The cognitive effects of adjuvant chemotherapy in early stage breast cancer: a prospective study

The cognitive effects of adjuvant chemotherapy in earlystage breast cancer: a prospective studyy

Angela Stewart1, Barbara Collins2*, Joyce Mackenzie2, Eva Tomiak3, Shailendra Verma4 and Catherine Bielajew1

1 School of Psychology, University of Ottawa, Ottawa, Ont., Canada2 The Ottawa Hospital, Civic Campus, Ottawa, Ont., Canada3 Department of Genetics, Children’s Hospital of Eastern Ontario, Ottawa, Ont., Canada4 The Ottawa Hospital Regional Cancer Centre, Ottawa, Ont., Canada

* Correspondence to: TheOttawa Hospital, CivicCampus, 1053 Carling Ave,Room A603, Ottawa, Ont.,Canada K1Y 4E9. E-mail:[email protected] portion of the baselinedata was presented in posterformat at the AmericanNeuropsychiatric AssociationAnnual Conference in 2003.

Abstract

Purpose: The primary purpose of this study was to evaluate the cognitive effects of adjuvant

chemotherapy in post-menopausal breast cancer patients.

Patients and methods: Breast cancer patients scheduled to receive adjuvant chemotherapy

(n ¼ 61) completed comprehensive cognitive testing before and after treatment. A control group

of women receiving adjuvant hormonal therapy (n ¼ 51) was tested at comparable intervals.

Results: Mean scores for both patient groups were within the normal range relative to

published norms on all cognitive tests at both time points, and generally inclined or stayed the

same from baseline to retest in both groups. However, in an analysis of individual change

scores, the chemotherapy patients were 3.3 times more likely than the hormonal patients to

show reliable cognitive decline (31 and 12%, respectively). Chemotherapy subjects showing

decline were less educated and had higher baseline depression scores than their counterparts

who did not decline. Working memory was the cognitive domain most vulnerable to the effects

of chemotherapy.

Conclusion: These data support previous findings of a subtle negative influence of

chemotherapy on cognitive function in a subgroup of breast cancer patients. The results are

discussed in terms of the importance of study design.

Copyright # 2007 John Wiley & Sons, Ltd.

Keywords: breast cancer; oncology; adjuvant chemotherapy; cognitive function; adjuvant

hormonal treatment

Introduction

With increasing numbers of breast cancerpatients achieving complete physical recovery andlooking to resume normal lives, there is a growingconcern about the long-term side effects oftreatments. Many cancer patients complain of‘chemo fog’ or ‘chemo brain’, cognitive changesthat they attribute to their chemotherapy. Thesechanges seem to be quite persistent in some cases,and so may have important implications for qualityof life.Several studies of chemo fog have now been

conducted in breast cancer patients [1–19], withmost of them finding reduced cognitive function inthose exposed to chemotherapy. Rates of cognitiveimpairment as high as 75% have been reported[19]. In most cases, findings indicate subtle effectson memory and mental processing speed.Most of the studies done to date have been

cross-sectional and retrospective. This makes inter-pretation difficult, as it has been shown that there isan increased risk of cognitive impairment incancer patients who have had no chemotherapy

exposure [20,21]. Thus, assessment before and afterchemotherapy is necessary in order to concludethat any cognitive deficits observed followingchemotherapy are attributable to chemotherapyexposure. The few prospective studies that havebeen conducted [3,10,15,18], while reporting smal-ler effect sizes than cross-sectional studies, havegenerally found an increased risk of cognitivedecline in chemotherapy patients [3,15,18]. How-ever, most of these studies failed to include a non-chemotherapy control group of breast cancerpatients, and thus did not account for otherdisease- and treatment-related factors that mighthave given rise to performance decrements. Theonly prospective study to include a breast cancercontrol group found no group differences in eithermean post-treatment cognitive function or rate ofreliable cognitive decline [10]. Thus, the literatureremains far from conclusive.

This paper describes the results of a prospectivestudy evaluating the cognitive effects of adjuvantchemotherapy in breast cancer patients. Ourcontrol group comprised patients receiving adju-vant hormonal therapy without chemotherapy, to

Received: 4 August 2006

Revised: 28 February 2007

Accepted: 7 March 2007

Copyright # 2007 John Wiley & Sons, Ltd.

