Behavioral vs Biochemical Prediction of Clinical Stability Following Haloperidol Withdrawal in Schizophrenia

Behavioral vs Biochemical Predictionof Clinical Stability Following HaloperidolWithdrawal in SchizophreniaDaniel P. van Kammen, MD, PhD; Mary E. Kelley; John A. Gurklis, MD;Mark W. Gilbertson, PhD; Jeffrey K. Yao, PhD; Jeffrey L. Peters, MD

Background: We sought to identify haloperidol-treated subjects who relapsed within 6 weeks of placeboreplacement and those who did not, using multivariateanalysis.Methods: In the week prior to discontinuation ofhaloperidol treatment, global behavioral ratings and a

lumbar puncture for cerebrospinal fluid monoaminemetabolites were obtained in 88 patients with chronicschizophrenia. Logistic regression analyses were used toevaluate two competing models of relapse prediction.The models were then compared using receiver operat-ing characteristic analysis and a final combined modelwas derived.

Results: The behavioral model was less variable in itsprediction than the cerebrospinal fluid monoaminemodel. The final model consisted of increased psycho-sis, decreased anxiety, higher cerebrospinal fluid homo-vanillic acid levels, and lower cerebrospinal fluid5-hydroxyindoleacetic acid levels.

Conclusions: Several monoamine systems are in-volved in psychotic relapse within 6 weeks of haloperi-dol withdrawal. Future studies of relapse predictionshould include both clinical and biological measures to

fully assess relapse risk.

(Arch Gen Psychiatry. 1995;52:673-678)

THE COURSE of schizophre¬nia is marked by periods ofclinical stability that are in¬terrupted by psychotic ex¬

acerbations or relapses.1"3 Amajor therapeutic challenge in schizo¬phrenia is to prevent such relapses. Therisk for relapse is affected by medicationstatus, the dose of antipsychotic drug, in¬creased stress or life events,4 and changesin the internal biochemical milieu.3 Thepresent recommendations for the preven¬tion of relapse suggest continued neuro¬

leptic treatment for up to 1 year follow¬ing the first episode, for up to 5 years afterthe second episode, and indefinitely afterthe third episode. Many patients, how¬ever, prefer not to take medication or are

not consistently compliant. Still otherswho are compliant may relapse in spite ofantipsychotic drug treatment. Relapsesduring antipsychotic drug treatment aremore associated with increases in inde¬pendent life events (ie, stress) than are re¬

lapses without medication treatment.4For many years clinicians have noted

that relapses are preceded by changes in be¬havior or prodromes.6 Several groups havedesigned studies to evaluate the possibil¬ity ofmedicating patients only when the pa-

tient or the therapist believed that relapsewas imminent.7"10 While antipsychoticmaintenance treatment clearly prevents psy¬chotic relapses,11 clinically identifying pa¬tients who relapse before the fact is nearlyimpossible12: too many patients relapsedbefore they could be returned to neurolep¬tic treatment, in spite of careful clinicalsupervision.71013 Others tried to predictrelapse following drug withdrawal withbehavioral prodromes,14"17 with limited re¬

sults. Unfortunately, many patients in theprodromal phase do not share emergingpsychotic symptoms with therapists.

In our series of studies of relapse pre¬diction, we have found that in 32 halo-peridol-treated schizophrenic patients, ce¬

rebrospinal fluid (CSF) norepinephrine(NE) levels and paranoia subscale scores

of the Brief Psychiatric Rating Scale(BPRS)18 were higher but that BPRS anxi¬ety ratings were lower in patients who re¬

lapsed after haloperidol withdrawal thanthose who did not relapse.19 By adding CSF

From the Veterans AffairsMedical Center, Pittsburgh, Pa,and the Western PsychiatricInstitute and Clinic, Universityof Pittsburgh, School ofMedicine.

Downloaded From: http://archpsyc.jamanetwork.com/ by a University of Pittsburgh User on 12/29/2013

METHODS

SUBJECTS

Eighty-eight physically healthy male psychiatric patients witha diagnosis ofchronic schizophrenia according to DSM-III-Rcriteria23 receiving antipsychotic maintenance treatment par¬ticipated in the study following admission to the Schizophre¬nia Research Unit at the Department ofVeterans Affairs Medi¬cal Center, Highland Drive, in Pittsburgh, Pa. All lived in thecommunityprior to the study and gave written informed con¬sent. Clinical and demographic information on the sampleis provided in Table 1. Seventy-nine of thesubjects were white,seven were black, and two were Hispanic. All the subjects were

