A Longitudinal Study of Correlations Among Tardive Dyskinesia, Drug-Induced Parkinsonism, and Psychosis Thomas Ronald William William Daniel

Tardive dyskinesia (TD) and drug-induced parkinsonism (DIP) have been hypothesized to reflect opposing states of dopamine (DA) function. In this longitudinal study, 57 psychotic in patients were rated repeatedly for TD, DIP, and psychosis while receiving neuroleptic medication. Cross-sectional correlations among TD, DIP, and psychosis were weak or nonexistent. Factor and cluster analyses found that 13 patients (23%) were classified into groups characterized by the expected negative correlations. Thus, only partial support was found for the hypothesis that TD and DIP represent opposing states of DA function. (The Journal Neurosciences

of Neuropsychiatry 1992; 4:29-35)

and

Clinical

E. Hansen, M.D. M. Weigel, Ph.D. L. Brown, B.A. F. Hoffman, Ph.D., E. Casey, M.D.

M.D.

Tardive dyskinesia (TD) and drug-induced parkinsonism (DIP), both extrapyramidal side effects of neuroleptic medication, have been hypothesized to represent opposite states of dopamine (DA) function in the striatum. DIP is postulated to reflect reduced DA activity following acute blockade of DA receptors, while TD purportedly involves increased DA activity after chronic exposure to DA antagonists.’3 This striatal DA hypothesis implies that TD and DIP should vary inversely; i.e., as one improves, the other should worsen. Nine case studies1’ found the expected inverse relationship, but these studies included only 13 patients, usually involved direct intervention in an attempt to alter the movement disorders, and did not provide quantitative estimates for the correlation between the disorders. Other ID-DIP correlation data do not consistently support the DA hypothesis. Unexpectedly, TD and DIP can coexist.12’4 Also, studies that have measured the correlation between TD and DIP have varied widely in their findings. Eleven reports with 13 samples compared the prevalence and/or severity of TD and DIP in patients rated from the same sample at one time. These cross-sectional correlations were negative in five samples’3”’8 (n =782), essentially zero in two samples’92#{176} (n =532), and positive in six samples (n =i752).12.132 Sample differences in patient characteristics may explain some of the variability.’3

Received

January

1991. From

the

18, 1991; revised Psychiatry Service,

Center, Department Portland. Address I 16A-P,

JOURNAL

OF

NEUROPSYCHIATRY

P.O.

Box

of Psychiatry, reprint 1034,

requests

Portland,

April Portland

22,

1991; accepted Veterans

Affairs

Oregon Health Sciences to Dr. Hansen, Portland OR

April

29,

Medical University, VAMC,

97207.

29

CORRELATIONS

OF TD, DIP, AND

PSYCHOSIS

Cross-sectional assessments measure the relationship between ID and DIP at one time point, and thus could fail to identify reciprocal relationships occurring within an individual over time. Longitudinal studies with repeated patient evaluations should provide the best direct test of the DA model for ID and DIP. However, the six available longitudinal studies do not consistently support the DA hypothesis. Three of these (n =50) found negative correlations between ID and DIP26; two (n =30) found no relationship’7’27; and one (n =7) found a positive correlation.2’ Few studies concurrently examine ID and psychosis, putative hyperdopaminergic states that should covary. One group reported that ID patients were more psychotic than non-ID patients,28 and another noted that TD and psychosis decreased together with treatment.29 However, a third study3#{176} found no association between Brief Psychiatric Rating Scale (BPRS)3’ measures and ID. The following study examines the hypothesis that ID and psychosis vary inversely with DIP during treatment using neuroleptic and anticholinergic medication. We calculated longitudinal correlations and examined the data for both syndrome and patient characteristics that might define subgroups associated with the expected relationships. Although the daily response of each symptom to medication changes may vary, we expected the overall correlation over multiple ratings to reflect the relationships between symptoms within each patient.

METHODS Patients We studied 57 psychotic patients hospitalized in 1985 and 1986; all gave informed consent. Subjects had a minimum total lifetime exposure to neuroleptics of 3 months and mild or worse ID (defined below). This sample of patients needing neuroleptics and already having ID was chosen to maximize the chance that ID and DIP would both occur and fluctuate relative to each other. Patients were included if they received neuroleptics for a minimum of 10 days and were not prescribed medications known to affect extrapyramidal side effects other than neuroleptic medications and benztropine. Clinicians determined the type and dosage of neuroleptic medication and prescribed benztropine to treat dystonia, DIP, and akathisia, if they occurred. Ratings The research staff rated psychiatric symptoms, ID, and DIP prior to the first dose of medication, and then twice weekly throughout hospitalization. Research staff were blind to dose (because clinical staff administered medi-