Psycho-OncologyPsycho-Oncology 17: 122–130 (2008)Published online 23 May 2007 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/pon.1210

account for other disease and treatment factorsthat might contribute to cognitive change in cancerpatients. Our main hypothesis was that thechemotherapy-treated patients would be morelikely to show cognitive decline than the patientsreceiving hormonal therapy only.

Method

Participants

All new breast cancer patients presenting tomedical oncology clinics at the Ottawa HospitalRegional Cancer Centre were pre-screened in ananonymous fashion to determine if they met basicinclusion and exclusion criteria. Patients whopassed this pre-screening were informed about thestudy by their oncologist and, if agreeable, werecontacted by the study coordinator, who providedfurther information and screening. Slightly morethan 50% of eligible women who were approachedagreed to participate in the study. Two groups ofearly stage breast cancer patients were recruited:one group received standard dose adjuvant che-motherapy with or without hormonal treatment(chemotherapy group); the other received adjuvanthormonal therapy only (hormonal group). Weincluded only post-menopausal women between50 and 65 in order to reduce variability in cognitivefunction associated with age [22] and circulatingestrogen levels [23]. Patients were excluded if theyhad a previous history of cancer, chemotherapy, orradiation; advanced disease (metastasis beyondaxillary lymph nodes); neo-adjuvant therapy; orunstable psychiatric or neurological disorders thatmight affect cognition. Individuals who wereactively abusing substances were excluded. Pastsubstance abuse was not necessarily grounds forexclusion, provided that there were no knownmedical or neurological sequelae. Subjects receiveda $50.00 honorarium for each assessment. Thestudy was approved by the ethics board of theOttawa Hospital and written informed consentwas obtained from all participants. The majority ofwomen in the chemotherapy group (39 of 61)received six cycles of an anthracycline basedchemotherapy regimen (FEC, CEF, or FAC).Many of the women in the chemotherapy groupwere also prescribed adjuvant hormonal therapybut, in most cases, this was not initiated until aftercompletion of their chemotherapy. Table 1 listsrelevant demographic and clinical characteristics ofthe groups.Meta-analyses of cognitive function in breast

cancer patients [24,25] indicate a small to mediumeffect size in most cognitive domains. Accordingly,we set our target sample size at 60 subjects for eachof the treatment groups in order to yield 80%power to detect a medium effect size [26].

Assessment

A baseline assessment (T1) including clinicalhistory, neuropsychological testing, and a mood-rating scale was conducted after surgery and priorto initiation of any chemotherapy. Reassessment(T2) occurred following the last chemotherapy

Table 1. Demographic and clinical characteristics ofchemotherapy and hormonal groups

Characteristic Chemotherapy

(n¼ 61)

Hormonal

(n¼ 51)

Age (in years at baseline)

Mean (SD) 57.5 (3.7) 57.9 (4.4)

Min/max 50–66 50–65

Educationa (in years at baseline)

Mean (SD) 14.5 (3.2) 14.3 (3.3)

Min/max 8–23 9–23

Estimated verbal IQ (at baseline)

Mean (SD) 107.6 (10.1) 106.8 (10.5)

Min/max 87–130 84–135

On depression meds (at baseline) 11.5% (n¼ 7) 13.7% (n¼ 7)

Test–retest interval (in days)

Mean (SD) 147.3 (35.8) 158.1 (31.2)

Min/max 91–252 119–245

Stage of disease

I n¼ 18 n¼ 45

II n¼ 40 n¼ 6

III n¼ 3 n¼ 0

Type of chemotherapy

FEC� 6 cycles n¼ 31

CEF� 6 cycles n¼ 5

FAC� 6 cycles n¼ 4

AC� 4 cycles n¼ 14

AC� 4 cycles/Taxol (2–6 cycles) n¼ 4

EC� 4 cycles/Taxol (2–4 cycles) n¼ 2

Adriamycin and Cisplatin

� 4 cycles

n¼ 1

Interval between last chemotherapy

cycle and retest (in days)

Mean (SD) 30.9 (26.2)

Min/max 6–179

Number of subjects receiving

hormonal therapy prior to

second testing (by type)

Tamoxifen n¼ 10 n¼ 31

Arimidex n¼ 1 n¼ 15

Letrozole n¼ 1 n¼ 0

Switched

Tamoxifen to Arimidex primarily

n¼ 0 n¼ 5

None n¼ 49 n¼ 0

Proportion exposed to radiation

prior to second testing

4.9% (n¼ 3) 75% (n¼ 38)

Notes: SD, standard deviation; IQ, intelligence quotient as measured by the Quick

Test. a Number of years in educational program leading to a diploma or degree.

