Psychiatry and Clinical Neurosciences 2015; 69: 283–291
doi:10.1111/pcn.12226
Regular Article
Evaluating subjective domains of antipsychotic-induced adverse effects using heart rate variability Jae Seung Chang, MD, PhD,1† Samuel Suk-Hyun Hwang, PhD,2† Sang Hoon Yi, Yeni Kim, MD, PhD,6 Yu-Sang Lee, MD, PhD,3 Yong Sik Kim, MD, PhD4 and Hee-Yeon Jung, MD, PhD7*
PhD,5
1 Department of Psychiatry, Seoul National University Bundang Hospital, 2Department of Addiction, Rehabilitation, and Social Welfare, Eulji University, Seongnam, 3Department of Psychiatry, Yong-in Mental Hospital, Yongin, 4Department of Psychiatry, Dongguk University Ilsan Hospital, Goyang, 5Department of Computer Simulation, Institute of Basic Science, Inje University, Gimhae, 6Department of Adolescent Psychiatry, National Center for Child and Adolescent Psychiatry, Seoul National Hospital, and 7Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, Korea
Aims: Antipsychotic-induced autonomic dysregulation may lead to a wide range of subjective sideeffects in schizophrenia patients. Using heart rate variability (HRV) measures, we prospectively examined the relationship between subjective side-effects and cardiac autonomic regulation in unmedicated schizophrenia patients.
effects domain were significantly correlated with the changes in time-domain HRV measures and sample entropy (SampEn). In addition, the change in SampEn was significantly associated with that in the scores of extrapyramidal, anticholinergic, miscellaneous, and red herring domains as well as the mean total LUNSERS score.
Methods: Forty-five unmedicated schizophrenia patients were assessed for antipsychotic-associated side-effects and HRV parameters at baseline and after 6 weeks of treatment. Psychiatric symptoms and subjective side-effects were assessed using the Positive and Negative Syndrome Scale (PANSS) and the Liverpool University Neuroleptic Side-effect Rating Scale (LUNSERS).
Conclusion: Baseline HRV measures may predict clinical response and adverse events associated with treatment adherence. Also, subjective side-effects may correspond well with the changes in neurocardiac dynamics, and the changes in SampEn may effectively reflect subjective discomfort in patients receiving antipsychotic treatment.
Results: Correlations between subjective adverse effects and HRV measures at baseline and at week 6 varied. Nonetheless, the changes in the psychic side-
LOSE MONITORING OF antipsychoticassociated adverse events is essential to enhance treatment adherence.1 Perceived side-effects (SE) explain approximately one-quarter to one-third of
C
*Correspondence: Hee-Yeon Jung, MD, PhD, Department of Psychiatry, SMG-SNU Boramae Medical Center, 20 Boramae-Ro 5-Gil, Dongjak-Gu, Seoul 156-707, Korea. Email: [email protected] † The first two authors contributed equally. Received 22 January 2014; revised 18 June 2014; accepted 23 July 2014.
Key words: antipsychotic, heart rate variability, schizophrenia, subjective side-effect, treatment adherence.
early discontinuation of typical antipsychotics2 and at least 10% of novel atypical antipsychotics.3 SE associated with specific pharmacological actions can be easily detected and managed by clinicians but a substantial proportion of patients may experience non-specific SE of a given medication.4 Psychiatric symptoms may also affect subjective SE and overall wellbeing regardless of antipsychotic medications.5,6 Altered autonomic regulation is involved in psychological, physical, and behavioral symptoms presented in schizophrenia patients.7 Cardiovagal
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activity measured by heart rate variability (HRV) is reduced in schizophrenia patients and correlated with symptom severity.8 Bodén et al. suggested that some electrocardiogram (ECG) autonomic indices may provide prognostic information on treatment discontinuation in patients with first-episode psychotic disorder.9 Recent studies suggest that antipsychotic treatment can lead to favorable changes in HRV measures in schizophrenia patients,10 although they were not consistent.11 In contrast, several studies indicate a possible role of HRV measures in predicting SE of antipsychotics.12 A recent study suggested an association between altered HRV and subjective restlessness in schizophrenia patients receiving antipsychotic treatment,13 thereby implicating clinical use of HRV parameters in predicting antipsychotic-induced subjective SE. In this study, we explored the possible association between the antipsychotic-induced changes in cardiac autonomic regulation and subjective SE, and prospectively investigated the changes in both subjective SE and HRV measures in schizophrenia patients following antipsychotic treatment.
METHODS
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Clinical assessment For the baseline and 6-week follow up, the patients were assessed on psychiatric symptoms using the Positive and Negative Syndrome Scale (PANSS).15 For SE, they underwent an examination with the 48-item Udvalg for Kliniske Undersøgelser (UKU)16 by a certified psychiatrist and also completed the Liverpool University Neuroleptic Side-effect Rating Scale (LUNSERS).17 The UKU is a 4-point, semi-structured interview that assesses 48 items structured into mainly psychic, neurological and autonomic domains. The LUNSERS was developed as a selfreport instrument for adverse effects related to antipsychotic treatment17 and consists of 41 SE items and 10 red herring (RH) items, all of which are rated on a 5-point scale. The SE items of the LUNSERS are further grouped into the specific domains such as psychic, extrapyramidal, hormonal, anticholinergic, other autonomic, allergic, and miscellaneous. The 10 RH items are not directly related to known SE but were found to be useful for detecting overreporting tendencies or psychopathologies.6 The total scores of the two SE scales had good correlation in both English (r = 0.82) and Korean schizophrenia patients (r = 0.81).17,18
Subjects This was an exploratory study using the psychophysiologic approach to subjective SE. Forty-five unmedicated patients with DSM-IV schizophrenia hospitalized for acute psychotic symptoms were recruited from Yong-in Mental Hospital, Republic of Korea. The HRV data from some of these patients were used in our previous studies on diagnosis and clinical response.10,14 All patients were drug naïve or drug free, defined as cessation of antipsychotic medication for at least 28 days or depot antipsychotics for at least 24 weeks prior to the index hospitalization. Patients treated with antipsychotic monotherapy for 6 weeks were included in 6-week assessment. Patients switched from initially prescribed antipsychotics were regarded as withdrawn. The choice of antipsychotic was made by a treating psychiatrist blind to the study protocol, and the chosen antipsychotic was freely titrated to clinical response. The study protocol was reviewed and approved by the institutional review board and written informed consent was obtained from each participant. All procedures used in this study were in accordance with the tenets of the Declaration of Helsinki.
Physiological assessment To minimize diurnal changes in cardiac autonomic modulation, ECG recordings were performed in a dimly lit, warm, and quiet room between 09.00 and 11.00 hours after prohibiting any smoking and caffeine consumption on the morning of ECG recording. All patients were requested to keep their eyes closed and to breathe regularly in a recumbent position. After a 15-min test session to stabilize respiration and heartbeats, 5-min ECG was acquired. The ECG analog signal was automatically digitized at a sampling rate of 250 Hz. RR intervals were filtered by an adaptive filter algorithm to replace and interpolate ventricular premature beats and artifacts.19 The entire 5-min ECG data were used for HRV analysis. In the time domain analysis of HRV, mean length of all RR intervals (mean RR), standard deviation of all normal-to-normal (NN) intervals (SDNN), square root of the mean squared differences of successive NN intervals (RMSSD), and percentage of successive RR interval differences with absolute value >20 ms (pNN20) were calculated. These time domain mea-
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sures reflect the responsiveness of HR. In the frequency domain, spectral analysis was performed using the standard autoregressive algorithm, after detrending and resampling the irregularly timesampled recording of consecutive RR intervals. The power spectrum density was conventionally estimated for two major frequency ranges, that is, the low-frequency (LF) band (0.04–0.15 Hz) and the high-frequency (HF) band (0.15–0.4 Hz), and the ratio of LF to HF power (LF/HF) was computed to evaluate sympathovagal balance.20 The corrected Shannon entropy (CSE) was calculated using binary symbols generated from a coarsegraining process, based on a threshold, which is the absolute difference of successive RR intervals.21 Lower CSE reflects a higher degree of regularity of symbolic patterns generated from the binary sequences.22,23 To measure the regularity of the fluctuations in a time series, approximate entropy (ApEn) and sample entropy (SampEn) were also computed. SampEn is free from the bias induced from the finite length and self-matches in ApEn,14,24 and a low value signifies a high degree of regularity.25 Last, a detrended fluctuation analysis was used to derive the alpha, quantifying the fractal properties of RR intervals. The alpha reflects the amount of short-term temporal fluctuation in the HR time series, with the low values associated with a highly random, or coarse, time series and the high values with a strongly correlated, or smooth, time series.26
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Statistical analysis Following the analysis of the descriptive data of the baseline and 6-week follow up, between-groups differences of the completers and the discontinuation group were examined using simple analysis of variance (ANOVA) or chi-squared test as appropriate. The baseline and 6-week data of the completers were then compared with paired t-test. The change (Δ) scores were obtained by subtracting the values obtained at the baseline from those of the 6-week data. For each domain (clinical effect, objective SE, subjective SE, and HRV), the Bonferroni correction was used to control for multiple comparisons. Last, correlation analysis was carried out using the Pearson correlations, r. All statistical analysis was done using SPSS 21.0 for Windows (SPSS, Chicago, IL, USA).