30

cations), and dosage data were collected only after discharge. The scales used were the Abnormal Involuntary Movement Scale (AIMS),32 the Sct. Hans Rating Scale for Extrapyramidal Syndromes-Parkinsonism subscale,33 and the BPRS (each item scored from 0 to 6). The Sct. Hans Parkinsonism subscale rates facial expression, bradykinesia, tremor, posture, arm swing, gait, rigidity, and salivation from 0 to 6. A minimum score of 2 (mild) in any body part on the AIMS defined ID cases, and global rating scores on the Sct. Hans Parkinsonism scale of at least 2 (mild) established the presence of DIP. We measured the severity of ID and parkinsonism with total scores on the AIMS (AT) and the Sct. Hans Parkinsonism subscale (PKT). Total BPRS score and the Thought Disorder (IH) subscale were analyzed for psychosis. Interrater reliability for the two raters (T.E.H. and W.L.B.) ranged from Spearman’s p=O.7’3 to p=O.93 for the total scores on all scales. Demographic data, diagnosis (Research Diagnostic Criteria),35 duration of past neuroleptic exposure, medication use on admission (e.g., on/off and type/dose neuroleptic), psychiatric history, and neuroleptic/benztropine use during hospitalization were obtained from chart reviews. To allow for comparison between patients, and for purposes of statistical analysis, neuroleptic dosage was converted into chiorpromazine equivalents (CPZe). These figures, derived from in vitro reception binding studies and clinical estimates of relative potency, represent approximately how much chlorpromazine a patient would need to take to experience the same antipsychotic effect as that produced by his or her actual medication. Statistical Analysis Variables measured that were anticipated to affect the relationships among ID, DIP, and psychosis included age, years of neuroleptic exposure, medication status (on/off neuroleptics immediately prior to hospitalization), initial ID severity, initial DIP severity, initial thought disorder, average neuroleptic dose in CPZe during hospitalization, and average benztropine dose. (Only 3 of 57 patients received low-potency neuroleptics, so no correction for anticholinergic activity of the neuroleptics was required.) A minimum of I week constituted “being off neuroleptics” because of the long half-life of neuroleptics. (Even at I week, residual neuroleptic effects might be anticipated. Neither measures of serum neuroleptic activity nor specific blood levels were available.) We calculated Pearson’s correlation coefficients between ID and DIP (r AI-PKT), ID and thought disorder (r AT-IH), and DIP and thought disorder (r PKT-IH) for each patient over time. These longitudinal (within subject) correlations numerically reflect how each disorder covaries with the others over time within each patient.

VOLUME

4

#{149} NUMBER

1

#{149} WINTER

1992

HANSEN

Prior to analysis, Fisher’s r-to-Z normalizing transformation was performed on each correlation.37 Variation in duration of hospitalization resulted in an unequal number of evaluations per patient. Therefore, individual cases were weighted by the reciprocal of the standard error of Fisher’s Z score Eweight=(n 3)”i, where n is the number of observations per patient.378 We followed standard methodology for identification of psychiatric syndromes of correlated traits and validation of patient clusters that exhibit these fraits.32 The sequence of steps was first to enter the intrapatient correlation coefficients and other patient variables into a principal components analysis and then to use the factor score for each variable to determine whether or not patients fell into groups (clusters) with characteristics resembling the “syndromes” (factors) found in the principal components analysis. To identify patient syndromes, we used the BMDP statistical software program 4M,43 performing a principal components analysis on the II patient variables, then a standard varimax rotation to simplify factor structure. (Factors with eigenvalues greater than I were retained.)39 The patient factor scores TABLE

1.

Mean

rating

Hans

Scale

BPRS

Thought

BPRS

total

Note: ing

6.5 ± 2.6 2.3 ± 2.6 6.9 ± 3.7

(TO)

Set.

TABLE

Factor

(DIP) Disorder

score

19.2

Values are means for Extrapyramidal

Scale

2.

Principal

on retained factors, indicating each patient’s deviation from the mean on the syndromes identified, was then output to the SAS FASTCLUS procedure7’ which conducted a cluster analysis of patient factor scores, attempting to identify subgroups of patients with similar clinical syndromes. A k-means clustering algorithm was employed,”#{176}’4’with the number of clusters set equal to the number of factors retained from the prior factor analysis. We compared clusters on the weighted means of the 11 primary variables, using SAS Procedure GLM to conduct a one-way analysis of variance (ANOVA) with a modified Newman-Keuls procedure for post hoc comparisons.37

RESULTS Most patients were white males (98%) with schizophrenia or schizoaffective disorder (91%). The mean age (±SD) was 45.0±12.5 years. The mean duration of past neuroleptic exposure was 13.5±7.8 years. Forty-seven percent of the patients had been off neuroleptics for at least I week

scores Initial

AIMS

± SD. AJMS Syndromes

components

analysis

with

5.9 ± 3.0

4.2 ± 1.3± 2.7± 9.5 ±

11.9 involuntary parkinsonism);

variniax

Movement BPRS

± 7.1

=

Scale Brief

(tardive Psychiatric

15.2%

Parkinsonism

Hans

7.8 ± 2.6 4.5 ± 3.4 8.0 ± 3.6 21.4±7.1

Scale

(DIP)

=

Sct.

