WO2015076544A2 - Method for determining side effect of antipsychotic drug using heart rate variability index - Google Patents

Description

Determination of Side Effects of Antipsychotic Drugs Using Heart Rate Variability Index

The present application relates to a technique for evaluating side effects of antipsychotic drugs using heart rate variability indicators.

In order to maintain the continued use of antipsychotic drugs used in the treatment of psychosis, such as schizophrenia (formerly schizophrenia) and to maintain their therapeutic effects, it is important to monitor the drug-related side effects. Schizophrenia is one of the most common psychiatric disorders, with a prevalence of 0.4-0.7%, and if not treated properly, not only does the patient lose his or her function but also the burden on the family and the overall socioeconomic costs. Let's do it. Therefore, it is important to monitor the side effects of antipsychotic drugs in order to effectively treat these schizophrenia.

About 25% of orthopedic antipsychotic drug discontinuations and about 10% of atypical antipsychotic drugs are discontinued due to perceived side effects (SE). Due to certain pharmacological mechanisms, SEs can be judged early, allowing psychiatrists to monitor and take appropriate action. However, schizophrenia patients who receive a significant number of medications suffer from nonspecific subjective side effects in the SE. These subjective side effects may be related to the patient's mental state or the patient's overall health regardless of the drug used.

Therefore, it is necessary to develop a method for developing biological indicators capable of objectively evaluating these subjective side effects, monitoring them continuously, and taking appropriate measures early.

Republic of Korea Patent No. 1070122 relates to an apparatus and method for diagnosing schizophrenia using a heart rate variability indicator and discloses a method for diagnosing schizophrenia by measuring a change in heart rate variability.

U.S. Patent No. 8412314 relates to heart rate analysis for determining the health status of an animal, and discloses a method of using the heart rate variability to assess the physiological and overall health status of an animal.

None of these documents describe the use of heart rate variability in the evaluation of adverse effects on antipsychotic drugs.

The purpose of this study is to develop a heart rate variability (HRV) index that can be used to evaluate changes in autonomic nervous system and to determine the autonomic nervous system and mental side effects of drug treatment.

In one aspect, provided herein is a method comprising providing a heart rate variability (HRV) derived from a subject; And mean heart rate of all RR intervals (RR), standard deviation of all RR intervals (SDNN), square root of the mean squared differences of successive normal sinus intervals (RMSD), percentage of successive RR interval differences pNN20 at least one selected from the group consisting of whose absolute value exceeded 20 ms), power spectrum density (LF, HF or LF / HF), ApEn (approximate entropy), SampEn (sample entropy), alpha and corrected Shannon entropy (CSE) Analyzing in the region; Providing subjective side effects test results for the antipsychotic drug of the subject; And associating the analysis result with a subjective side effect evaluation result.

Subjective adverse effects in the method according to the present invention may be performed using one or more of a positive and negative syndrome scale (PANSS), a Udvalg for Kliniske Undersøgelser (UKU), or a Liverpool University Neuroleptic Side-effect Rating Scale (LUNERS). no.

Subjective side effects in the methods according to the present disclosure may be measured at one or more of the sub configuration measures such as, but not limited to, mentality, extraneous, hormonal, cholinergic, other autonomic, and allergic items.

The method according to the present disclosure may be used for, but not limited to, various antipsychotic drugs, such as antipsychotic drugs, for evaluating side effects on risperidone, olanzapine, amisulfride, or aripiprazole used in the treatment of schizophrenia.

The method according to the present invention can be particularly useful for evaluating side effects for antipsychotic drugs, which can predict the side effects associated with treatment response or treatment compliance with antipsychotic drugs.

The step of associating in the method according to the present invention includes comparing the HRV analysis result of the basal state measured before taking the antipsychotic drug with the HRV analysis result after taking the drug to determine whether there is a change.

In the method according to the present disclosure, the details of the subjective adverse event assessment may be associated with specific HRV indicators. For example, the UKU mentality category may be associated with a change in the LF indicator or the anticholinergic category with a change in the alpha indicator, or the LUNSERS mentality item may be associated with a change in one or more of the SDNN, RMSSD, LF, ApEn, and SampEn indicators. have.

