure within 10% of preoperative baseline values.

To avoid hyperalgesia, ultrashort opiates, such as remifentanil and sufentanil, were not used in this study, but ketamine at antihyperalgesic doses was allowed, if decided by the anesthesiologist in charge (bolus of 0.150–0.200 mg/kg at the beginning of surgery, without infusion during or after surgery). Tracheal extubation was performed at the end of surgery.

Postoperative analgesia was achieved by PPNCA or PCA, according to the Pediatric Anesthetic Unit Guidelines (bolus: 20 μg/kg, continuous infusion: 20 μg/kg/h, lockout time: 8 minutes, and bolus demand: 6 maximum bolus per hour; if visual analog scale (VAS) score is >4 and bolus demand exceeds 6, then bolus is increased to 30 μg/kg; if analgesia is still unsatisfactory, continuous infusion increases to 30 μg/kg). Paracetamol and nonsteroid antiinflammatory drugs (NSAID) were systematically prescribed in the absence of surgical contraindications (e.g., callus formation).

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Laboratory Investigations

For genetic analysis, 3 mL of EDTA blood was drawn during the child’s procedure, and from each parent, if accessible.

Total genomic DNA was extracted according to a standard protocol of the Molecular Diagnostic Laboratory (Gentra Autopure, QIAGEN, Switzerland). Samples were stored in DNA hydration solution (Gentra) at 6°C.

The following SNPs were analyzed: ABCB1C3435T, COMTVal158Met, NTRK1His40Tyr, OPRMA118G, POMC Arg236Gln, and a haplotype of CYP2D6 (CYP2D6*3, CYP2D6*4, CYP2D6*5, CYP2D6*6, and CYP2D6*2×N).

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Statistical Analysis

Genetic Analysis

Each SNP was categorized into 3 possible genotypes. However, when the frequency of children who were homozygous for the variant at a locus was too low, we pooled them with those who were heterozygous for the variant. Hardy-Weinberg equilibrium was tested using χ2 analysis, by comparing the observed and the expected heterozygosity.

Genotyping the parents permitted us to categorize the children according to the mating type of their parents. For example, for ABCB1, the parental mating type of a CC child could be either CC × CC, CT × CT, or CC × CT. Parents having the same mating type are more likely to have the same ethnic and genetic background.21

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Statistical Power Analysis

We expected that a sample size of 180, assuming α = 0.05, would give a statistical power >0.8 to detect an odds ratio of 2.0 if the prevalence of the reference genotype was >10% (as for NTRK1), and >1.8 if the prevalence of the reference genotype was >20% (as for ABCB1, COMT, and OPRM).

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Definition and Analysis of Pain Outcomes

Morphine consumption, number of pain peaks >6 on the FPS, and mean FPS score at rest and at mobilization were the dependant variables of interest.

  1. Total morphine consumption per kilogram per 24 hours and bolus morphine consumption per kilogram per 24 hours were calculated for each child. Being lognormally distributed, total morphine consumption was log-transformed for the analysis and described using means and medians. High morphine consumption was defined as more than the median of 440.5 μg/kg/24 h.
  2. FPS scores were analyzed separately at rest and during mobilization. Nonassessed FPS scores, because of the child being asleep, were coded as missing values. For ethical reasons, the nurses were instructed not to insist that the child cough or move when the child was in too much pain. In these cases, the FPS score at rest was imputed as the FPS score at mobilization, because the latter would be at least as high. This approach underestimates the true pain level at mobilization, but less so than if these assessments were treated as missing values, because children accept movement mostly when their pain is under control.

Up to 22 longitudinal pain intensity measures were available for each child, 11 at rest and 11 at mobilization. The distribution of the mean FPS scores per child was, as expected, close to normally distributed, allowing us to perform group comparisons treating pain at rest and at mobilization as continuous variables. The consensus is that successful analgesia keeps the FPS score <4.22 Thus, a polymorphism can be deemed to clinically increase pain intensity when its presence is associated with a higher number of pain bouts of FPS score ≥4. We therefore compared the different genotypes separately for rest and mobilization assessments for (a) the number of pain peaks of FPS score ≥4 during the first 24 hours, and (b) having or not having at least 4 pain peaks of FPS score ≥6. The latter cutoff was chosen because it segregated the upper 15% of the whole sample with the highest postoperative pain intensity. We used analysis of variance (SAS Proc GLM) to estimate the mean number of pain peaks across genotypes adjusted for age, gender, Caucasian origin, NSAID prescription, type of surgery (orthopedic or general surgery), and all other SNPs and multiple logistic regression (SAS Proc logistic) to estimate the adjusted odds ratio of having at least 4 pain peaks of VAS score ≥6. Given that this is a cohort study, these odds ratios can be interpreted as risk ratios, exactly.

