Prevalence of QTc Prolongation in Patients With Advanced Cancer Receiving Palliative Care—A Cause for Concern?

Journal Pre-proof Prevalence of QTc prolongation in patients with advanced cancer receiving palliative care – a cause for concern? Janet R. Hardy, BSc, FRACP, MD, Prof, Dr Daniel Bundock, Dr Jessica Cross, Kristen Gibbons, Senior Epidemiologist, A/Prof, Ross Pinkerton, Prof, Dr Korana Kindl, Phillip Good, A/Prof, Jennifer Philip, Prof PII:

S0885-3924(19)31061-9

DOI:

https://doi.org/10.1016/j.jpainsymman.2019.12.356

Reference:

JPS 10338

To appear in:

Journal of Pain and Symptom Management

Received Date: 18 September 2019 Revised Date:

10 December 2019

Accepted Date: 11 December 2019

Please cite this article as: Hardy JR, Bundock D, Cross J, Gibbons K, Pinkerton R, Kindl K, Good P, Philip J, Prevalence of QTc prolongation in patients with advanced cancer receiving palliative care – a cause for concern?, Journal of Pain and Symptom Management (2020), doi: https://doi.org/10.1016/ j.jpainsymman.2019.12.356. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc. on behalf of American Academy of Hospice and Palliative Medicine

Prevalence of QTc prolongation in patients with advanced cancer receiving palliative care – a cause for concern?

Prof Janet R. Hardy (corresponding author) BSc, FRACP, MD Department of Palliative and Supportive Care Mater Misericordiae Ltd Mater Research Institute – University of Queensland Raymond Terrace South Brisbane Queensland Australia 4101

Dr Daniel Bundock Department of Palliative and Supportive Care Mater Misericordiae Ltd Mater Research Institute – University of Queensland South Brisbane Queensland Australia 4101

Dr Jessica Cross Department of Palliative and Supportive Care Mater Misericordiae Ltd Mater Research Institute – University of Queensland South Brisbane Queensland Australia 4101

A/Prof Kristen Gibbons, Senior Epidemiologist

Mater Research Institute – University of Queensland South Brisbane Queensland Australia 4101

Prof Ross Pinkerton Hummingbird House Hospice Chermside Queensland Australia 4032

Dr Korana Kindl St Vincent’s Private Hospital Brisbane Kangaroo Point Queensland Australia 4169

A/Prof Phillip Good St Vincent’s Private Hospital Brisbane Kangaroo Point Queensland Australia 4169

Prof Jennifer Philip University of Melbourne and St Vincent’s Hospital Melbourne, Victoria Australia 3065

Word count = 2482

Tables = 3 Figures = 4 References = 30

Abstract Context Medications commonly used for symptom control along with other known risk factors have the potential to prolong ventricular repolarization as measured by the QT interval on a standard ECG. Objectives To document the prevalence of a prolonged QTc interval in the palliative/oncology setting, compare automatic ECG QTc measurements with manual readings and identify any correlation between QTc prolongation and the use of drugs or other risk factors. Methods A convenience sample of consecutive patients with cancer, admitted under or known to the palliative/supportive care teams in two metropolitan hospitals and willing to provide an ECG recording and basic demographic information including QTc risk factors were included. Both automated and manually calculated QTc intervals were recorded. Multivariable analysis was used to determine risk factors independently associated with prolonged QTc intervals. Results Of the 389 participants, there was a significant difference in mean QTc between sites using automated but not manual calculations. Manual readings were therefore used with predetermined cut-offs of 0.44sec (males) and 0.46sec (females). 72 (18.5%) of participants had a prolonged QTc with 6 (1.5%) having a prolongation >0.50sec. “At-risk” drugs were being taken by 218 participants (56.0% of total cohort). Factors shown to be associated with QTc prolongation included age, gender, performance status and hypocalcaemia. No specific medication was associated with increased risk. Conclusion. Although almost 20% of patients receiving palliative care had prolongation of QTc, the risk of serious consequences appeared to be low despite the frequent occurrence of risk factors. Key message: •

QTc prolongation is not uncommon in patients receiving palliative care

•

Many of the medications used commonly for symptom management have the potential to prolong QTc

•

Clinicians should be aware of the risk factors and potential consequences of QT prolongation and screen those likely to be on long term treatment or at greatest risk

•

Manual calculation is recommended to confirm QTc prolongation due to the variability of automated recordings.

Key words: electrocardiogram; QTc prolongation; palliative care.