screened with a complete physical, neurological, and psychi¬atric evaluation conducted by board-certified psychiatrists.Trained staff obtained the diagnostic data from a structuredinterview using the Schedule for Affective Disorders andSchizophrenia-Lifetime Version,24 and a DSM-ÍÍI-R checklist.Patients who met DSM-IÍÍ-R criteria for alcohol or substanceabuse/dependence were excluded at the time of the study un¬

less they had been in remission for at least 6 months. All thesubjects were put on a low-monoamine, caffeine- and alcohol-free diet on admission, and their medication was convertedto haloperidol for at least 3 months if they were not alreadybeing treated with haloperidol. Following this, they wereswitched to treatment with haloperidol in unmarked capsulesfor 2 to 4 weeks. If benztropine mesylate (1 to 3 mg/d) was

given, this was discontinued at least 2 weeks prior to the lum¬bar puncture. No other medications were given. Identical-looking placebo capsules replaced the haloperidol capsulesovernight within days following the lumbar puncture.

RATINGS

Patients were rated daily by the nursing staff on the globalpsychosis scale of Bunney and Hamburg.21 The 1- to 15-point psychosis item of this scale is subdivided into fiveconsecutive levels of intensity, each consisting of threepoints. Relapse was considered to have taken place if therewas a 3-day mean increase in 3 points in the global psy¬chosis ratings compared with the mean of those psychosisratings of the last 7 days of haloperidol treatment done bythe nursing staff. A rating of 6 and higher was required,indicating a clear presence of psychotic symptoms, whichensured that an increase into the range of psychotic be¬havior had taken place.25 These criteria have been previ¬ously validated.20 26 Of the 88 subjects, 54 relapsed within6 weeks. The 34 patients who did not meet the criteria forrelapse by 6 weeks were considered to be clinically stablefor the purpose of the study.

Therapists who were blinded to patients' status inde-pendendy rated patients weekly on the Bunney-Hamburg Glo¬bal Rating Scale21 and the Scale for the Assessment of Nega¬tive Symptoms.22 Weekly meetings were conducted to ensureinterrater reliability, computed using intraclass correlationcoefficients; the interrater reliability was greater than 85%for all behavioral measures. The ratings of the last week ofhaloperidol treatment were used in the analysis.LUMBAR PUNCTURES

Lumbar punctures were obtained during the last week ofhaloperidol treatment, with the patient in the lateral de-

cubitus position between 7:30 and 8:30 am after over¬

night fasting and bed rest as previously described.27 TheCSF was collected on ice and stored at

—

80°C. The CSFmeasures included HVA, 5-HIAA, NE, and MHPG, as¬

sayed according to the modified assays by Scheinin et al28from aliquots of the first 12-mL pool.STATISTICAL PROCEDURES

Logistic regression29 (SPSS-PC, SPSS Ine, Chicago, 111) was

used to predict dichotomous relapse status using variousbiochemical and behavioral measures as independent pre¬dictors. Logistic regression models, using relapse as the de¬pendent variable, were performed with the following groupsof independent variables: (1) a biochemical model consist¬ing of CSF HVA, 5-HIAA, MHPG, and NE and dosage ofhaloperidol in milligrams per kilograms per day; (2) a be¬havioral model consisting of the global items of the Bunney-Hamburg scale21 (except for mania, which had insuffi¬cient variance), as well as the Scale for the Assessment ofNegative Symptoms total score; and (3) a reduced combi¬nation of the variables from the behavioral and biochemi¬cal models. The probability of relapse was calculated as

l/[ 1+e"<exp)], where the exponent of the logit function (exp)is a linear combination of the independent variables (seethe "Results" section).

The Hosmer-Lemeshow goodness-of-fit 2 statistic29was used to determine whether any of the models pre¬sented had a global lack of fit, which would result if theerror or residual deviance (analogous to the residual sum

of squares in linear regression) was high. Likelihood ratio 2 statistics were used to evaluate each individual vari¬able's contribution to the model as well as the significanceof the model as a whole.

The evaluation of the success ofclassification producedby the logistic regression model is often presented as a 2X 2table, indicating the number of correctly predicted membersin each group. However, the data in the 2X2 table are basedon an arbitrary cutoffpoint for the probability of relapse com¬

puted by the model. The steps involved are depicted inFigure 1 ; the equation resulting from the logistic regressionprocedure gives a probability for relapse between 0 and 1 foreach subject based on the levels of his covariates.