RESULTS Demographic and clinical variables The subject demographic and clinical characteristics are listed in Table 1. Among 45 patients, 11 (six men and five women) refused or were incapable of completing baseline evaluation hence were excluded from the final analysis. These patients did not significantly differ from the rest of the patients in sex, age, and type of antipsychotics, but they had significantly more severe psychopathology (108.09 ± 14.96) than those included (91.71 ± 17.81), according to PANSS
Table 1 Subject characteristics Baseline (n = 34) n (%) or mean ± SD Male Age (years) Schizophrenia Paranoid type Undifferentiated type PANSS score UKU score LUNSERS SE score LUNSERS RH score Accumulated antipsychotic dose (mg in chlorpromazine equivalents)
18 (52.9) 35.0 ± 10.9 29 (85.3) 5 (14.7) 91.71 ± 17.8 10.96 ± 10.48 26.53 ± 23.57 4.62 ± 6.20 –
Week 6 (n = 25)† n (%) or mean ± SD 14 (56.0) 36.2 ± 11.21 22 (88.0) 3 (12.0) 63.60 ± 12.2 19.29 ± 10.07 18.89 ± 17.83 2.92 ± 6.67 9168.00 ± 3777.87
†
Three patients who did not complete ECG assessment were excluded. LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; PANSS, Positive And Negative Syndrome Scale; RH, red herring; SE, side-effect; UKU, Udvalg for Kliniske Undersøgelser.
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total score (t = 2.75, P < .01). As for 6-week assessment, six of 34 patients were switched from initially prescribed antipsychotics and thus were withdrawn from the study, and three patients did not complete the second evaluation due to lack of cooperation. Age, gender distribution, and antipsychotic type did not significantly differ between the nine excluded patients and those included in the final analysis. In addition, they did not significantly differ in the baseline UKU subscales or LUNSERS subscales including the RH. During 6-week antipsychotic treatment, 16 patients (64%) were taking risperidone, five (20%) olanzapine, three (12%) amisulpride, and one (4%), aripiprazole. Based on paired comparison, significant differences were found between the baseline and 6-week scores in PANSS total score (t = 9.83, P < 0.001), UKU total score (t = −2.30, P < 0.05) and LUNSERS SE total score (t = 2.33, P < 0.05). Listed in Table 2 are the psychopathology, SE, and HRV measures of the completed and discontinuation patients at baseline and 6-week assessment. In terms of completers, significant improvements in psychopathology, lower psychic, autonomic, and total SE scores, and decreases in SD, RMSSD, and CSE were evident over the study period. After Bonferroni correction was applied, significant changes were seen only in CSE; no significant changes were detected between the two timepoints in subjective SE measured using the LUNSERS and HRV measures. In contrast, the modified subscales of the clinician-rated UKU extrapyramidal SE and anticholinergic SE increased significantly. In the comparison of the baseline data between the completers and discontinuation patients, no significant group differences in the psychopathology and SE measures were found, but the discontinuation patients had higher HF in HRV measures compared to the completers. No significant associations were found between accumulated antipsychotic dose and changes in the PANSS total, positive subscale, and negative subscale scores. The UKU and the LUNSERS subscale scores were not associated with accumulated antipsychotic dose. The level of tension (G4 item on the PANSS) was not associated with RR interval at baseline (r = 0.15, P = 0.41) or 6-week assessment (r = 0.06, P = 0.80).
and UKU counterparts, respectively (Table 3). At baseline, all LUNSERS subscales significantly correlated with the UKU subscales, except for extrapyramidal and allergic symptoms. At 6-week assessment, all LUNSERS subscales significantly correlated with their UKU counterparts, except for psychic and autonomic symptoms, which were marginally correlated (P < 0.06). Last, the changed scores of the respective LUNSERS and the UKU counterpart subscales were significantly correlated for all subscales, except for allergic and miscellaneous subscales.
Correlations between LUNSERS and UKU subscales We examined the cross-sectional and longitudinal correlations between the subscales of the LUNSERS
Cross-sectional correlations at baseline and 6-month follow up At baseline there were no significant correlations between HRV parameters and SE measures except that LF/HF significantly correlated with UKU extrapyramidal SE and hormonal SE (r = 0.62, P < 0.001; r = 0.42, P < 0.01, respectively). At 6-week assessment, UKU extrapyramidal SE significantly correlated with SampEn (r = −0.41, P < 0.05), and miscellaneous with RMSSD (r = 0.51, P < 0.01). The LUNSERS extrapyramidal SE subscale correlated significantly with SampEn (r = −0.43, P < 0.05), hormonal with pNN20 and CSE (r = 0.40, P < 0.05; r = 0.43, P < 0.05, respectively), anticholinergic with RMSSD (r = 0.48, P < 0.05), and RH with SDNN and RMSSD (r = 0.45, P < 0.05; r = 0.44, P < 0.05, respectively).
Correlations between ΔSE and ΔHRV Table 4 lists the results of correlations analysis of the changes in the corresponding UKU and LUNSERS subscale scores and HRV parameters. The only significant correlations found between the UKU and HRV were those between psychic SE and LF, miscellaneous SE and mean RR, and anticholinergic SE and alpha. In contrast, change in LUNSERS psychic SE subscale was significantly correlated with changes in SDNN, RMSSD, LF, ApEn and SampEn among HRV parameters, and changes in both SE and RH scores correlated significantly with changes in SDNN, RMSSD, and SampEn. It should also be noted that SampEn, as did alpha, had significant correlations with extrapyramidal, anticholinergic, and miscellaneous subscales of the LUNSERS, except hormonal, extrapyramidal, and allergic SE.
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Table 2 Psychopathology, side-effects, and HRV measures Completed (n = 25)
PANSS
UKU
LUNSERS
HRV
Positive Negative Total Psychic Extrapyramidal Hormonal anticholinergic Autonomic Miscellaneous Allergic Total side-effects Psychic Extrapyramidal Hormonal anticholinergic Autonomic Miscellaneous Allergic Total side-effects Red herring Mean RR SDNN RMSSD pNN20 LF HF LF/HF ApEn SampEn Alpha CSE
Discontinued (n = 6)
Baseline Mean ± SD
6 weeks Mean ± SD
t†
Baseline Mean ± SD
U‡
25.48 ± 5.87 22.48 ± 6.61 91.64 ± 20.29 8.24 ± 6.17 0.52 ± 1.26 0.20 ± 0.65 0.32 ± 0.63 0.64 ± 1.15 0.72 ± 0.98 0.04 ± 0.20 11.88 ± 10.72 10.6 ± 6.75 4.48 ± 5.17 1.84 ± 2.78 3.32 ± 3.25 3.24 ± 3.93 2.32 ± 2.82 1.24 ± 2.79 27.04 ± 22.75 4.96 ± 6.15 791.49 ± 145.88 50.97 ± 29.51 59.24 ± 42.96 36.55 ± 26.28 330.43 ± 332.58 412.12 ± 491.58 1.34 ± 1.07 1.00 ± 0.16 1.57 ± 0.51 0.82 ± 0.22 1.93 ± 0.74
15.08 ± 4.65 17.20 ± 5.06 63.60 ± 12.18 8.12 ± 7.93 2.52 ± 2.37 0.48 ± 1.00 1.16 ± 1.21 0.60 ± 1.04 0.96 ± 1.17 0.20 ± 0.58 10.56 ± 9.02 7.52 ± 6.75 2.44 ± 3.08 1.52 ± 2.54 3.16 ± 3.34 1.92 ± 2.34 1.76 ± 2.13 0.56 ± 1.45 18.88 ± 17.83 2.92 ± 4.67 813.60 ± 139.30 36.35 ± 16.60 36.55 ± 24.49 33.27 ± 20.49 243.51 ± 359.97 223.53 ± 211.78 1.31 ± 1.12 1.00 ± 0.20 1.72 ± 0.57 0.80 ± 0.21 1.41 ± 0.50
11.00*** 4.76*** 8.66*** 0.07 –4.20*** –1.10 –2.87** 0.14 –1.03 –1.45 0.68 2.42* 1.97 0.60 0.21 2.26* 1.34 1.41 2.28* 1.84 –0.91 2.14* 2.13* 0.60 0.88 1.82 0.11 0.10 –1.02 0.22 2.94**
22.33 ± 6.06 25.83 ± 5.38 93.00 ± 9.51 12.17 ± 8.64 0.50 ± 0.55 0.33 ± 0.82 0.17 ± 0.41 0.67 ± 1.21 0.67 ± 1.03 0.00 ± 0.00 16.50 ± 12.49 12.67 ± 8.96 5.50 ± 8.57 2.17 ± 2.32 3.83 ± 4.62 3.33 ± 3.88 2.17 ± 2.32 1.50 ± 3.67 31.17 ± 31.71 4.33 ± 8.21 881.61 ± 185.55 97.53 ± 64.97 133.22 ± 94.30 55.45 ± 28.53 738.05 ± 663.00 1297.20 ± 1005.71 1.69 ± 0.85 1.03 ± 0.08 1.58 ± 0.32 0.68 ± 0.20 2.24 ± 1.04
100.00 46.50 63.50 52.50 61.50 71.00 81.50 72.00 79.00 78.00 52.00 64.00 90.00 62.00 77.50 69.00 74.50 81.00 68.00 95.00 40.00 37.00 37.00 49.00 37.00 34.00* 49.00 66.50 69.00 100.00 47.50
*P < 0.05, **P < 0.01, ***P < 0.001. †Paired t-test between baseline and 6-week assessments of the completers. ‡ Independent-samples Mann–Whitney U-test between completers and non-completers at baseline. ApEn, approximate entropy; CSE, corrected Shannon entropy; HF, high-frequency component of HRV; HRV, heart rate variability; LF, low-frequency component of HRV; LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; mean RR, mean length of all RR intervals; PANSS, Positive and Negative Syndrome Scale; pNN20, percentage of successive NN interval differences with absolute value >20 ms; RMSSD, square root of the mean squared differences of successive NN intervals; SampEn, sample entropy; SDNN, standard deviation of all NN intervals; UKU, Udvalg for Kliniske Undersøgelser.