Running title: Prevalence of QTc prolongation in palliative care patients

Introduction The QT interval on a standard 12-lead electrocardiogram (ECG), includes the QRS complex, the ST segment, and T wave (Figure1) and is primarily a measure of ventricular repolarization. The time for ventricular repolarization and therefore the QT interval is dependent upon the heart rate; it is shorter at faster heart rates and longer with slower rates. The QTc is the QT interval corrected for heart rate. There is no consensus regarding the normal range of QTc values and this has led to differences in what is defined as ‘abnormal’ in reported studies. In general, an upper limit of normal is taken as around 0.45sec. Some differentiate by gender, suggesting that QTc is 10-20msec longer in women. A generally accepted normal value for the QTc is ≤0.44sec in men and ≤ 0.46sec in women (1). A prolonged QTc is a pro-arrhythmic state that can be associated with an increased incidence of ventricular arrhythmias, especially Torsades de Pointes (TdP) and may be associated with sudden cardiac death (2). QTc prolongation can be due to a wide variety of causes; congenital conduction

defects,

underlying

cardiac

abnormalities,

hypothyroidism,

metabolic

abnormalities and as a consequence of drug administration (3). There are extensive data on the effect of a wide range of drugs that prolong QTc and these have been classified into groups of relative risk (4-7). Many of these are used in palliative care(8-13), including chlorpromazine, domperidone, droperidol and ondansetron. Opioids are regarded as a particular contributing factor, especially methadone (14). The association of drugs with QTc prolongation has led to the screening and potential exclusion of certain medications on the grounds of ECG abnormalities (15-17). The actual risk of severe complications such as TdP is difficult to define but appears to be small. It is unclear which factors could precipitate potentially fatal complications. There is, however, evidence that a

QTc greater than 0.50sec carries a higher risk of clinical sequelae and should lead to concern when continuing any suspect drug (5,15,17). Oversensitivity to the possible risks involved could lead to inappropriate cessation of effective symptom control medications in the palliative care population. With the earlier involvement of palliative care in the management of patients with advanced cancer and the more active use of appropriate analgesics, antiemetics and sedatives, it is of relevance to gather information on QTc intervals in this population and the possible causal factors.

The primary goal in this study was to identify the prevalence of a prolonged QTc interval in the palliative/oncology setting. Secondary goals were to: compare automatic ECG QTc measurements with manual readings using Bazett’s formula (1), document the use of medications with the potential to prolong QT in palliative care patients, the frequency of occurrence of other risk factors with the potential to prolong QT and to determine whether there was a correlation between QTc prolongation and the use of drugs or risk factors known to prolong QT.

Methods.

Patient population A convenience sample of 471 consecutive in-patients admitted under or referred to the palliative/supportive care team in the Mater Adult Hospital Brisbane (site 1) and St Vincent’s Hospital Melbourne (site 2) were screened daily by research officers. Included patients had malignant disease, were over 18 years of age and were admitted for a non- cardiac event. Patients were excluded if it was not possible to obtain a standard ECG recording or demographic data.

All participants were given information about the study and asked to sign a consent to allow for the collection of: a standard ECG, routine admission blood tests, demographic information and a medication history. All published QTc risk factors (4-13 ) were documented. Blood tests taken within a week of the ECG was accepted. Normal laboratory values at site 1 were used to define abnormal values. Medications considered to have the potential of prolonging QTc (at risk drugs) taken within 24 hours of the ECG (regular or “as required”) were noted. For patients who had already had an ECG but were unable to provide consent, proxy consent from a legally acceptable representative was obtained.

No medication was withheld in the study population as a consequence of QTc prolongation. The study was approved by the Human Research Ethics Committees of the Mater Hospital Brisbane and St Vincent’s Hospital Melbourne.

Assessment of QT interval The methods used to evaluate QTc have previously been extensively investigated and the value of automated versus manual measurements considered (1,18-20). Both methods were utilised in this study. 12-lead ECG machines at both sites were standardised. An ECG was taken once only for each participant, unless required more frequently for clinical monitoring, or already available through the admission process. If there were several ECGs in the chart from the current admission, the most recent ECG was used. Each ECG was anonymised and numbered and the automated QTc interval shown on the print out documented.