To assess the number ofsubjects correctly predicted, sub¬jects must be assigned to a group based on their probabilityscore. A reasonable cutoff point, and the cutoff used as thedefault in the majority of logistic regression output results(the SPSS, SAS, BMDP statistical software programs), can bemade at the probability level of .5 (or 50%), with those receiv¬ing a probability lower than .5 being classified as nonrelaps-ers and those with a probability above .5 being classified as re-

lapsers. However, other cutoffs are often not explored. Receiveroperating characteristic (ROC) analysis50 is a graphical rep¬resentation of a series of 2 X 2 tables, each table being gener¬ated by using a different cutoffpoint. Thus, each point on thecurve represents a sensitivity and ( 1

—

specificity) combinationthat defines the ability of the model to discriminate at the givencutoff point. In this assessment of the logistic regressionmodel, there is no specific sensitivity or specificity associatedwith the model, but rathera set ofvalues that can be tested againstthe null hypothesis ofa random allocation. Comparison of theROC curves allows us to compare the discriminating abilityof the models at all cutoff points, and one can judge by thesmoothness of the curve how sensitive each model is to thechoice of the cutoff point, ie, the consistency of the model.


*Data are given as mean±SD. CSF indicates cerebrospinal fluid; NE,norepinephrine; HVA, homovanillic acid; 5-HIAA, 5-hydroxyindoleaceticacid; MHPG, 3-methoxy-4-hydroxyphenylglycol; BH, Bunney-HamburgGlobal Rating Scale?'; and SANS, Scale for the Assessment of NegativeSymptoms.

^Ratings made by blinded therapists, not nurses' ratings used forestablishing relapse.

Regression Classification Prediction

Data

ILogistic Regression

IProbabilityof Relapse

Probabilityof Relapse

RelapseNoRelapse

_

Status,Predicted

True-Positive RateFalse-Positive Rate

Arbitrary Cutoff Point

Figure 1. The steps from regression to prediction of relapse inschizophrenia.

3-methoxy-4-hydroxyphenylglycol (MHPG) and chro-mogranin -like immunoreactivity and the dose of halo¬peridol to those items, we developed a logistic regres¬sion model in 50 schizophrenic patients that correctlyidentified relapsers and nonrelapsers with high specific¬ity.20 We decided to test both biochemical (CSF homo¬vanillic acid [HVA], 5-hydroxyindoleacetic acid [5-HIAA],MHPG, and NE) and behavioral models of relapse pre¬diction and compare their abilities to discriminate be¬tween those patients who would relapse and those whowould not. We hypothesized that changes in serotonin(5-HT) and NE levels in addition to dopamine turnoverwould be involved based on the observations that 5-HTand NE affect dopamine activity and that indirect dopa¬mine agonists and serotonin-dopamine antagonists af¬fect psychosis more profoundly than more specific or di¬rect dopaminergic agents. We report herein on a groupof 88 well-diagnosed and carefully screened neuroleptic-treated schizophrenic patients. Fifty of these patients par¬ticipated in a previous exploratory study.20 For this study,

we used the therapists' global ratings of psychosis, de¬pression, and anxiety21 and the total score of the Scalefor the Assessment of Negative Symptoms22 during an¬

tipsychotic treatment for the behavioral prediction model;it has been our experience that the BPRS clusters do not

always reflect changes in global clinical behavior as wellas do these items. We believe that these once-a-week rat¬

ings more resembled the clinical practice setting. Thesepatients were stabilized on treatment with haloperidoland participated in a 6-week placebo replacement studyof relapse prediction, using operationally defined psy¬chotic relapse criteria.

LOGISTIC REGRESSION

The behavioral and CSF monoamine models are pre¬sented in Table 2. In the behavioral model, anxiety andpsychosis were significant contributors, while CSF HVA,5-HIAA, and NE contributed significantly to the bio¬chemical model. The probability of relapse can be cal¬culated for each subject; for example, for the biochemi¬cal model the probability of relapse=l/[ 14-e"(exp)], wherethe exponent of the logit function (exp) = 0.5466 4- 0.0144(CSF HVA) 4- 1.1773 (CSF NE)

-

0.0282 (CSF MHPG)-

0.0274 (CSF 5HIAA) 4- 3.5109 (dose/weight). Noneof the models had a significant lack of fit.

ROC CURVES

The cutoff points used for classification were the quin¬tiles (those values that separate the subjects into fiveequal-sized groups) of the distribution of predictedvalues for each model. The resulting quintiles were

0.502, 0.572, 0.644, 0.724, for the biochemical modeland 0.454, 0.594, 0.681, 0.783 for the behavioralmodel. The curves are depicted in Figure 2; the graydiagonal line represents a 50-50 chance, or randomcurve. A random curve is expected if the variableshave no predictive value; thus, the larger the area

under the curve, the larger the significance. The area

under the ROC curve is a measure of the model's pre¬dictive value; by testing this area vs the null hypoth¬esis of the area=0.5 (no prediction), we arrive at thesignificance values presented in Table 2.31

The use of the ROC curves allows us to see that thebiochemical model is more varied in its predictability,ie, it is more sensitive to the choice of cutoff point, whilethe behavioral model is not. In addition, one can choosethe cutoff point that has the highest sensitivity and speci¬ficity combination (the highest value of the two added)to be considered the "best" cutoff point.

