Operational Definition Identifying Osteoporotic Vertebral Fractures in the Claims Database

Min Heui Yu; Namki Hong; Seunghyun Lee; Ha-Young Kim; Hye-Sun Park; Sang-Min Park; Young-Kyun Lee; Tae-Young Kim; Yong-Chan Ha; Yumie Rhee; Kyung-Hoi Koo

doi:10.3346/jkms.2022.37.e249

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Yu, Hong, Lee, Kim, Park, Park, Lee, Kim, Ha, Rhee, and Koo: Operational Definition Identifying Osteoporotic Vertebral Fractures in the Claims Database

Original Article

Musculoskeletal Disorders, Rehabilitation & Sports Medicine

Journal of Korean Medical Science 2022; 37(32): e249.

Published online: 4 August 2022

DOI: https://doi.org/10.3346/jkms.2022.37.e249

Operational Definition Identifying Osteoporotic Vertebral Fractures in the Claims Database

Min Heui Yu^1,^*

, Namki Hong^1,^*

, Seunghyun Lee²

, Ha-Young Kim³

, Hye-Sun Park⁴

, Sang-Min Park⁵

, Young-Kyun Lee⁵

, Tae-Young Kim⁶

, Yong-Chan Ha^7,^†

, Yumie Rhee¹

, Kyung-Hoi Koo⁸

¹Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

²Department of Internal Medicine, Yonsei Wonju College of Medicine, Wonju, Korea.

³Department of Internal Medicine, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea.

⁴Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

⁵Department of Orthopaedic Surgery, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, Korea.

⁶Department of Orthopaedic Surgery, School of Medicine, Konkuk University, Seoul, Korea.

⁷Department of Orthopaedic Surgery, College of Medicine, Chung-Ang University, Seoul, Korea.

⁸Department of Orthopaedic Surgery, Seoul National University Bundang Hospital and Kay Joint Center at Cheil Orthopaedic Hospital, Seoul, Korea.

Address for Correspondence: Sang-Min Park, MD. Department of Orthopaedic Surgery, Seoul National University College of Medicine and Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Republic of Korea. psmini@naver.com

Address for Correspondence: Young-Kyun Lee, MD. Department of Orthopaedic Surgery, Seoul National University College of Medicine and Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Republic of Korea. ykleemd@gmail.com

Equal contributor:

^*Min Heui Yu and Namki Hong contributed equally to this study, and should be considered as co-first authors.

Current address:

^†Present address: Department of Orthopaedic Surgery, Seoul Bumin Hospital, Seoul, Korea

Received 15 June 2022 Accepted 7 July 2022

The Korean Academy of Medical Sciences

https://creativecommons.org/licenses/by-nc/4.0/

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

We analyzed the International Classification of Diseases, 10th edition (ICD-10) diagnostic codes, procedure codes, and radiographic image codes for vertebral fracture (VF) used in the database of Health Insurance Review and Assessment Service (HIRA) of Korea to establish a validated operational definition for identifying patients with osteoporotic VF in claims data.

Methods

We developed three operational definitions for detecting VFs using 9 diagnostic codes, 5 procedure codes and 4 imaging codes. Medical records and radiographs of 2,819 patients, who had primary and subordinated codes of VF between January 2016 and December 2016 at two institutions, were reviewed to detect true vertebral fractures. We evaluated the sensitivity and positive predictive value (PPV) of the operational definition in detecting true osteoporotic VF and obtained the receiver operating characteristic (ROC) curve.

Results

Among the 2,819 patients who had primary or secondary diagnosis codes for VF, 995 patients satisfied at least one of the criteria for the operational definition of osteoporotic VF. Of these patients, 594 were judged as having true fractures based on medical records and radiographic examinations. The sensitivity and PPV were 62.5 (95% confidence interval [CI], 59.4–65.6) and 59.7(95% CI, 56.6–62.8) respectively. In the receiver operating characteristic analysis, area under the curve (AUC) was 0.706 (95% CI, 0.688–0.724).

Conclusion

Graphical Abstract

Keywords: Claim Database, Vertebral Fracture, Operational Definition, Osteoporosis

INTRODUCTION

Vertebral fracture (VF) is the most common osteoporotic fracture, that occurs in 30–50% of people aged over 50 years.1 2 3 4 5 Patients with VF experience marked impairment in mobility and increased mortality; this impairs the patients’ quality-of-life and poses a serious socioeconomic burden on the healthcare system, especially among older adults.4 6 7 8 9 10 VF can present with non-specific symptoms or be asymptomatic, thus making clinical detection difficult. The diagnosis of VFs requires spine imaging by lateral radiography. However, it may also be diagnosed by chance on an X-ray requested for other reasons.