Copyright # 2007 John Wiley & Sons, Ltd. Psycho-Oncology 17: 122–130 (2008)

DOI: 10.1002/pon

123Cognitive function following chemotherapy for early stage breast cancer

cycle in the chemotherapy group, and at anequivalent time point in the hormonal group. TheT1–T2 interval varied considerably among che-motherapy patients, due both to the differences intreatment duration and the subjects’ availabilityfor retest. We monitored the mean T1–T2 intervalfor the chemotherapy group, and adjusted theinterval for the hormonal group accordingly, suchthat there was no significant difference in the meanT1–T2 interval between the two groups. Ninety-three percent of the chemotherapy sample wasreassessed within 2 months of completing che-motherapy. There were no significant correlationsbetween T1–T2 interval and change on any of theneuropsychological measures.The Quick Test [27], a measure of receptive

vocabulary, was used to estimate IQ. Psychologicaldistress was assessed using three subscale scoresfrom the Profile of Mood States (POMS; [28])}depression–dejection (depression), fatigue–inertia(fatigue), and tension–anxiety (tension). A total of23 scores from 18 neuropsychological tests wasanalyzed. All published tests were administeredaccording to their respective manuals. Ideally,alternate forms would have been used on retesting[29]. However, as alternate forms were not avail-able for most of the tests in the battery, for the sakeof uniformity, we elected to use a single version andcontrol for practice effects statistically. Scores to beanalyzed were selected to capture the mostinformation with the fewest variables. The neurop-sychological tests, and the scores analyzed fromeach, are briefly described in Table 2.

Data analysis

The Statistical Package for the Social Sciences(version 13.0) was used for all data analyses. Alphawas set at 0.05.

Individual change analyses

A standardized regression-based (SRB) approachwas used to assess cognitive change at theindividual level [41,42]. This method uses the test/retest scores of a reference group (the hormonalgroup in this case) to develop a regression equationthat predicts retest scores from baseline scores.Next, an SRB score is obtained for each subject, oneach neuropsychological variable, by subtractingtheir actual retest score from the predicted retestscore and dividing by the standard error ofestimate derived from the control group. Thedifference between the predicted and obtainedscores divided by the standard error of theestimate produces a difference score that reflectsthe deviation from the change that would beexpected if there were no differences in thecognitive effect of the treatments. This score isexpressed in standard deviation (SD) units and

reflects the direction and magnitude of deviation.Although other methods of individual changeover time have been employed in this researchdomain (e.g. [15]) they cannot accommodate theinclusion of moderator variables, regression tothe mean, and comparisons across measures as canthe SRB approach.To illustrate this approach, assume that the

regression equation in the hormonal groupthat best predicts Time 2 Digit Span scores fromTime 1 Digit Span scores is as follows: DigitSpanT2¼ (Digit SpanT1� 1.05)+2. If a givensubject obtained a score of 16 at Time 1, herpredicted Time 2 score would be ð16� 1:05Þ þ 2 ¼18:8: Now, let us assume her actual score at Time 2was 17 and that the SE of estimate in the controlgroup was 2.5. Her SRB score derived from theformula (Observed Score�Predicted Score)/SEEst

would be �0.72. In other words, her change scoreis 0.72 SD units lower than would be predicted onthe basis of the hormonal group. Note that, eventhough her score has improved from T1 to T2, theSRB score is negative, because the Time 2score does not improve as much as would beexpected on the basis of the change observed in thecontrol group.We considered an individual subject to have

shown reliable overall cognitive decline if she hadtwo or more SRB scores of �2.0 or less or, in otherwords, if her actual T2 score was two SDs ormore below her predicted score on two or more ofthe 23 cognitive measures. Note that this doesnot necessarily imply that a subject’s score actuallydeclined from T1 to T2, simply that it fell shortof the score that was predicted according to theforegoing equation. Reliable cognitive improve-ment was defined as a change score greater than orequal to two SDs above the mean on two or morecognitive measures. Chi-square was used to deter-mine group differences in frequency of reliablecognitive decline and improvement.

