Physician assessment of disease activity in JIA subtypes

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Research

Physician assessment of disease activity in JIA subtypes. Analysis of data

extracted from electronic medical records

The electronic version of this article is the complete one and can be found

online at: http://www.ped-rheum.com/content/9/1/9

Pediatric Rheumatology 2011, 9:9doi:10.1186/1546-0096-9-9

Published: 14 April 2011

Abstract

Objective

Although electronic medical records (EMRs) have facilitated care for

children with juvenile idiopathic arthritis (JIA), analyses of treatment

outcomes have required paper based or manually re-entered data. We have

started EMR discrete data entry for JIA patient visits, including joint

examination and global assessment, by physician and patient. In this

preliminary study, we extracted data from the EMR to Xenobaseâ„¢ (TransMed

Systems, Inc., Cupertino, CA), an application permitting cohort analyses of

the relationship between global assessment to joint examination and subtype.

Methods

During clinic visits, data were entered into discrete fields in ambulatory

visit forms in the EMR (EpicCareâ„¢, Epic Systems, Verona, WI). Data were

extracted using Clarity Reports, then de-identified and uploaded for

analyses to Xenobaseâ„¢. Parameters included joint examination, ILAR

diagnostic classification, physician global assessment, patient global

assessment, and patient pain score. Data for a single visit for each of 160

patients over a 2 month period, beginning March, 2010, were analyzed.

Results

In systemic JIA patients, strong correlations for physician global

assessment were found with pain score, joint count and patient assessment.

In contrast, physician assessment for patients with persistent

oligoarticular and rheumatoid factor negative patients showed strong

correlation with joint counts, but only moderate correlation with pain

scores and patient global assessment. Conversely, for enthesitis patients,

physician assessment correlated strongly with pain scores, and moderately

with joint count and patient global assessment. Rheumatoid factor positive

patients, the smallest group studied, showed moderate correlation for all

three measures. Patient global assessment for systemic patients showed

strong correlations with pain scores and joint count, similar to data for

physician assessment. For polyarticular and enthesitis patients, correlation

of patient global assessment with pain scores was strong. Moderate

correlations were found between patient global assessment and joint count in

oligoarticular and polyarticular patients.

Conclusion

Data extraction from the EMR is feasible and useful to evaluate JIA patients

for indicators of treatment responsiveness. In this pilot study, we found

correlates for physician global assessment of arthritis differed, according

to disease subtype. Further data extraction and analyses will determine if

these findings can be confirmed, and will assess other outcome measures,

compare longitudinal responses to treatment, and export extracted data to

multi-center databases.

Introduction

Juvenile idiopathic arthritis (JIA) is characterized by joint inflammation

with onset at or before sixteen years of age [1]. Recent studies of biologic

agents [2,3], have relied upon a core set of outcome measures, including

components of physical examination, physician and patient global assessment,

and pain scores [4]. Other disease status measures validated to measure

remission [5] and define minimally active disease [6] have found that,

despite current treatment, many children, including those with initial

response, have disease recurrences, measured by remission criteria [7].

Physician global assessment is an important component of all these outcome

measures, distinct from joint counts, patient pain assessment, and other

components. Physician and patient global assessments have been used to

define minimal disease activity [6]. Studies have not determined the extent

to which physician global assessment correlates with joint counts or pain

assessments, depending upon JIA subtype. The advent of the electronic

medical record (EMR) permits data extraction to address this type of

question more efficiently than permitted by a study of paper based records.

Since 2008, we have used an EMR to document all outpatient visits, resulting

in more rapid and better organized access to medical information. We

recently incorporated the ability to enter discrete data for all visits of

children with juvenile idiopathic arthritis. In addition to extracting data

in preparation for participation in multi-center studies, we have started to

analyze extracted data concerning clinical status and assessment of JIA

patients. In this preliminary study, we used EMR data extraction to study

the relationship of physician global assessment to patient global assessment

and pain scores in patients with different JIA subtypes.

Patients and methods

Patients

We studied data for all patients meeting ILAR diagnostic criteria for

juvenile idiopathic arthritis [8] seen by M.M. and M.K.G. at Children's

Memorial Hospital pediatric rheumatology clinics for 2 months starting March

2010. Patients in the Undifferentiated arthritis category were not included

because of heterogeneous characteristics of these patients. In the other

ILAR subtypes, 160 patients were seen.

