CHALLENGES IN IDENTIFYING SLEEP STAGES FROM PATIENTS TREATED WITH SEDATIVE-HYPNOTIC DRUGS AND ANTIDEPRESSANTS

Acda RN, Fernandez A, Raman S, Williams V, Fafowora O

Integrated Sleep Disorders Center, McGuire Veterans Medical Center, Richmond, VA

INTRODUCTION:In February 2016, results of a study released by the U.S.Centers for Disease Control and Prevention (CDC) indicatedthat more than a third of American adults are not gettingenough sleep on a regular basis. As of 2010, about 4% ofadult Americans take prescription sleep aids, while close to13% of Americans over the age of 12 reported use of anti-depressants in 2014.

IMPLICATIONSPerforming diagnostics and application of therapeutic PAPtreatments during live acquisitions often involve qualitativeand appreciable decision making processes from aregistered technologist or technician. Historically, it hasbeen understood that staging polysomnograms havetendencies to be subjective in nature as shown in recentAASM Sleep ISR with varying degrees of difficulty.Contributing factors such as pharmacological effects inrelation to a person’s sleep architecture,electroencephalography (EEG), electrooculography (EOG)and electromyogram (EMG) are common challenges thattechnologists encounter in delivering quality staging forphysicians and care to patients.

Shown in Figure 1.0 is a perfect example of stagingdisagreements where only 9.07% of the total population ofSleep ISR takers agreed with the Gold Standard. Much worseyet is the percentage that are in Full Agreement with theassessment of this particular epoch at 5.27%..

Antidepressants include tricyclic agents (TCAs), monoamine oxidaseinhibitors (MAOIs), serotonin antagonist reuptake inhibitors (SARIs),and selective serotonin reuptake inhibitors (SSRIs). Some physicianshave been prescribing antidepressants due to the strong bi-directional correlation between depression and insomnia.

EFFECTS ON SLEEP QUALITYVariable effects on sleep quality have been observed on differentkinds of antidepressants. Some are sedating while others arestimulating.

EFFECTS ON SLEEP ARCHITECTURE- Suppresses Stage R sleep except for SARIs- Increases latency to the onset of Stage R sleep (See Fig 3.0)- SSRIs appear to increase stage N1 sleep- REM rebound (exaggerated) effects have been observed when

they are abruptly stopped

POTENTIAL PITFALLS- An observed effect of antidepressants is excessive EOG activity

(commonly referred as Prozac Eyes). Technologists usuallymistaken epochs such as shown in Figure 2.0 as Stage R.

- Figure 2.1 shows the same subject’s real Stage R. Please notethe differences in EEG and EMG amplitude against Fig 2.0.

REFERENCES1. Liu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of healthy sleep duration among adults — United States, 2014. MMWR Morb Mortal Wkly Rep. 2016;65(6):137–141.2. Chong Y, Fryar CD, Gu Q. Prescription sleep aid use among adults: United States, 2005–2010. NCHS data brief, no 127. Hyattsville, MD: National Center for Health Statistics. 2013.3. Pratt LA, Brody DJ, Gu Q. Antidepressant use among persons aged 12 and over: United States, 2011–2014. NCHS data brief, no 283. Hyattsville, MD: National Center for Health Statistics. 20174. Kilduff TS, Mendelson WB. Mechanisms of action and pharmacologic effects. In: Principles and Practices of Sleep Medicine, 6th ed, Kryger MH, Roth T, Dement WC (Eds), Elsevier Saunders, St Louis, MO 2016. p.424.5. Buysse DJ, Tyagi S. Clinical pharmacology of other drugs used as hypnotics. In: Principles and Practices of Sleep Medicine, 6th ed, Kryger MH, Roth T, Dement WC (Eds), Elsevier Saunders, St Louis, MO 2016. p.432.6. Chawala, J, MD. "Insomnia Medication." Medscape. Updated: Aug 01, 2016.7. Pagel, J.F, and Parnes, B.L., 2001, Medication for the Treatment of Sleep Disorders: An Overview, Primary care Companion p118-125.

Benzodiazepines, barbiturates and nonbenzodiazepine receptoragonists are sedative-hypnotics medications prescribed to slowdown the body's functions. Over the years, barbiturates has largelybeen replaced by benzodiazepines and nonbenzodiazepine drugsdue to the lower risk of addiction and overdose.