Partial correlations between SampEn and LUNSERS SE and RH controlling for psychopathology Low SampEn has been previously linked with more severe psychopathology27 and SE has been found to be partly determined by illness stage.6 This in turn led
to the question of whether psychopathology may have served as a covariate between the SampEn and LUNSERS SE and RH. Thus, we conducted a post-hoc partial correlation of the changed scores by controlling for change in PANSS total score, and still found the changes in the SampEn to be significantly correlated with the changes in psychic, extrapyramidal,
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Table 3 Correlations between LUNSERS and UKU
Psychic Extrapyramidal Hormonal Anticholinergic Autonomic Allergic Miscellaneous
Baseline (n = 34)
6 weeks (n = 28)
Delta (n = 28)
0.61*** 0.29 0.49** 0.49** 0.73*** −0.01 0.53**
0.37 0.50** 0.58** 0.43* 0.36 0.56** 0.54**
0.56** 0.42* 0.56** 0.57** 0.43* 0.09 0.26
*P < 0.05, **P < 0.01, ***P < 0.001. LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; UKU, Udvalg for Kliniske Undersøgelser.
anticholinergic, autonomic, total SE and RH scores (r = −0.67, P < 0.001; r = −0.58, P < 0.01; r = −0.43, P < 0.05; r = −0.54, P < 0.01; r = −0.69, P < 0.001; r = −0.51, P < 0.01, respectively).
DISCUSSION In this study, we explored the feasibility of using HRV measures to detect changes in subjective adverse
effects in antipsychotic-treated schizophrenia patients. Differential patterns of associations between SE measures and HRV parameters were found in cross-sectional and longitudinal data. In addition, patients who did not complete 6-week antipsychotic treatment had significantly different baseline HRV compared with those who remained. These findings may hold some important implications for future research and management of adverse effects, particularly self-rated adverse effects, in schizophrenia patients. At baseline, the sympathetic dominance indicated by LF/HF appears to be associated with the discomforts largely associated with extrapyramidal SE and UKU hormonal SE, while CSE was associated with UKU autonomic SE. The association of UKU subscales with HRV parameters may be attributable to abnormalities in hypothalamic–pituitary–adrenal (HPA) axis activity in schizophrenia.28,29 A broad number of neuromotor abnormalities, particularly spontaneous parkinsonism, have been reported in drug-naïve schizophrenia patients and these symptoms are closely related to decreased vagal activity.30,31 HRV therefore could be linked to HPA axis32,33 and parkinsonian manifestations, particularly hypokinesia and rigidity.34,35 In contrast, there
Table 4 Changes in UKU and LUNSERS SE subscale scores and HRV parameters
UKU
LUNSERS
Psychic Extrapyramidal Hormonal Anticholinergic Miscellaneous Autonomic Allergic Psychic Extrapyramidal Hormonal Anticholinergic Miscellaneous Autonomic Allergic Red herring Side-effect
Mean RR
SDNN
RMSSD
pNN20
LF
HF
LF/HF
ApEn
SampEn
Alpha
CSE
−0.17 −0.05 −0.17 −0.14 −0.44* −0.29 0.09 −0.11 −0.05 −0.13 0.16 −0.04 −0.05 0.01 0.17 −0.05
0.04 −0.07 −0.19 −0.11 0.03 −0.05 0.13 0.54** 0.31 0.02 0.39* 0.20 0.13 0.05 0.43* 0.42*
0.00 −0.08 −0.13 −0.13 0.11 −0.05 0.17 0.51** 0.33 0.09 0.36 0.19 0.08 0.04 0.39* 0.41*
−0.06 −0.12 −0.01 −0.34 0.03 −0.28 0.24 0.04 0.02 −0.02 −0.10 −0.11 0.12 −0.09 0.10 −0.02
0.44* −0.15 −0.04 −0.03 −0.04 0.02 −0.05 0.55*** 0.17 −0.07 0.12 0.10 −0.08 −0.04 0.22 0.26
−0.34 0.12 −0.02 −0.16 −0.09 −0.13 0.10 0.18 0.27 0.18 0.38 0.07 0.26 0.24 0.39 0.32
−0.10 0.29 0.31 0.04 −0.13 −0.17 −0.12 −0.16 0.00 0.05 0.02 −0.19 0.24 0.10 −0.06 −0.03
0.31 0.29 0.23 0.12 0.31 0.30 −0.08 0.44* 0.25 0.26 0.11 0.19 0.37 −0.02 0.13 0.36
−0.31 −0.23 0.05 −0.26 −0.17 −0.30 −0.14 −0.66** −0.58** −0.21 −0.42* −0.54** −0.35 −0.17 −0.52** −0.67***
−0.36 −0.36 −0.01 −0.41* −0.08 −0.27 −0.07 −0.32 −0.45* −0.20 −0.51** −0.49* −0.19 −0.21 −0.35 −0.52**
0.19 −0.11 0.09 0.00 −0.09 0.06 0.02 0.11 −0.19 0.00 0.05 −0.09 −0.13 −0.24 −0.08 −0.07
*P < 0.05, **P < 0.01, ***P < 0.001. ApEn, approximate entropy; CSE, corrected Shannon entropy; HF, high-frequency component of HRV; HRV, heart rate variability; LF, low-frequency component of HRV; LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; mean RR, mean length of all RR intervals; pNN20, percentage of successive RR interval differences with absolute value >20 ms; RMSSD, square root of the mean squared differences of successive normal sinus intervals; SampEn, sample entropy; SDNN, standard deviation of all RR intervals; SE, side-effect; UKU, Udvalg for Kliniske Undersøgelser.