Demographic and disease characteristics

Age data (birth date, date of each visit) were extracted. Additional

extractable disease characteristics consisted of diagnostic subtype, joint

count (active joints, as defined in reference [4]), pain score, physician

and patient global assessment. ANA, rheumatoid factor (RF), and B27 status

had been tested within the first 4 months of diagnosis and available (as

noted in parentheses) for the subgroups as follows: 18 systemic patients (18

ANA, 12 RF, 0 B27); 63 oligoarticular persistent patients (59 ANA, 43 RF, 21

B27); 6 oligoarticular extended patients (6 ANA, 5 RF, 2 B27); 40 RF

negative polyarticular patients (38 ANA, 35 RF, 15 B27); 12 RF positive

polyarticular patients (12 ANA, 12 RF, 6 B27); 3 psoriatic arthritis

patients (3 ANA, 3 RF, 2 B27), 18 enthesitis patients (18 ANA, 15 RF, 15

B27). Per cent positive for each test is expressed as per cent of all

patients in the subgroup.

Physician and patient assessment

Physician global assessment of overall disease activity [4] used a scale

from 0 to 10. Patient pain was rated on a Likert scale (with 0 = no pain and

10 = very severe pain) in response to the question " By giving a number

between 0 and 10, with 0 being no pain and 10 being the worst possible pain,

how much pain on average have you experienced from your arthritis over the

past week? " Patient global assessment was rated on a Likert scale (with 0 =

doing very poorly and 10 = doing very well) in response to the question " By

giving a number between 0 and 10, with 0 being doing very poorly and 10

being doing very well, how have you experienced your arthritis in general

over the past week? Include not only pain, but also how you feel about your

arthritis, how having arthritis affects your getting along with family and

friends, and how well you can move around. " Of the 160 patients, 132 (83%)

self reported pain and global assessments (13.0 ± 4.0 years). Of the other

28 patients (6.3 ± 2.7 years), mothers of 26 patients reported, and fathers

reported for 2 patients.

Data Entry into the Electronic Medical Record

Since July 2008, all patient visits have been documented in an EMR

(EpicCareâ„¢, Epic Systems, Verona, WI). Starting in 2010, a discrete data

structure (called flow sheet rows in the Epic EMR) for JIA patients, named

RHE modules, for JIA patients has been incorporated into the EMR, based upon

the same data entry structure in use at Cincinnati Children's Hospital. All

patients with JIA have disease subtype (Figure 1), joint examination and

related clinical data entered as discrete data into these flow sheet rows,

as part of routine care for each outpatient encounter. During the period of

study, 3.75% of assessments were incomplete, reflecting adjustment of

physican work flows to the new data entry method.

Figure 1. Appearance of the flow sheet rows for diagnosis within the

EpicCareâ„¢ EMR. Entry of ILAR diagnosis is more accurate than can be

accommodated by the ICD-9 system, implemented primarily for billing

purposes. The ICD-9 system, for example, has no specific code for systemic

JIA.

Data Extraction from the Electronic Medical Record

A System Development Lifecycle process was established by the Department of

Information Technology, Children's Memorial Research Center for data

extraction (Figure 2). Once flow sheet rows were established, an Extract,

Transform, Load (ETL) procedure employed data queries that extracted

relevant data, which was then de-identified prior to uploading to Xenobaseâ„¢.

Data is extracted each month for all RHE modules.

Figure 2. System Development Lifecycle. See Methods for explanation.

Development initially affected appearance of the ambulatory EMR by adding

forms for entry of specific values for the joint examination, physician and

patient global assessment, and patient pain score. The ETL (extract,

transform, load) process was implemented external to both the EMR source and

analytic software target.

Xenobase

Data was analyzed using the XenoBase-BioIntegration Suite (Xenobaseâ„¢), an

application residing in servers maintained by the Xenobase team, CMRC.