The method according to the present invention utilizes a reliable mathematical concept to provide objective indicators of side effects caused by antipsychotic drugs, particularly subjective side effects that are difficult to identify, through the use of economical and noninvasive digital electrocardiograms. More effective medications for schizophrenia can be predicted for side effects associated with, for example, therapeutic responses or treatment compliance with antipsychotic drugs, allowing for more effective customized medications.

In particular, the method of the present application may be useful for predicting the prognosis of treatment by applying to patients with severe self-reported or unseverely serious patients who have been found to be reliably self-reported, or who have discontinued previous medications due to severe side effects.

1 shows the relationship between the subjective extrapyramidal side effects and the objective electrophysiological indicators of antipsychotics.

FIG. 2 compares baseline HRV indices between patients who completed six weeks of treatment and dropouts, indicating that SD, RMSSD, LF, and HF of baseline HRVs in dropouts were significantly higher. The units of the numerical values described on the Y axis of the graph of FIG. 2 are as follows: MEAN, SD and RMSSD are ms (millisecond); LF and HF are ms ² (millisecond squared).

Based on the results of determining the autonomic nervous system and mental side effects of the drug treatment by evaluating the changes in central autonomic nervous system regulation of the cardiovascular system in response to the adverse effects caused by the antipsychotic drugs disclosed herein. Diagnostic and evaluation methods have not yet been developed.

Therefore, in one aspect, the present invention relates to a method for the diagnosis and objective evaluation of side effects, particularly subjective side effects caused by antipsychotic drugs using heart rate variability through the development of HRV indicators.

In one embodiment the method comprises providing a heart rate variability (HRV) derived from a subject; And mean heart rate of all RR intervals (RR), standard deviation of all RR intervals (SDNN), square root of the mean squared differences of successive normal sinus intervals (RMSD), percentage of successive RR interval differences pNN20 at least one selected from the group consisting of whose absolute value exceeded 20 ms), power spectrum density (LF, HF or LF / HF), ApEn (approximate entropy), SampEn (sample entropy), alpha and corrected Shannon entropy (CSE) Analyzing in the region; Providing subjective side effects test results for the antipsychotic drug of the subject; And associating the analysis result with a subjective side effect evaluation result.

The method according to the present invention is for the objective evaluation of adverse effects on antipsychotic drugs, wherein the antipsychotic drugs are used for the treatment of schizophrenia, other psychotic disorders and recurrent depressive disorders and bipolar disorders. Schizophrenia is a disease that shows symptoms such as delusions, hallucinations, abnormal and nonsense words and behaviors, avoiding interpersonal relationships, expressionlessness, loss of motivation, neurotransmitter abnormalities, genetic causes, immunological causes, neurodevelopmental causes. Due to a combination of psychological, social, and social causes. It is not a symptom resulting from physical abnormality, psychosis caused by drugs, depression or manic depression accompanied by mood swings, and refers to a disease that lasts more than 6 months and causes social-occupational problems.

Drugs to which the methods according to the invention can be applied can be used for the treatment of schizophrenia, including but not limited to risperidone, olanzapine, amisulfride, or aripiprazole.

Drug side effects herein are any adverse reactions to patients as well as therapeutically expected effects after drug administration, in which case a causal relationship between the administered drug and the side effects is suspected or at least not possible. Include. Drug side effects also include unexpected side effects and known side effects, as well as symptoms and abnormal findings in the sample.

It also includes objective side effects based on physiological mechanisms and subjective side effects based on subjective judgments of patients. Subjective adverse events are abnormal levels of adverse events not supported by the currently available medical tests or that can be expected from laboratory findings. Finally, control of the central nervous system is linked to the balance of multi-organ interactions, endocrine or neurotransmitters. In connection with the above, the object has the characteristics of appeal.

