Paxton Elizabeth W, Inacio Maria C S, Khatod Monti, Yue Eric, Funahashi Tadashi, Barber Thomas
Surgical Outcomes and Analysis, Kaiser Permanente, 8954 Rio San Diego Drive, Suite 406, San Diego, CA, 92108, USA.
Department of Orthopaedic Surgery, Southern California Permanente Medical Group, West Los Angeles, CA, USA.
Clin Orthop Relat Res. 2015 Dec;473(12):3965-73. doi: 10.1007/s11999-015-4506-4. Epub 2015 Sep 1.
Considering the cost and risk associated with revision Total knee arthroplasty (TKAs) and Total hip arthroplasty (THAs), steps to prevent these operations will help patients and reduce healthcare costs. Revision risk calculators for patients may reduce revision surgery by supporting clinical decision-making at the point of care.
QUESTIONS/PURPOSES: We sought to develop a TKA and THA revision risk calculator using data from a large health-maintenance organization's arthroplasty registry and determine the best set of predictors for the revision risk calculator.
Revision risk calculators for THAs and TKAs were developed using a patient cohort from a total joint replacement registry and data from a large US integrated healthcare system. The cohort included all patients who had primary procedures performed in our healthcare system between April 2001 and July 2008 and were followed until January 2014 (TKAs, n = 41,750; THAs, n = 22,721), During the study period, 9% of patients (TKA = 3066/34,686; THA=1898/20,285) were lost to followup and 7% died (TKA= 2350/41,750; THA=1419/20,285). The outcome of interest was revision surgery and was defined as replacement of any component for any reason within 5 years postoperatively. Candidate predictors for the revision risk calculator were limited to preoperative patient demographics, comorbidities, and procedure diagnoses. Logistic regression models were used to identify predictors and the Hosmer-Lemeshow goodness-of-fit test and c-statistic were used to choose final models for the revision risk calculator.
The best predictors for the TKA revision risk calculator were age (odds ratio [OR], 0.96; 95% CI, 0.95-0.97; p < 0.001), sex (OR, 0.84; 95% CI, 0.75-0.95; p = 0.004), square-root BMI (OR, 1.05; 95% CI, 0.99-1.11; p = 0.140), diabetes (OR, 1.32; 95% CI, 1.17-1.48; p < 0.001), osteoarthritis (OR, 1.16; 95% CI, 0.84-1.62; p = 0.368), posttraumatic arthritis (OR, 1.66; 95% CI, 1.07-2.56; p = 0.022), and osteonecrosis (OR, 2.54; 95% CI, 1.31-4.92; p = 0.006). The best predictors for the THA revision risk calculator were sex (OR, 1.24; 95% CI, 1.05-1.46; p = 0.010), age (OR, 0.98; 95% CI, 0.98-0.99; p < 0.001), square-root BMI (OR, 1.07; 95% CI, 1.00-1.15; p = 0.066), and osteoarthritis (OR, 0.85; 95% CI, 0.66-1.09; p = 0.190).
Study model parameters can be used to create web-based calculators. Surgeons can enter personalized patient data in the risk calculators for identification of risk of revision which can be used for clinical decision making at the point of care. Future prospective studies will be needed to validate these calculators and to refine them with time.
Level III, prognostic study.
考虑到翻修全膝关节置换术(TKA)和全髋关节置换术(THA)的成本及风险,采取措施预防这些手术将有助于患者并降低医疗成本。用于患者的翻修风险计算器通过在医疗点支持临床决策,可能会减少翻修手术。
问题/目的:我们试图利用来自一个大型健康维护组织的关节置换登记处的数据,开发一个TKA和THA翻修风险计算器,并确定翻修风险计算器的最佳预测指标集。
使用来自全关节置换登记处的患者队列以及来自美国一个大型综合医疗系统的数据,开发THA和TKA的翻修风险计算器。该队列包括2001年4月至2008年7月在我们医疗系统接受初次手术并随访至2014年1月的所有患者(TKA,n = 41,750;THA,n = 22,721)。在研究期间,9%的患者(TKA = 3066/34,686;THA = 1898/20,285)失访,7%的患者死亡(TKA = 2350/41,750;THA = 1419/20,285)。感兴趣的结局是翻修手术,定义为术后5年内因任何原因更换任何部件。翻修风险计算器的候选预测指标限于术前患者人口统计学特征、合并症和手术诊断。使用逻辑回归模型识别预测指标,并使用Hosmer-Lemeshow拟合优度检验和c统计量为翻修风险计算器选择最终模型。
TKA翻修风险计算器的最佳预测指标为年龄(比值比[OR],0.96;95%置信区间[CI],0.95 - 0.97;p < 0.001)、性别(OR,0.84;95% CI,0.75 - 0.95;p = 0.004)、BMI平方根(OR,1.05;95% CI,0.99 - 1.11;p = 0.140)、糖尿病(OR,1.32;95% CI,1.17 - 1.48;p < 0.001)、骨关节炎(OR,1.16;95% CI,0.84 - 1.62;p = 0.368)、创伤后关节炎(OR,1.66;95% CI,1.07 - 2.56;p = 0.022)和骨坏死(OR,2.54;95% CI,1.31 - 4.92;p = 0.006)。THA翻修风险计算器的最佳预测指标为性别(OR,1.24;95% CI,1.05 - 1.46;p = 0.010)、年龄(OR,0.98;95% CI,0.98 - 0.99;p < 0.001)、BMI平方根(OR,1.07;95% CI,1.00 - 1.15;p = 0.066)和骨关节炎(OR,0.85;95% CI,0.66 - 1.09;p = 0.190)。
研究模型参数可用于创建基于网络的计算器。外科医生可以在风险计算器中输入个性化的患者数据,以识别翻修风险,该风险可用于医疗点的临床决策。未来需要进行前瞻性研究来验证这些计算器并随着时间进行完善。
III级,预后研究。