Xenobase, developed in the Program of Translational Medicine at the Van

Andel Research Institute, and available commercially (TransMed Systems,

Inc., Cupertino, CA), provides a common interface for consolidating

disparate clinical, preclinical and molecular data; it provides statistical

and graphical data analytic functions (Figure 3). To provide patient

privacy, Xenobase uses an offset of up to 45 days applied to all uploaded

patient dates (including birthdates). With IRB approval, this offset was

re-identified, permitting retrieval of legacy paper based data (dates of

onset of symptoms and diagnosis for patients whose first visits antedated

EMR usage) and data quality validation.

Figure 3. Appearance of a data query window within the Xenobaseâ„¢ analytic

software. Data queries, based upon JIA subtype, can be carried out on

uploaded data extracted from the EMR.

Statistical methods

Descriptive statistics are presented as median (minimum, maximum) for

continuous variables or frequency (percentage) for categorical variables. We

used Spearmann Rank correlation coefficients to measure pair-wised

correlations among physician assessment and patient pain score, global

assessments, and joint counts for all subtypes except extended

oligoarticular and psoriatic arthritis, due to the small numbers of patients

in those groups. Correlations were considered strong for values >0.7,

moderate for 0.4 - 0.7, and weak for <0.4. All statistical analyses were

performed using SAS® version 9.2 (SAS Institute, Inc., Cary, NC).

Results

Patients

We analyzed data extracted from 160 patients (Table 1). Racial and ethnic

group distributions (Table 1) were notable for the paucity of African

American and Asian patients seen with arthritis, relative to their

representation among outpatients seen in specialty clinics at Children's

Memorial Hospital (data not shown). In addition, Hispanic patients tended to

have either oligoarticular or polyarticular disease, more than other

subtypes.

Table 1. Demographic and disease characteristics

Half of children with enthesitis related arthritis were ANA positive. Few

patients with non-polyarticular disease were rheumatoid factor positive. One

third of enthesitis related arthritis patients were B27 positive. Although

no discernible differences among the subtypes were found for median scores

for physician global assessment, patient global assessment, or patient pain

score (Table 2), relationships between physician assessment and other

parameters were found for particular disease subtypes, as noted below.

Table 2. Values of outcome measures

Physician Global Assessment in JIA subtypes

Correlation of physician assessment with other outcome measures varied with

JIA subtype (Table 3). In systemic JIA patients, strong correlations were

found for pain scores, joint count and patient assessment. In contrast,

physician assessment for patients with persistent oligoarticular and

rheumatoid factor negative patients showed strong correlation with joint

counts, but moderate correlation with pain scores and patient global

assessment. Conversely, for enthesitis patients, physician global assessment

correlated strongly with pain scores, and moderately with joint count (and,

similarly to persistent oligoarticular and rheumatoid factor negative

polyarticular patients, moderately with patient global assessment).

Rheumatoid factor positive patients, the smallest group studied, showed only

moderate correlation for all three measures.

Table 3. Spearman's correlations between Physician Global Assessment and

other outcome measures in JIA subtypes

Patient Global Assessment in JIA subtypes

Correlation of patient assessment with pain scores and joint counts also

varied with JIA subtype (Table 4). For systemic patients, patient assessment

showed strong correlations with pain scores and joint count, similar to data

for physician assessment. For polyarticular and enthesitis patients,

correlation of patient global assessment with pain scores was strong.

Moderate correlations were found between patient global assessment and joint

count in oligoarticular and polyarticular patients.

Table 4. Spearman's correlations Patient Global Assessment and other outcome

measures in JIA subtypes

Discussion

The aim of this study was to determine the extent to which physician global

assessment correlates with pain and joint count for JIA patients with

different subtypes, using data extracted from the electronic medical record.

We found the extraction process efficient, permitting a study too time

consuming to perform in our center, if data were only available from paper

records. Distribution of JIA subtypes was similar to that previously

reported [9], except for fewer numbers of psoriatic arthritis patients,

perhaps because of different ethnic distributions or shorter period of time

for data collection in our study. Half of our enthesitis patients were ANA

positive, possibly a result of small sample size.

Validating data was necessary to ensure data extraction yielded identical

target and source data. For example, we found incorrect EMR formatting

caused some data appear to be absent, when physician or patient global

assessment was entered as zero. This was easily corrected, but would have

been missed had there not been a validation process. When dates of disease

onset and diagnosis were prior to EMR implementation, requiring data

extraction from paper records, we used at least two inspections of the data.