Early detection and rapid response to changes in cardiovascular system for side effects of antipsychotics, especially subjective side effects, are very important for patient health and treatment compliance. In particular, the diagnosis and evaluation method of side effects caused by antipsychotics using heart rate variability through the development of HRV indicators for the evaluation and diagnosis of side effects caused by antipsychotics in patients with severe self-reported or severely ill patients. Development can be used to improve the prognosis of treatment by minimizing the possibility of discontinuing the previous drug for reasons of severe side effects.

In addition, the subjective side effects that the patient feels are often difficult to distinguish from the actual psychiatric symptoms, the patient is difficult to explain what they feel, and the therapist also difficult to determine the subjective side effects to the patient.

The method of the present invention enables early detection of adverse effects on antipsychotic drugs, especially subjective side effects, through changes in HRV-related indicators. And treatment planning.

Heart rate variability in the method according to the invention is analyzed in time domain, frequency domain and complexity domain. HRV analysis results analyzed in the time domain, frequency domain, and complexity domain described below are referred to as HRV related indicators or HRV indicators.

In the time domain, heart rate (HR), mean length of all RR intervals (SD) (standard deviation of all RR intervals), and SDSD (square root of the square) mean squared differences of successive normal sinus intervals: The square root mean square of consecutive R peak intervals on an electrocardiogram that effectively reflects the average value of a sine wave-like biosignal change, such as an electrocardiogram, STD (Standard Deviation), or continuous normal RR interval. PNNx (the proportion of interval differences of successive normal RR intervals> x msec), which is the ratio of RR intervals greater than x msec, is an indicator for checking the fluctuation degree of the RR interval. A sequential difference of 20 ms or more).

The frequency domain includes, but is not limited to, indicators such as Very Low Frequency (VLF), Low Frequency (LF), High Frequency (HF), and LF / HF. VLF (<0.04 Hz) is a very low frequency component of heart rate variability and represents an indicator of thermoregulation or endocrine activity. LF (0.04-0.15 Hz) is an index of sympathetic and parasympathetic activity in the low frequency component of heart rate variability. HF (0.15-0.4 Hz) is an index of parasympathetic activity in the high frequency component of heart rate variability. LF / HF: A ratio of the sympathetic-parasympathetic activity ratio in the ratio of low frequency region to high frequency region of heart rate variability.

Complexity indicators include DFA (Detrended Fluctuation Analysis), which evaluates the fractal correlation in the time domain, SD1 representing the long distance in the cross section of the attractor embedded in the phase space, SD2 representing the short distance, The plugs embedded in the phase space are alpha, ApEn (Approximate Entropy), and ApEn, which are indicators of changes in the regularity and complexity of the drag as the dimensions of the space increase. Short data length and low noise include SampEn (Sample Entropy) and CSE (corrected Sahnnon Entropy).

Alpha represents a fractal correlation indicator of time series data such as electrocardiogram. SampEn (sample entropy) is an index that quantifies the change in spatial correlation according to the dimensional change of a nonlinear draggile composed of HRV. While the existing linear indices reflect the heart rate control function of the autonomic nervous system, SampEn is the central nervous system. Reflect the degree of involvement in heart rate control. The encoding analysis index includes corrected Shannon entropy (CSEx), which is an index for quantifying short-term correlation by calculating entropy of a reconstructed code sequence by encoding 0 or 1 according to the magnitude x of successive R-R differences. CSE measures nonlinear symbolic dynamic and performs binary symbolization based on the absolute difference of consecutive RR intervals. For example, if the threshold is set to 20 ms, the RR interval above it is symbolized as 1 and the following value is 0.