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was no association between LUNSERS and any parameter of HRV in acute schizophrenia patients. Lack of association between subjective SE and HRV parameter at baseline might be partly due to the tendency of the patients to overrate LUNSERS in frequency and severity compared to clinicians,36,37 especially during acute phase.6 Further studies are necessary to confirm the lack of relationship between HRV and LUNSERS. At 6-week follow up, there were negative associations of both UKU extrapyramidal SE and LUNSERS extrapyramidal SE with SampEn. Lower SampEn reflects a higher degree of regularity, which means less randomness within the framework of the nonlinear dynamical systems.38 The irregularity or complexity of biological rhythm is greater in healthy controls.24,39 A strong association between the UKU and LUNSERS extrapyramidal SE subscales lends further support to the validity of self-reported measure of extrapyramidal SE and its possible utilization in psychotic patients. The LUNSERS anticholinergic subscale score at week 6 was correlated with RMSSD and the LUNSERS hormonal SE with pNN20 and CSE. Along with pNN20, RMSSD mainly reflects cardiovagal activity independent of long-term trends.40,41 The positive correlations of hormonal SE with pNN20 and CSE may be indicative of sexual dysfunction associated with anticholinergic SE. The positive association of RMSSD with RH also suggests heightened awareness of non-specific bodily discomfort.6 Interestingly, lack of correlation between autonomic nervous system activity and daily dose of anticholinergic drugs,12 between decreased vagal function and differences due to medication SE, and between HRV and change in drug-induced parkinsonism have been reported elsewhere.23,42 The present results, however, suggest that self-reported anticholinergic SE is reflective of the corresponding HRV parameters, providing support for self-reported SE as a viable measure of autonomic dysregulation associated with antipsychotic treatment. As for the 6-week changes (Δ) in scores, overall number and strength of correlations between psychic, extrapyramidal, anticholinergic, miscellaneous subscales of the LUNSERS and HRV parameters, especially SampEn and alpha, far outweighed those between the UKU and HRV. This suggests that the changes in self-reported, rather than clinicianrated, SE were better reflected by the changes in HRV dynamics and confirms the utility of LUNSERS
to effectively assess antipsychotic-induced adverse effects.43 Furthermore, SampEn and/or alpha had a higher correlation with subscales and SE total compared to the linear parameters, suggesting that non-linear HRV properties better reflected SE. A recent study suggested that central autonomic activity might be a biomarker for SE of antipsychotic drugs rather than severity of schizophrenia.12 Post-hoc partial correlation analysis also confirmed that SampEn may be directly linked with subjective discomfort independent of psychopathology. Given that the discrepancy between clinician-rated and patient-rated adverse effects is evident,43 and the adverse effects of antipsychotics are the cause of non-compliance and the limiting factor for further improvement regardless of mode of assessment,44 the present findings suggest that subjective experience of antipsychotics should be used to complement clinician judgment of adverse effects in clinical routine.45 Last, between-groups comparison of the baseline data showed that the discontinuation group had a threefold higher HF compared with the completers. These measures have been reported to be associated with vagal tone in both healthy control and atrial fibrillation groups.40,46 While schizophrenia patients have been characterized by decreased vagal tone,14,47,48 the discontinuation patients are likely to have experienced some anticholinergic symptoms. And it has been reported that HR was associated with subsequent treatment discontinuation and of symptomatic outcome of schizophrenia spectrum disorders.9 There is a possibility that the presence of such a symptom, in combination with cardiovascular discomfort, may have contributed to the discontinuation. The total LUNSERS and UKU scores exhibit good correlation.17,43,49 Nonetheless, Lambert et al. found that on an individual item basis, only approximately half of the LUNSERS items were significantly correlated with their predicted counterpart from the UKU, with 11 pairs of items showing a high level of correlation (Spearman’s rho ≥ 0.6).49 One of the plausible explanations may come from the discrepancy between clinician and patient evaluation of distress.43 Patients have been found to report SE more frequently than as assessed by clinicians,36 thus highlighting the underlying difficulties in effectively conveying discomfort to the clinician. The present study has several limitations. First, the sample size was too small to allow generalization of
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the present results. In particular, the discontinuation group was too small to allow calculation of parametric statistics. Second, because type of antipsychotic varied among patients, a dose–response relationship could not be fully explored. Finally, we could not evaluate if there was a placebo effect, because this was not designed as a confirmatory study. This study examined whether self-reported SE is consistent with clinician-rated SE with regard to HRV parameters. It was found that, although most respective SE subscales strongly correlated with each other and there were some correlations between the selfreported and clinician-rated measures and HRV, selfreported SE were more consistently and strongly correlated with the specific HRV measures, especially in change scores. Further systematic examination of linkages between self-reported psychic and anticholinergic SE and particularly non-linear HRV parameters of SampEn and alpha may be necessary for neurocardiac monitoring.
7. Fujibayashi M, Matsumoto T, Kishida I et al. Autonomic nervous system activity and psychiatric severity in schizophrenia. Psychiatry Clin. Neurosci. 2009; 63: 538–545. 8. Bär KJ, Wernich K, Boettger S et al. Relationship between cardiovagal modulation and psychotic state in patients with paranoid schizophrenia. Psychiatry Res. 2008; 157: 255–257. 9. Bodén R, Lindström L, Rautaharju P, Sundström J. Electrocardiographic signs of autonomic imbalance in medicated patients with first-episode schizophrenia spectrum disorders: Relations to first treatment discontinuation and fiveyear remission status. Eur. Psychiatry 2012; 27: 213–218. 10. Chang JS, Yoo CS, Yi SH et al. Changes in heart rate dynamics of patients with schizophrenia treated with risperidone. Prog. Neuropsychopharmacol. Biol. Psychiatry 2010; 34: 924–929. 11. Hempel R, Tulen J, van Beveren N, Roder C, Hengeveld M. Cardiovascular variability during treatment with haloperidol, olanzapine or risperidone in recent-onset schizophrenia. J. Psychopharmacol. 2009; 23: 697–707. 12. Iwamoto Y, Kawanishi C, Kishida I et al. Dose-dependent effect of antipsychotic drugs on autonomic nervous system activity in schizophrenia. BMC Psychiatry 2012; 12: 199. 13. Kim JH, Ann JH, Lee J, Kim MH, Han AY. Altered heart rate dynamics associated with antipsychotic-induced subjective restlessness in patients with schizophrenia. Neuropsychiatr. Dis. Treat. 2013; 9: 989–994. 14. Chang JS, Yoo CS, Yi SH et al. Differential pattern of heart rate variability in patients with schizophrenia. Prog. Neuropsychopharmacol. Biol. Psychiatry 2009; 33: 991–995. 15. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 1987; 13: 261–276. 16. Lingjaerde O, Ahlfors UG, Bech P, Dencker SJ, Elgen K. The UKU side effect rating scale. A new comprehensive rating scale for psychotropic drugs and a cross-sectional study of side effects in neuroleptic-treated patients. Acta Psychiatr. Scand. Suppl. 1987; 334: 1–100. 17. Day JC, Wood G, Dewey M, Bentall RP. A self-rating scale for measuring neuroleptic side-effects. Validation in a group of schizophrenic patients. Br. J. Psychiatry 1995; 166: 650–653. 18. Jung HY, Joo YH, Shin HK et al. A validation study of Korean-version of Liverpool University Neuroleptic Side Effect Rating Scale (LUNSERS). J Korean Neuropsychiatr. Assoc. 2002; 41: 138–145. 19. Lippman N, Stein KM, Lerman BB. Comparison of methods for removal of ectopy in measurement of heart rate variability. Am. J. Physiol. 1994; 267: H411–H418. 20. Lim YJ, Yi SH, Shin DG, Jeon GR. Effects of aging, Denger and metabolic syndrome on the dynamics of a heart. J. Korean Phys. Soc. 2006; 48: S163–S169. 21. Kurths J, Voss A, Saparin P, Witt A, Kleiner HJ, Wessel N. Quantitative analysis of heart rate variability. Chaos 1995; 5: 88–94.
ACKNOWLEDGMENTS This research was supported by the Seoul National University Hospital (SNUH) Research Fund (Grant no. 04-2009-93) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant no. 2010-0010274). The authors have no conflicts of interest to declare.
REFERENCES 1. Kane JM. Addressing side effects from antipsychotic treatment in schizophrenia. J. Clin. Psychiatry 2011; 72: e07. 2. Fenton WS, Blyler CR, Heinssen RK. Determinants of medication compliance in schizophrenia: Empirical and clinical findings. Schizophr. Bull. 1997; 23: 637–651. 3. Lieberman JA, Stroup TS, McEvoy JP et al. Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N. Engl. J. Med. 2005; 353: 1209–1223. 4. Barsky AJ, Saintfort R, Rogers MP, Borus JF. Nonspecific medication side effects and the nocebo phenomenon. JAMA 2002; 287: 622–627. 5. de Haan L, Weisfelt M, Dingemans PM, Linszen DH, Wouters L. Psychometric properties of the Subjective WellBeing Under Neuroleptics scale and the Subjective Deficit Syndrome Scale. Psychopharmacology (Berl) 2002; 162: 24–28. 6. Hwang SS, Jung DC, Ahn YM, Kim SH, Kim YS. The effects of psychopathology on subjective experience of side effects of antipsychotics in patients with schizophrenia. Int. Clin. Psychopharmacol. 2010; 25: 83–90.
