ORIGINAL ARTICLE

Assessment of atrial electromechanical delay in patientswith migraine

Asuman Celikbilek • Savas Sarikaya •

Gokmen Zararsiz • Nermin Tanik • Ali Riza Erbay

Received: 22 October 2013 / Accepted: 17 December 2013

� Belgian Neurological Society 2013

Abstract Evidence suggests that symptoms of migraine

are related to the involvement of the autonomic nervous

system. Data on atrial conduction system are limited in

migraineurs. We aimed to assess atrial electromechanical

delay using tissue Doppler imaging (TDI) in patients with

migraine. Forty-five migraine patients and age- and sex-

matched 26 control subjects were enrolled in the study. All

the patients and controls underwent resting surface elec-

trocardiogram (ECG) and TDI. The maximum P-wave

duration (Pmax), minimum P-wave duration (Pmin) and

P-wave dispersion (Pd) were measured from the 12-lead

ECG. Atrial conduction time was determined from the

lateral mitral annulus (PA lateral), septal mitral annulus

(PA septal), and lateral tricuspid annulus (PA tricuspid) by

TDI. Interatrial (PA lateral–PA tricuspid) and intraatrial

(PA septal–PA tricuspid) electromechanical delays were

calculated. Pd was significantly higher in migraine patients

than in controls (p \ 0.05), whereas Pmax and Pmin were

not different between both groups (p [ 0.05). PA lateral

and PA septal durations were significantly higher in

migraine patients than in controls (p \ 0.001 and p \ 0.05,

respectively). However, PA tricuspid duration was similar

between the groups (p [ 0.05). Both interatrial and intra-

atrial conduction times were delayed in migraineurs as

compared to the controls (p \ 0.001). Interatrial delay and

intraatrial delay variables were found as an independent

risk factors separately on predicting atrial conduction

abnormalities in migraineurs. An interatrial delay of 18 ms

and an intraatrial delay of 5 ms were found to be cutoff

values in ROC analysis (p \ 0.001). This is the first report

to provide a hypothetical suggestion that there is an atrial

electromechanical delay in patients with migraine.

Keywords Atrial electromechanical delay � Autonomic

nervous system � Electrocardiogram � Migraine � P-wave

dispersion � Tissue Doppler imaging

Introduction

Migraine is a primary, chronic intermittent neurovascular

disorder characterized by episodic headache, vegetative

symptoms, and in a minority of patients, by other neuro-

logical signs such as aura [1]. Evidence suggests that

symptoms of migraine are related to the involvement of the

autonomic nervous system (ANS) [2, 3]. The ANS has also

known to play a potentially important role in the occur-

rence of cardiac repolarization due to the rich autonomic

innervation of the heart [3]. Dysfunction of the ANS may,

therefore, cause cardiac repolarization abnormalities [3, 4].

Within this context, the ANS is considered to be involved

in the conduction characteristics, although the precise

mechanisms underlying this interaction are not clear [3–5].

Then, we may speculate that patients with migraine could

have higher atrial conduction abnormalities, as a substrate

for cardiac arrhythmias, than those without migraine.

However, the data are limited on this ground [4, 6, 7].

A. Celikbilek (&) � N. Tanik

Department of Neurology, Medical School, Bozok University,

Yozgat 66200, Turkey

e-mail: asunebioglu@yahoo.com

S. Sarikaya � A. R. Erbay

Department of Cardiology, Medical School, Bozok University,

Yozgat, Turkey

G. Zararsiz

Department of Biostatistics, Medical School, Hacettepe

University, Ankara, Turkey

123

Acta Neurol Belg

DOI 10.1007/s13760-013-0273-8

With recent developments, it is possible to evaluate the

atrial conduction abnormalities using tissue Doppler

imaging (TDI) and two simple electrocardiographic

markers, maximum P-wave duration (Pmax) and P-wave

dispersion (Pd) [8]. M-mode, Doppler, and finally TDI,

which is a noninvasive method alternative to invasive

electrophysiological studies, have been shown to be a

useful technique in evaluating conduction characteristics

[9]. To date, Pmax and Pd parameters were evaluated in

patients with migraine [6, 7], but TDI findings have not

been studied in migraineurs. For the first time in literature,

we aimed to assess atrial electromechanical delay using

TDI in patients with migraine.