Examination of subjective side effects, including the segmentation and quantification of subjective side effects herein, is described with reference to the Examples described herein according to the Positive and Negative Syndrome Scale (PANSS), the Udvalg for Kliniske Undersøgelser (UKU), or the Patient Reporting Scale. Can be performed using one or more of the LUNSER (Liverpool University Neuroleptic Side-effect Rating Scale). LUNSER (LUNSERS; Day et al. J. of Psychiatr; 166: 650-653, 1995), a questionnaire for self-reported subjective adverse events, has 81% accuracy for Parkinson and Non-Parkinson groups with one Shakiness. The combination of the four items can be used to evaluate the presence of left anorexia with an accuracy of 76.2% (Jung et al, Human Psychopharmacol; 204: 41-45, 2005). In one embodiment according to the present invention the subjective side effects are measured in particular on a subscale of items such as mentality, extraneous, hormonal, cholinergic, other autonomous, and allergic. It is not limited to this.

The method according to the present invention comprises the step of correlating HRV-related indicators with the quantified subjective side effects, which is a change in basal state before and after taking the antipsychotic drug of the subject and changes in the HRV value after taking the drug or during an existing antipsychotic drug. The objective is to quantify and evaluate adverse effects on antipsychotic drugs by determining the change, that is, increase or decrease, of HRV measured at predetermined intervals. The increase or decrease may vary according to the type of HRV indicator, and a person of ordinary skill in the art may make a clinical decision by determining an appropriate increase and decrease and a change value.

The step of correlating may, for example, correlate each of the UKU and LUNSERS subscales, which are assessment measures based on subjective reporting of the patient, with individual HRV indicators. For example, if there is a change in the UKU subjective side effects psychological category associated with the LF indicator, or other subjective adverse events related to the average RR index, and the anticholinergic subjective adverse events related to the alpha indicator, objective and quantified Can provide an assessment. In addition, mentality items in LUNSERS can be associated with changes in SDNN, RMSSD, LF, ApEn and SampEn among HRV indicators. In other implementations, subjective side effect and red herring items can be associated with changes in SDNN, RMSSD, and SampEn. In other respects, SampEn and alpha may be associated with changes in anticholinergic and other factors, in addition to LUNSERS.

The method of the present invention enables early detection of adverse effects on antipsychotic drugs, especially subjective side effects, by changes in HRV-related indicators. And treatment planning.

Hereinafter, examples are provided to help understand the present invention. However, the following examples are provided only to more easily understand the present invention, and the present invention is not limited to the following examples.

Example

Example 1 Experimental Method

Subject

Forty-five patients with DSM-IV schizophrenia (schizophrenia) who were admitted to Yongin Mental Hospital due to acute psychotic symptoms participated in the study. For 30 of these patients the HRV baseline was measured as described previously and compared with healthy controls (Chang JS et al., Biol Psychiatry; 33: 991-995, 2009). In addition, 16 patients receiving risperidone participated in the trial. All patients were drug free because they did not take antipsychotic drugs for at least 28 days prior to the admission or did not administer Depot antipsychotic drugs for 24 weeks. Patients were given a single-dose regimen that only administered antipsychotic drugs during the study, but received anticholinergic drugs or benzodiazepines for side effects control during the first two weeks of the study. High informed consent was obtained from all patients, and under the approval of the Ethics Committee, all studies were conducted in accordance with the Helsinki Declaration.

Assessment of psychotic symptoms and perceived side effects (SE)

During the initial results and following six weeks of follow-up, all patients were evaluated for psychotic symptoms using the Positive and Negative Syndrome Scale (PANSS) (Kay SR et al, Schizophr Bull; 13: 261-276, 1987). . SE has been recognized by accredited psychologists as UKU (48-item Udvalg for Kliniske Undersøgelser) (Lingjaerade O et al, Acta Psychiatr Scand Suppl; 334: 1-100, 1987) and LUNSERS (Liverpool University Neuroleptic Side-effect Rating Scale) (Day Jc et al, ibid ). UKU is a four-point, semistructured interview that evaluates 48 items in the mental, neurological, and autonomous areas. LUNSERS consists of 41 SE items and 10 Red Herring items, which are scored on a five-point scale in the form of a questionnaire for self-reported subjective adverse events that self-measure side effects associated with taking antipsychotic drugs. LUNSERS's SE is further subdivided into mentality, extraordinary, hormonal, cholinergic, other autonomous, allergic and others. The 10 RH items are not directly related to known SE but are useful for detecting overreporting trends or psychopathological phenomena. The total scores of the two SE scales showed a good correlation in English-speaking (r = 0.81) and Korean-speaking patients (r = 0.82) with excellent schizophrenia.

