Objective: Early readmission following total hip arthroplasty (THA) is not uncommon and impacts patient outcomes and healthcare costs. However, easily accessible biomarkers for early identification of high-risk patients remain limited. This study aims to evaluate the association between various blood component-derived ratios and 14-day readmission after THA.
Methods: Data from the Chang Gung Medical Research Database (CGRD) from 2014 to 2022 were retrospectively analyzed. Patients ≥ 20 years old who underwent primary THA by a single surgeon were included. The primary outcome was 14-day readmission. Five hematologic markers were evaluated: monocyte-to-albumin ratio (MAR), red cell distribution width (RDW)-to-albumin ratio (RAR), hemoglobin-to-albumin ratio (HAR), leukocyte-to-albumin ratio (LAR), and RDW-to-platelet ratio (RPR). Ratios were calculated from blood collected within 1 month before to 1 week after surgery. Receiver operating characteristic (ROC) Curve analysis was used to determine their optimal thresholds, and multivariable logistic regression assessed associations between these markers and readmission risk.
Results: A total of 307 patients were included in the analysis. Among the ratios evaluated, only high RPR (≥ 0.10; aOR = 5.92, 95% CI: 2.19–16.00, p = 0.001) was significantly associated with increased risk of 14-day readmission after adjustment in the multivariable analysis.
Conclusion: RPR is independently associated with 14-day readmission following THA in this exploratory study. As an easily obtainable marker, it may aid postoperative risk stratification, and the findings provide a foundation for future multicenter prospective investigations incorporating more granular perioperative factors and additional biomarkers before clinical application.
| [1] |
T. S. Karachalios, A. A. Koutalos, and G. A. Komnos, “Total Hip Arthroplasty in Patients With Osteoporosis,” Hip International 30 (2020): 370–379.
|
| [2] |
I. Shichman, M. Roof, N. Askew, et al., “Projections and Epidemiology of Primary Hip and Knee Arthroplasty in Medicare Patients to 2040-2060,” JB & JS Open Access 8 (2023): e22.00112.
|
| [3] |
K. K. Tung, Y. H. Lee, C. C. Lin, C. H. Lee, M. C. Lin, and J. C. C. Wei, “Opposing Trends in Total Knee and Hip Arthroplasties for Patients With Rheumatoid Arthritis vs. the General Population-A 14-Year Retrospective Study in Taiwan,” Frontiers in Medicine 8 (2021): 640275.
|
| [4] |
L. Yan, L. Ge, S. Dong, et al., “Evaluation of Comparative Efficacy and Safety of Surgical Approaches for Total Hip Arthroplasty: A Systematic Review and Network Meta-Analysis,” JAMA Network Open 6 (2023): e2253942.
|
| [5] |
R. Schwarzkopf, O. A. Behery, H. Yu, L. G. Suter, L. Li, and L. I. Horwitz, “Patterns and Costs of 90-Day Readmission for Surgical and Medical Complications Following Total Hip and Knee Arthroplasty,” Journal of Arthroplasty 34 (2019): 2304–2307.
|
| [6] |
F. Rezaeitalab, M. Esmaeili, A. Saberi, et al., “Predictive Value of Inflammatory Markers for Functional Outcomes in Patients With Ischemic Stroke,” Current Journal of Neurology 19 (2020): 47–52.
|
| [7] |
J. Watson, C. Salisbury, J. Banks, P. Whiting, and W. Hamilton, “Predictive Value of Inflammatory Markers for Cancer Diagnosis in Primary Care: A Prospective Cohort Study Using Electronic Health Records,” British Journal of Cancer 120 (2019): 1045–1051.
|
| [8] |
C. L. Zhang, X. C. Jiang, Y. Li, et al., “Independent Predictive Value of Blood Inflammatory Composite Markers in Ovarian Cancer: Recent Clinical Evidence and Perspective Focusing on NLR and PLR,” Journal of Ovarian Research 16 (2023): 36.
|
| [9] |
B. Chen, B. Ye, J. Zhang, L. Ying, and Y. Chen, “RDW to Platelet Ratio: A Novel Noninvasive Index for Predicting Hepatic Fibrosis and Cirrhosis in Chronic Hepatitis B,” PLoS One 8 (2013): e68780.
|
| [10] |
B. Z. Eraslan, S. K. Cengiz, S. B. Sagmen, K. Z. Yalçınkaya, N. Kıral, and S. Cömert, “The Importance of the Erythrocyte Distribution Width/Albumin Ratio in Patients With Chronic Obstructive Pulmonary Disease Exacerbation,” Saudi Medical Journal 45 (2024): 27–33.