  • 3. Mean 24-hour FPS values allowed us to take advantage of the longitudinal measurements over 24 hours. We used mixed linear regression models with random intercept for repeated measurements (SAS Proc mixed). Instead of modeling 1 mean per subject, these models repeat each record for as many times as there are repeated observations for that subject. For example, a child with 10 FPS values at rest would count as 10 records. The model estimates the average of the 10 records, and computes a variance that takes into consideration the intracorrelation of the FPS values for the same subject. Because, to our knowledge, there is no evidence that 2 consecutive pain measurements are more similar than more distant evaluations, we assumed equal correlations among all measurements, whether they are closer in time or not.

Mixed models had several advantages in this study: (a) they increased statistical power, and (b) they allowed for adjustment by parental mating types because FPS means could be computed after stratification by parental mating types, even though mating types observed for fewer than 5 child–parental trios had to be excluded from the analysis.23 The disadvantages were that (a) we were limited to 110 sets of complete child–parental trios, and (b) extensive adjustment for covariates was not feasible because, after having stratified these 110 children for parental mating types, strata became too thin to further adjust for gender, age, and surgery.

The statistical significance was defined at P < 0.05. However, we used Bonferroni continuity correction to address the risk of statistically significant findings resulting from multiple comparisons. Because we studied 4 genes and 2 types of pain (rest and mobilization), we set the α level at 0.05/8 = 0.00625.

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RESULTS

Among the 201 children enrolled in the cohort, 19 (9.2%) were ineligible because of parent refusal to give informed consent, parents not speaking French, psychiatric disorder, psychomotor disability or attention-deficit disorder, chronic opiate treatment before surgery, or because of a history of drug dependence or drug abuse in parents. Three patients did not have surgery at all. Three PPNCA were stopped before the 24th postoperative hour. One patient returned home before the 24th postoperative hour, and 5 parents could not be interviewed. The final cohort comprised 170 children.

The 170 patients were genotyped for all SNPs, but there were 2 laboratory failures for ABCB1, OPRM, and COMT, resulting in a fully genotyped cohort of 168 children. There were 110 complete trios and 58 duos (only 1 genotyped parent). The number of children harboring the variant allele was too small to allow for a meaningful comparison between pain and POMCArg236GIy (165 GG and 2 GC) or CYP2D6 haplotype (6 poor, 151 extensive, and 10 ultrarapid metabolizers). Thus, these polymorphisms were not further analyzed.

The first row of Table 2 provides demographic data for the 168 children. Table 2 also provides the distribution of the demographic, surgical, and analgesic characteristics according to the genotypes of the 4 genes analyzed. As expected, most associations were not statistically significant but some associations were prominent in terms of gender, age, Caucasian origin, orthopedic surgery, and NSAID. These factors were added as covariates in the subsequent analyses.

Table 2

Table 2

There were no differences in genetic frequencies of each SNP among the children, the mothers, and the fathers (Table 3). For each SNP, alleles were in Hardy-Weinberg equilibrium.

Table 3

Table 3

The longitudinal analysis of pain scores showed that fewer than 1% of the children had 24-hour streaks of FPS score >4. However, some children had peaks of intense pain, which would generally be considered as unsuccessful analgesia. These pain peaks differed by genotypes. The total number of peaks with FPS score >4, adjusted for gender, age, Caucasian origin, NSAID prescription, type of surgery, and all other SNPs, was greater in ABCB1_CC than ABCB1_CT or ABCB1_TT at rest (3.1 vs 1.9 peaks, P = 0.002) and at mobilization (4.4 vs 3.2, P = 0.02).