Manual calculation of all QTc intervals was undertaken by one member of the research team (DB) with cross-checking of a subset by another (KK). ECGs were inspected visually for quality. ECGs with missing leads, excessive electrical artefact and insufficient voltage to determine QT interval were removed, as were ECGs without 3 consecutive QRS complexes. QT interval was measured during stable sinus rhythm and averaged over 3 consecutive beats to control for beat to beat variability. Patients were excluded from further analysis in the presence of overt arrhythmia, atrial flutter or fibrillation. Any ECGs with concerning features were brought to the attention of the treating clinicians. The tangent method of manual calculation was performed as described by Postema (21). Their standardised method is said to provide clear criteria for QT measurement, with increased accuracy and reduced inter-observer variation (Figure 1). The end of the T wave was defined as the intersection of a tangent to the steepest slope of the last limb of the T wave and the baseline, in lead II or V5. QTc was defined as QT/RR from the RR interval between the measured and the preceding complex according to Bazett's formula (QTc=QT interval ÷ square root of the RR interval in sec).

A normal value for QTc was defined as ≤0.44sec in men and ≤ 0.46sec in women (22). Statistical analysis

A sample size of 384 participants was required to report the prevalence of prolonged QTc with a 95% confidence interval (CI) and width of 10%; a prolonged QTc prevalence of 50% was used in the sample size calculation to ensure that the CI width would be 10% or less, regardless of the resulting prevalence. Comparison of the automatic ECG QTc measurements with manual readings was undertaken visually using a Bland-Altman agreement plot. Analysis of the relationship between prolonged QTc and risk factors (including medication use) was undertaken using a series of Fisher’s exacts tests for categorical factors and t-tests or Mann-Whitney U tests for continuous factors. Those factors with a p-value less than 0.25 on bivariate analysis were entered into a multiple logistic regression to identify independent factors associated with prolonged QTc. Analyses were undertaken in StataSE version 14 (StataCorp Pty Ltd, College Station, Texas) and statistical significance was set at 0.05. Results 471 participants were recruited into the study, 276 (58.6%) at site 1 and 195 (41.4%) at site 2 (Figure 2). Of the 471 participants, 82 (17.4%) were excluded (34 (12.3%) site 1 and 48 (24.6%) site 2). The main reason for exclusion (77 cases (94%)) was that a manual reading was not possible because of atrial fibrillation, poor quality recording, abnormal or low voltage ECG, or machine error. The mean participant age (standard deviation [SD]) was 65[14] years with a preponderance of females (62.5%). The most common cancer diagnoses were lung (18.5%), gynaecological (17.8%,) breast (17%), genitourinary (10%) and gastrointestinal (10%). The majority of participants (>80%) had metastatic disease. Participants were of a relatively good performance status (Australian-modified Karnofsky Performance Scale mean score 61/100 [16]).

The Bland-Altman plot indicated good agreement between the manual and automatic measures (Figure 3). However, while there was no significant difference between sites for the manual reading (mean [SD] QTc 0.422 [0.033] and 0.426 [0.029], p=0.233) there was a significant difference between sites in mean QTc from automatic readings (0.442 [0.028] and 0.410 [0.030], p<0.001) (Figure 4a). Furthermore, while there was no significant difference between the mean (SD) QTc manual readings for males and females (0.422 [0.032] vs 0.425

[0.032], p=0.373), there was a significant difference between males and females using automated readings (0.425 [0.030] vs 0.433 [0.034], p=0.034) (Figure 4b).

Using predetermined cut-offs of 0.44 seconds for males and 0.46 seconds for females, 72 (18.5%) of all patients had a prolonged QTc interval on manual readings and 87 (22.5%) on automatic readings. Six patients (1.5%) on manual readings and 10 (2.6%) on automatic readings had a prolongation >0.50s (Table 1). Due to the inconsistencies between sites using the automated method, it was decided to use manual readings (n=389) only for all subsequent analyses.

Two hundred and eighteen patients (56.0% of the total cohort) were taking an “at-risk drug” at the time of the ECG (Table 2). Opioids had been prescribed in 272 (69.9%) patients. In 83.2% the daily oral morphine equivalent was <100 mg (range 0 to >500). A small number (n=25, 6.4%) were on methadone with a median daily dose of 20mg. 13.6% were on a drug classed as an anti-psychotic. Both patient numbers in these subsets and mean daily doses were low (eg haloperidol 1mg, methotrimeprazine 6.25mg).

Using the gender-specific cut-offs, those demographic factors shown to be associated with QTc prolongation included age, gender and performance status (Table 3). Similarly, hypocalcaemia but not hypomagnesaemia or hypokalaemia was also associated with an increased risk of prolongation. No medication was associated with increased risk and surprisingly, the use of any opioid appeared protective (normal vs QTc prolongation 73 vs 58%, p=0.022). Multivariable analysis confirmed that age (adjusted odds ratio [aOR] 1.054, 95% confidence interval [CI] 1.02, 1.07, p<0.001), male gender (aOR 0.40, 95% CI0.22, 0.73, p=0.003) and hypocalcaemia (aOR 6.96, 95%CI 2.09, 23.21, p=0.002) were all independently associated with prolonged QTc (Table 3). When using the 0.45 cut off for both males and females, only performance status was predictive but once again, opioid medication use appreared protective (data not shown). In the small number on methadone no association was found with prolongation of QTc.