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22. Steuer R, Ebeling W, Russell DF, Bahar S, Neiman A, Moss F. Entropy and local uncertainty of data from sensory neurons. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2001; 64: 061911. 23. Mujica-Parodi LR, Yeragani V, Malaspina D. Nonlinear complexity and spectral analyses of heart rate variability in medicated and unmedicated patients with schizophrenia. Neuropsychobiology 2005; 51: 10–15. 24. Bornas X, Llabres J, Noguera M, Pez A. Sample entropy of ECG time series of fearful flyers: Preliminary results. Nonlinear Dynamics Psychol. Life Sci. 2006; 10: 301–318. 25. Hautala AJ, Karjalainen J, Kiviniemi AM et al. Physical activity and heart rate variability measured simultaneously during waking hours. Am. J. Physiol. Heart Circ. Physiol. 2010; 298: H874–H880. 26. Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS. Heart rate variability: A review. Med. Biol. Eng. Comput. 2006; 44: 1031–1051. 27. Kim JH, Yi SH, Yoo CS et al. Heart rate dynamics and their relationship to psychotic symptom severity in clozapinetreated schizophrenic subjects. Prog. Neuropsychopharmacol. Biol. Psychiatry 2004; 28: 371–378. 28. Ryan MC, Sharifi N, Condren R, Thakore JH. Evidence of basal pituitary-adrenal overactivity in first episode, drug naive patients with schizophrenia. Psychoneuroendocrinology 2004; 29: 1065–1070. 29. Belvederi Murri M, Pariante CM, Dazzan P et al. Hypothalamic-pituitary-adrenal axis and clinical symptoms in first-episode psychosis. Psychoneuroendocrinology 2012; 37: 629–644. 30. Peralta V, Basterra V, Campos MS, de Jalon EG, Moreno-Izco L, Cuesta MJ. Characterization of spontaneous Parkinsonism in drug-naive patients with nonaffective psychotic disorders. Eur. Arch. Psychiatry Clin. Neurosci. 2012; 262: 131–138. 31. Mietus JE, Peng CK, Henry I, Goldsmith RL, Goldberger AL. The pNNx files: Re-examining a widely used heart rate variability measure. Heart 2002; 88: 378–380. 32. Thayer JF, Lane RD. Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neurosci. Biobehav. Rev. 2009; 33: 81–88. 33. Thayer JF, Sternberg E. Beyond heart rate variability: Vagal regulation of allostatic systems. Ann. N. Y. Acad. Sci. 2006; 1088: 361–372. 34. Kallio M, Haapaniemi T, Turkka J et al. Heart rate variability in patients with untreated Parkinson’s disease. Eur. J. Neurol. 2000; 7: 667–672. 35. Valappil RA, Black JE, Broderick MJ et al. Exploring the electrocardiogram as a potential tool to screen for premotor Parkinson’s disease. Mov. Disord. 2010; 25: 2296–2303. 36. Lindström E, Lewander T, Malm U, Malt UF, Lublin H, Ahlfors UG. Patient-rated versus clinician-rated side effects of drug treatment in schizophrenia. Clinical validation of
a self-rating version of the UKU Side Effect Rating Scale (UKU-SERS-Pat). Nord. J. Psychiatry 2001; 55 (Suppl. 44): 5–69. Wolters HA, Knegtering H, van den Bosch RJ, Wiersma D. Effects and side effects of antipsychotic treatment in schizophrenia: Pros and cons of available self-rating scales. Schizophr. Res. 2009; 112: 114–118. Chang JS, Ha K, Yoon IY et al. Patterns of cardiorespiratory coordination in young women with recurrent major depressive disorder treated with escitalopram or venlafaxine. Prog. Neuropsychopharmacol. Biol. Psychiatry 2012; 39: 136–142. Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 2000; 278: H2039–H2049. Hayano J, Sakakibara Y, Yamada A et al. Accuracy of assessment of cardiac vagal tone by heart rate variability in normal subjects. Am. J. Cardiol. 1991; 67: 199–204. Bigger JT, Jr, Albrecht P, Steinman RC, Rolnitzky LM, Fleiss JL, Cohen RJ. Comparison of time- and frequency domain-based measures of cardiac parasympathetic activity in Holter recordings after myocardial infarction. Am. J. Cardiol. 1989; 64: 536–538. Huang WL, Chang LR, Kuo TB, Lin YH, Chen YZ, Yang CC. Impact of antipsychotics and anticholinergics on autonomic modulation in patients with schizophrenia. J. Clin. Psychopharmacol. 2013; 33: 170–177. Jung HY, Kim JH, Ahn YM, Kim SC, Hwang SS, Kim YS. Liverpool University Neuroleptic Side-Effect Rating Scale (LUNSERS) as a subjective measure of drug-induced parkinsonism and akathisia. Hum. Psychopharmacol. 2005; 20: 41–45. Voruganti LP, Baker LK, Awad AG. New generation antipsychotic drugs and compliance behaviour. Curr. Opin. Psychiatry 2008; 21: 133–139. Ritsner M, Perelroyzen G, Ilan H, Gibel A. Subjective response to antipsychotics of schizophrenia patients treated in routine clinical practice: A naturalistic comparative study. J. Clin. Psychopharmacol. 2004; 24: 245– 254. van den Berg MP, Haaksma J, Brouwer J, Tieleman RG, Mulder G, Crijns HJ. Heart rate variability in patients with atrial fibrillation is related to vagal tone. Circulation 1997; 96: 1209–1216. Bär KJ, Letzsch A, Jochum T, Wagner G, Greiner W, Sauer H. Loss of efferent vagal activity in acute schizophrenia. J. Psychiatr. Res. 2005; 39: 519–527. Valkonen-Korhonen M, Tarvainen MP, Ranta-Aho P et al. Heart rate variability in acute psychosis. Psychophysiology 2003; 40: 716–726. Lambert TJ, Cock N, Alcock SJ, Kelly DL, Conley RR. Measurement of antipsychotic-induced side effects: Support for the validity of a self-report (LUNSERS) versus structured interview (UKU) approach to measurement. Hum. Psychopharmacol. 2003; 18: 405–411.
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doi:10.1111/pcn.12226
Regular Article
Evaluating subjective domains of antipsychotic-induced adverse effects using heart rate variability Jae Seung Chang, MD, PhD,1† Samuel Suk-Hyun Hwang, PhD,2† Sang Hoon Yi, Yeni Kim, MD, PhD,6 Yu-Sang Lee, MD, PhD,3 Yong Sik Kim, MD, PhD4 and Hee-Yeon Jung, MD, PhD7*
PhD,5
1 Department of Psychiatry, Seoul National University Bundang Hospital, 2Department of Addiction, Rehabilitation, and Social Welfare, Eulji University, Seongnam, 3Department of Psychiatry, Yong-in Mental Hospital, Yongin, 4Department of Psychiatry, Dongguk University Ilsan Hospital, Goyang, 5Department of Computer Simulation, Institute of Basic Science, Inje University, Gimhae, 6Department of Adolescent Psychiatry, National Center for Child and Adolescent Psychiatry, Seoul National Hospital, and 7Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, Korea
Aims: Antipsychotic-induced autonomic dysregulation may lead to a wide range of subjective sideeffects in schizophrenia patients. Using heart rate variability (HRV) measures, we prospectively examined the relationship between subjective side-effects and cardiac autonomic regulation in unmedicated schizophrenia patients.
effects domain were significantly correlated with the changes in time-domain HRV measures and sample entropy (SampEn). In addition, the change in SampEn was significantly associated with that in the scores of extrapyramidal, anticholinergic, miscellaneous, and red herring domains as well as the mean total LUNSERS score.
Methods: Forty-five unmedicated schizophrenia patients were assessed for antipsychotic-associated side-effects and HRV parameters at baseline and after 6 weeks of treatment. Psychiatric symptoms and subjective side-effects were assessed using the Positive and Negative Syndrome Scale (PANSS) and the Liverpool University Neuroleptic Side-effect Rating Scale (LUNSERS).
Conclusion: Baseline HRV measures may predict clinical response and adverse events associated with treatment adherence. Also, subjective side-effects may correspond well with the changes in neurocardiac dynamics, and the changes in SampEn may effectively reflect subjective discomfort in patients receiving antipsychotic treatment.
Results: Correlations between subjective adverse effects and HRV measures at baseline and at week 6 varied. Nonetheless, the changes in the psychic side-
LOSE MONITORING OF antipsychoticassociated adverse events is essential to enhance treatment adherence.1 Perceived side-effects (SE) explain approximately one-quarter to one-third of
C
*Correspondence: Hee-Yeon Jung, MD, PhD, Department of Psychiatry, SMG-SNU Boramae Medical Center, 20 Boramae-Ro 5-Gil, Dongjak-Gu, Seoul 156-707, Korea. Email: [email protected] † The first two authors contributed equally. Received 22 January 2014; revised 18 June 2014; accepted 23 July 2014.
