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SUPPLEMENTARY MATERIALS

 

Construction of an open-access QT database for detecting the proarrhythmic potential of marketed drugs: ECG-ViEW

 

Authors: MY Park, MS, D Yoon, MD, MS, NK Choi, PhD, J Lee, MD, MS, K Lee, PhD, HS Lim,

BJ Park, MD, MPH, PhD, JH Kim, MD, PhD, RW Park, MD, PhD

 

DATA EXTRACTION

 

Parameter extraction from ECG printouts using OCR software

Graphic software was used to pre-process scanned ECG printouts. Gray-scale conversion, cropping to text area, and removing background dirt by adjusting the brightness level were conducted in sequence. Image pre-processing of all the images was performed automatically using a macro function of the graphic software. OCR software was used to extract parameters from a pre-processed image. OCR is the electronic translation widely used for converting books and documents into electronic files, to computerize a record-keeping system in an office, or to publish text on a website. The macro function of the OCR software enabled the recognition of ECG readings from the ECG images into text files automatically. Incorrect cases were collected, and falsely recognized characters in the images were trained by using the character-training function provided by the OCR software. We developed a Java-based text-parsing software that parsed ECG parameters from the recognized texts files. Overall processing is illustrated in the following diagram.

 

ECG-ViEW_sFig2

<Overall process to extract ECG parameters from scanned ECG printout>

 

Web-parsing software for parameter extraction from ECG management system

We developed Java-based web-parsing software that sends a request to the web viewer of the ECG management system. Returned PDF files containing ECG parameters and ECG waveforms from the ECG management system were saved into a file folder. All the stored ECG records in the ECG management system were retrieved and saved as PDF files by sequentially iterating the request and saving the loop for all patients who ever visited the subject hospital. A pause of 0.5 sec was placed between sequential requests to prevent unexpected shut down or breakdown of the ECG management system due to overload. A 5-minute pause every 55 minutes and a 30-minute pause every day were also included.

A centralized ECG management system should always be running without a pause. Thus, extracting all stored ECG records may hinder the performance of the system or can cause a breakdown. We tried to minimize the burden on the system by pausing at various intervals before the next request to the ECG management system. The stability or performance of the ECG management systems of individual hospitals may vary. Thus, our strategy to pause between sequential requests must be adapted to the individual system. We believe that the suggested pause between requests is sufficient.

 

De-identification of data

 

<Removed diagnoses from the database, which are highly stigmatized >

ICD-10 codes

Descriptions

A50-A64

Infections with a predominantly sexual mode of transmission               

B20-B24

Human immunodeficiency virus [HIV] disease                        

F10-F19

Mental and behavioural disorders due to psychoactive substance use           

F52

Sexual dysfunction, not caused by organic disorder or disease

F65

Disorders of sexual preference

F70-F79

Mental retardation                                    

N46

Male infertility                                    

N48

Other disorders of penis                                

N50

Other disorders of male genital organs                         

O00-O08

Pregnancy with abortive outcome                              

O30-O48

Maternal care related to the fetus and amniotic cavity and possible delivery problems  

Q00-Q07

Congenital malformations of the nervous system                     

Q10-Q18

Congenital malformations of eye, ear, face and neck                   

Q20-Q28

Congenital malformations of the circulatory system                   

Q30-Q34

Congenital malformations of the respiratory system                   

Q35-Q37

Cleft lip and cleft palate                               

Q38-Q45

Other congenital malformations of the digestive system                 

Q50-Q56

Congenital malformations of genital organs                       

Q60-Q64

Congenital malformations of the urinary system                     

Q65-Q79

Congenital malformations and deformations of the musculoskeletal system         

Q80-Q89

Other congenital malformations                             

Q90-Q99

Chromosomal abnormalities, not elsewhere classified                   

T73

Effects of other deprivation 

T74

Maltreatment syndromes 

X60-X84

Intentional self-harm

X85-Y09

Assault

Z72

Problems related to lifestyle

Z80-Z99

Persons with potential health hazards related to family and personal history and certain conditions influencing health status 

Software tools

We used Eclipse 3.2.2 (IBM, Riverton, NJ) as a Java programming tool for the web-parsing and text-parsing software. MS-SQL 2000 (Microsoft, Redmond, WA) was used as the database-management system. Paint Shop Pro 8 (Jasc Software, Eden Prairie, MN) was used to pre-process the scanned ECG images. ABBYY FineReader 11.0 (ABBYY Software, Moscow, Russia) was used for OCR.