Obtaining and preprocessing heart rate data

Electrocardiogram (ECG) results were obtained from 9 am to 11 am for all patients. All patients were advised not to consume tobacco and coffee prior to measurement, with their eyes closed and their breathing as usual while lying down. After 15 minutes of breathing and heart rate stabilization period d, ECG results were measured for 5 minutes. Analog ECG results were digitized at a sampling rate of 250 Hz. The RR intervals were filtered using an adaptive filter algorithm to replace and interpolate ventricular pacing and artificial values. All data were used for HRV analysis for 5 minutes.

Linear HRV Measurement

HRV indicators included linear and nonlinear measurements. In the time domain, mean length of all RR intervals (Mean RR), standard deviation of all RR intervals (SDNN), square root of the mean squared differences of successive normal sinus intervals, And pNN20 (percentage of successive RR interval differences whose absolute value exceeded 20 ms). In the frequency domain, spectral analysis was performed using standard autoregressive algorithms after detrending and resampling of consecutive RR intervals sampled irregularly over time. Power spectral density is typically calculated in two main frequency ranges: 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 the LF to HF power (LF / HF) was used to assess the balance of sympathetic-vaginal nerves.

Nonlinear HRV Measurement

Corrected Shannon entropy (CSE) was calculated using binary symbols through coarse graining, based on a threshold value, the absolute difference between successive RR intervals. Low CSE values indicate a high degree of regularity for symbolic patterns generated from binary sequences. In previous studies, the most significant difference between control and schizophrenia was 20 ms (Park KT et al, J Korean Phys Soc 44: 569-576, 2004). In order to measure the regularity of the time series variation, the root mean value entropy (ApEn) and sample entropy (SampEn) were calculated. SmpEn has no bias derived from the finite length and self-match found in ApEn, and a low value indicates a high degree of regularity. Next, alpha was extracted using detrended variance analysis to quantify the fractal characteristics of the RR interval. Alpha represents a short-term temporary variation in clock-driven heartbeats, while low numbers are very random, or are associated with a “coarse” time series, and high values are highly relevant or “smooth”. It is related to time series.

Statistical analysis

After the baseline value and six weeks of follow-up study by the method described above, the difference between the studied and incomplete subject groups was analyzed using ANOVA (simple analysis of variance) or χ ² test. Baseline and six-week data of completers were compared by paired t-test. The change value (Δ) was obtained by subtracting the base value from the value obtained for 6 weeks. Association analysis was performed using the Pearson association method, r. All statistical analyzes were performed using SPSS 15.0 for Windows (SPSS Inc., Chicago, Il, USA).

Example 2 Demographic and Clinical Variables

The demographic and clinical characteristics of the patients are listed in Table 1. Of 45 patients, 11 (6 males, 5 females) did not participate in baseline measurements and were excluded from the final analysis. These patients were not significantly different from other patients in gender, age and antipsychotic drug type. However, the excluded patients were more severe than those who participated in terms of psychopathology (108.09 ± 14.96) (91.71 ± 17.81) as measured by the PANSS total score (t = 2.75, p <.01). In a six-week evaluation, six of the 34 patients changed their initially prescribed antipsychotic medications and were excluded from the study, and three patients did not cooperate and did not participate until the end of the study. Age, gender distribution, and antipsychotic drug types were not significantly different between the 9 excluded and participating groups, and were not significantly different on either the base UKU subscale or LUNSERS subscale, including RH. . During 6 weeks, 16 patients (64%) took risperidone, 5 patients (20%) olanzapine, 3 patients (12%) amisulfride, and 1 patient (4%) aripiprazole. Based on the pairwise comparison, the baseline for 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) There was a significant difference between the value and the 6 week score.