Key words: antipsychotic, heart rate variability, schizophrenia, subjective side-effect, treatment adherence.
early discontinuation of typical antipsychotics2 and at least 10% of novel atypical antipsychotics.3 SE associated with specific pharmacological actions can be easily detected and managed by clinicians but a substantial proportion of patients may experience non-specific SE of a given medication.4 Psychiatric symptoms may also affect subjective SE and overall wellbeing regardless of antipsychotic medications.5,6 Altered autonomic regulation is involved in psychological, physical, and behavioral symptoms presented in schizophrenia patients.7 Cardiovagal
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activity measured by heart rate variability (HRV) is reduced in schizophrenia patients and correlated with symptom severity.8 Bodén et al. suggested that some electrocardiogram (ECG) autonomic indices may provide prognostic information on treatment discontinuation in patients with first-episode psychotic disorder.9 Recent studies suggest that antipsychotic treatment can lead to favorable changes in HRV measures in schizophrenia patients,10 although they were not consistent.11 In contrast, several studies indicate a possible role of HRV measures in predicting SE of antipsychotics.12 A recent study suggested an association between altered HRV and subjective restlessness in schizophrenia patients receiving antipsychotic treatment,13 thereby implicating clinical use of HRV parameters in predicting antipsychotic-induced subjective SE. In this study, we explored the possible association between the antipsychotic-induced changes in cardiac autonomic regulation and subjective SE, and prospectively investigated the changes in both subjective SE and HRV measures in schizophrenia patients following antipsychotic treatment.
METHODS
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Clinical assessment For the baseline and 6-week follow up, the patients were assessed on psychiatric symptoms using the Positive and Negative Syndrome Scale (PANSS).15 For SE, they underwent an examination with the 48-item Udvalg for Kliniske Undersøgelser (UKU)16 by a certified psychiatrist and also completed the Liverpool University Neuroleptic Side-effect Rating Scale (LUNSERS).17 The UKU is a 4-point, semi-structured interview that assesses 48 items structured into mainly psychic, neurological and autonomic domains. The LUNSERS was developed as a selfreport instrument for adverse effects related to antipsychotic treatment17 and consists of 41 SE items and 10 red herring (RH) items, all of which are rated on a 5-point scale. The SE items of the LUNSERS are further grouped into the specific domains such as psychic, extrapyramidal, hormonal, anticholinergic, other autonomic, allergic, and miscellaneous. The 10 RH items are not directly related to known SE but were found to be useful for detecting overreporting tendencies or psychopathologies.6 The total scores of the two SE scales had good correlation in both English (r = 0.82) and Korean schizophrenia patients (r = 0.81).17,18
Subjects This was an exploratory study using the psychophysiologic approach to subjective SE. Forty-five unmedicated patients with DSM-IV schizophrenia hospitalized for acute psychotic symptoms were recruited from Yong-in Mental Hospital, Republic of Korea. The HRV data from some of these patients were used in our previous studies on diagnosis and clinical response.10,14 All patients were drug naïve or drug free, defined as cessation of antipsychotic medication for at least 28 days or depot antipsychotics for at least 24 weeks prior to the index hospitalization. Patients treated with antipsychotic monotherapy for 6 weeks were included in 6-week assessment. Patients switched from initially prescribed antipsychotics were regarded as withdrawn. The choice of antipsychotic was made by a treating psychiatrist blind to the study protocol, and the chosen antipsychotic was freely titrated to clinical response. The study protocol was reviewed and approved by the institutional review board and written informed consent was obtained from each participant. All procedures used in this study were in accordance with the tenets of the Declaration of Helsinki.
Physiological assessment To minimize diurnal changes in cardiac autonomic modulation, ECG recordings were performed in a dimly lit, warm, and quiet room between 09.00 and 11.00 hours after prohibiting any smoking and caffeine consumption on the morning of ECG recording. All patients were requested to keep their eyes closed and to breathe regularly in a recumbent position. After a 15-min test session to stabilize respiration and heartbeats, 5-min ECG was acquired. The ECG analog signal was automatically digitized at a sampling rate of 250 Hz. RR intervals were filtered by an adaptive filter algorithm to replace and interpolate ventricular premature beats and artifacts.19 The entire 5-min ECG data were used for HRV analysis. In the time domain analysis of HRV, mean length of all RR intervals (mean RR), standard deviation of all normal-to-normal (NN) intervals (SDNN), square root of the mean squared differences of successive NN intervals (RMSSD), and percentage of successive RR interval differences with absolute value >20 ms (pNN20) were calculated. These time domain mea-
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sures reflect the responsiveness of HR. In the frequency domain, spectral analysis was performed using the standard autoregressive algorithm, after detrending and resampling the irregularly timesampled recording of consecutive RR intervals. The power spectrum density was conventionally estimated for two major frequency ranges, that is, the low-frequency (LF) band (0.04–0.15 Hz) and the high-frequency (HF) band (0.15–0.4 Hz), and the ratio of LF to HF power (LF/HF) was computed to evaluate sympathovagal balance.20 The corrected Shannon entropy (CSE) was calculated using binary symbols generated from a coarsegraining process, based on a threshold, which is the absolute difference of successive RR intervals.21 Lower CSE reflects a higher degree of regularity of symbolic patterns generated from the binary sequences.22,23 To measure the regularity of the fluctuations in a time series, approximate entropy (ApEn) and sample entropy (SampEn) were also computed. SampEn is free from the bias induced from the finite length and self-matches in ApEn,14,24 and a low value signifies a high degree of regularity.25 Last, a detrended fluctuation analysis was used to derive the alpha, quantifying the fractal properties of RR intervals. The alpha reflects the amount of short-term temporal fluctuation in the HR time series, with the low values associated with a highly random, or coarse, time series and the high values with a strongly correlated, or smooth, time series.26
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Statistical analysis Following the analysis of the descriptive data of the baseline and 6-week follow up, between-groups differences of the completers and the discontinuation group were examined using simple analysis of variance (ANOVA) or chi-squared test as appropriate. The baseline and 6-week data of the completers were then compared with paired t-test. The change (Δ) scores were obtained by subtracting the values obtained at the baseline from those of the 6-week data. For each domain (clinical effect, objective SE, subjective SE, and HRV), the Bonferroni correction was used to control for multiple comparisons. Last, correlation analysis was carried out using the Pearson correlations, r. All statistical analysis was done using SPSS 21.0 for Windows (SPSS, Chicago, IL, USA).
RESULTS Demographic and clinical variables The subject demographic and clinical characteristics are listed in Table 1. Among 45 patients, 11 (six men and five women) refused or were incapable of completing baseline evaluation hence were excluded from the final analysis. These patients did not significantly differ from the rest of the patients in sex, age, and type of antipsychotics, but they had significantly more severe psychopathology (108.09 ± 14.96) than those included (91.71 ± 17.81), according to PANSS
Table 1 Subject characteristics Baseline (n = 34) n (%) or mean ± SD Male Age (years) Schizophrenia Paranoid type Undifferentiated type PANSS score UKU score LUNSERS SE score LUNSERS RH score Accumulated antipsychotic dose (mg in chlorpromazine equivalents)
18 (52.9) 35.0 ± 10.9 29 (85.3) 5 (14.7) 91.71 ± 17.8 10.96 ± 10.48 26.53 ± 23.57 4.62 ± 6.20 –
Week 6 (n = 25)† n (%) or mean ± SD 14 (56.0) 36.2 ± 11.21 22 (88.0) 3 (12.0) 63.60 ± 12.2 19.29 ± 10.07 18.89 ± 17.83 2.92 ± 6.67 9168.00 ± 3777.87
†
Three patients who did not complete ECG assessment were excluded. LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; PANSS, Positive And Negative Syndrome Scale; RH, red herring; SE, side-effect; UKU, Udvalg for Kliniske Undersøgelser.
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total score (t = 2.75, P < .01). As for 6-week assessment, six of 34 patients were switched from initially prescribed antipsychotics and thus were withdrawn from the study, and three patients did not complete the second evaluation due to lack of cooperation. Age, gender distribution, and antipsychotic type did not significantly differ between the nine excluded patients and those included in the final analysis. In addition, they did not significantly differ in the baseline UKU subscales or LUNSERS subscales including the RH. During 6-week antipsychotic treatment, 16 patients (64%) were taking risperidone, five (20%) olanzapine, three (12%) amisulpride, and one (4%), aripiprazole. Based on paired comparison, significant differences were found between the baseline and 6-week scores in PANSS total score (t = 9.83, P < 0.001), UKU total score (t = −2.30, P < 0.05) and LUNSERS SE total score (t = 2.33, P < 0.05). Listed in Table 2 are the psychopathology, SE, and HRV measures of the completed and discontinuation patients at baseline and 6-week assessment. In terms of completers, significant improvements in psychopathology, lower psychic, autonomic, and total SE scores, and decreases in SD, RMSSD, and CSE were evident over the study period. After Bonferroni correction was applied, significant changes were seen only in CSE; no significant changes were detected between the two timepoints in subjective SE measured using the LUNSERS and HRV measures. In contrast, the modified subscales of the clinician-rated UKU extrapyramidal SE and anticholinergic SE increased significantly. In the comparison of the baseline data between the completers and discontinuation patients, no significant group differences in the psychopathology and SE measures were found, but the discontinuation patients had higher HF in HRV measures compared to the completers. No significant associations were found between accumulated antipsychotic dose and changes in the PANSS total, positive subscale, and negative subscale scores. The UKU and the LUNSERS subscale scores were not associated with accumulated antipsychotic dose. The level of tension (G4 item on the PANSS) was not associated with RR interval at baseline (r = 0.15, P = 0.41) or 6-week assessment (r = 0.06, P = 0.80).
and UKU counterparts, respectively (Table 3). At baseline, all LUNSERS subscales significantly correlated with the UKU subscales, except for extrapyramidal and allergic symptoms. At 6-week assessment, all LUNSERS subscales significantly correlated with their UKU counterparts, except for psychic and autonomic symptoms, which were marginally correlated (P < 0.06). Last, the changed scores of the respective LUNSERS and the UKU counterpart subscales were significantly correlated for all subscales, except for allergic and miscellaneous subscales.