Selection of covariates

Where indicated, covariates, including age, educa-tion, IQ, and disease stage, as well as POMSdepression, tension–anxiety, and fatigue scores,were included in the regression analyses used tocalculate the SRB scores. However, we did notwant to detract from our statistical power by usingcovariates unnecessarily (i.e. when they were notrelated to the dependent variable). Thus, to qualifyas a covariate in any given SRB analysis, thecandidate variable had to be significantly related tochanges in the dependent variable from Time 1 toTime 2 (i.e. neuropsychological change scores). Inthe case of the constant covariates, age, education,IQ, and disease stage, we conducted a seriesof stepwise linear regressions to determinewhich of them independently predicted the various

124 A. Stewart et al.


DOI: 10.1002/pon

Table 2. Test battery identified by cognitive domain

Tests (by Cognitive

Domain)

Description Variable(s)

analyzed

Executive function

Paced Auditory Serial

Addition Task (PASAT; [30])

Subject is presented with a series of 61 single-digit numbers at a fixed pace on a

tape recorder, and instructed to add each pair of consecutive numbers. Rate of

presentation of the numbers increases over four consecutive trials. This is a

very demanding task, especially for older adults, and many of our subjects

discontinued their performance following the first trial. Therefore, only trial 1

(2.4 s interval) scores were analyzed

Number correct on

2.4 s trial

Trail Making Test B

(Trails B; [31])

A pencil-and-paper test of visuomotor tracking, requiring subjects to alternately

connect, in sequence, numbers and letters randomly distributed on a page

Completion time

in seconds

Wisconsin Card Sorting Test

(WCST; [32])

Requires the subject to sort cards in relation to four key cards, alternating

sorting strategies deduced from examiner feedback. This test generates

numerous scores. In an effort to keep our number of variables manageable, we

opted to analyze the number of trials required to meet the criterion of six

successful sorts, as this captures the most information in a single score

Trials administered

(max 128)

Language function

Boston Naming Test [33] Subject is required to name pictures of well-known objects. This test is known

to be sensitive to anomia

Number correct with

or without category cue

Controlled Oral Word

Association Test (FAS; [34])

Scores on this test reflect the total number of words beginning with the letters

F, A, and S generated orally in respective 1-min intervals

Total number correct for

all letters

Motor

Grooved Pegboard [35] A test of manual speed and dexterity requiring the subject to insert 25 pegs

into keyhole slots, using first the dominant, then the nondominant, hand

Completion time in

seconds, both hands

Processing speed

Digit-Symbol Coding,

WAIS-III [36]

A timed pencil-and-paper test which requires the subject to copy symbols to

correspond with numbers, according to a key

Number correct in 120 s

Symbol Search,

WAIS-III [36]

A timed test requiring the subject to scan a group of symbols in search of target

symbols

Number correct in 120 s

less errors

Trail Making Test A (Trails A;

[31])

A pencil-and-paper test of visuomotor tracking, requiring subjects to connect,

in sequence, numbers randomly distributed on a page

Completion time in

seconds

Verbal learning and memory

California Verbal Learning

Test II (CVLT; [37])

Assesses ability to learn a list of 16 words over 5 trials, and to recall the list after

a 20-min delay. Variables analyzed included recall of the list on trial 1 (CVLT

Trial 1; a measure of short-term memory), free recall after delay (CVLT delayed

recall; captures both learning and retention), and the score on a delayed

multiple-choice test (CVLT delayed recognition; allows differentiation between

retention and retrieval)

Trial 1 (number correct)

Delayed recall (number

correct)

Delayed recognition

(true positives+true

negatives)

Logical Memory II,

WMS-III [38]