Methods

Study population

Forty-five newly diagnosed migraine patients and age- and

sex-matched 26 control subjects ranging 18–50 years were

enrolled in this cross-sectional prospective study. Patients

with malignancy, chronic renal, hepatic or autoimmune

diseases, diabetes mellitus, thyroid disease, anemia, elec-

trolyte imbalance, pregnancy, morbid obesity, smoking,

alcohol consumption, hyperlipidemia, hypertension, medi-

cations known to alter cardiac conduction, a history of

coronary artery disease, atrial fibrillation or other arrhyth-

mias and poor echocardiographic imaging were excluded

from the study. Migraine diagnosis was settled according to

the International Classification of Headache Disorders-II

diagnostic criteria [10]. Twenty-nine patients had migraine

with aura, while the rest had migraine without aura. The

control subjects were enrolled as healthy individuals who

had no headache of any kind. The patient’s medical history,

physical and neurological examinations were performed by

the same neurologist. Migraine patients were evaluated

according to the headache characteristics including aura,

severity, frequency and duration of the migraine attack and

the duration of the disease. Based on visual analog scale,

the headache was defined as mild (score 1–3), moderate [4–

6], severe (score 7–8) or very severe (score 9–10) [11].

Migraine headache attack frequency was noted as the

number of attacks per month [12]. Duration of the head-

ache attack was defined as hours whereas disease duration

as the year. All patients were studied during the headache-

free period. These subjects were not on any medication.

Systolic blood pressure (SBP) and diastolic blood pressure

(DBP) were measured for each patient. Body mass index

(BMI) was calculated as weight in kilograms divided by

the square of height in meters [13]. Using standard labo-

ratory methods, baseline venous blood samples were

obtained from the antecubital vein after an overnight fast

for measurements of plasma glucose, total cholesterol

(TC), low-density lipoprotein (LDL) cholesterol, high-

density lipoprotein (HDL) cholesterol, triglycerides (TG),

creatinine, alanine aminotransferase (ALT), thyroid stim-

ulating hormone and complete blood count which were

routinely performed for each patient. The study protocol

was approved by the Bozok University Local Research

Ethics Committee and written informed consent was

obtained from all patients.

Electrocardiographic analysis

All subjects underwent a 12-lead electrocardiogram (ECG)

recording after a 20 min resting period in supine position at

a paper speed of 50 mm/s and 2 mV/cm standardization.

To decrease the error, P-wave duration was measured

manually in all simultaneously recorded 12 leads of the

surface ECG by two qualified investigators unaware of the

study hypothesis. In each lead, the mean values for the

three complexes were calculated. The onset of the P-wave

was defined as the point of first visible upward departure

from baseline for positive waveforms and as the point of

first downward departure from the baseline for negative

waveforms. The return to the baseline was considered to be

the end of the P-wave. The Pmax measured in any of the 12

leads of the surface ECG was used as the longest atrial

conduction time. The difference between Pmax and the

minimum P-wave duration (Pmin) was calculated and

defined as Pd (Pd = Pmax-Pmin). Intraobserver and inter-

observer coefficients of variation were 3.0 and 3.4 % for

Pmax, and 3.5 and 3.8 % for Pd, respectively.

Echocardiographic analysis

All patients were evaluated by transthoracic two-dimen-

sional, M-mode, pulsed-wave, continuous wave, color

flow, and TDI. The echocardiographic measurements and

analysis were performed with the Aloka system (Aloka,

Japan) by a 2–4 MHz transducer at a depth of 16 cm, by a

cardiologist who was blinded to the clinical details and

results of the other investigations of each patient and

control. During echocardiography, 1-lead ECG was recor-

ded continuously. All patients were imaged while resting at

the left lateral decubitus position. M-mode measurements

were performed according to the criteria of American

Society of Echocardiography [14]. Three consecutive

cycles were averaged for every parameter. Left atrial

diameter (LAD) and left ventricular ejection fraction

(LVEF) were measured by M-mode echocardiography.