EFFECTS ON SLEEP QUALITYReduced time to sleep onset and potentially reduce wakefulnessand increase the total sleep time.

EFFECTS ON SLEEP ARCHITECTURE- Reduces the amount of stage N1 sleep.- Potentially increases spindle activity during N2- Modestly reduces rapid eye movement (REM) sleep when used

at higher than indicated doses.

POTENTIAL PITFALLS- Patients with advanced Parkinson’s Disease are usually

prescribed with sedative-hypnotics to help reduce wakefulness.Due to slowing of the alpha rhythm and increase of activity inslower frequencies, techs need to practice vigilance in scoringNREM and Wake transitions as PD patients often suffer fromthe worst cases of disrupted sleep.

- Traditionally, the success of PAP therapy depends on a patient’sresponse resulting in Stage R. Increasing PAP pressures in thehopes of “forcing” REM should be discouraged as it may lead toadditional discomfort and PAP-induced events (Fig 3.0).

- Figure 3.0 shows Central Apneas being observed due tocontinuous increase in PAP pressure despite the absence ofrespiratory events.

ANTIDEPRESSANTS

CONCLUSIONRecognizing the possible pharmacological effects on apatient’s sleep architecture is very important in aidingtechnologists and other sleep professionals in stagingpolysomnograms based on usual interpretation methodsthat are applied to the majority of acquisitions. Being ableto appropriately score and stage acquisitions of varyingdegrees of difficulty helps in minimizing misdiagnosis,delays in diagnosis and treatments, as well as results inbetter overall patient engagement and outcomes.

SEDATIVE-HYPNOTICS Below are lists of the most commonly prescribed antidepressantsand sedative-hypnotics. Please note that this is not acomprehensive list of drugs that may be available in the markettoday.

Impact of montelukast and fluticasone on quality of life in mild pediatric sleep apneaAndrew E Bluher, MD1, Craig C Brawley, MD2, Tina D Cunningham, PhD3, Cristina M Baldassari, MD4

1 Department of Otolaryngology – Head and Neck Surgery, Eastern Virginia Medical School, Norfolk, Virginia, USA2 Department of Otolaryngology – Head and Neck Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA 3 Healthcare Delivery Science Program, Eastern Virginia Medical School, Norfolk, Virginia, USA4 Department of Pediatric Otolaryngology, Children’s Hospital of The King’s Daughters, Norfolk, Virginia, USA

Research has shown improvement in apnea-

hypopnea index in children with mild obstructive sleep

apnea treated with anti-inflammatory medications.

Data on quality of life outcomes in children receiving

these medications is lacking. We aim to assess

quality of life in children with mild obstructive sleep

apnea treated with montelukast and fluticasone.

Children between 3 and 16 years old with mild sleep

apnea (apnea-hypopnea index > 1 and ≤5)

presenting to a tertiary care hospital were recruited

prospectively and treated with 4 months oxf

montelukast and fluticasone. Subjects’ caregivers

completed the OSA-18, a validated quality of life

survey, at baseline and 4 months. Children with

ongoing obstruction at follow-up underwent

adenotonsillectomy.

A power analysis was conducted prior to undertaking

this study. A group size of 16 was deemed sufficient

to detect a 20-point difference in OSA-18 QOL scores

with 80% power and a .05 significance level.

Descriptive statistics were reported. Means and

standard deviations were used for continuous

outcomes and frequencies and counts were used for

categorical outcomes. Paired t-test was used to

compare continuous outcomes between baseline and

follow-up, and two-sample t-test was utilized to detect

differences between groups. To investigate factors

that were associated with failure of medical therapy,

logistic regression models were fitted. Odds ratios

and 95% confidence intervals were reported. The

analysis was performed using SAS software version

9.3 (SAS Institute, Cary, North Carolina).

Thirty-one patients were included. Mean (SD) age was

6.8 (3.9) years. Most subjects (54.8%) were black and

48% were obese. Mean (SD) apnea-hypopnea index of

the subjects was 2.8 (1.0). The mean (SD) baseline

OSA-18 score was 60.2 (18.5), indicating a moderate

impact of sleep disturbance on quality of life. Following

treatment, there was significant improvement (p<0.005)

in mean OSA-18 score. Four children discontinued

montelukast due to behavioral side effects. Seven

children (22%) underwent adenotonsillectomy after failing

medical therapy. Demographic factors such as obesity

[OR 0.63 (0.11, 3.49)] and apnea hypopnea index [OR

1.38 (0.59, 3.66)] failed to predict which children would

respond to anti-inflammatory medications.