COMBINED MODEL

We used all the independent variables from the first twomodels as competing predictors in a backward stepwiseprocedure; the remaining variables were CSF HVA and5-HIAA, anxiety, and psychosis (Table 2). The quintilesfor the combined model were 0.472,0.591,0.689,0.768,respectively. The area under the ROC curve was more


*ROC indicates receiver operating characteristic. See Table 1 for expansion of other abbreviations.fP values are based on the significance of the likelihood ratio 2 statistic; boldface values indicate P<.05.

significant for the combined model than either the bio¬chemical or behavioral models (Figure 2).

BENEFIT OF ROC

It can be seen from the "best" cutoff values for the curves

(0.502, 0.681, 0.591) that the default value of 0.5 is not

always the best cutoff point, and thus examination of dif¬ferent cutoff points, using ROC, is beneficial in inter¬preting the logistic regression results.

COMMENT

BEHAVIORAL VS BIOCHEMICALPREDICTION OF RELAPSE

A variety of behavioral measures have been found to pre¬dict relapse. Among others, Dencker et al14 reported higherparanoia and Subotnik and Nuechterlein12 reported higherlevels of BPRS hostility and grandiosity prior to psy¬chotic relapse following drug withdrawal. Buchanan etal13 reported nonsignificantly higher BPRS thinking dis¬turbance cluster values in the relapsers prior to drug with¬drawal. In the logistic regression model, it is the combi¬nation of variables and the weights that each carry in theequation that determines the risk for relapse. The vari¬ables by themselves do not need to be significantly dif¬ferent between the two groups, which may lead to seem¬

ingly paradoxical observations; ie, there may be a complexrelationship among predictors. This relationship may ex¬

plain why clinical assessment of specific symptoms can¬

not reliably indicate relapse risk in drug-free patients.The relapse rates reported in this sample seem higher

than those usually reported.11 This may be because thecriteria for relapse reported in the literature varies con¬

siderably. Our criteria are based on specific behavioralcriteria that are conservative in nature, thus, one wouldexpect the relapse rate to be increased in our sample.

We have previously included (BPRS) anxiety be¬cause of its potential relationship with locus coeruleusactivity and found these ratings to be paradoxically lowerin the patients receiving haloperidol who were going to

relapse within 6 weeks. " One interpretation may be thatlower anxiety in the future relapsers may reflect lowerakathisia levels, because akathisia may have contami¬nated the anxiety ratings. When used, anticholinergictherapy was discontinued more than 2 weeks prior to test¬

ing and in most cases more than 3 weeks prior to test¬

ing. Therefore, akathisia was present in some patients,albeit tolerably. We found that the presence of akathisiadelayed the actual relapse (D.P.v.K. et al, unpublisheddata, February 1994). We are presently assessing the roleof akathisia in the relapse process.

Negative symptoms were not included in the model,suggesting that negative symptoms associated with halo¬peridol treatment have no predictive value. This may bebecause the negative symptoms observed with neurolep¬tic treatment may be partially induced by the treatmentitself, rather than by a primary phenomenon.

Higher neuroleptic doses have been associated withrelapse following drug withdrawal,13·20·32·33 but this phe¬nomenon was observed within different dosage ranges.32Although the simplest explanation is that clinicians treatthe most symptomatic patients with higher doses, the ac¬

tual differences between the groups do not support thisnotion. In the current study, dose/weight was not a sig¬nificant predictor of relapse, either by itself (mean±SD,0.15±0.9 vs 0.13±0.14 mg/kg per day; t=0.79, P=.43)or in the models (Table 2). This may be because mediandoses for the relapsers and nonrelapsers were 10 and 7mg/d, respectively, neither of which is a relatively highdose. Recent evidence indicates that most patients are ef¬fectively treated with haloperidol in doses between 5 and15 mg/d. The generalizability of our results obtained inan all-chronic, male, veteran patient sample may be lim¬ited. Similar studies will have to be done in women, par-


Figure 2. Receiver operating characteristic curves. The larger the areaunder the curve, the more discriminating the model; the bestdiscriminating points are those that are the farthest from the gray diagonal(random) curve and are marked with solid circles. The best discriminatingpoints refer to cutoff points of 0.502 for the biochemical model, 0.681 forthe behavioral model, and 0.591 for the combined model.