Subjects try to recall two stories presented orally. Delayed recall was analyzed

in order to capture both initial encoding and retention (immediate and delayed

recall were highly correlated)

Delayed recall, total score

Visual learning and memory

Rey Visual Learning Test

(RVLT; [39])

A visual analogue of the CVLT that assesses an individual’s ability to learn a

series of 15 nonsense designs over 5 trials, and to recall the designs after a 20-

min delay. Variables analyzed were chosen to correspond to those of the CVLT

Trial 1 (number correct)

Delayed recall (number

correct)

Delayed recognition (true

positives)

Family Pictures II,

WMS-III [38]

A visual analogue of Logical Memory II where subjects try to recall four

thematic pictures. Delayed recall was analyzed in order to capture both initial

encoding and retention

Delayed recall, total score

Visuospatial function

Block Design,

WAIS-III [36]

Subjects attempt to reconstruct geometric designs using three-dimensional

blocks and are scored according to speed and accuracy

Total raw score

Working memory

Arithmetic, WAIS-III [36] Subjects are required to mentally solve a series of orally presented arithmetic

problems

Total raw score

Consonant Trigrams

(CCCs; [40])

Subjects attempt to retain orally presented letter trigrams for 0, 3, 9, or 18 s

while simultaneously carrying out a serial subtraction task

Total letters correctly

recalled for all intervals



DOI: 10.1002/pon

neuropsychological change scores. Only the signi-ficant predictors from these regressions wereincluded as covariates in the subsequent SRBanalyses. Raw baseline scores were used for thesestable covariates.Unlike the constant covariates described above,

the mood measures from the POMS might changesystematically over time in relation to differentcancer treatments. It is the change in thesemeasures over time, and the relationship betweenthese changes in psychological state and changes inneuropsychological performance, that is pertinentto our understanding of these data. Thus, changescores (from T1 to T2) for the 3 POMS measureswere regressed in a stepwise fashion on the 23neuropsychological change scores in turn. When agiven neuropsychological change score was pre-dicted by one or more of the POMS change scores,we included the significant POMS predictor(s) as arunning covariate in the subsequent SRB analysisfor that dependent variable. We achieved this byregressing the relevant POMS score(s) on thecontemporaneous neuropsychological score, firstusing T1 scores, then T2 scores. The residuals fromthese latter analyses, rather than the raw neurop-sychological test scores, were then used in thesubsequent SRB analyses. The residuals from theseregressions represent the variation in the neurop-sychological measure that remains after removingthe effects of anxiety, depression, and/or fatigue.Table 3 shows which covariates were used witheach of the neuropsychological measures in theSRB analyses.

Group comparisons on composite SRB scores

Domain-specific cognitive composite scores werecomputed by adding the SRB scores for allvariables within a given cognitive domain (seeTable 2). A global neuropsychological compositescore was obtained by adding all SRB scores. Thesecomposite scores were compared for chemotherapyand hormonal groups using t-tests.

Results

Rate of attrition was identical (7.5%) in bothtreatment groups. Three of the chemotherapy

subjects were excluded due to cancer recurrence.In all remaining cases, attrition was due to subjectsdeclining retest. Given the low number of drop-outs, statistical comparison to those who remainedin the study was not feasible. Only data frompatients who completed both testing sessions wereanalyzed. The two patient groups did not differfrom each other with respect to age, education, IQ,or test–retest interval (Table 1). There were morepatients with stage II and III disease in thechemotherapy group than in the hormonal group.Few of the chemotherapy subjects had had radia-tion by T2, whereas most of the hormonal subjectshad. Similarly, most of the chemotherapy subjectshad not yet commenced hormonal treatment atT2, whereas all of the hormonal subjects had.Scores for both groups on all neuropsychologicaland POMS measures at T1 and T2 are presented inTable 4.