Children with mild OSA treated with montelukast and

fluticasone experience significant improvements in QOL.

Baseline disease-specific QOL scores may be useful in

predicting which children will respond to medical therapy.

Further research is needed to determine the optimal duration

of anti-inflammatory medications and to compare outcomes for

medical and surgical therapy for non-severe pediatric OSA.

Introduction

Procedures

Results Conclusion

1. Aldea Perona A, García-Sáiz M, Sanz Álvarez E. Psychiatric Disorders and

Montelukast in Children: A Disproportionality Analysis of the VigiBase(®). Drug Saf.

2016;39(1):69-78.

2. Baldassari CM, Mitchell RB, Schubert C, Rudnick EF. Pediatric obstructive sleep

apnea and quality of life: a meta-analysis. Otolaryngol Head Neck Surg.

2008;138(3):265-273.

3. Calapai G, Casciaro M, Miroddi M, Calapai F, Navarra M, Gangemi S. Montelukast-

induced adverse drug reactions: a review of case reports in the literature.

Pharmacology. 2014;94(1-2):60-70.

4. Chan CC, Au CT, Lam HS, Lee DL, Wing YK, Li AM. Intranasal corticosteroids for

mild childhood obstructive sleep apnea--a randomized, placebo-controlled study.

Sleep Med. 2015;16(3):358-363.

5. Chervin RD, Ellenberg SS, Hou X, et al. Prognosis for Spontaneous Resolution of

OSA in Children. Chest. 2015;148(5):1204-1213.

6. Franco RA, Rosenfeld RM, Rao M. First place--resident clinical science award 1999.

Quality of life for children with obstructive sleep apnea. Otolaryngol Head Neck

Surg. 2000;123(1 Pt 1):9-16.

7. Goldbart AD, Greenberg-Dotan S, Tal A. Montelukast for children with obstructive

sleep apnea: a double-blind, placebo-controlled study. Pediatrics. 2012;130(3):e575-

580.

8. Kheirandish-Gozal L, Bandla HP, Gozal D. Montelukast for Children with Obstructive

Sleep Apnea: Results of a Double-Blind, Randomized, Placebo-Controlled Trial. Ann

Am Thorac Soc. 2016;13(10):1736-1741.

9. Kheirandish-Gozal L, Bhattacharjee R, Bandla HPR, Gozal D. Antiinflammatory

therapy outcomes for mild OSA in children. Chest. 2014;146(1):88-95.

10. Landau YE, Bar-Yishay O, Greenberg-Dotan S, Goldbart AD, Tarasiuk A, Tal A.

Impaired behavioral and neurocognitive function in preschool children with

obstructive sleep apnea. Pediatr Pulmonol. 2012;47(2):180-188.

11. Marcus CL, Moore RH, Rosen CL, et al. A randomized trial of adenotonsillectomy for

childhood sleep apnea. N Engl J Med. 2013;368(25):2366-2376.

12. Volsky PG, Woughter MA, Beydoun HA, Derkay CS, Baldassari CM.

Adenotonsillectomy vs observation for management of mild obstructive sleep apnea

in children. Otolaryngol Head Neck Surg. 2014;150(1):126-132.

ReferencesCategory Results

Age, mean SD, yrs 6.8 3.9

BMI percentile, mean SD 71.2 29.7

Obese 15 (48.4%)

Obstructive AHI 2.8 1.0

Central AHI 1.1 0.9

Arousal Index 9.5 2.8

Oxygen Nadir (%) 90.6 2.7

Race

Black 17 (54.8%)

White 13 (41.9%)

Multiracial 1 (3.2%)

Asthma 8 (25.8%)

Second hand smoke 7 (24.8%)

Allergic rhinitis 5 (16.1%)

Table 1. Subject Demographic Data (n = 31).