TABLE 1

The abbreviations used in Table 1 are as follows: a: Excluding three patients who did not complete the ECG test; LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; PANSS, Positive And Negative Syndrome Scale; RH, red herring; SD, standard deviation; SE, side effect; UKU, Udvalg for Kliniske Undersøgelser.

TABLE 2

Table 2 lists the psychopathology, adverse events and HRV measurements of complete and incomplete patients at the start and 6 weeks of study. The group completing the study showed significant improvement in psychopathology, mental problem reporting, autonomic neurological, and total SE scores, and decreased in SE, RMSDD, and CSE. On the other hand, the clinician's out-of-UKU trend and the modified subconstitutional scale of mental SE increased significantly. Comparison of baseline data between complete and incomplete showed no significant differences in psychopathological and SE measurements between groups. However, incomplete patients showed significantly higher SD, RMSSD, LF, and HF results in HRV compared to completers.

Example 3 Correlation between LUNSERS Subconfiguration Scale and Reconstructed UKU Subconfiguration Scale

The cross-sectional and longitudinal correlations between the substructure measures of the LUNSERS and UKU counterparts are shown in Table 3. At baseline, all subscale measures of LUNSERS showed significant correlations with subunit scales of UKU, except for extraneous and allergic symptoms. In the six-week assessment, all subscale measures of LUNSERS were significantly correlated with UKU subscale measures except for mental and autonomic symptoms (minimal correlations) ( p <0.06). Finally, the changed scores of the LUNSERS subscale and UKU counterparts showed significant correlations in all subscales, with the exception of allergy and other subscales.

TABLE 3

* p <0.05, ** p <0.01, *** p <0.001

Example 4 Yellow Cross-sectional Correlation of Initiation and Six-Week Results

At initiation, HRV indicators and SE measurements were excluded, except that LF / HF correlated significantly with UKU out of trend and with hormone SE (r = 0.62, p <0.001; r = 0.42, p <0.01, respectively). There was no significant correlation between the livers. In the six-week evaluation, UKU EPS was found to be significantly correlated with other values with SampEn (r = -0.41, p <0.05) and RMSSD (r = 0.51, p <0.01). LUNSERS EPS subscales were SampEn (r = -0.43, p <0.05), hormonal pNN20 and CSE (r = 0.40, p <0.05; r = 0.43, p <0.05, respectively), anticholinergic RMSSD (r = 0.48 , p <0.05), and RH SDNN and RMSSD (r = 0.45, p <0.05; r = 0.44, p <0.05, respectively).

Example 5 Correlation between SE and HRV Indicator Change (Δ)

Table 4 lists the results of the correlation analysis between changes in the UKU and LUNSERS subscale scale scores and HRV indicators. Significant correlations between HRV indicators and UKU were found to be between psychological SE (subjective side effects) and LF, other SE and mean RR, and anticholinergic SE and alpha. On the other hand, changes in the mental SE subconfiguration scale in LUNSERS were significantly correlated with changes in SDNN, RMSSD, LF, ApEn, and SampEn among HRV indicators, and changes in SE and RH were significant in SDNN, RMSSD, and SampEn. There was a significant correlation with the change of. SampEn, like alpha, has been shown to significantly correlate with anticholinergic and other sub-constitutive measures, except for the hormonal, off-trend, and allergic SE, out of LUNSERS trend.

In summary, the Udvalg for Kliniske Undersøgelser scale and the Liverpool University Neuroleptic Side-effect for 45 schizophrenic patients who were not on medication and at 6 weeks Rating Scale (LUNSERS) was written and heart rate was measured to investigate the correlation between drug side effects and HRV. Since the LUNSERS scale was developed based on the UKU scale, the items of the UKU were reconstructed and used according to the composition of the scale of the LUNSERS. As a result, there was a high correlation between 0.42 and 0.57 between UKU and LUNSERS scales except for Allergic and Miscellaneous scales (Table 1). The psychosocial side effects induced by antipsychotics were found to be significantly correlated with the variables in the time domain of HRV and SampEn. In addition, changes in SampEn were significantly correlated with changes in anticholinergic, miscellaneous, and reliability categories (measures for nonspecific discomfort) and overall mean scores, outside of the trend in LUNSERS (Table 2). Comparison between baseline HRV measurements between patients who completed six weeks of treatment and dropouts showed significantly higher SD, RMSSD, LF, and HF of baseline HRV in dropouts (FIG. 2). This indicates that baseline HRV measurements can predict side effects associated with treatment compliance along with treatment response. In conclusion, subjective side effects reflect changes in cardiovascular dynamics, and changes in SampEn may effectively reflect subjective pain associated with antipsychotics.