Correlations between LUNSERS and UKU subscales We examined the cross-sectional and longitudinal correlations between the subscales of the LUNSERS
Cross-sectional correlations at baseline and 6-month follow up At baseline there were no significant correlations between HRV parameters and SE measures except that LF/HF significantly correlated with UKU extrapyramidal SE and hormonal SE (r = 0.62, P < 0.001; r = 0.42, P < 0.01, respectively). At 6-week assessment, UKU extrapyramidal SE significantly correlated with SampEn (r = −0.41, P < 0.05), and miscellaneous with RMSSD (r = 0.51, P < 0.01). The LUNSERS extrapyramidal SE subscale correlated significantly with SampEn (r = −0.43, P < 0.05), hormonal with pNN20 and CSE (r = 0.40, P < 0.05; r = 0.43, P < 0.05, respectively), anticholinergic with RMSSD (r = 0.48, P < 0.05), and RH with SDNN and RMSSD (r = 0.45, P < 0.05; r = 0.44, P < 0.05, respectively).
Correlations between ΔSE and ΔHRV Table 4 lists the results of correlations analysis of the changes in the corresponding UKU and LUNSERS subscale scores and HRV parameters. The only significant correlations found between the UKU and HRV were those between psychic SE and LF, miscellaneous SE and mean RR, and anticholinergic SE and alpha. In contrast, change in LUNSERS psychic SE subscale was significantly correlated with changes in SDNN, RMSSD, LF, ApEn and SampEn among HRV parameters, and changes in both SE and RH scores correlated significantly with changes in SDNN, RMSSD, and SampEn. It should also be noted that SampEn, as did alpha, had significant correlations with extrapyramidal, anticholinergic, and miscellaneous subscales of the LUNSERS, except hormonal, extrapyramidal, and allergic SE.
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Table 2 Psychopathology, side-effects, and HRV measures Completed (n = 25)
PANSS
UKU
LUNSERS
HRV
Positive Negative Total Psychic Extrapyramidal Hormonal anticholinergic Autonomic Miscellaneous Allergic Total side-effects Psychic Extrapyramidal Hormonal anticholinergic Autonomic Miscellaneous Allergic Total side-effects Red herring Mean RR SDNN RMSSD pNN20 LF HF LF/HF ApEn SampEn Alpha CSE
Discontinued (n = 6)
Baseline Mean ± SD
6 weeks Mean ± SD
t†
Baseline Mean ± SD
U‡
25.48 ± 5.87 22.48 ± 6.61 91.64 ± 20.29 8.24 ± 6.17 0.52 ± 1.26 0.20 ± 0.65 0.32 ± 0.63 0.64 ± 1.15 0.72 ± 0.98 0.04 ± 0.20 11.88 ± 10.72 10.6 ± 6.75 4.48 ± 5.17 1.84 ± 2.78 3.32 ± 3.25 3.24 ± 3.93 2.32 ± 2.82 1.24 ± 2.79 27.04 ± 22.75 4.96 ± 6.15 791.49 ± 145.88 50.97 ± 29.51 59.24 ± 42.96 36.55 ± 26.28 330.43 ± 332.58 412.12 ± 491.58 1.34 ± 1.07 1.00 ± 0.16 1.57 ± 0.51 0.82 ± 0.22 1.93 ± 0.74
15.08 ± 4.65 17.20 ± 5.06 63.60 ± 12.18 8.12 ± 7.93 2.52 ± 2.37 0.48 ± 1.00 1.16 ± 1.21 0.60 ± 1.04 0.96 ± 1.17 0.20 ± 0.58 10.56 ± 9.02 7.52 ± 6.75 2.44 ± 3.08 1.52 ± 2.54 3.16 ± 3.34 1.92 ± 2.34 1.76 ± 2.13 0.56 ± 1.45 18.88 ± 17.83 2.92 ± 4.67 813.60 ± 139.30 36.35 ± 16.60 36.55 ± 24.49 33.27 ± 20.49 243.51 ± 359.97 223.53 ± 211.78 1.31 ± 1.12 1.00 ± 0.20 1.72 ± 0.57 0.80 ± 0.21 1.41 ± 0.50
11.00*** 4.76*** 8.66*** 0.07 –4.20*** –1.10 –2.87** 0.14 –1.03 –1.45 0.68 2.42* 1.97 0.60 0.21 2.26* 1.34 1.41 2.28* 1.84 –0.91 2.14* 2.13* 0.60 0.88 1.82 0.11 0.10 –1.02 0.22 2.94**
22.33 ± 6.06 25.83 ± 5.38 93.00 ± 9.51 12.17 ± 8.64 0.50 ± 0.55 0.33 ± 0.82 0.17 ± 0.41 0.67 ± 1.21 0.67 ± 1.03 0.00 ± 0.00 16.50 ± 12.49 12.67 ± 8.96 5.50 ± 8.57 2.17 ± 2.32 3.83 ± 4.62 3.33 ± 3.88 2.17 ± 2.32 1.50 ± 3.67 31.17 ± 31.71 4.33 ± 8.21 881.61 ± 185.55 97.53 ± 64.97 133.22 ± 94.30 55.45 ± 28.53 738.05 ± 663.00 1297.20 ± 1005.71 1.69 ± 0.85 1.03 ± 0.08 1.58 ± 0.32 0.68 ± 0.20 2.24 ± 1.04
100.00 46.50 63.50 52.50 61.50 71.00 81.50 72.00 79.00 78.00 52.00 64.00 90.00 62.00 77.50 69.00 74.50 81.00 68.00 95.00 40.00 37.00 37.00 49.00 37.00 34.00* 49.00 66.50 69.00 100.00 47.50
*P < 0.05, **P < 0.01, ***P < 0.001. †Paired t-test between baseline and 6-week assessments of the completers. ‡ Independent-samples Mann–Whitney U-test between completers and non-completers at baseline. ApEn, approximate entropy; CSE, corrected Shannon entropy; HF, high-frequency component of HRV; HRV, heart rate variability; LF, low-frequency component of HRV; LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; mean RR, mean length of all RR intervals; PANSS, Positive and Negative Syndrome Scale; pNN20, percentage of successive NN interval differences with absolute value >20 ms; RMSSD, square root of the mean squared differences of successive NN intervals; SampEn, sample entropy; SDNN, standard deviation of all NN intervals; UKU, Udvalg for Kliniske Undersøgelser.
Partial correlations between SampEn and LUNSERS SE and RH controlling for psychopathology Low SampEn has been previously linked with more severe psychopathology27 and SE has been found to be partly determined by illness stage.6 This in turn led
to the question of whether psychopathology may have served as a covariate between the SampEn and LUNSERS SE and RH. Thus, we conducted a post-hoc partial correlation of the changed scores by controlling for change in PANSS total score, and still found the changes in the SampEn to be significantly correlated with the changes in psychic, extrapyramidal,
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Table 3 Correlations between LUNSERS and UKU
Psychic Extrapyramidal Hormonal Anticholinergic Autonomic Allergic Miscellaneous
Baseline (n = 34)
6 weeks (n = 28)
Delta (n = 28)
0.61*** 0.29 0.49** 0.49** 0.73*** −0.01 0.53**
0.37 0.50** 0.58** 0.43* 0.36 0.56** 0.54**
0.56** 0.42* 0.56** 0.57** 0.43* 0.09 0.26
*P < 0.05, **P < 0.01, ***P < 0.001. LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; UKU, Udvalg for Kliniske Undersøgelser.
anticholinergic, autonomic, total SE and RH scores (r = −0.67, P < 0.001; r = −0.58, P < 0.01; r = −0.43, P < 0.05; r = −0.54, P < 0.01; r = −0.69, P < 0.001; r = −0.51, P < 0.01, respectively).