 

 

CHARACTERISTICS OF ECG-ViEW

 

Characteristics of ECGs in ECG-ViEW. The QT database contains 710,369 evaluable ECG records from 371,401 patients over a 17-year study period, including 508,978 patient years. The average observation period was 502 ± 1,008 days, and the patients had an average of 1.9 ECGs. Most patients had only one ECG; the interval between the consecutive ECGs was decreased as the number of ECGs/patient increased. The interval between ECG dates in patients who had more than two ECGs was 544.7 ± 813.3 days.

 

ECG-ViEW_Fig2.tif
<The intervals between consecutive ECG measurements by the number of ECGs recorded>

 


The mean age of patients was 42.4 years, and 50.0% of patients were female. Most of the study population was Korean (99.1%). Age-adjusted CCI values of 5–6 were most prevalent (34.3%). The average QT and QTc (by Bazett's formula) were 383.2 ± 41.0 and 414.9 ± 26.3 ms for males, and 387.3 ± 42.2 and 423.9 ± 27.1 ms for females, respectively.

 

ECG-ViEW_Fig3.tif

<The QTc distribution and size of study populations in ECG-ViEW according to the department where the ECG was measured>

 

The proportion of QTc prolongation (M >450 ms, F >460 ms) was 8.1%. There were 37.9 million prescriptions for 911 classes of drugs and 2.9 million laboratory test results for selected electrolytes (serum potassium, calcium, and magnesium) during the observation period in ECG-ViEW.

 


Characteristics of the ECGs by department

About half (44.5%) of ECG recordings were performed at outpatient departments. QTc prolongation was most prevalent at emergency departments (25.4%), followed by inpatient (17.1%), outpatient (7.5%), and health examination departments (4.1%), in descending order.

 

<Characteristics of ECGs in ECG-ViEW by departments>

Variables

Department

p-value

Total

Health examination

Outpatient

Emergency

Inpatient

n

125794 (17.7%)

316158 (44.5%)

108802 (15.3%)

159615 (22.5%)

710369 (100%)

RR interval, ms

953.2 ± 138.5

850.7 ± 172.4

790.9 ± 190.8

820.4 ± 191.7

<0.001*

852.9 ± 181.9

QT interval, ms

406.6 ± 29.0

383.6 ± 41.7

387.2 ± 50.3

385.2 ± 49.8

<0.001*

388.6 ± 44.0

QTc interval, ms

417.5 ± 20.7

417.9 ± 26.4

438.8 ± 36.5

428.4 ± 36.3

<0.001*

423.4 ± 30.8

QTc interval by category, ms

 

 

 

 

<0.001

 

<390

7623 (6.1%)

26002 (8.2%)

2193 (2.0%)

7247 (4.5%)

43065 (6.1%)

390 to <420

66343 (52.7%)

164918 (52.2%)

34062 (31.3%)

72616 (45.5%)

337939 (47.6%)

420 to <450

42934 (34.1%)

91558 (29.0%)

38824 (35.7%)

46961 (29.4%)

220277 (31.0%)

450 to <480

7918 (6.3%)

26344 (8.3%)

21830 (20.1%)

20409 (12.8%)

76501 (10.8%)

480 to <500

739 (0.6%)

4147 (1.3%)

5894 (5.4%)

5444 (3.4%)

16224 (2.3%)

500

237 (0.2%)

3189 (1.0%)

5999 (5.5%)

6938 (4.3%)

16363 (2.3%)

 QTc prolongation

5177 (4.1%)

23574 (7.5%)

27639 (25.4%)

27299 (17.1%)

<0.001

83689 (11.8%)

Data are n (%) or mean ± SD.

An individual ECG measurement was regarded as an observation in this table; thus, a patient with many serial ECG measurements was included multiple times.

QTc prolongation, M >450 ms, F >460 ms

*ANOVA test, Chi-square test

 

Study for proof of concept

To prove the usefulness of the database as a surveillance database for detecting QT prolongation associated with medical products, we conducted a surveillance study against amiodarone, which is well known to be associated with QT prolongation.1,2 This drug is listed on the first line of the Drugs with a Risk of Torsades de Pointes on the Arizona CERT website (http://www.azcert.org/). For the evaluation, reporting odds ratio (ROR) and proportional reporting ratio (PRR),3 widely used data-mining algorithms for ADR signal detection from SRS data, were used. However, the data in this study were observed instead of reporting data. Thus, we refer to ROR as observed odds ratio and PRR as proportional observed ratio hereafter. To calculate them, a two-by-two table was created according to whether the patient took amiodarone and whether QTc was prolonged.