|
| [11] |
C. G. Hu, B. E. Hu, J. F. Zhu, Z. M. Zhu, and C. Huang, “Prognostic Significance of the Preoperative Hemoglobin to Albumin Ratio for the Short-Term Survival of Gastric Cancer Patients,” World Journal of Gastrointestinal Surgery 14 (2022): 580–593.
|
| [12] |
F. Y. N. Lessomo, Q. Fan, Z. Q. Wang, and C. Mukuka, “The Relationship Between Leukocyte to Albumin Ratio and Atrial Fibrillation Severity,” BMC Cardiovascular Disorders 23 (2023): 67.
|
| [13] |
E. Rahamim, D. R. Zwas, A. Keren, et al., “The Ratio of Hemoglobin to Red Cell Distribution Width: A Strong Predictor of Clinical Outcome in Patients With Heart Failure,” Journal of Clinical Medicine 11 (2022): 886.
|
| [14] |
B. Yilmaz, E. Somay, E. Topkan, A. Kucuk, B. Pehlivan, and U. Selek, “The Predictive Value of Pretreatment Hemoglobin-to-Platelet Ratio on Osteoradionecrosis Incidence Rates of Locally Advanced Nasopharyngeal Cancer Patients Managed With Concurrent Chemoradiotherapy,” BMC Oral Health 23 (2023): 231.
|
| [15] |
B. Zhai, J. Chen, J. Wu, et al., “Predictive Value of the Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Lymphocyte-To-Monocyte Ratio (LMR) in Patients With Non-Small Cell Lung Cancer After Radical Lung Cancer Surgery,” Annals of Translational Medicine 9 (2021): 976.
|
| [16] |
Z. L. Zhang, Q. Q. Guo, J. N. Tang, et al., “Monocyte-to-Albumin Ratio as a Novel Predictor of Long-Term Adverse Outcomes in Patients After Percutaneous Coronary Intervention,” Bioscience Reports 41 (2021): BSR20210154.
|
| [17] |
Y. T. Huang, Y. J. Chen, S. H. Chang, C. F. Kuo, and M. H. Chen, “Discharge Status Validation of the Chang Gung Research Database in Taiwan,” Biomedical Journal 45 (2022): 907–913.
|
| [18] |
S. C. Shao, Y. Y. Chan, Y. H. Kao Yang, et al., “The Chang Gung Research Database-A Multi-Institutional Electronic Medical Records Database for Real-World Epidemiological Studies in Taiwan,” Pharmacoepidemiology and Drug Safety 28 (2019): 593–600.
|
| [19] |
G. L. Salvagno, F. Sanchis-Gomar, A. Picanza, and G. Lippi, “Red Blood Cell Distribution Width: A Simple Parameter With Multiple Clinical Applications,” Critical Reviews in Clinical Laboratory Sciences 52, no. 2 (2015): 86–105.
|
| [20] |
L. Hu, M. Li, Y. Ding, et al., “Prognostic Value of RDW in Cancers: A Systematic Review and Meta-Analysis,” Oncotarget 8, no. 9 (2017): 16027–16035.
|
| [21] |
R. L. Gauer and D. J. Whitaker, “Thrombocytopenia: Evaluation and Management,” American Family Physician 106, no. 3 (2022): 288–298.
|
| [22] |
L. Rivadeneyra, H. Falet, and K. M. Hoffmeister, “Circulating Platelet Count and Glycans,” Current Opinion in Hematology 28, no. 6 (2021): 431–437.
|
| [23] |
A. M. Ali, M. D. Loeffler, P. Aylin, and A. Bottle, “Factors Associated With 30-Day Readmission After Primary Total Hip Arthroplasty: Analysis of 514 455 Procedures in the UK National Health Service,” JAMA Surgery 152 (2017): e173949.
|
| [24] |
C. K. Cantrell, H. A. DeBell, E. J. Lehtonen, et al., “Risk Factors for Readmission Within Thirty Days Following Revision Total Hip Arthroplasty,” Journal of Clinical Orthopaedics and Trauma 11 (2020): 38–42.
|
| [25] |
H. Yao, L. Lian, R. Zheng, and C. Chen, “Red Blood Cell Distribution Width/Platelet Ratio on Admission as a Predictor for In-Hospital Mortality in Patients With Acute Myocardial Infarction: A Retrospective Analysis From MIMIC-IV Database,” BMC Anesthesiology 23 (2023): 113.
|
| [26] |
N. Xu and C. Peng, “Association Between Red Cell Distribution Width-To-Platelet Ratio and Short-Term and Long-Term Mortality Risk in Patients With Acute Ischemic Stroke,” BMC Neurology 23 (2023): 191.