Individual change analyses

Thirty-one percent (19 of 61) of the chemo-therapy subjects met our criterion for reliablecognitive decline as compared to 12% (6 of 51) ofthe hormonal group (OR ¼ 3:39; w2 ¼ 6:02;p ¼ 0:014). There was no difference in the fre-quency of reliable cognitive improvement (5% inchemotherapy group; 6% in hormonal group;p ¼ 0:82).Within the chemotherapy group, there were no

differences between those who declined and thosewho did not in terms of chemotherapy regimen,

Table 2. (continued)

Tests (by Cognitive

Domain)

Description Variable(s)

analyzed

Digit Span, WAIS-III [36] Subjects recite strings of random digits of increasing length, first forward, then

backward

Total raw score forward

and backward

Letter-Number-Sequencing,

WAIS-III [36]

Subjects are required to re-order random alphanumeric sequences presented

orally

Total raw score

Spatial Span, WMS-III [38] Subjects are asked to tap out a spatial sequence illustrated by the examiner, first

forward and then backward

Total raw score forward

and backward

Notes: WAIS-III, Wechsler Adult Intelligence Scale-III and WMS-III, Wechsler Memory Scale-III.

Table 3. Covariates used with the various neuropsychologicalmeasures in SRB analyses

Cognitive measure Stable

covariates

Changing

covariates

PASAT Fatigue

Boston Naming Test Fatigue

Grooved pegboard IQ

Digit–symbol coding Tension, depression

CVLT Trial 1 Disease stage

RVLT Trial 1 Education

Arithmetic Tension, depression,

fatigue

Letter–number sequencing Disease stage



DOI: 10.1002/pon

number of cycles, age, IQ, disease stage, or inter-test interval. Decliners and non-decliners wereequally likely to have started concomitant hormo-nal therapy or to have received radiation. Declinerswere less educated than non-decliners (p ¼ 0:03).Decliners had higher POMS depression scores thannon-decliners at T1 (t¼ 2.081, p ¼ 0:049) but notat T2. There was no interaction between declinestatus and change in depression score in a mixeddesign ANOVA. Decliners and non-decliners didnot differ on fatigue or tension/anxiety scores ateither T1 or T2, and there were no group-by-time

interactions on these variables in respective mixeddesign ANOVAs.

Group analyses

The Working Memory Composite Score was lowerin the chemotherapy group than the hormonalgroup (t ¼ �2:0; p ¼ 0:05). There were no groupdifferences in the other domain-specific compositescores, or in the global neuropsychological compo-site score. In terms of individual cognitivemeasures, frequency of decline was greater in the

Table 4. Unadjusted means and standard deviation for chemotherapy and hormonal groups on cognitive measures and POMSsubscales

Cognitive domain/measure Time 1 Time 2

Chemo

mean (SD)

n Hormonal

mean (SD)

n Chemo

mean (SD)

n Hormonal

mean (SD)