ticularly because women in general require lower dosesof antipsychotic drugs. However, gender-specific re¬

lapse rates are not available from the literature.We have previously shown in 50 patients that a

model consisting of CSF NE, MHPG, chromogranin A,dose of haloperidol, weight, and the BPRS paranoia sub-scale and anxiety item predicted relapse at 6 weeks with86% accuracy using a cutoff value of O.5.20 The purposeof the present study was to examine the options of dif¬ferent cutoff values and their ability to predict relapse.It can be seen from Figure 2 that the traditionally used0.5 value is not the best discriminating point of the dis¬tribution. Cerebrospinal fluid NE was almost significantat P=.06 in the biochemical model, but did not add sig¬nificantly to the final model, suggesting that the norad-renergic contribution was the result of its relationshipto other items that were significant in the model. Possi¬bly, this is because HVA is derived in part from dopa¬mine broken down in noradrenergic neurons.

The inclusion of a serotonergic measure (ie, CSF5-HIAA) in the biochemical model is of particular inter¬est, considering the pharmacological properties of thenewer antipsychotic agents, such as clozapine, risperi-done, sertindole, olanzapine, and others.34 Becausethese atypical agents have strong 5-HT2 receptor block¬ade as well as ctx and a2 receptor blockade and may bemore effective antipsychotics, in addition to the factthat the NE, 5-HT, and dopamine systems interact, a

role for 5-HT in psychotic decompensation is plausible.Further support for a role of 5-HT in psychotic relapse,if psychotic relapse represents a loss of impulse control,may come from the association of impulsive behaviorswith decreased 5-HT turnover.33 Furthermore, Hsiao etal36 have shown that the relationships between theseCSF monoamines decrease with psychotic deteriora-

tion, while Joyce37 has reported that the 5-HT systemswere altered in autopsy results of the brains of schizo¬phrenics. Because haloperidol does not have discernible5-HT2 or a2 receptor blockade, our data imply thatpatients with lower 5-HT turnover or higher 5-HT2receptor activity are more likely to relapse followinghaloperidol discontinuation. Dopamine receptor block¬ade may normalize the relationships between the 5-HTand dopamine systems.36

This multimonoamine involvement in the psy¬chotic relapse process supports the notion that duringrelapse, relationships between these neurotransmittersare disturbed38 and that the interactions among the mono-

aminergic systems are more important than each neuro-transmitter by itself.39 The more compromised these re¬

lationships are, the more likely the patient will relapse.Patients who are in a prerelapse state may have failingfeedback systems that maintain behavioral and biochemi¬cal homeostasis.40·41 Conceivably, dopamine involve¬ment in schizophrenia reflects the failure of a homeo-static mechanism that allows for integration of differentbrain functional parts as needed.

CONCLUSIONS

If antipsychotic drugs raise the stress threshold and "nor¬malize" relationships between monoamines, and if re¬

lapse risk is also a function of this stress sensitivity, thenrelapse is a failed attempt to contain stress. We have pro¬posed that clinical instability, as exposed by the state-

dependent response to stimulant challenges, is due tobiochemical instability, reflecting increased stress sen¬

sitivity.42·43 We hypothesize now that increased stress sen¬

sitivity and decreased impulse control (ie, relapse risk)causes relapse when the stress threshold is lowered withhaloperidol withdrawal. If we can identify when pa¬tients change into a high-risk condition, appropriate stressand performance demand reduction can be imple¬mented while receiving medication,4 and drug-free pa¬tients can be returned to medication treatment in a timelyfashion.

We are continuing our search for a measure or mea¬sures that will reflect stress sensitivity to identify thosepatients who will remain clinically stable without anti¬psychotic drug treatment, or inversely will relapse earlyfollowing haloperidol withdrawal.

Accepted for publication March 29, 1995.Funding for this project was provided to Dr van

Kämmen in part by the National Institute of MentalHealth (R01MH44-841), the Department of VeteransAffairs Research and Development Service (MeritReview), and the American Defenders of Bataan andCorregidor Medical Fund.

The authors thank the patients and nursing staff ofthe Schizophrenia Research Unit under the leadership ofDoris McAdam, RN, for their participation and collabo¬ration.

Reprint requests to Professor of Psychiatry, Chief ofStaff VAMC, 7180 Highland Drive, Pittsburgh, PA 15206(Dr van Kämmen).


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Documents

Behavioral vs Biochemical Prediction of Clinical Stability Following Haloperidol Withdrawal in Schizophrenia