DISCUSSION In this study, we explored the feasibility of using HRV measures to detect changes in subjective adverse
effects in antipsychotic-treated schizophrenia patients. Differential patterns of associations between SE measures and HRV parameters were found in cross-sectional and longitudinal data. In addition, patients who did not complete 6-week antipsychotic treatment had significantly different baseline HRV compared with those who remained. These findings may hold some important implications for future research and management of adverse effects, particularly self-rated adverse effects, in schizophrenia patients. At baseline, the sympathetic dominance indicated by LF/HF appears to be associated with the discomforts largely associated with extrapyramidal SE and UKU hormonal SE, while CSE was associated with UKU autonomic SE. The association of UKU subscales with HRV parameters may be attributable to abnormalities in hypothalamic–pituitary–adrenal (HPA) axis activity in schizophrenia.28,29 A broad number of neuromotor abnormalities, particularly spontaneous parkinsonism, have been reported in drug-naïve schizophrenia patients and these symptoms are closely related to decreased vagal activity.30,31 HRV therefore could be linked to HPA axis32,33 and parkinsonian manifestations, particularly hypokinesia and rigidity.34,35 In contrast, there
Table 4 Changes in UKU and LUNSERS SE subscale scores and HRV parameters
UKU
LUNSERS
Psychic Extrapyramidal Hormonal Anticholinergic Miscellaneous Autonomic Allergic Psychic Extrapyramidal Hormonal Anticholinergic Miscellaneous Autonomic Allergic Red herring Side-effect
Mean RR
SDNN
RMSSD
pNN20
LF
HF
LF/HF
ApEn
SampEn
Alpha
CSE
−0.17 −0.05 −0.17 −0.14 −0.44* −0.29 0.09 −0.11 −0.05 −0.13 0.16 −0.04 −0.05 0.01 0.17 −0.05
0.04 −0.07 −0.19 −0.11 0.03 −0.05 0.13 0.54** 0.31 0.02 0.39* 0.20 0.13 0.05 0.43* 0.42*
0.00 −0.08 −0.13 −0.13 0.11 −0.05 0.17 0.51** 0.33 0.09 0.36 0.19 0.08 0.04 0.39* 0.41*
−0.06 −0.12 −0.01 −0.34 0.03 −0.28 0.24 0.04 0.02 −0.02 −0.10 −0.11 0.12 −0.09 0.10 −0.02
0.44* −0.15 −0.04 −0.03 −0.04 0.02 −0.05 0.55*** 0.17 −0.07 0.12 0.10 −0.08 −0.04 0.22 0.26
−0.34 0.12 −0.02 −0.16 −0.09 −0.13 0.10 0.18 0.27 0.18 0.38 0.07 0.26 0.24 0.39 0.32
−0.10 0.29 0.31 0.04 −0.13 −0.17 −0.12 −0.16 0.00 0.05 0.02 −0.19 0.24 0.10 −0.06 −0.03
0.31 0.29 0.23 0.12 0.31 0.30 −0.08 0.44* 0.25 0.26 0.11 0.19 0.37 −0.02 0.13 0.36
−0.31 −0.23 0.05 −0.26 −0.17 −0.30 −0.14 −0.66** −0.58** −0.21 −0.42* −0.54** −0.35 −0.17 −0.52** −0.67***
−0.36 −0.36 −0.01 −0.41* −0.08 −0.27 −0.07 −0.32 −0.45* −0.20 −0.51** −0.49* −0.19 −0.21 −0.35 −0.52**
0.19 −0.11 0.09 0.00 −0.09 0.06 0.02 0.11 −0.19 0.00 0.05 −0.09 −0.13 −0.24 −0.08 −0.07
*P < 0.05, **P < 0.01, ***P < 0.001. ApEn, approximate entropy; CSE, corrected Shannon entropy; HF, high-frequency component of HRV; HRV, heart rate variability; LF, low-frequency component of HRV; LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; mean RR, mean length of all RR intervals; pNN20, percentage of successive RR interval differences with absolute value >20 ms; RMSSD, square root of the mean squared differences of successive normal sinus intervals; SampEn, sample entropy; SDNN, standard deviation of all RR intervals; SE, side-effect; UKU, Udvalg for Kliniske Undersøgelser.
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Side-effects and cardiovagal modulation 289
was no association between LUNSERS and any parameter of HRV in acute schizophrenia patients. Lack of association between subjective SE and HRV parameter at baseline might be partly due to the tendency of the patients to overrate LUNSERS in frequency and severity compared to clinicians,36,37 especially during acute phase.6 Further studies are necessary to confirm the lack of relationship between HRV and LUNSERS. At 6-week follow up, there were negative associations of both UKU extrapyramidal SE and LUNSERS extrapyramidal SE with SampEn. Lower SampEn reflects a higher degree of regularity, which means less randomness within the framework of the nonlinear dynamical systems.38 The irregularity or complexity of biological rhythm is greater in healthy controls.24,39 A strong association between the UKU and LUNSERS extrapyramidal SE subscales lends further support to the validity of self-reported measure of extrapyramidal SE and its possible utilization in psychotic patients. The LUNSERS anticholinergic subscale score at week 6 was correlated with RMSSD and the LUNSERS hormonal SE with pNN20 and CSE. Along with pNN20, RMSSD mainly reflects cardiovagal activity independent of long-term trends.40,41 The positive correlations of hormonal SE with pNN20 and CSE may be indicative of sexual dysfunction associated with anticholinergic SE. The positive association of RMSSD with RH also suggests heightened awareness of non-specific bodily discomfort.6 Interestingly, lack of correlation between autonomic nervous system activity and daily dose of anticholinergic drugs,12 between decreased vagal function and differences due to medication SE, and between HRV and change in drug-induced parkinsonism have been reported elsewhere.23,42 The present results, however, suggest that self-reported anticholinergic SE is reflective of the corresponding HRV parameters, providing support for self-reported SE as a viable measure of autonomic dysregulation associated with antipsychotic treatment. As for the 6-week changes (Δ) in scores, overall number and strength of correlations between psychic, extrapyramidal, anticholinergic, miscellaneous subscales of the LUNSERS and HRV parameters, especially SampEn and alpha, far outweighed those between the UKU and HRV. This suggests that the changes in self-reported, rather than clinicianrated, SE were better reflected by the changes in HRV dynamics and confirms the utility of LUNSERS
to effectively assess antipsychotic-induced adverse effects.43 Furthermore, SampEn and/or alpha had a higher correlation with subscales and SE total compared to the linear parameters, suggesting that non-linear HRV properties better reflected SE. A recent study suggested that central autonomic activity might be a biomarker for SE of antipsychotic drugs rather than severity of schizophrenia.12 Post-hoc partial correlation analysis also confirmed that SampEn may be directly linked with subjective discomfort independent of psychopathology. Given that the discrepancy between clinician-rated and patient-rated adverse effects is evident,43 and the adverse effects of antipsychotics are the cause of non-compliance and the limiting factor for further improvement regardless of mode of assessment,44 the present findings suggest that subjective experience of antipsychotics should be used to complement clinician judgment of adverse effects in clinical routine.45 Last, between-groups comparison of the baseline data showed that the discontinuation group had a threefold higher HF compared with the completers. These measures have been reported to be associated with vagal tone in both healthy control and atrial fibrillation groups.40,46 While schizophrenia patients have been characterized by decreased vagal tone,14,47,48 the discontinuation patients are likely to have experienced some anticholinergic symptoms. And it has been reported that HR was associated with subsequent treatment discontinuation and of symptomatic outcome of schizophrenia spectrum disorders.9 There is a possibility that the presence of such a symptom, in combination with cardiovascular discomfort, may have contributed to the discontinuation. The total LUNSERS and UKU scores exhibit good correlation.17,43,49 Nonetheless, Lambert et al. found that on an individual item basis, only approximately half of the LUNSERS items were significantly correlated with their predicted counterpart from the UKU, with 11 pairs of items showing a high level of correlation (Spearman’s rho ≥ 0.6).49 One of the plausible explanations may come from the discrepancy between clinician and patient evaluation of distress.43 Patients have been found to report SE more frequently than as assessed by clinicians,36 thus highlighting the underlying difficulties in effectively conveying discomfort to the clinician. The present study has several limitations. First, the sample size was too small to allow generalization of
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the present results. In particular, the discontinuation group was too small to allow calculation of parametric statistics. Second, because type of antipsychotic varied among patients, a dose–response relationship could not be fully explored. Finally, we could not evaluate if there was a placebo effect, because this was not designed as a confirmatory study. This study examined whether self-reported SE is consistent with clinician-rated SE with regard to HRV parameters. It was found that, although most respective SE subscales strongly correlated with each other and there were some correlations between the selfreported and clinician-rated measures and HRV, selfreported SE were more consistently and strongly correlated with the specific HRV measures, especially in change scores. Further systematic examination of linkages between self-reported psychic and anticholinergic SE and particularly non-linear HRV parameters of SampEn and alpha may be necessary for neurocardiac monitoring.
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ACKNOWLEDGMENTS This research was supported by the Seoul National University Hospital (SNUH) Research Fund (Grant no. 04-2009-93) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant no. 2010-0010274). The authors have no conflicts of interest to declare.
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