There were no unexpected deaths thought to be cardiac related.

Discussion

The results of this study are consistent with those found by Walker (13) who analysed automated QTc readings in a population of adults with advanced cancer and documented an incidence of QTc prolongation of 16% (47/300) when defining normal maximum values of 0.45sec (males) and 0.47 (females). Two patients (both with pre-existing cardiac disease) had a value >0.5sec. Prolongation appeared to be correlated with female gender, cardiac disease, low potassium, low calcium and elevated ALP and bilirubin. Full details of the medications administered were not provided and no correlation was noted in relation to groups of drugs that were associated with QTc prolongation. Differences in the values chosen as “normal” renders comparison between studies difficult. Normal cut off values documented in the literature vary and may be described as age or sex dependent or independent. Moreover, the determination of QT interval poses a number of technical issues. Significant variation in the calculation of QT interval and the recognition of prolonged QT interval amongst cardiologists and non-cardiologists as compared to a goldstandard as set by 25 QT “experts” has been demonstrated (23). In the present study, a significant difference was found between sites with respect to mean automated QTc reading despite there being no difference in median QTc values from manual readings. These data have confirmed previous concerns regarding the reliability of automatic readings, particularly if acting on the results will change management. Although previous studies have also shown an overall high correlation between automated and manual methods, concern has been expressed in relation to arrhythmia evaluation (20). It seems appropriate that if the automatic result is clearly normal, this can be taken as sufficient evidence for no concern. If, however, it is outside the normal range a manual calculation by an experienced or specifically trained observer should be carried out. . Holter monitoring can detect self-limited episodes of prolonged QTc that would have been missed on a single study (24). A focus in palliative care has been the effect of methadone on QTc. This has been extensively explored in non-cancer and addiction populations (25). A few studies have addressed this issue in patients with cancer (26-29). In a retrospective review (26), a prolonged QTc was found in 28% patients receiving methadone (median dose 23mg). 75% were receiving other “at risk” drugs. Only one patient had a QTc > 0.50sec and no significant arrythmias or sudden deaths were documented. Two more recent studies reached similar conclusions (27,28). The surprising finding in the current study was that when analysed as a single group, opioids appeared protective with

respect to QTc prolongation. While the number of patients on methadone was relatively small, the median dose was similar to that of Reddy (26). Due to the relatively small number of patients receiving specific opioids, no significant correlation was sought with individual drugs. Guidelines in relation to methadone used in drug withdrawal programmes are less applicable in palliative care where the relative risk versus benefit ratio is different, especially during end of life care. It has been suggested that a ‘common sense’ approach be taken with an ECG being irrelevant in end of life care (30). However, where palliative care is introduced earlier in the disease trajectory, it is appropriate to identify specific risk factors and consider monitoring as recommended by the European Medicines Agency (6). The present study has indicated that in the palliative care setting, advanced age, male gender, poor performance status and hypocalcaemia are significant factors that should raise awareness of increased risk for QTc prolongation. Other drug groups such as anti-emetics or psychotropic agents did not appear to increase risk. Limitations of this study include the heterogeneous population with a wide range of different cancers receiving a wide variety of medications. The disparity between automatic ECG reading between the two sites is of concern. Two tertiary metropolitan centres participated, and this predominantly inpatient population may not be reflective of the situation in community or home care. No baseline ECG was taken prior to the use of opioids (making it impossible to establish any cause-effect relationship) and identifying the role of specific drugs was not feasible with this sample size. In conclusion, although almost 20% of patients receiving palliative care had a prolonged QTc, very few exceeded 0.5secs and the risk of serious consequences appears to be low. Risk factors are similar to those reported in other clinical settings. In those situations where drugs known to prolong QTc are introduced early in care, in particular where there are other clinical risk factors, it is appropriate to perform a baseline ECG and consider drug modification if indicated. Disclosures and acknowledgements The authors report no conflicts of interest.