TABLE 4

The abbreviations used in Table 4 are as follows: ApEn, approximate entropy; CSE, corrected Shannon entropy; HF, high frequency component of heart rate variability; HRV, heart rate variability; LF, low frequency component of heart rate variability; LUNSERS, Liverpool University Neuroleptic Side-effect Rating Scale; Mean RR, mean length of all RR intervals; pNN20, the percentage of successive RR interval differences whose absolute value exceeded 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; UKU, Udvalg for Kliniske Undersøgelser; * p <0.05, ** p <0.01, *** p <0.001

Example 6 Postmortem Partial Correlation Between Psychopathological Regulatory SampEn and LUNSERS SE and RH

Low SampEn has previously been associated with severe levels of psychopathology, and SE has been shown to be partially related to the stage of the disease. From these results, assuming that psychopathology could function as a covariate between SampEn and LUNSERS SE and RH, the change in the PANSS total score was adjusted to analyze the posttest partial correlations for the changed scores. As a result, the change in SampEn was significantly correlated with mental, EPS, anticholinergic, autonomic nerve, 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) (see Table 4).

Although the exemplary embodiments of the present application have been described in detail above, the scope of the present application is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. It belongs to.

All technical terms used in the present invention, unless defined otherwise, are used in the meaning as commonly understood by those skilled in the art in the related field of the present invention. The contents of all publications described herein by reference are incorporated into the present invention.

Claims (8)

1. Providing a heart rate variability (HRV) derived from the subject; And

   Mean heart rate of all RR intervals (RR), standard deviation of all RR intervals (SDNN), square root of the mean squared differences of successive normal sinus intervals (RMSD), percentage of successive RR interval differences whose one or more regions selected from the group consisting of absolute value exceeded 20 ms), power spectrum density (LF, HF or LF / HF), ApEn (approximate entropy), SampEn (sample entropy), alpha, and corrected Shannon entropy (CSE) Analyzing in the;

   Providing subjective side effects test results for the antipsychotic drug of the subject; And

   Correlating the analysis result with subjective side effects test results.
2. The method of claim 1, wherein the subjective side effects are examined using one or more of a Positive and Negative Syndrome Scale (PANSS), an Udvalg for Kliniske Undersøgelser (UKU), and the Liverpool University Neuroleptic Side-effect Rating Scale (LUNSER). Method of evaluating side effects for psychotic drugs.
3. The method of claim 2, wherein the subjective side effects are measured in one or more of the following categories: mental, extratraditional, hormonal, cholinergic, other autonomous, and allergic.
4. The antipsychotic drug according to claim 1, wherein the associating step is to determine whether the subject's HRV analysis result is compared with the HRV analysis result after taking the drug before taking the antipsychotic drug. How to assess side effects for.
5. 4. The method of claim 3, wherein associating the mentality item of the UKU. Or associating an anticholinergic entry with a change in alpha indicator.
6. 4. The method of claim 3 wherein the associating step associates the mentality item of the LUNSERS with a change in one or more indicators of SDNN, RMSSD, LF, ApEn and SampEn.
7. The method of claim 1, wherein the antipsychotic drug is risperidone, olanzapine, ammisulfide, or aripiprazole used in the treatment of schizophrenia.
8. The method of claim 1, wherein the method is capable of predicting side effects associated with a therapeutic response or treatment compliance with an antipsychotic drug.

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