LVEF was estimated by Simpson’s rule [15]. TDI was

performed by the same echocardiograph machine, adjust-

ing the spectral pulsed Doppler signal filters until a Nyquist

limit of 15–20 cm/s was reached and using the minimal

Acta Neurol Belg

123

optimal gain. The monitor sweep speed was set at

50–100 mm/s to optimize the spectral display of myocar-

dial velocities. In an apical four-chamber view, the pulsed

Doppler sample volume was placed at the level of LV

lateral mitral annulus, septal mitral annulus, and lateral

tricuspid annulus. The sampling window was positioned as

parallel as possible to the myocardial segment of interest to

ensure the optimal angle of imaging. The time interval

from the onset of the P-wave on surface ECG to the

beginning of the late diastolic wave (Am wave), which is

called atrial electromechanical coupling (PA), was

obtained from the lateral mitral annulus (PA lateral), septal

mitral annulus (PA septal), and lateral tricuspid annulus

(PA tricuspid), respectively. The difference between PA

lateral and PA tricuspid was defined as interatrial electro-

mechanical delay (PA lateral–PA tricuspid), and the dif-

ference between PA septal and PA tricuspid was defined as

intraatrial electromechanical delay (PA septal–PA tricus-

pid). Values were averaged over three consecutive beats.

Intraobserver variability was 5.3 % for PA lateral, 5.8 %

for PA septal, and 5.3 % for PA tricuspid, respectively.

Interobserver variability was 5.0 % for PA lateral, 5.2 %

for PA septal, and 5.7 % for PA tricuspid, respectively.

Statistical analysis

The Shapiro–Wilk’s test, histograms, and q–q plots were

used to test the normality of the data, and Levene’s test was

used to assess variance homogeneity. A two-way inde-

pendent-sample t tests, Welch t test and Mann–Whitney

U tests were used to compare differences between contin-

uous variables and Fisher’s exact test was used to assess

the differences between categorical variables. Pearson

correlation was used to examine the relationship between

laboratory data, electrophysiological findings and headache

characteristics including aura, attack severity, frequency,

duration, and the disease duration. Univariate and multiple

logistic regression analyses were conducted to identify the

risk factors of atrial conduction abnormalities in migrai-

neurs. Odds ratios (OR) are calculated with 95 % confi-

dence intervals (CI). Only for the parameters of creatinine

and LAD, we have limited values of which the cutoff

values were used to calculate OR. Significant variables in

univariate analysis were included into multiple models and

backward elimination procedure was applied using likeli-

hood ratio statistic. Correlated variables were considered

separately in multiple logistic regression models. More-

over, receiving operating characteristic (ROC) curve

analyses were applied and the area under these curves

(AUC) was calculated with 95 % CI for PA lateral, PA

septal, interatrial delay, intraatrial delay, Pd variables and

compared between them. Also, cutoff values were deter-

mined for each variable and sensitivity, specificity, positive

predictive value, negative predictive value diagnostic

measures (with 95 % CI) and Kappa test statistic were

calculated. Values are expressed as frequencies and per-

centages, means and standard deviations, or medians and

interquartile ranges. Analyses were conducted using R

3.0.0 software. A p probability level\5 % was considered

as statistically significant.

Results

The demographic and laboratory data of the migraine and

control patients are summarized in Table 1. With respect to

age and gender, no significant difference was found

between the two groups (p [ 0.05). Similarly, there was no

significant association between the parameters of BMI,

SBP, DBP in migraine patients as compared with the

controls (p [ 0.05). The laboratory results revealed that

fasting glucose, renal and liver function tests, complete

blood count and thyroid stimulating hormone did not sig-

nificantly differ in migraineurs as compared to the controls

(p [ 0.05). Regarding lipid profile, total cholesterol was

only significantly higher in migraine patients than in con-

trols (p \ 0.05).

The electrocardiographic and echocardiographic mea-

surement results are listed in Table 2. Pd was significantly

higher in migraine patients than in controls (p \ 0.05),

whereas Pmax and Pmin were not different between the two

groups (p [ 0.05). The heart rates, LVEF and LAD were

similar between the two groups (p \ 0.05). PA lateral and

PA septal durations were significantly higher in patients

with migraine as compared to the control group (p \ 0.001

and p \ 0.05, respectively). However, PA tricuspid dura-

tion was similar between both groups (p [ 0.05). Both

interatrial and intraatrial conduction times were delayed in

patients with migraine when compared with the controls

(p \ 0.001). We applied four multiple logistic regression

models due to high correlations between PA lateral-PA

septal (r = 0.791, p \ 0.001) and between interatrial

delay-intraatrial delay (r = 0.680, p \ 0.001). First model

contained the PA lateral-interatrial delay pair and the other

significant variables in univariate analysis. Second model

contained the PA lateral-intraatrial delay pair and the other

significant variables in univariate analysis. Third model

contained the PA septal-interatrial delay pair and the other

significant variables in univariate analysis. Last model

contained the PA septal-intraatrial delay pair and the other

significant variables in univariate analysis. After multiple

logistic regression results, interatrial delay and intraatrial

delay variables were found as an independent risk factors

separately on predicting atrial conduction abnormalities in

migraine patients [OR and 95 % CI are 1.24 (1.11–1.38)