They acknowledge all the patients who allowed their ECGs to be included in this study. We also acknowledge the contributions of Dr Jennifer Weil who co-ordinated this study at the Melbourne site and Dr Sandy Prasad who gave cardiology advice. This research did not receive any specific grant from the funding agencies in the public, commercial, or not-for-profit sectors. All participants provided written informed consent and the study was approved by the investigator’s institutional review board/ethics committee. Approval reference: Mater - HREC/14/MHS/150 St Vincent’s - LRR 117/14

Figure 1. Calculation of QTc using Bazett’s formula and tangent method of manual calculation (21)

Figure 2. Flowchart describing participant recruitment

Total Recruited (n=471) - Mater n=276 - St Vincent’s n=195 Excluded – Missing Data (n=5) ECGs for Review (n=464)

No Automatic QTc Reading (n=2) Still Included in analysis of Manual QTc Reading

Manual QTc Reading (n=389)

Automatic QTc Reading (n=387)

Excluded - Manual QTc Reading not possible (n=77) - Artefact (n=25) - Low voltage (n=17) - Abnormal ECG (n=16) - Atrial fibrillation (n=12) - Poor quality ECG copy (n=3) - ECG machine error (n=1) - Other (n=3)

Figure 3. Bland-Altman plot assessing agreement between QTc values using manual and automatic methods

Difference of QTc Auto and QTc Manual -.1 -.05 0 .05 .1

Bland Altman Plot

.35

.4 .45 .5 Average of QTc Auto and QTc Manual Obs Mean-1.96*SD

Mean Mean+1.96*SD

.55

.55 .5 .45 .4 .35

.35

.4

.45

.5

.55

Figure 4. Boxplot comparing the distribution of QTc manual and automatic readings between a) sites and b) genders

Mater QTc Manual (sec)

a)

St Vincent's

Male

QTc Auto (sec)

QTc Manual (sec)

b)

Female QTc Auto (sec)

Table 1. Prevalence of prolonged QTc using gender based normal value and non-gender norms Variable

Manual

Auto

Category

Prolonged*

Male (N=146)

Female (N=243)

n

n

%

pvalue

%

Total (N=389) n

%

43 29.5 29 11.9 <0.001 72 18.5

Prolonged>0.45s** 24 16.4 46 18.9

0.587 70 18.0

Prolonged>0.50s**

0.417

Prolonged*

1

0.7

5

2.1

6

1.5

72 30.0 15 10.2 <0.001 87 22.5

Prolonged>0.45s** 78 32.5 12

8.2 <0.001 90 23.3

Prolonged>0.50s**

1.4

8

3.3

2

*gender-based normal values: 0.44secs (female), 0.46 (male) ** non-gender-based value: 0.45secs

0.330 10

2.6

Table 2. Medication taken by those with a prolonged QTc reading versus those without (using gender-specific norms on manual readings) Medication

Total using any at-risk drug Antipsychotics Opioids Methadone*

Normal QTc (N=317)

Prolonged QTc (N=72)

n

%

n

%

180

56.4

38

54.3

0.791

218

56.0

44

13.9

9

12.5

0.851

53

13.6

230

72.6

42

58.3

0.022

272

69.9

22

6.9

3

4.2

0.594

25

6.4

* Daily dose range 2.5mg – 150mg; median dose 20mg

pvalue

Total (N=389)

n

%

Table 3. Comparison of demographic and clinical factors for patients with a prolonged QTc reading versus those without (using gender-specific norms on manual readings)

Bivariate Analysis

Multivariable Analysis (N=346)

Variable

Normal QTc (N=317) n

%

Prolonged QTc (N=72) n

pvalue

%

Total (N=389)

n

aOR (95% CI)

pvalue

%

Age (years)*

63.6 13.7 71.3 11.0 <0.001 65.0 13.5

1.05 <0.001 (1.02, 1.07)

Gender - Male

103 32.5

146 37.5

0.40 (0.22, 0.73)

0.003

AKPS#

62.2 16.0 56.7 15.2

0.011 61.2 16.0

-

-

3.5

0.999

15

4.6

-

-

10 17.9

0.535

48 14.9

-

-

2.6

6 10.7

0.014

13

4.1

6.96 (2.06,23.2)

0.002

230 72.6

42 58.3

0.022

272 69.9

0.60 (0.32, 1.11)

0.105

6.4

-

-

131 33.7

0.93 (0.49, 1.76)

0.826

Hypokalaemia#

13

Hypomagnesaemia#

38 14.2

Hypocalcaemia#

Any opioids

Any methadone “At risk”GI

7

22

4.9

6.9

112 35.3

43 59.7 <0.001

2

3

4.2

0.594

19 26.4

0.168

drugs* #

Missing data present

*Loperamide, ondansetron, granisetron, metoclopramide

25

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