Although prospective studies eventually will not derive data from paper

records, data validation from EMR sources and targets will always be

necessary, if only because necessary EMR software upgrades have potential to

alter data flow from source to target applications. Re-identification of

de-identified data is also critical for data validation in research using

data extracted from EMRs. To verify that data contents and formats have been

maintained from the moment of data entry into the EMR to the time of data

uploading to the data target, cross checking against data from several

re-identified patients is best carried out by visual comparisons by the

investigator.

In the current study, we found physician global assessment of persistent

oligoarticular and rheumatoid factor negative polyarticular patients

strongly correlated with joint count but moderately with patient pain scores

and global assessment (Table 3). However, patient assessments showed higher

correlation with pain scores than joint counts in polyarticular patients

(Table 4), raising the possibility that these patients factored pain more

than extent of arthritis into their global assessments. In contrast to these

JIA subtypes, only for systemic JIA patients were there strong correlations

between physician global assessments and all the other outcome measures

tested (Table 3). This possibly reflects differences in disease

characteristics in this subtype compared to others, although low joint

counts in this group probably accounted for the high correlation

coefficients between physician and patient assessments.

In contrast to oligoarticular and rheumatoid factor negative patients, in

enthesitis patients we found a strong correlation between physician global

assessment and pain score, rather than joint count. Most likely, enthesitis

contributed to these correlations. A quantitative measure of enthesitis on

physical examination, that might correlate with physician and patient

assessments, is lacking, perhaps because of difficulties inherent in

validation. Correlations between pain scores and joint counts in all JIA

subtypes tended to be lower than other correlations (data not shown),

possibly because pain assessment during clinic visits can be insensitive,

suggested by a study using daily pain diaries [10].

Physician global assessment, while a subjective interpretation of patient

status, has been used as a component of outcome scores, validated for drug

studies [4] and JIA remission criteria [11]. Few investigators have explored

its relationship to other outcome score components. In one study with

findings similar to ours, Berntson et al found a strong relationship with

joint count [12] in 312 Scandinavian patients with all JIA subtypes,

excluding systemic onset. This study did not compare physician global

assessment to patient global assessment or pain scores, as it addressed the

role of joint size in physician assessment.

We found no differences among JIA subtypes for data distributions of

physician or patient global assessments, or for joint count, perhaps because

of small sample sizes and large numbers of patients with zero joint counts.

Findings from this preliminary study need to be confirmed with studies of

larger numbers of patients, which may identify differences among subgroups.

We did not characterize whether physician assessment correlated to

medication status; multi-center longitudinal studies may be better able to

address this question. Finally, although it is possible that physician

assessment varied, when we analyzed data from patients by physician, no such

variability was found.

Ours is one of the first studies of JIA patients to use the EMR as a source

of data, which can be used for quality improvement studies [13], as has been

used for adults with arthritis [14]. While quality studies for children with

arthritis using EMR derived data remain to be published [15], the Child

Arthritis and Rheumatology Research Alliance is establishing registries for

childhood rheumatic diseases, based upon widespread interest and

participation of pediatric rheumatologists [16,17].

In conclusion, we have established extractable clinical data in electronic

medical records for the purpose of monitoring the status of JIA patients. In

this preliminary study, we found correlates of physician assessment vary

with disease subtype. We anticipate adding other response measures and using

extracted data to contribute to multi-center national registries.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MLM contributed to study conception and design, entered data in the EMR, had

full access to all data, and is responsible for data integrity and analysis.

MKG contributed to study design, entered data in the EMR, and contributed to

manuscript drafts and revision. JR, GL, and SM carried out implementation of

the data extraction and uploading process. DW and YZ carried out all

statistical analyses. All authors were involved in drafting and approving

the final version of the manuscript.

Acknowledgements

For their assistance in implementing discrete data structures in the EMR, we

thank our colleagues in Information Management, Children's Memorial

Hospital, Arnold Butiu and Lattice Bell. For sharing information on data

entry structure and operational assistance, we thank Ting, MD,

Lovell, MD, MPH, both of the Division of Rheumatology, and Spooner,

MD, Chief Medical Information Officer, all of Cincinnati Children's

Hospital.

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