Recently, large-scale medical databases have been used for big data research worldwide.5 6 11 12 13 Medical databases are powerful tools that support clinical and epidemiologic studies of disease burden and treatment outcomes.14 15 16 17 18 19 20 21 22 Specifically, a claims database can provide large-scale nationwide information for analysis while also preventing selection bias.14 15 17 23 24 When properly used, this information allows researchers to simulate predictive scenarios through the use of artificial intelligence such as machine learning and deep learning.25 26 However, claims databases were established for financial reimbursement and not medical research; therefore, analysis using claims data may be limited due to potential lack of clinical information and possibility of coding errors.5 20 27 28 29 30 31 32

The lack of details on individual cases, such as VF injury mechanism (i.e., trauma type, osteoporosis), radiographs, and bone mineral density results,20 28 29 poses significant limitations on conclusions that may be drawn through analysis using claims database. Among previous studies, osteoporotic VF has had varied definitions when using large-scale medical claim data; there is no standard operational definition for osteoporotic VFs.33 34 35 Therefore, researchers should develop algorithms to identify osteoporotic VF prior to the study to reliably validate their operational definitions.23 36 37 For this reason, establishing and validating the operational definition of osteoporotic VF is necessary, especially when conducting research using national health claim data.

Here, we analyzed the International Classification of Diseases, 10th edition (ICD-10) diagnostic codes, procedure codes, and radiographic image codes for VF used in the database of Health Insurance Review and Assessment Service (HIRA) of South Korea to establish a validated operational definition for identifying patients with osteoporotic VF in claims data.

METHODS

Development of operational definition to identify osteoporotic VFs

Claims data submitted by our hospital to the national health insurance service were used in this study. The dataset contains demographic data, including age and sex; physician information; and hospital-related comorbidities, as described by the diagnostic codes.

Three of the authors (SMP, a spine surgery specialist with 7 years of experience; YKL, a hip surgery and osteoporosis specialist with 13 years of experience; and TYK, a hip surgery and osteoporosis specialist with 16 years of experience) collaboratively developed three criteria using 9 diagnostic codes (M48.4, M48.5, M49.5, M80.8, S22.0, S22.1, S32.0, S32.7, T08.0), 5 procedure codes (N0471, N0472, N0473, N0474, N0630) and 4 imaging codes (G430, G440, G450, G460) of the ICD-10 code (Table 1).18 33 35 Meeting any one of the three criteria was indicative of a VF. If the patient had multiple claims for VF, only the first claim was included. We excluded patients with conditions that were considered high-impact traumas (multiple fractures).

Table 1

Codes for operational definition of osteoporotic vertebral fractures

Code		Description
Diagnostic code
	M48.4	Fatigue fracture of vertebra
	M48.5	Collapsed vertebra, not elsewhere classified
	M49.5	Collapsed vertebra in diseases classified elsewhere
	M80.8	Other osteoporosis with pathological fracture
	S22.0	Fracture of thoracic vertebra
	S22.1	Multiple fractures of thoracic spine
	S32.0	Fracture of lumbar vertebra
	S32.7	Multiple fractures of lumbar spine and pelvis
	T08.0	Fracture of spine, level unspecified
Procedure code
	N0471	Percutaneous vertebroplasty – 1 level
	N0472	Percutaneous vertebroplasty – multi-level
	N0473	Percutaneous kyphoplasty – 1 level
	N0474	Percutaneous kyphoplasty – multi-level
	N0630	Closed reduction of vertebral fracture
Imaging procedure codes
	G430	Radiograph of thoracic vertebra
	G440	Radiograph of thoracolumbar vertebra
	G450	Radiograph of lumbar vertebra
	G460	Radiograph of lumbosacral vertebra

Criterion 1 was a combination of 9 diagnostic codes and 5 procedure codes. The date of procedure coding was defined as the index date. A VF diagnostic code should have been coded within 1 month before the procedure codes.

Criterion 2 indicated an admission with 9 diagnostic codes as primary diagnosis. Date of the admission was defined as the index date.

Criterion 3 was a combination of 9 diagnostic codes as primary diagnosis and 4 imaging procedure codes irrespective of admission of the patient. The coding date of imaging was defined as the index date. The diagnosis of VF should have been coded as primary diagnosis within 1 month before or after the coding of imaging procedure.