0

2

4

6

8

10

12

14

16

Within normallimits (18-40)

Mild QOLimpairment

(40-59)

ModerateQOL

impairment(60-79)

Severe QOLimpairment

(80-126)

Num

ber

of P

atients

Total OSA-18 QOL Scores

Baseline Follow-up

Variables Baseline OSA-18

Scores

mean (SD)

Follow up OSA-

18 Scores

mean (SD)

OSA-18 Total 60.2 (18.5) 40.4 (16.1)

Sleep Disturbances 15.3 (5.5) 9.8 (4.7)

Physical Symptoms 12.0 (4.8) 7.3 (3.8)

Emotional

Symptoms

10.1 (5.4) 7.2 (5.1)

Daytime Function 11.0 (4.6) 7.9 (4.5)

Caregiver Concerns 11.7 (5.6) 7.4 (3.9)

Table 2. OSA-18 QOL Scores for children with Mild OSA

treated with Anti-Inflammatory Therapy

Figure 2. Mean Total OSA-18 QOL Scores Before and

After Medical Therapy for Children with Mild OSA.

Patient CharacteristicsThirty-five patients were enrolled in the study with 4

patients failing to return for follow-up. Thus, thirty-one

patients were included in the analysis. Subjects’ AHI

ranged from 1.1 to 4.3, with a mean of 2.8 (SD 1.0).

Mean nadir oxygen saturation was 90.6% with a range

from 84% to 95%.

Patients with obstructive sleep apnea (OSA)

treated with continuous positive airway pressure

(CPAP) have variable compliance. Level of

support provided by the healthcare team can

influence adherence. Compliance is particularly

difficult in the Veteran population. This is a

retrospective chart review of patients seen in the

Hampton Veterans Affairs Medical Center (VAMC)

CPAP Clinic with attention to time since last CPAP

Clinic visit and the relationship to CPAP

compliance. A high degree of variability in

compliance is seen at inter-visit intervals of up to

15 months; following 18 months, data is limited,

but compliance appears remarkably poor.

• To determine whether there is a particular time

frame since last follow-up visit that is

associated with lack of adherence to CPAP.

• To determine whether a change in the protocol

for ongoing monitoring of CPAP therapy at the

our institution is needed.

• To provide the most appropriate support to our

patient population to enable the best possible

healthcare outcomes.Figure 1. High

degree of

variation in

compliance is

seen at time

intervals up to

15 months. Low

numbers of

patients were

seen at intervals

of greater than

18 months;

these patients

had markedly

low compliance.

This study assesses our manner of processing referrals

to CPAP Clinic, specifically addressing time since last

visit, in order to improve system efficiency.

Based on our preliminary data, the majority of patients

who had not been seen in over 18 months demonstrated

very low compliance. However, the number of patients in

this category was low, thus limiting the generalizability of

this association.

Next steps:

• Evaluate more patient encounters to determine

whether additional data support an association

between > 18 months since last visit and unacceptably

low compliance with CPAP

• Multivariate analysis to determine whether additional

variables, such as data from sleep studies, may

interact with the time interval since last visit to better

predict CPAP compliance.

CPAP compliance can be challenging in patients,

including Veterans, with OSA1, although benefits

have been demonstrated2-4. The Hampton VAMC

CPAP clinic sees patients with OSA on CPAP at

least annually to assess treatment and reorder

supplies. CPAP therapy orders are often provided

by the Sleep Medicine physician based on chart

review and discussion with CPAP clinic techs. VA

and Centers for Medicare and Medicaid Services

(CMS) require patients who are noncompliant with

CPAP use to be reevaluated by polysomnography

prior to renewal of CPAP therapy5. Thus,

determination of the most appropriate next step

(routine CPAP Clinic follow-up, repeat sleep

testing, or Sleep physician visit) is a challenge.

We hypothesized that comparing CPAP

compliance with the time since most recent CPAP

Clinic appointment will yield a cutoff point after

which the rate of CPAP compliance is

unacceptably low, requiring clinical re-evaluation

by the provider.

CPAP Compliance and Time Since Last CPAP Clinic VisitMolly M. Herring, DO, and Sean Hesselbacher, MD, FAASM | Eastern Virginia Medical School | Hampton Veterans Affairs Medical Center

Conflicts of Interest: None

Abstract

Background

Methods

Results Conclusions

• Retrospective chart review

• Adult patients with

OSA treated with

CPAP

• All follow up visits

seen in Hampton

VAMC CPAP Clinic

over a 3-month period

in 2018

• Data collected:

• Demographics

• CPAP settings and

compliance

• Time since last CPAP

Clinic visit.