n

Executive function

PASAT 40.4 (9.1) 52 39.6 (9.7) 42 43.7 (10.1) 47 41.9 (9.3) 40

Trails B 73.1 (29.6) 61 70.1 (22.0) 51 69.5 (27.8) 61 70.3 (22.0) 51

WCST 100.7 (21.9) 59 104.1 (23.9) 50 96.8 (22.3) 59 100.8 (23.6) 48

Language

Boston Naming Test 54.8 (4.9) 58 54.1 (5.4) 51 55.6 (4.8) 58 55.1 (4.9) 51

FAS 40.7 (12.7) 60 37.7 (11.0) 51 39.5 (12.4) 60 39.0 (10.9) 51

Motor

Grooved Pegboard 154.5 (40.9) 60 159.1 (29.9) 49 151.9 (33.8) 60 153.6 (25.1) 48

Processing speed

Digit–symbol coding 67.0 (12.2) 61 68.9 (12.0) 51 67.6 (12.9) 61 69.0 (12.5) 51

Symbol Search 30.4 (6.3) 61 30.2 (5.1) 51 30.8 (6.8) 61 30.3 (5.0) 51

Trail Making Test A 28.2 (8.9) 61 27.2 (8.8) 51 27.4 (9.0) 61 26.6 (7.4) 51

Verbal learning and memory

CVLT Trial 1 7.2 (2.2) 61 6.1 (1.3) 51 7.3 (2.1) 61 7.3 (2.2) 51

CVLT delayed recall 12.5 (2.9) 61 12.1 (2.7) 50 13.2 (2.5) 61 12.8 (2.4) 51

CVLT delayed recognition 29.4 (3.0) 60 29.2 (2.7) 50 29.9 (3.0) 61 29.2 (2.8) 51

Logical memory II 26.8 (6.8) 60 23.7 (7.2) 51 30.2 (5.7) 61 27.3 (7.0) 51

Visual learning and memory

RVLT Trial 1 4.6 (1.5) 61 4.1 (1.5) 51 5.0 (1.7) 61 4.9 (1.9) 51

RVLT delayed recall 8.0 (2.3) 61 7.4 (2.5) 51 8.6 (2.4) 61 8.3 (2.5) 51

RVLT delayed recognition 13.1 (1.1) 61 12.5 (1.4) 51 13.1 (1.4) 61 13.2 (1.2) 51

Family Pictures II 45.1 (9.1) 60 41.7 (9.5) 50 46.1 (9.3) 61 46.9 (8.7) 50

Visuospatial function

Block design 36.1 (11.1) 61 33.8 (10.0) 51 37.0 (11.4) 61 34.5 (10.5) 51

Working memory

Arithmetic 14.0 (3.4) 60 13.8 (3.0) 51 14.3 (3.3) 60 13.8 (3.1) 51

CCCs 43.1 (7.2) 57 42.9 (7.1) 50 43.0 (8.4) 57 45.0 (7.6) 49

Digit span 17.0 (4.3) 60 17.2 (4.2) 50 17.2 (4.0) 61 18.3 (3.6) 51

Letter–number sequencing 10.7 (2.7) 61 10.5 (2.2) 51 10.3 (2.6) 61 11.0 (2.2) 51

Spatial span 15.2 (2.6) 61 15.2 (2.5) 51 15.4 (2.7) 61 15.5 (2.5) 51

POMS

Depression-Dejection 8.5 (9.3) 60 5.0 (6.4) 51 6.2 (9.4) 61 5.6 (7.1) 51

Fatigue–Inertia 7.8 (6.9) 60 8.2 (5.8) 51 10.7 (7.1) 61 9.0 (7.1) 51

Tension–Anxiety 11.2 (6.7) 60 8.8 (6.4) 51 7.9 (6.6) 61 8.3 (6.5) 51

Notes: SD, standard deviation and POMS, Profile of Mood States.



DOI: 10.1002/pon

chemotherapy group than in the hormonal groupon CCCs (w2 ¼ 5:4; p ¼ 0:02) and Digit Span(w2 ¼ 3:8; p ¼ 0:05).

Discussion

The results of this prospective controlled studysupport the growing body of literature indicatingan association between chemotherapy and cogni-tive function. Using a SRB method to assessindividual cognitive change, we found a threefoldgreater risk of cognitive decline in the chemother-apy patients compared to the hormonal patients(31 and 12%, respectively), even after statisticallyaccounting for age, education, intelligence, fatigue,psychological distress, and regression to the mean.Effects were generally not evident in comparing

group means. It is possible that group comparisonsmight be misleading in estimating effect size if, assome studies have shown, hormonal therapy itselfexerted some negative impact on cognition[5,23,43–45] or if only a small subgroup ofchemotherapy treated women was appreciablyaffected. However, the fact that no mean score,and very few individual scores, fell in the impairedrange in either group, at either time point, suggeststhat any negative impact of chemotherapy oncognition is very subtle. (Considering measuresfor which reliable norms were available, only 1.3%of scores in the chemotherapy group, and only0.8% of scores in the hormonal group, fell two SDsbelow the published mean.) Our findings are inkeeping with the few other prospective studies ofthe cognitive effects of chemotherapy done to date,which have tended to show smaller effect sizes thanretrospective cross-sectional studies [46].Choice of control group has probably also

contributed to the difference in magnitude of effectsfrom one study to another. Cancer patients are atincreased risk for cognitive impairment even if theyhave not been exposed to chemotherapy [18,20],and so we would expect to see larger effect sizes instudies with healthy controls than in those withpatient controls. However, studies with healthycontrols do not allow any causal inferences aboutthe role of chemotherapy in the cognitive deficits.We attempted to find a control group that wasas similar as possible to our chemotherapy groupon all variables except chemotherapy exposure. Ifanything, our choice of control group may haveresulted in an underestimation of chemotherapyeffects due to possible competing detrimental effectsof hormonal treatment and radiation in the controlgroup. In view of this, the finding of increased riskof cognitive decline in our chemotherapy group isall the more meaningful.Differences in the characteristics of the chemo-