Evaluation of the criterion-related validity

Two evaluators (NH, an endocrinology specialist with 6 years of experience and SL, an endocrinology specialist with 3 years of experience) independently reviewed the medical records and radiographs of 2,819 patients who had at least one of the diagnostic codes for VF as primary or secondary diagnosis at the outpatient clinic or at admission between 1 January 2016 and 31 December 2016 in our tertiary institutions. Patients were diagnosed with incident osteoporotic VF when 1) radiographs showed a decrease in vertebral height > 25% according to semiquantitative Genant method, 2) the patient had developed acute back pain within the most recent 3 months, and 3) patients had no history of major trauma or falls greater than 2-meter based on medical records.38 Any disparity in the diagnosis was resolved by adjudication of third reviewer (YR, an endocrinology specialist with 20 years of experience).

Statistical analysis

We evaluated the sensitivity and positive predictive value (PPV) of the operational definition in detecting true osteoporotic VF and obtained the receiver operating characteristic (ROC) curve. All analyses were performed using STATA v16.0 (Stata Corp., College Station, TX, USA).

Ethics statement

The Institutional Review Board (IRB) of our hospital approved this study (IRB number: 4-2021-1531). The requirement for informed consent was waived by the review board due to retrospective nature of the study design.

RESULTS

Demographic data

Among the 2,819 patients who had primary or secondary diagnosis codes for VF, 995 patients satisfied at least one of the criteria for the operational definition of osteoporotic VF. There was a female preponderance (651/955, 65.43%), and the mean patient age was 65.27 ± 16.71 years (Table 2). Of these patients, 594 were judged as having true fractures based on medical records and radiographic examinations; furthermore, individuals who satisfied operational definition for osteoporotic VF had higher prevalence of prior VF compared to those without.

Table 2

Patient demographics and clinical characteristics of the study population

Characteristics	Total (N = 2,819)	Incident VF by operational definition (n = 995)	Non-VF by operational definition (n = 1,824)	P value
Age, yr	65.40 ± 16.76	65.27 ± 16.71	65.48 ± 16.80	0.747
Sex, female	1871 (66.37)	651 (65.43)	1,220 (66.89)	0.433
Prior diagnosis codes of vertebral fracture	1,121 (39.8)	440 (44.22)	681 (37.34)	< 0.001
CKD	172 (6.10)	60 (6.03)	112 (6.14)	0.907
COPD	67 (2.38)	25 (2.51)	42 (2.30)	0.727
Heart failure	109 (3.87)	32 (3.22)	77 (4.22)	0.186
RA	58 (2.06)	16 (1.61)	42 (2.30)	0.214
Diabetes	457 (16.21)	135 (13.57)	322 (17.65)	0.005
Hypertension	985 (34.94)	293 (29.45)	692 (37.94)	< 0.001
Atrial fibrillation	144 (6.85)	36 (6.70)	78 (6.92)	0.870
Any malignancy	290 (10.29)	88 (8.84)	202 (11.07)	0.063
True incident VF cases	950 (33.66)	594 (59.70)	356 (19.49)	< 0.001

Numeric parameters are expressed as mean ± standard deviation in parentheses. Categorical parameters are expressed as counts and percentages in parentheses.

CKD = chronic kidney disease, COPD = chronic obstructive pulmonary disease, RA = rheumatoid arthritis, VF = vertebral fracture.

Validity of the 3 operational definitions

Criteria 1 and 2 identified 5.05% and 1.01% of the patients with VF, respectively, while criterion 3 identified 53.20% of patients with VF (316/594) (Fig. 1).

Fig. 1

Proportion of criteria for vertebral fracture.

The sensitivity and PPV of the operational definition of incident VF were 62.5 (95% confidence interval [CI], 59.4–65.6) and 59.7 (95% CI, 56.6–62.8) respectively (Table 3). Area under the ROC curve for operational definition was 0.706 (95% CI, 0.688–0.724).

Table 3

Diagnostic performance of operational definitions for VF

No. of VF case	No. of TP	No. of FN	No. of FP	No. of TN	Sensitivity, % (95% CI)	PPV, % (95% CI)	AUC (95% CI)
950	594	356	401	1,471	62.5 (59.4–65.6)	59.7 (56.6–62.8)	0.706 (0.688–0.724)

VF = vertebral fracture, TP = true positive, FN = false negative, FP = false positive, TN = true negative, PPV = positive predictive value, AUC = area under cover.