 

<Study for proof of concept: amiodaron and QTc prolongation>

Amiodarone

All other drugs

Overall

OOR (CI)

POR (CI)

Prolonged QTc

1876 (N1)

 50614 (N2)

 52490

5.54

(5.18–5.91)

3.19
(3.09–3.30)

Normal QTc

1754 (N3)

261992 (N4)

263746

Overall

3630

312606

316236

OOR, Observed odds ratio; POR, proportional observed ratio; CI, 95% confidence interval

 

This leads to the definition of observed odds ratio and proportional observed ratio as

 

Observed odds ratio = (N1/N3)/(N2/N4)

Proportional observed ratio = (N1/(N1+N3))/(N2/(N2+N4)),

where

N1: QTc prolonged after study drug (herein amiodarone) prescription

N3: QTc not prolonged after study drug prescription

N2: QTc prolonged after all other drugs (except study drug) prescribed

N4: QTc not prolonged after all other drugs (except study drug) prescribed

 

Drugs prescribed within one day before ECG examination were considered as drugs that could affect ECG parameters. QTc prolongation was defined as intervals longer than 450 msec for males and 460 msec for females.4 Of the 3,630 ECG records conducted within one day after amiodarone prescription, 1,876 ECG records showed QTc prolongation. In contrast, of the 312,606 ECG records conducted within one day after prescription of all drugs other than amiodarone, 50,614 ECG recordings showed QTc prolongation. The observed odds ratio and proportional observed ratio were 5.54 (95% confidence interval [CI], 5.18–5.91) and 3.19 (95% CI, 3.09–3.30), respectively.


ECG-ViEW database specification

The ECG-ViEW database consists of five data tables and two reference tables

 

<Data-table lists and descriptions>

Table name

Description

Person

Demographic and clinical information about a person

Electrocardiogram

ECG recording for a patient at a certain time

Drug

Drug prescribed at a certain time

Diagnosis

Diagnosis recorded for a patient at a certain time

Laboratory

Laboratory test result for a patient at a certain time

DrugCodeMaster

Prescription code and name of a drug

DiagnosisCodeMaster

A mapping table between local diagnostic code and ICD-10 code

 


<Table specifications>

Table name

Field

Type, precision

Description

Person

personid

integer

Unique identifier, randomly assigned

sex

boolean

1 = Male, 0 = Female

birthday

date

Date of birth, randomly shifted within ± 90 days

ethnicity

boolean

1 = Korean, 0 = Non-Korean

Electrocardiogram

personid

integer

Unique identifier, randomly assigned

ecgdate

date

Date and time when the ECG was recorded. The date was shifted from the original date by the same number of days as were applied to the patient's birthday.

RR

integer

RR interval, ms

QT

integer

QT, ms

QTc

integer

QTc, ms (by Bazett's formula)

ACCI

integer

Age-adjusted Charlson comorbidity index

ecgdept

character (1)

E = Emergency, H = Health examination, O = Outpatient, I = Inpatient

ecgsource

character (1)

M = ECG management system, P = scanned paper ECG, E = EHR

Drug

personid

integer

Unique identifier, randomly assigned

drugdate

date

Date and time when the drug was prescribed. The date was shifted from the original date by the same number of days as were applied to the patient's birthday.

druglocalcode

character (8)

A system-generated local drug code

atccode

character (7)

ATC code, up to level 5

drugdept

character (1)

E = Emergency, H = Health examination, O = Outpatient, I = Inpatient

route

character (1)

Route of drug administration, P = parenteral (injection), E = enteral

duration

integer

Duration of drug use

DrugcodeMaster

druglocalcode

character (8)

Local code for a drug

igrdname

character (50)

Drug ingredient

Diagnosis

personid

integer

Unique identifier, randomly assigned

diagdate

date

Date and time when the diagnosis was made. The date was shifted from the original date by the same number of days as were applied to the patient's birthday.

diaglocalcode

Character (8)

Local code for  diagnosis

icdcode

character(7)

ICD-10 code for diagnosis

diagdept

character(1)

E = Emergency, H = Health examination, O = Outpatient, I = Inpatient

DiagnosisCodeMaster

diaglocalcode

character (8)

Local code for diagnosis

diagnosis

character (190)

Diagnosis, full text

Laboratory

personid

integer

Unique identifier, randomly assigned

labdate

date

Date and time when the laboratory sample was drawn from the patient. The date was shifted from the original date by the same number of days as were applied to the patient's birthday.