therapy sample may also influence effect sizes. Forexample, if chemotherapy affects cognition by

means of its effects on ovarian function and theinduction of sudden menopause, we might expectto see smaller effects in post-menopausal womensuch as those in our study. Jenkins et al. [10] notedthat patients who experienced a treatment-inducedmenopause were at higher risk for cognitive declinethan women who were post-menopausal at base-line. It is important to bear this in mind whenconsidering the generalizability of our findings.In prospective studies lacking a control group,

effect size will also be greatly affected by themanner in which ‘reliable decline’ is defined [47].The more stringent the criterion for decline, the lessfrequently it will be observed. However, while thestringency of the decline criterion will have aprofound effect on the rate of decline within a givengroup, it does not as easily account for a significantgroup difference in rate of decline such as thatobserved here. This underlines the importance of acontrolled prospective study design, that allows forcomparison both within and between groups.The fact that the cognitive effects of chemother-

apy are so subtle and elusive does not necessarilyimply that they are clinically insignificant. Thevalidity of cognitive complaints in other neuro-logical disorders such as sports-related concussion[48], HIV-1-associated mild neurocognitive disor-der [49], and multiple sclerosis [50] was similarlyquestioned, but refinement of assessment tools andstudy design has since led to the recognition ofcharacteristic neurocognitive syndromes. The neu-ropsychological profile associated with chemo fogmay be similar, qualitatively and quantitatively, tothese other disorders. For example, recent findings,including our own observations, suggest thatworking memory measures are particularly sensi-tive to chemotherapy [13,15,51]. Indeed, a recentfMRI study showed more diffuse brain activationduring a working memory task in chemotherapypatients than in controls, even when performancewas maintained [52]. These changes in activationpatterns may reflect a compensation for subtledysfunction, and may underlie patients’ subjectivereports of cognitive disturbance and increasedmental fatigue.There has been considerable speculation as to

the mechanism of chemotherapy-related cognitivedysfunction. Changes in endocrine status, fatigue,and psychological distress are often proposed asmediating factors. Changes in menopausal statusclearly do not explain our findings given that allsubjects were post-menopausal at study outset.Fatigue was included as a covariate in our studyand so is also unlikely to explain group differencesin cognitive decline. Within the chemotherapygroup, the non-decliners actually showed a greaterincrease in fatigue than the decliners. Thenon-decliners also tended to show less reduc-tion in depression scores after treatment thanthe decliners. However, the decliners did have



DOI: 10.1002/pon

significantly higher baseline depression scores,suggesting that characterological factors suchas poor stress tolerance may be risk factors forchemo fog.We failed to find differences between decliners

and non-decliners in the chemotherapy group on anumber of other putative risk factors, includingage, disease stage, test–retest interval, chemother-apy regimen, and number of chemotherapy cycles.The two groups did differ on education, such thatlower levels of education seemed to be a risk factorfor cognitive decline. It has been posited thatindividuals with more education have greater‘cognitive reserve’ and can better tolerate braininsult than those with less education [53]. It wouldbe interesting in future studies to evaluate this, aswell as various personality constructs, as concomi-tants of limited neural reserve and risk factors forchemotherapy-induced cognitive dysfunction.In conclusion, results of this study are in keeping

with the emerging consensus that chemotherapyexposure is associated with subtle cognitivechanges. Refinement of assessment tools and studydesign is necessary to improve our understandingof this complex and elusive phenomenon. This, inturn, will allow treating professionals to offerappropriate education, support, and interventionto those individuals who may experience distressingcognitive side effects of their cancer treatments.

Acknowledgements

This research was made possible by the generous support ofthe Canadian Breast Cancer Foundation}Ontario Chapter.We would like to thank the women who volunteered assubjects, the oncologists, nurses, and support staff at theOttawa Hospital Regional Cancer Centre, and NesrineAwad Shimoon for their support.

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The cognitive effects of adjuvant chemotherapy in early stage breast cancer: a prospective study