Table 4 shows the associations between each SNP and having or not having ≥4 peaks of pain of FPS score >6, at rest and during mobilization, adjusted for gender, age, Caucasian origin, NSAID prescription, type of surgery, and all other SNPs. Statistically significant risk ratios were observed, at rest, for ABCB1_CC (risk ratio = 4.5, 95% confidence interval [CI], 1.5–13.4) and OPRM_GA (risk ratio = 3.5, 95% CI, 1.1–11.2). The corrected CIs for multiple comparisons were 0.98–20.55 for ABCB1_CC and 0.70–17.30 for OPRM_GA.

Table 4

Table 4

Table 5 shows the mean FPS scores across the 24 postoperative hours, for each genotype, after adjusting for parental mating types using a mixed linear regression model and Bonferroni correction for multiple comparisons. Children with genotypes CT and TT of NTRK1 presented significantly higher mean FPS scores at mobilization, 4.3 and 3.1, respectively (P = 0.002), compared with those with the wild-type CC. The effect of OPRM was significant at rest, with mean FPS score of 2.4 for AA and 3.3 for GA (P < 0.0002). The effect of COMT was significant during mobilization, with the wild-type GG having a mean FPS score of 3.2 compared with GA (3.9) and AA (4.1; P = 0.005).

Table 5

Table 5

The maximum 24-hour cumulative morphine consumption was 2071 μg/kg (median = 440.5, SD = 415). Table 6 shows the cumulative morphine consumption after stratification by age and type of surgery (orthopedic or general surgery).

Table 6

Table 6

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DISCUSSION

In this cohort of children undergoing major surgery, the polymorphisms of at least 2 genes appeared to play a clinically significant role in the intensity of postoperative pain. Children harboring these genotypes were in intense pain for at least 36% of their assessments (4/11) during the first 24 hours after surgery. Children with genotype ABCB1_CC were 4.5 times more likely to experience ≥4 peaks of intense pain (FPS score >6) compared with children with genotypes ABCB1_CT or ABCB1_TT. Children with genotype OPRM_GA were 3.5 times more likely to experience ≥4 peaks of intense pain (FPS score >6) compared with children with genotype OPRM_AA.

We further explored the effect of the polymorphisms on mean pain over the 24 hours after surgery, using mixed linear models. The 24-hour FPS means were higher with the C allele of ABCB1C3435T, the A allele of COMTVal158Met, the T allele of NTRK1H604Y, and the G allele of OPRMAsn40Asp. As expected with PCA analgesia, these means were below or around 4 because, for a majority of children, pain is under control for most of the postoperative time. Over 24 hours, pain peaks were therefore diluted. With stronger criteria for statistical analysis (Bonferroni correction for multiple correlation), the statistically significant associations were those between OPRM and pain at rest and between COMT or NTRK1 and pain at mobilization.

The crude analyses of 24-hour means, before adjusting for parental mating types, confirmed the association of ABCB1 with both pain at rest and at mobilization, and of OPRM with pain at rest. However, mixed models allowed us to stratify the sample by parental mating types in the subsample of 110 complete child–parental trios. Adjusting for the parent mating types revealed more associations, but the sample size did not allow us to further adjust for additional confounders. These findings therefore need to be corroborated in larger studies.

Many of our results remained statistically significant after accounting for chance findings associated with having linked 2 related pain facial scores with the 4 same SNPs. This is of moderate interest however because, in an observational cohort study, adjustment for multiple comparisons dramatically reduces its ability to detect potentially important findings.24

It is noteworthy that mating type of both parents could not be established for 58 children. There are techniques to use the duos information for case-control studies, using a statistical technique, in which only data of the sick children are used.19,23 In this study, sick children were those with intense pain. We tried these approaches in the present study, but they were even less powerful than those we used, because this is a cohort study and not a case series of sick children.

The present results are plausible given the known functionality of the candidate genes (Table 1 and Fig. 1), and are consistent with the findings in adults. For ABCB1, CC adult cancer patients had more intense pain,25 but there was no difference in postoperative morphine consumption after colorectal procedures.26 The G allele of OPRMA118G was associated with higher VAS scores and morphine consumption in 994 women undergoing cesarean delivery,27 with a lower pain tolerance threshold (electric stimulation) and higher fentanyl consumption in 177 gynecologic patients.28 Higher postoperative pain scores have been reported for COMT Met/Met (AA) genotype compared with Val/Val (GG) genotype, without significant differences in morphine consumption.29 Finally, a haplotype GGGG from 4 SNPs (including COMT Val/Met) has been associated with lower30 and higher15 pain ratings.