|
| [27] |
Y. Zhou, L. Zhong, W. Chen, F. Liang, Y. Liao, and Y. Zhong, “Enhanced Red Blood Cell Distribution Width to Platelet Ratio Is a Predictor of Mortality in Patients With Sepsis: A Propensity Score Matching Analysis Based on the MIMIC-IV Database,” BMJ Open 12 (2022): e062245.
|
| [28] |
H. S. Lee, E. J. Jung, J. M. Kim, et al., “The Usefulness of Red Blood Cell Distribution Width and Its Ratio With Platelet Count in Breast Cancer After Surgery and Adjuvant Treatment: A Retrospective Study,” Gland Surgery 11 (2022): 1864–1873.
|
| [29] |
R. Wang, M. He, J. Zhang, S. Wang, and J. Xu, “A Prognostic Model Incorporating Red Cell Distribution Width to Platelet Ratio for Patients With Traumatic Brain Injury,” Therapeutics and Clinical Risk Management 17 (2021): 1239–1248.
|
| [30] |
A. Aali-Rezaie, P. Alijanipour, N. Shohat, et al., “Red Cell Distribution Width: An Unacknowledged Predictor of Mortality and Adverse Outcomes Following Revision Arthroplasty,” Journal of Arthroplasty 33 (2018): 3514–3519.
|
| [31] |
A. Aali-Rezaie, F. C. Kuo, E. Kozaily, H. Vahedi, J. Parvizi, and P. F. Sharkey, “Red Cell Distribution Width: Commonly Performed Test Predicts Mortality in Primary Total Joint Arthroplasty,” Journal of Arthroplasty 36 (2021): 3646–3649.
|
| [32] |
X. F. Zhu, H. Y. Weng, and S. F. Huang, “Red Cell Distribution Width as a Predictor of Mortality After Hip Fracture: A Systematic Review and Meta-Analysis,” European Review for Medical and Pharmacological Sciences 27 (2023): 6996–7004.
|
| [33] |
X. Xiong, T. Li, S. Yu, and B. Cheng, “Association Between Red Blood Cell Indices and Preoperative Deep Vein Thrombosis in Patients Undergoing Total Joint Arthroplasty: A Retrospective Study,” Clinical and Applied Thrombosis/Hemostasis 28 (2022): 10760296221149029.
|
| [34] |
T. Paziuk, A. J. Rondon, K. Goswami, T. L. Tan, and J. Parvizi, “A Novel Adjunct Indicator of Periprosthetic Joint Infection: Platelet Count and Mean Platelet Volume,” Journal of Arthroplasty 35 (2020): 836–839.
|
| [35] |
X. Xiong, T. Li, S. Yu, and B. Cheng, “Association Between Platelet Indices and Preoperative Deep Vein Thrombosis in Elderly Patients Undergoing Total Joint Arthroplasty: A Retrospective Study,” Clinical and Applied Thrombosis/Hemostasis 29 (2023): 10760296221149699.
|
| [36] |
R. Malpani, M. S. Haynes, M. G. Clark, A. R. Galivanche, P. Bovonratwet, and J. N. Grauer, “Abnormally High, as Well as Low, Preoperative Platelet Counts Correlate With Adverse Outcomes and Readmissions After Elective Total Knee Arthroplasty,” Journal of Arthroplasty 34 (2019): 1670–1676.
|
| [37] |
B. Hanczar, J. Hua, C. Sima, J. Weinstein, M. Bittner, and E. R. Dougherty, “Small-Sample Precision of ROC-Related Estimates,” Bioinformatics 26, no. 6 (2010): 822–830.
|
| [38] |
E. W. Steyerberg, Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, 2nd ed. (Springer, 2019).
|
| [39] |
S. Kim and Y. Huang, “Combining Biomarkers for Classification With Covariate Adjustment,” Statistics in Medicine 36, no. 15 (2017): 2347–2362.
|
| [40] |
M. S. Pepe, T. Cai, and G. Longton, “Combining Predictors for Classification Using the Area Under the Receiver Operating Characteristic Curve,” Biometrics 62, no. 1 (2006): 221–229.
|
| [41] |
S. Mazzara, R. L. Rossi, R. Grifantini, S. Donizetti, S. Abrignani, and M. Bombaci, “CombiROC: An Interactive Web Tool for Selecting Accurate Marker Combinations of Omics Data,” Scientific Reports 7 (2017): 45477.
|
| [42] |
L. Qiu, C. Chen, S.-J. Li, et al., “Prognostic Values of Red Blood Cell Distribution Width, Platelet Count, and Red Cell Distribution Width-To-Platelet Ratio for Severe Burn Injury,” Scientific Reports 7, no. 1 (2017): 13720.
|
RIGHTS & PERMISSIONS
2025 The Author(s). Orthopaedic Surgery published by Tianjin Hospital and John Wiley & Sons Australia, Ltd.
PDF