and 1.52 (1.17–1.96), respectively, Table 2]. ROC analysis

Acta Neurol Belg

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was applied for PA lateral, PA septal, interatrial delay,

intraatrial delay and Pd variables (Fig. 1). AUC values

were found as 0.76 (0.65–0.86), 0.66 (0.54–0.77), 0.83

(0.73–0.91), 0.80 (0.69–0.89) and 0.67 (0.55–0.78),

respectively, as presented in Table 3. An interatrial delay

of 18 ms and an intraatrial delay of 5 ms were found to be

cutoff values with a sensitivity and specificity of 0.87

(0.73–0.95) and 0.65 (0.44–0.83), 0.89 (0.76–0.96) and

0.62 (0.41–0.80) to predict atrial conduction abnormalities,

respectively, in the ROC analysis (j = 0.534 and

j = 0.526, respectively, p \ 0.001, Table 3). Also, with

an alternative cutoff value of 22 ms for interatrial delay,

sensitivity is calculated as 0.73 (0.58–0.85) and specificity

as 0.88 (0.70–0.97).

Based on correlation analysis, no correlation was

detected between the electrophysiological findings and

headache characteristics (p [ 0.05).

Discussion

The salient findings of our study were as follows: (1) PA

lateral and PA septal durations and Pd were significantly

higher in patients with migraine than in controls; (2)

interatrial and intraatrial conduction times were delayed in

patients with migraine as compared to the controls; (3)

interatrial delay and intraatrial delay variables were inde-

pendent parameters in migraine patients; (4) an interatrial

delay of 18 ms and an intraatrial delay of 5 ms were found

to be cutoff values with a sensitivity and specificity of 0.87

and 0.65, 0.89 and 0.62, respectively, in ROC analysis.

The pathophysiology of migraine is not completely

understood, but it is assumed that migraine is commonly

associated with a variety of autonomic accompaniments

such as nausea, vomiting, pallor, flushing, piloerection and

diaphoresis [2]. This may be explained by varied auto-

nomic dysregulation, perhaps the result of an imbalance

between the sympathetic and parasympathetic systems [3].

There are both sympathetic and parasympathetic nerve

fibers in venous and perivenous tissue. The interaction

between the sympathetic and parasympathetic systems,

particularly changes in sympathovagal balance, may con-

tribute to the various clinical features of migraineurs [3, 5].

The ANS has also known to play an important role in the

occurrence of cardiac repolarization due to the rich auto-

nomic innervation of the heart [3]. Dysfunction of the ANS

may, therefore cause cardiac repolarization abnormalities

[3, 4]. Then, the ANS is considered to be involved in the

conduction characteristics [3–5]. Recent studies have

demonstrated that Pd and Pmax, electrocardiographic

markers of atrial conduction abnormalities, get longer in

various rheumatologic disorders, including scleroderma,

rheumatoid arthritis, and Behcet’s disease [16–19]. In

addition, disrupted autonomic innervation of the heart and

coronary arteries in migraine patients was linked to the

possible ECG abnormalities, particularly PR and corrected

Table 1 Demographic and laboratory data of migraine patients and controls

Variable Control (n = 26) Migraine (n = 45) p OR (95 % CI)

Age (years) 30 ± 7.01 33.47 ± 8.17 0.074 1.06 (0.99–1.13)

Gender (male/female) 3 (12.0)/23 (88.0) 3 (6.7)/42 (93.3) 0.999 0.86 (0.13–5.50)

BMI (kg/m2) 25.31 (21.95–26.8) 26.12 (23.4–27.1) 0.110 1.16 (0.96–1.39)

SBP (mmHg) 110 (100–120) 110 (100–120) 0.576 0.99 (0.95–1.03)