On the other hand, we could not compare our results about the SNP NTRK1H604Y to other publications because this is the first study investigating its association with pain intensity. The variants of CYP2D6 and POMCArg236Gly were too rare in our cohort to permit meaningful analyses.

We were surprised not to find differences in average morphine consumption across the genotypes of the investigated genes, when matching the associations observed between genes and FPS pain levels. There are several possible explanations for this apparent inconsistency. First, genotypes may influence the pattern of morphine consumption over 24 hours, that is, whether it was mostly consumed immediately after surgery or several hours later, rather than the cumulative amount. We could not test this hypothesis because we only measured the cumulated morphine received over 24 hours after surgery. Second, the association between genotypes and morphine consumption may be confounded by other factors, such as the use of NSAIDs, type of surgery, and so on. However, this did not seem to be an issue in this study because morphine differences were adjusted for these factors. Moreover, the median FPS scores at rest and during mobilization, and morphine consumption levels were almost identical in the abdominal and orthopedic surgery groups. Third, PPNCA used for younger children could have distorted the association between pain and morphine consumption, if parents or nurses did not activate PPNCA adequately. This is unlikely because there was no heterogeneity of FPS–morphine consumption association across age subgroups. Fourth, other factors than pain, such as psychological factors,31could restrain adults from using PCA.31–33 For example, Bayar et al.34 observed that, compared with an intramuscular meperidine group, PCA users were more satisfied with their analgesia, although their pain scores were higher than those of the meperidine group for the same procedure. In adults, the placebo component of PCA, quantified by Keeri-Szanto35 and varying from 22% to 70% among patients, may interfere with PCA use. These factors, not yet investigated in children, could attenuate differences in consumption across genotypes, particularly in our cohort, where the differences in mean FPS scores were small.

The present study provides clues to further explore the genetic foundations of pediatric pain. The effect magnitudes reported here will be useful to establish the sample sizes of future studies. Future multicenter studies, having access to large populations of surgical patients, may be able to limit the eligibility to 1 type of surgical procedure, to standardize the pain stimulus. Also, a longitudinal assessment of FPS scores over the first 24 hours should be matched with a longitudinal assessment of morphine consumption to determine whether there is a genetic influence on the pattern rather than on the average amount of morphine consumption. In addition to FPS scores and morphine consumption, pre- and postoperative quantitative sensory testing could be performed to measure sensitization areas. The strong confounding by parental mating type stresses the importance of collecting parental DNA in pediatric genetic epidemiology studies. Pediatric studies are optimal for collecting blood from parental–child trios because parents are more likely to be alive in these studies than they would be in adult cohort studies. Finally, future studies should consider genotyping 4 variants of COMT30 instead of 1 as in this study.

In conclusion, our study is highly suggestive of a genetic component in pain response among children: ABCB1C3435T and OPRMAsn40Asp appear to have clinically significant effects whereas those of NTRK1H604Y and COMTVal158Met are more subtle. More research is needed to confirm these associations and, if confirmed, to test how they can be translated into clinical practice.

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DISCLOSURES

Name: Chantal Mamie, MD.

Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.

Attestation: Chantal Mamie has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

Name: Michela C. Rebsamen, PhD.

Contribution: This author helped conduct the study, analyze the data, and write the manuscript.

Attestation: Michela C. Rebsamen has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Michael A. Morris, DPhil.

Contribution: This author helped conduct the study, analyze the data, and write the manuscript.

Attestation: Michael A. Morris has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

Name: Alfredo Morabia, MD, PhD.

Contribution: This author helped design the study, analyze the data, and write the manuscript.

Attestation: Alfredo Morabia has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

This manuscript was handled by: Peter J. Davis, MD

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ACKNOWLEDGMENTS

We are grateful to Laurence Faou and PA Meyer (nurses, Unit of Pediatric Anesthesiology, Division of Anesthesiology, Geneva University Hospitals, Geneva) for their patient care and data collection and to Simon Merolle (laboratory assistant, Molecular Diagnostic Laboratory, Service of Genetic Medicine, Geneva University Hospitals, Geneva) for his expert technical assistance.

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