• Data analysis

• Correlation and linear

regression models

• Association between

time since last CPAP

Clinic visit and CPAP

compliance.

1. El-Solh, AA; Ayyar, L; Akinnusi, M; Relia, S; and Akinnusi, O. Positive Airway

Pressure Adherence in Veterans with Posttraumatic Stress Disorder. SLEEP

2010; 33(11):1495-1500.

2. Tamanna, S; Parker, J; Lyons, J; and Ullah, MI. The Effect of Continuous

Positive Air Pressure (CPAP) on Nightmares in Patients with Posttraumatic

Stress Disorder (PTSD) and Obstructive Sleep Apnea (OSA). Journal of

Clinical Sleep Medicine 2014; 10(6):631-636.

3. El-Solh, AA; Vermont, L; Homish, G; and Kufel, T. The effect of continuous

positive airway pressure on post-traumatic stress disorder symptoms in

veterans with post-traumatic stress disorder and obstructive sleep apnea: a

prospective study. Sleep Medicine 2017; 33:145-150.

4. Ullah, MI; Campbell, DG; Bhagat, R: Lyons, J; and Tamanna, S. Improving

PTSD Symptoms and Preventing Progression of Subclinical PTSD to an Overt

Disorder by Treating Comorbid OSA With CPAP. Journal of Clinical Sleep

Medicine 2017; 13(10):1191-1198.

5. “Positive Airway Pressure (PAP) Devices: Complying with Documentation &

Coverage Requirements.” Medicare Learning Network. ICN 905064, October

2016. Accessed online <https://www.cms.gov/Outreach-and-

Education/Medicare-Learning-Network-

MLN/MLNProducts/downloads/PAP_DocCvg_Factsheet_ICN905064.pdf> 9

August 2018.

6. Bouloukaki, I; Giannadaki, K; Mermigkis, C; Tzanakis, N; Mauroudi, E;

Moniaki, V; Michelakis, S; Siafakas, N; and Schiza, S. Intensive versus

standard follow-up to improve continuous positive airway pressure

compliance. European Respiratory Journal 2014; 44:1262-1274.

References

ObjectivesTable 1. Demographic and overall CPAP usage data.

Mean or Number

Standard Deviation or Percentage

Age (years) 53.2 11.3Male gender 104/115 90.4%

Time since last CPAP Clinic visit (months) 8.1 5.6AHI, treated(events/hour) 2.5 3.9Days with CPAP use ≥4 h (%) 55.9 35.9Days with any CPAP use (%) 68.7 31.8

Figure 2.

Similarly, there

is a high degree

of variability in

compliance with

any daily CPAP

use above an

inter-visit period

of 18 months.

Association of attention deficit hyperactivity disorder and obstructive sleep apnea in adults.Sean E. Hesselbacher1, 2, Akshar A. Ayer, Salim R. Surani3, Zoya S. Surani, Alishah A. Suleman, Joseph Varon, Shyamsunder Subramanian4

Hampton VA Medical Center, Hampton, VA1; Eastern Virginia Medical School, Norfolk, VA2; Texas A&M University, Corpus Christi, TX3; University Hospitals Case Medical Center, Cleveland, OH4

Results

Conclusions

References

1. Kessler RC, Adler L, Barkley R, et al. The prevalence and correlates

of adult ADHD in the United States: results from the National

Comorbidity Survey Replication. Am J Psychiatry. 2006: 163: 716-

723.

2. Kessler RC, Adler L, et al. The World Health Organization adult

ADHD self-report scale (ASRS): a short screening scale for use in

the general population. Psychol Med. 2005; 25: 245-256.

3. Kessler RC, Adler L, Gruber MJ, et al. Validity of the World Health

Organization Adult ADHD Self-Report Scale (ASRS) Screener in a

representative sample of health plan members. Int J Methods

Psychiatr Res. 2007; 16: 52-65.

• The association between obstructive sleep apnea (OSA)

and attention deficit hyperactivity disorder (ADHD) is well-

established in children.

• There is a paucity of literature regarding this association in

adults.

• ADHD is common in adults with estimated prevalence of

4.4%1.