DBP (mmHg) 60 (60–80) 70 (60–70) 0.892 0.99 (0.94–1.05)

TC (mg/dL) 171.62 ± 35.21 188.42 ± 27.5 0.029 1.02 (1.00–1.04)

LDL-C (mg/dL) 106.38 ± 26.37 115.91 ± 20.81 0.097 1.02 (0.99–1.04)

HDL-C (mg/dL) 43.5 (40–48) 42 (40–50) 0.928 1.02 (0.96–1.08)

TG (mg/dL) 112 ± 34.71 102.53 ± 37.89 0.300 0.99 (0.98–1.01)

Fasting glucose (mg/dL) 88 (85–91) 87 (83–89) 0.245 0.94 (0.86–1.03)

Creatinine (mg/dL) (\0.6/[0.6) 0.6 (0.6–0.8) 0.6 (0.5–0.7) 0.065 1.62 (0.60–4.42)

WBC (103/mm3) 6.95 (5.9–9) 7.4 (6.6–8.2) 0.971 0.97 (0.67–1.42)

Hemoglobin (mg/dL) 13.25 (12.8–14.5) 13.5 (12.5–14.4) 0.844 0.94 (0.64–1.38)

Platelet (103/mm3) 261 (228–322) 257 (219–307) 0.716 1.00 (0.99–1.01)

ALT (IU/L) 15.81 ± 5.19 16.31 ± 7.89 0.772 1.01 (0.94–1.09)

TSH (uIU/mL) 2.05 ± 1.12 1.89 ± 0.95 0.526 0.86 (0.53–1.38)

Values are expressed as n (%), mean ± SD or median (1st–3rd quartile)

BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, TC total cholesterol, LDL-C low-density lipoprotein

cholesterol, HDL-C high-density lipoprotein cholesterol, TG triglyceride, WBC white blood cells, ALT alanine aminotransferase, TSH thyroid

stimulating hormone

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QT interval lengthening during a migraine attack [4]. In

parallel, Duru et al. [6] showed that migraine attacks were

associated with a prolonged QT interval and Pd which

could be predictors of arrhythmia. Besides, Kocer et al. [7]

found that Pd, being higher in migraineurs than in controls,

was positively correlated with migraine attack number per

month. In agreement with these reports, the increased Pd

values in our migraine patients may suggest that migrai-

neurs might have an increased risk of developing atrial

rhythm disturbances relative to non-migraineurs.

On the other hand, the prolongation of intraatrial and

interatrial conduction times and the inhomogeneous propa-

gation of sinus impulses are well-known electrophysiologic

characteristics of the atrium prone to fibrillate [20]. In recent

years, interatrial and intraatrial electromechanical delay,

another indicator of cardiac arrhythmias, has been identified

in patients with systemic lupus erythematosus, familial

Mediterranean fever, ankylosing spondylitis and Behcet’s

disease [19, 21–23]. The proposed mechanism in these dis-

orders is the inflammation causing damage involving left

atrial tissue resulting in left atrial fibrosis. To our knowledge,

atrial electromechanical delay has not been evaluated in

patients with migraine before. In concordance with those

reports, we found that interatrial and intraatrial conduction

times were independently delayed in migraineurs. Our

patients were asymptomatic and did not have either any kind

of cardiac arrhythmia or history of beta-blocker usage, which

might influence the conduction characteristics. This may

probably indicate the conduction system involvement in

migraine patients. The actual mechanistic pathway for this

condition is difficult to explain; however, it could be attrib-

utable to the structural and electrophysiologic changes in the

atrial myocardium through the ANS-related effects, result-

ing in the prolonged atrial activation time rather than a part of

an increased atrial size and/or a decreased pump function of

the heart. The similarity for LAD and LVEF between

migraine patients and controls supported this hypothesis. In

contrast, the electrophysiological findings did not correlate

with migraine characteristics, but larger cohorts are needed

to draw any conclusion.

The present study has certain limitations. First, our study

is cross-sectional, hence, we cannot determine a causal link

between migraine and atrial electromechanical delay, and

we lacked follow-up data for future arrhythmic events in

migraineurs, both of which considered together, represent

the greatest limitation of this study. Second, atrial con-

duction time was not examined by invasive electrophysi-

ologic techniques which are the gold standard methods for

this evaluation. Third, we measured the atrial electrome-

chanical delay only in three points at the annulus level,

measurements at different atrial regions might give us more

detailed information about the atrial conduction times.