0.88

PKT

NEX

0.83

NLPAV

zr AT-TI-I’

0.79

TH

0.71

JOURNAL

years

of neuroleptic

correlation

OF

exposure;

NLPAV

=

mean

dose

neuroleptic;

0.26

AT

0.31

-0.75

NLPAV AT

OFF zr ATPKTa

0.71 0.51

zr pIcr-TH’

0.81

AT

PKT

0.62

zr AT-PKT’

BNZAV

0.44 -0.28

1’H

=

mean

dose

ben.ztropine;

OFF

=

-0.37 0.31

0.48 0.32

TH

-0.27

OFF

-0.26

AGE =

Rat-

0.63

BNZAV

15.2%

Note: NEX admission. aZ transformed

Hans

Variables with “Moderate” (O.25) Factor Loadings

AGE

NLPAV use

Set. Scale.

2.3 1.9 2.5 5.7

Variables with “High” (O.50) Factor Loadings

15.3%

neuroleptic

dyskinesia); Rating

Maximum

rotation

17.1%

Psychosis

Minimum

4.0 ± 3.2

±7.4

Variance Explained

Label

Final

2.4 ± 2.9

(TD)=Abnormal (drug-induced

Age

Recent

et al.

off neuroleptics

0.25 on

scores.

NEUROPSYCHIATRY

31

CORRELATIONS

OF ID,

DIP, AND

PSYCHOSIS

prior to admission. Mean final daily doses were 641±493 mg CPZe (range, 97-2,665 mg) for neuroleptics and 0.9±1.5 mg (range, 0-5.6 mg) for benztropine. Patients were followed for a mean of 19.2±8.6 days. Initial and final rating scale scores (Table 1) indicate that psychosis improved while little change occurred in mean ID and DIP severity, but comparison of minimum and maximum ID and DIP scores reveals that substantial fluctuations occurred during the study period. In fact, the total scores changed by three points or more on the AIMS in 41 patients (77%) and on the Sct. Hans DIP scale in 34 patients (64%). Fourteen of the 20 patients who developed DIP during the study received benztropine. (Eleven other patients received benztropine from day I for DIP.) Initial scores for these 14 patients were compared with ratings just prior to starting benztropine. The total AIMS score fell from 6.5±2.5 to 4.9±2.2, and the total Sct. Hans DIP score increased from 1.8±2.0 to 4.3±3.6. At study completion, the total AIMS score had increased again to 6.9±1.3, and the total Sct. Hans DIP score fell to 2.3±2.1 (P 0.5) or moderate (I r I >0.25) factor loadings. Four factors with eigenvalues greater than 1.0 were extracted, accounting for 62.7% of the total variance. The factors were labeled by variables with high factor loadings. The first, “Age,” reflected a correlation with age, years of neuroleptic exposure, and DIP. The other three factors each had one longitudinal correlation with a high positive loading: zr AI-IH on “Psychosis,” zr AT-PKT on “Recent Neuroleptic Use,” and zr PKT-TH on “Parkinsonism” (zr AT-PKT also had a moderate positive loading on this factor). The k-means cluster analysis (with a predefined criterion of four clusters) was conducted utilizing patient factor scores on the four factors extracted above. Table 3 provides statistics on the cluster means for the 11 variables used in the statistical analyses. The P values reported for the one-way ANOVAs and the post hoc comparisons are biased toward statistical significance

(0.1)

06ab

Mean

0.3”

#4

ANO P

7 (0.3)

VA1

(SEM)

6

31

(0.2)

Cluster

Cluster #3 Mean (SEM)

0.0’

-

(0.0)

0.006

(5.39)

44.8

(3.0)

45.6

(5.7)

54.8

(8.0)

NS

(5.8)

0.049

NEX3

8.7’

(2.0)

13.8”

(1.9)

I9.5a

(3.9)

I6.6a

zrAT-PK4

0.2Ia

(0.12)

-0.1?”

(0.10)

-0.40”

(0.22)

-0.31”

(0.18)

0.003

zrAT-TH5

0.50’

(0.14)

-0.27”

(0.14)

(0.07)

-0.13”

(0.12)