DISCUSSION

This study demonstrated the diagnostic validity of three operational definitions of VFs that were developed to identify osteoporosis-related VFs from the HIRA claims database. The VF operational definitions were based on a combination of diagnosis, procedure and imaging codes from the medical records. And we demonstrated theses operational definitions based on medical records and radiographs. Our operational definition identified patients with osteoporosis-related VF with a high accuracy by combining diagnosis, procedure, and imaging codes.

In our country, the diagnosis code of claim data is assigned according to the importance of treatment or examination during the treatment period. A primary diagnosis is the condition that consumes the most medical resources, especially for newly diagnosed diseases. Secondary, or subordinated, diagnostic codes imply the use of fewer medical resources than those needed for primary diagnoses. For this reason, the order of diagnostic codes is often used as a criterion for developing operational definitions using claims data,39 40 and classifying the codes as primary or secondary. Additionally, imaging and procedure codes were used along with diagnostic codes to increase the accuracy of the operational definitions.35 Here, we analyzed the operational definition developed using these codes.

To date, various operational definitions have been proposed for the identification of patients with osteoporotic fractures including VF, hip fracture and distal radius fracture.35 40 41 Unlike other osteoporotic fractures,40 41 identification of patients with osteoporosis-related VFs in claims data is challenging because the claims database does not include data on injury mechanism or bone mineral density. Moreover, the patient’s condition often improves with simple conservative treatments, such as prescription pain relievers, precluding the need for specialty treatments such as vertebroplasty or fusion surgery. Moreover, multiple diagnostic codes assigned during follow-up evaluations after the VF are problematic in accurate diagnostic identification though medical records.

Most osteoporotic VFs are treated conservatively, and there is a wide variation in the therapeutic options used and treatment lengths followed by different physicians. Therefore, previous claims database studies, which mainly used diagnosis codes and/or procedure codes, reported inconsistent incidence rates of osteoporotic VFs.18 28 35 Thus, diagnostic, imaging, and procedural codes should be combined to enable accurate identification of cases of osteoporotic VF. Furthermore, prior medical history of VF leads to further inconsistency between the true VF rate and the rate of VFs detected by currently used operational definitions; in the currently available literature, most (99.9%) patients who did not have incident VF but had primary or subordinated codes of VF had a previous history of VF (Table 1). In a previous study, the PPV of incident VF using only the diagnostic code was relatively low by 46%; however, it slightly increased to 61% in this study owing to the inclusion of imaging codes.39

There were some limitations to this study. First, we sourced our data from a tertiary referral center, and a substantial number of the selected patients had comorbidities. If patient populations from other institutions were studied, the results could be inconsistent. Therefore, a multi-institutional study that combines a larger population data set from various databases should be conducted in the future to corroborate our results. Second, our operational definitions cannot be generalized to other countries that use different coding systems. Hence, although further validation is needed to translate our approach to other coding systems and study the populations of different countries, our results may serve as a foundation for such studies. Third, the PPV of our operational definition for VF was lower than those reported for hip fracture (59.1–77.4%)41 and wrist fracture (95.6–98.2%).40 Most patients with VF are treated conservatively and visit the outpatient clinic several times during the treatment course; this unique clinical feature may account for this difference.

Our findings demonstrate the validity of our operational definitions to identify VFs more accurately using claims data. This algorithm to identify VF is likely to be useful in future studies for diagnosing osteoporotic VF. Although the use of three operational definitions could misclassify incident VF, these results may be useful for future observational studies. Additional validation studies that can reduce the effect of confounding factors with improved algorithms and larger datasets should be conducted to corroborate and expand on our findings.

Notes

Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI18C0284).

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Kim HY, Park SM, Lee YK, Kim TY, Ha YC.
Data curation: Lee S, Park HS.
Formal analysis: Yu MH, Hong N, Rhee Y.
Funding acquisition: Lee YK, Koo KH.
Investigation: Yu MH, Hong N, Rhee Y.
Methodology: Kim HY, Park SM, Lee YK, Kim TY, Ha YC.
Software: Yu MH, Hong N.
Validation: Yu MH, Hong N, Rhee Y.
Visualization: Yu MH, Hong N, Rhee Y.
Writing – original draft: Yu MH, Hong N.
Writing – review & editing: Park SM, Lee YK.

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