Fourth, the measurements were done in the headache-free

period, so we were unable to compare the findings during

Table 2 Comparison of the

electrocardiographic and tissue

Doppler echocardiographic

findings of migraine patients

and controls

Values are expressed as n (%),

mean ± SD or median (1st–3rd

quartile)

LVEF Left ventricular ejection

fraction, LAD left atrial

diameter

Variable Control (n = 26) Migraine (n = 45) p OR (95 % CI)

Heart rate (beats/min) 75 (68.7–80) 78 (65–80.5) 0.879 0.99 (0.94–1.05)

LVEF (%) 64 (63–66) 64 (62–66) 0.595 1.08 (0.86–1.35)

LAD (cm) ([3.3/\3.3) 3.3 (3.2–3.3) 3.3 (3.2–3.4) 0.135 2.44 (0.82–7.23)

PA lateral (ms) 55 (48–64) 64 (60–72) \0.001 1.14 (1.06–1.22)

PA septal (ms) 36 (36–50) 48 (42–50) 0.002 1.08 (1.01–1.16)

PA tricuspid (ms) 32.5 (30–46) 39 (32–42) 0.529 1.01 (0.95–1.08)

Interatrial delay (ms) 18.19 ± 4.85 27.38 ± 8.2 \0.001 1.24 (1.11–1.38)

Intraatrial delay (ms) 5.15 ± 2.68 9.33 ± 5.56 0.001 1.52 (1.17–1.96)

Pmax (ms) 93.61 ± 4.84 95.60 ± 6.34 0.173 1.07 (0.97–1.17)

Pmin (ms) 55.69 ± 5.06 53.96 ± 2.66 0.114 0.88 (0.77–1.01)

Pd (ms) 37.81 ± 6.6 41.56 ± 6.03 0.017 1.11 (1.01–1.21)

Fig. 1 Comparison of ROC curves of PA lateral, PA septal,

interatrial delay, intraatrial delay and Pd in patients with migraine

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and after an attack. Fifth, we lacked ambulatory 24 h

Holter recording for the detection of cardiac extrasystole

burden in the study group.

In conclusion, despite the abovementioned limitations,

this is the first report to provide a hypothetical suggestion that

there is an atrial electromechanical delay in patients with

migraine. Indeed, it is usually difficult to measure the con-

duction times in practice. Future large-scale longitudinal

studies that overcome the current study’s limitations will

present a more detailed view of underlying mechanisms of

the tendency for cardiac arrhythmias in patients with

migraine.

Conflict of interest None.

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Table 3 ROC analysis, diagnostic measures and Kappa test results of variables in the detection of atrial conduction abnormalities in migraine

patients

Variables ROC analysis Diagnostic measures Kappa test

AUC (95 % CI) Cutoff

value

SEN (95 % CI) SPE (95 % CI) PPV (95 % CI) NPV (95 % CI) j p

PA lateral 0.76 (0.65–0.86)ab [56 0.84 (0.71–0.94) 0.62 (0.41–0.80) 0.79 (0.65–0.90) 0.70 (0.47–0.87) 0.471 \0.001

PA septal 0.66 (0.54–0.77)b [36 0.87 (0.73–0.95) 0.54 (0.33–0.73) 0.76 (0.63–0.87) 0.70 (0.45–0.89) 0.426 \0.001

Interatrial

delay

0.83 (0.73–0.91)a [18 0.87 (0.73–0.95) 0.65 (0.44–0.83) 0.81 (0.67–0.91) 0.74 (0.51–0.90) 0.534 \0.001

Intraatrial

delay

0.80 (0.69–0.89)a [5 0.89 (0.76–0.96) 0.62 (0.41–0.80) 0.80 (0.66–0.90) 0.76 (0.53–0.92) 0.526 \0.001

Pd 0.67 (0.55–0.78)b [34 0.89 (0.76–0.96) 0.38 (0.20–0.59) 0.71 (0.58–0.83) 0.67 (0.38–0.88) 0.300 0.007

Different superscripts indicate statistically significant differences

AUC Area under curve, CI confidence interval, SEN sensitivity, SPE specificity, PPV positive predictive value, NPV negative predictive value, jKappa statistic

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