labname

character (1)

1 = serum potassium, 2 = serum magnesium, 3 = serum calcium

labvalue

number (7,2)

Laboratory test result

ECG-ViEW_sFig1.tif

<The entity-relationship diagram (ERD) for the ECG-ViEW data tables and their relationships>

 


Comparison with previous studies

Although the literature regarding QT intervals is substantial, there are no standards for distribution of QT intervals in the population. Based on the criteria presented in previous studies, the average QTc of ECG-ViEW was similar to that of an English study that evaluated 3,596 older (60–79 years) participants.5 However, it was longer than that reported in a Finnish study involving 10,822 participants.6 QTc prolongation in ECG-ViEW was comparable to, or less than, that in a Dutch study (390 patients)7 or the English study noted above. The average QTc in ECG-ViEW was between two US studies involving 7,828 participants aged 40 years or older8 and 46,129 normal volunteers.9 The differences may result from differences in the ethnicity and/or study populations among the study groups. Only ECG-ViEW contains associated clinical data and is open access, in contrast to the previous studies and ECG databases.

 

<Comparison of ECG-ViEW with previous studies>

Study

Country

Population type

ECG type

HR correction formula

Age, years

No. of subjects

Gender

QTc, ms

QTc prolongation

Criteria, ms

Proportion

Zhang8

U.S.

general

Standard*

Bazett

≥40

7828

-

429 ± 23

-

-

ECG-ViEW

Korea

HE

37376

-

420 ± 22

-

-

Algra7

Netherlands

patient

Ambulatory

Bazett

-

390

-

-

≥440

0.22

ECG-ViEW

Korea

patient

Standard

308825

-

-

≥440

0.19

Sohaib5

England

general

Standard

Hodge

60–79

3596

-

419 ± 26

>440

0.19

>500

0.01

ECG-ViEW

Korea

HE

6397

-

424 ± 22

>440

0.21

>500

0.01

Anttonen6

Finland

general

Standard

Bazett

30–59

10822

M

402 ± 54

-

-

F

415 ± 52

ECG-ViEW

Korea

HE

52160

M

411 ± 19

-

-

F

424 ± 20

Mason9

U.S.

general

Standard

Bazett

-

46129

M

401 (median)

≥449

0.02

F

414 (median)

≥460

0.02

ECG-ViEW

Korea

HE

62576

M

409 (median)

≥449

0.04

F

422 (median)

≥469

0.06

Data are n (%) or mean ± SD

*Standard resting 12-lead ECG

24-hour ambulatory ECG

ECG, Electrocardiogram; HR, heart rate; HE, health examination

 

 


REFERENCES

1.         Riera, A.R., et al. Relationship among amiodarone, new class III antiarrhythmics, miscellaneous agents and acquired long QT syndrome. Cardiol J 15, 209-219 (2008).

2.         Taira, C.A., Opezzo, J.A., Mayer, M.A. & Hocht, C. Cardiovascular drugs inducing QT prolongation: facts and evidence. Curr Drug Saf 5, 65-72 (2010).

3.         Brian L. Strom, S.E.K. textbook of pharmacoepidemiology, (John Wiley & Sons Ltd, Chichester, 2006).

4.         Rautaharju, P.M., et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part IV: the ST segment, T and U waves, and the QT interval: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol 53, 982-991 (2009).

5.         Sohaib, S.M., Papacosta, O., Morris, R.W., Macfarlane, P.W. & Whincup, P.H. Length of the QT interval: determinants and prognostic implications in a population-based prospective study of older men. J Electrocardiol 41, 704-710 (2008).

6.         Anttonen, O., et al. Prevalence and prognostic significance of short QT interval in a middle-aged Finnish population. Circulation 116, 714-720 (2007).

7.         Algra, A., Tijssen, J.G., Roelandt, J.R., Pool, J. & Lubsen, J. QTc prolongation measured by standard 12-lead electrocardiography is an independent risk factor for sudden death due to cardiac arrest. Circulation 83, 1888-1894 (1991).

8.         Zhang, Y., et al. QT-interval duration and mortality rate: results from the Third National Health and Nutrition Examination Survey. Arch Intern Med 171, 1727-1733 (2011).

9.         Mason, J.W., et al. Electrocardiographic reference ranges derived from 79,743 ambulatory subjects. J Electrocardiol 40, 228-234 (2007).

 

 

 

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