How Accurate Is ICD-10 Coding for Revision Total Knee Arthroplasty?

Roof, Mackenzie A.; Lygrisse, Katherine A.; Keitel, Lauren; Piuzzi, Nicolás S.; Chen, Antonia F.; Callaghan, John J.; Schwarzkopf, Ran; Bedard, Nicholas A.

doi:10.1016/j.arth.2021.08.021

Cited by 18 publications

(13 citation statements)

References 11 publications

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“…Measuring social support through the Risk Assessment and Prediction Tool (RAPT) presents a valuable future avenue 41. Lastly, while this retrospective study utilized ICD10 codes to acquire components of patient data, some uncertainty on accuracy of ICD10 codes has been reported that could have resulted in an underestimation of risk factors 42…”

Section: Discussionmentioning

confidence: 99%

Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator

Arora

Lituiev

Jain

et al. 2022

Spine

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Study Design. A retrospective study at a single academic institution.Objective. The purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator. Summary of Background Data. A total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record. Methods. Patients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N = 587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days). Results. Of 3678 patients analyzed, 51.4% were male (n = 1890) and 48.6% were female (n = 1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R 2 = 0.16), the predictions of poisson regression (R 2 = 0.29) and LASSO (R 2 = 0.29) models were significantly more correlated with observed LOS (P = 0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP (P = 0.135). Conclusion. The predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

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Section: Discussionmentioning

confidence: 99%

Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator

Arora

Lituiev

Jain

et al. 2022

Spine

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“…However, in a recent validation study comparing billing records against operative reports, Roof et al. found that ICD-10 procedural coding for rTKA is often imprecise [ 35 ]. Therefore, it must be acknowledged that including ICD-10 procedural codes in this study consequently introduced coding bias.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Hepatitis C on Complication Rates After Revision Total Knee Arthroplasty: A Matched Cohort Study

Ross¹,

Ross²,

Cole³

et al. 2022

Arthroplasty Today

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“…The accuracy of ICD-10 procedural coding was recently evaluated with 2 large multicenter studies of 1,906 revision TKAs and 895 revision THAs 25,26 . The results of these studies indicated that ICD-10 procedural coding was very accurate (98% to 100%) in determining that a revision procedure had occurred, but was quite inaccurate (22% to 75% accurate) in identifying which specific components (i.e., the liner, the acetabular component, or the tibial component) were truly revised during surgery 25,26 . These results highlight that administrative claims data can be powerful, but they cannot be used to answer every question, and researchers must be cautious when trying to report granular details from ICD and CPT codes.…”

Section: Pitfalls Of Coding In Databasesmentioning

confidence: 99%

“…Due to the complexity of the coding for these procedures, there is a potential for error and improperly coded revision procedures. The accuracy of ICD-10 procedural coding was recently evaluated with 2 large multicenter studies of 1,906 revision TKAs and 895 revision THAs 25,26 . The results of these studies indicated that ICD-10 procedural coding was very accurate (98% to 100%) in determining that a revision procedure had occurred, but was quite inaccurate (22% to 75% accurate) in identifying which specific components (i.e., the liner, the acetabular component, or the tibial component) were truly revised during surgery 25,26 .…”

Section: Pitfalls Of Coding In Databasesmentioning

confidence: 99%

Administrative Data Use in National Registry Efforts: Blessing or Curse?

Bedard

Katz

Losina

et al. 2022

Journal of Bone and Joint Surgery

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Big data" refers to a growing field of large database research. Administrative data, a subset of big data, includes information from insurance claims, electronic medical records, and registries that can be useful for investigating novel research questions. While its use provides salient advantages, potential researchers relying on big data would benefit from knowing about how these databases are coded, common errors they may encounter, and how to best use large data to address various research questions. In the first section of this paper, Dr. Nicholas A. Bedard addresses the four major pitfalls to avoid with diagnosis and procedure codes in administrative data. In the next section, Dr. Jeffrey N. Katz et al. focus on the strengths and limitations of administrative data, suggesting methods to mitigate these limitations. Lastly, Dr. Elena Losina et al. review the uses and misuses of large databases for cost-effectiveness research, detailing methods for careful economic evaluations. Pitfalls of Coding in DatabasesU tilization of administrative claims data for the purposes of orthopaedic research and quality assessment has increased exponentially over the past decade [1][2][3][4][5] . Administrative claims data are most often associated with large insurance-based data sets, with data derived from billing records following the delivery of health-care services. However, administrative claims data are also utilized by clinical registries, such as the American Joint Replacement Registry (AJRR), to help provide important datapoints regarding surgical procedures, patient comorbidities, and postoperative complications.Administrative claims data can be an important resource, and many studies utilizing these data have led to improvements in patient care, health-care policy, and payment reform 6,7 . However, the primary purpose of Current Procedural Terminology (CPT) and International Classification of Diseases (ICD) codes is to allow providers to notify payers of health-care services provided so that they may be reimbursed for their services. CPT and ICD codes were not initially designed for research purposes and, as such, researchers must be aware of the unique characteristics of CPT and ICD codes to avoid the pitfalls that will be encountered when utilizing claims data for orthopaedic research. These pitfalls apply not only to researchers but also to clinical registries that utilize administrative claims to populate registry datapoints. Failure to recognize these pitfalls can impact the utility of administrative claims databases or clinical registries that rely on CPT, ICD-9 (Ninth Revision), and ICD-10 (Tenth Revision) codes.Disclosure: Research reported in this publication was supported in part by grants from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS): R01 AR074290, P30 AR072577. The funding source did not play any role in designing, conducting, or reporting this analysis. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (ht...

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How Accurate Is ICD-10 Coding for Revision Total Knee Arthroplasty?

Cited by 18 publications

References 11 publications

Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator

Predictive Models for Length of Stay and Discharge Disposition in Elective Spine Surgery: Development, Validation, and Comparison to the ACS NSQIP Risk Calculator

The Impact of Hepatitis C on Complication Rates After Revision Total Knee Arthroplasty: A Matched Cohort Study

Administrative Data Use in National Registry Efforts: Blessing or Curse?

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