• The Adult ADHD Self-Report Scale (ASRS) is a validated

18-question screen for adult ADHD2.

• Part A is 6 questions; positive screen if 4/6 questions are

answered abnormally

• Sensitivity 68.7%

• Specificity 99.5%

•These data do not suggest a relationship between the presence of OSA and the

presence of ADHD symptoms.

•ADHD+ screen was associated with higher ESS scores, most notably in patients with

any severity of OSA.

•Proportion of ADHD+ screen was substantially higher than the reported prevalence

of ADHD (4.4%)2,3. A positive screen would be expected in ~7.3% of the general

population; therefore, it appears this study population may have a higher prevalence

of ADHD than the general population.

•A similar study of a broader population may yield different results.

Abstract

Objectives: The association between obstructive sleep apnea (OSA) and

attention deficit hyperactivity disorder (ADHD) is well-established in children.

However, there is a paucity of literature regarding this association in adults.

The aim of this study was to determine if ADHD is more common in adult

patients with OSA.

Materials and Methods: All patients referred to a sleep center for sleep

evaluation were administered an adult ADHD screening questionnaire (Adult

ADHD Self-Report Scale (ASRS-v1.1)) and diagnostic polysomnogram. The

ADHD screen is considered positive if 4 of 6 questions in part A of the

screening questionnaire were answered abnormally.

Results: The study population consisted of 194 participants, predominantly

male (62%), Caucasian (54%), and Hispanic (44%). OSA (AHI ≥5

events/hour) was identified in 160 (83%) of participants, with 116 (60%)

having moderate to severe OSA (AHI ≥15 events/hour). The ADHD screen

was positive in 37 (19%) of participants. There was no significant association

between the presence of OSA of any severity and results on the ADHD

screen. Likewise, the presence of a positive ADHD screen was not

associated with any significant difference in AHI.

Conclusions: These data do not suggest a relationship between the

presence of OSA and the presence of ADHD symptoms. This may be

affected by the population studied, which had a high prevalence of OSA. It

should be noted that the proportion of subjects with a positive ADHD screen

was substantially higher than the reported prevalence of ADHD; therefore, a

similar study of a broader population may yield different results.

Conflicts of Interest Disclosure: I do not have any relationships with

any entities producing, marketing, re-selling, or distributing health

care goods or services consumed by, or used on, patients.

Background

Objectives

Methods

• Consecutive patients referred to Baylor College of Medicine/Methodist Hospital Sleep

Center for sleep evaluation were administered the ASRS screen for adult ADHD and

diagnostic polysomnogram (PSG).

• ASRS results were compared with demographic and PSG data for significant

associations. P>0.05 considered significant.

Demographic characteristics of the participants.

Association of ADHD symptoms with sleepiness.

• The primary aim of this study was to determine if ADHD is more common

in adult patients with OSA.

• Secondary objectives were to determine associations between OSA and

ADHD in subgroups of adults

• Gender

• Ethnicity

• Age

• Sleepiness

*P<0.05; **P<0.01; ***P<0.001

ADHD+ OSA(5)+ P-value OSA(15)+ P-value

All (n=194) 37 160 n.s. 116 n.s.

Male (n=120) 17 105 n.s. 80 n.s.

Female (n=74) 20 55 n.s. 36 n.s.

Caucasian (n=105) 19 86 n.s. 61 n.s.

Hispanic (n=85) 16 72 n.s. 53 n.s.

Obese (n=151) 33 131 n.s. 100 n.s.

Non-obese (n=41) 4 27 n.s. 14 n.s.

Age <50 (n=66) 19 50 n.s 39 n.s.

Age 50-64 (n=69) 12 63 n.s. 46 n.s.

Age ≥65 (n=59) 6 47 n.s. 31 n.s.

Total participants: 194

Age (years): 55.6 ±13.9

Gender: 62% male; 38% female.

Ethnicity: 54% Caucasian, 44% Hispanic, 2% other

OSA (AHI ≥5): 160 (82.5%)

Moderate to severe OSA (AHI ≥15): 116 (59.8%)

ADHD screen positive (≥4 abnormal responses): 37 (19.1%)

Association* between OSA and ADHD

Effects of social media by bedtime use.

n.s. = not significant

*Contigency analysis; Fisher’s exact test