60歲以上退化性關節炎患者首次人工膝關節置換術後死亡率及關節失效率分析

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Title:	60歲以上退化性關節炎患者首次人工膝關節置換術後死亡率及關節失效率分析 Analysis of Mortality and Prosthesis Failure Rate after Primary Total Knee Replacement for Osteoarthritis in Patients Aged 60 or Older
Authors:	邱建勳;Jian-Shiun Chiou
Contributors:	生物統計研究所碩士班
Keywords:	全民健康保險資料庫;人工膝關節置換術;死亡率;關節失效率;競爭風險;National Health Insurance Research Database;knee replacement;mortality;prosthesis failure rate;competing risk
Date:	2013-06-30
Issue Date:	2013-10-02 11:00:28 (UTC+8)
Publisher:	中國醫藥大學
Abstract:	背景：在台灣骨科施行人工膝關節置換術的患者，多數是因為退化性關節炎所導致。有關首次接受人工膝關節術後的死亡率或關節失效率之估計，以及相關危險因子分析的研究，在亞洲尚不多見。本研究利用台灣健保資料庫，針對60歲以上退化性關節炎患者，在首次接受人工膝關節置換術後，探討其發生死亡與關節失效之風險，同時找出相關之危險因子。材料與方法：資料來自全民健康保險資料庫之住院資料，篩選在2000至2005年間，60歲以上首次接受人工膝關節置換術的退化性關節炎患者並追蹤至2010年。估計手術之發生率、一年內死亡率、標準化死亡比、一年內關節失效率 (包含膝關節再置換術或膝關節移除術)，並使用 Kaplan-Meier 方法估計術後累積死亡率和累積關節失效率，以 Cox 等比例風險迴歸模型，分析年齡、性別、置換部位 (分別為單側或雙側人工膝關節置換術) 以及共病症個數，對於術後死亡與關節失效之影響。更進一步針對關節失效率，將死亡作為其競爭風險，以 Fine 和 Gray 迴歸模型估計累積關節失效率並分析其相關危險因子。結果：在2000年至2005年間，首次接受人工膝關節置換術的患者共有35,805人，平均年齡為72歲、男性有9,842人 (27.49%)、女性有25,963人 (72.51%)、單側有26,819人 (74.90%)、雙側有8,986人 (25.10%)、發生死亡有8,253人 (23.05%)、發生關節失效有1,357人 (3.79%)、罹患1個共病症的患者有8,088人 (22.59%) 而罹患2個以上共病症的患者有4,703人 (13.14%)。接受人工膝關節置換術之人數從2000年的每十萬人180人增加至2005年的每十萬人245人。從術後1年內至5年內之標準化死亡比，分別為7.62、3.51、2.14、1.41和0.97。術後的關節失效率從1年內至5年內，分別為1.23、1.02、0.87、0.79、1.12和0.77 以Kaplan-Meier分析結果顯示，術後之累積死亡率從第1年內的0.28%，增加至第10年的32.40%；術後之累積關節失效率，從第1年的0.97%，累積至第10年的4.86%。進一步以Fine 和 Gray分析術後之累積關節失效率，從第1年的0.96%，累積至第10年的4.35%。使用多變量 Cox 等比例風險迴歸模型分析死亡風險，年齡越高的患者其死亡風險越高 (以60-64歲為參考組，65-69歲之 HR=1.31；70-74歲之 HR=1.75；75-79歲之 HR=2.49；80歲以上之 HR=3.96)，男性比女性高 (HR=1.52)，雙側手術比單側手術低 (HR=0.75)、共病症個數越多其死亡風險越高 (以沒有罹患共病症為參考組，罹患一個共病症之HR=1.57；罹患兩個以上共病症之HR=2.93)，以上均達到統計上的顯著。在術後關節失效風險方面，年齡越輕的患者其關節失效風險越高 (以60-64歲為參考組，65-69歲之 HR=0.84；70-74歲之 HR=0.68；75-79歲之 HR=0.58；80歲以上之 HR=0.55)，男性比女性高 (HR=1.39)、罹患2個以上共病症的患者發生關節失效的風險會比沒有罹患共病症的患者高出1.21倍 (95 % CI=1.03-1.43)，以上均達到統計上的顯著。更進一步，以死亡為競爭風險，利用Fine 和 Gray 迴歸模型探討關節失效的危險因子，結果顯示共病症個數多寡與關節失效是沒有統計上的相關性。結論：從首次接受人工膝關節置換術後的情況來看，年齡越輕、男性以及共病症個數越多的患者，有較高的關節失效風險。然而從Fine 和 Gray 迴歸模型分析結果顯示共病症個數對於關節失效之發生率並無顯著影響，在評估術後關節失效之發生率及風險時，使用Fine 和 Gray 迴歸模型可得到更準確之結果。 Background: Patients who undergo artificial total knee replacement (TKR) surgery mostly due to osteoarthritis In Taiwan. Few studies focused on mortality, prosthesis failure rate and risk factors after primary knee replacement for osteoarthritis in Asian. Our aim was to estimate the rates of prosthesis failure and survival and to examine their potential risk factors among inpatients aged 60 years or older after primary TKR surgery from a nationwide population database in Taiwan. Methods: From the inpatient data, we selected 35,805 inpatients (? 60 years) with TKR between 2000 and 2005. Each subject was followed up to 2010. Annual incidence, mortality and standardized mortality ratio (SMR), and annual prosthesis failure rate after TKR were measured. After adjusting for age, gender, surgery type (unilateral or bilateral surgery) and comorbidity number, we used the Kaplan-Meier to estimate the cumulative prosthesis failure and survival rates and applied the Cox’s proportional hazards model to assess risk factors about death and prosthesis failure. Furthermore, we considered death event as a competing risk event to prosthesis failure using the Fine and Gray regression model to calculate the cumulative rates and subdistribution hazards for prosthesis failure. Results: Between 2000 and 2005, 35,805 inpatients were admitted to undergo primary TKR surgery. Among these patients, average age were 72 years old, 9,842 (27.49%) were male, 25,963 (72.51%) were female, 26,819 (74.90%) were with unilateral TKR surgery, 8,986 (25.10%) were with bilateral TKR surgery, 8,253 (23.05%) died, 1,357 (3.79%) had prosthesis failure, 8,088 (22.59%) were with 1 comorbidity number, and 4,703 (13.14%) were with ?2 comorbidity number. The annual knee replacement rate increased from 180/100,000 in 2000 to 245/100,000 in 2005. Follow-up SMR at 1-year, 2-year, 3-year, 4-year and 5-year post-replacement were 7.62, 3.51, 2.14, 1.42, and 0.97, respectively. Prosthesis failure rate at 1-year, 2-year, 3-year, 4-year and 5-year post-replacement were 1.23, 1.02, 0.87, 0.79, 1.12, and 0.77, respectively. Using the Kaplan-Meier for the cumulative mortality at one and ten years were 0.28% and 32.40%; the cumulative prosthesis failure rate at one and ten were 0.97% and 4.86%. Furthermore, the Fine and Gray regression model showed the cumulative prosthesis failure rate at one and ten years were 0.96% and 4.35%. Using the multi-variable Cox proportional hazard model, statistically significant risk factors of overall death were older age (compared with 60-64 years group, the HRs were 1.31, 1.75, 2.49, and 3.96 for the 65-69, 70-74, 75-79, and ?80 years age groups, respectively), male (compared with female, the HR was 1.52 for the male), unilateral surgery (the HR was 0.75 for bilateral surgery vs. unilateral surgery), and higher comorbidity number (1 vs. 0 HR=1.57, ?2 vs. 0 HR=2.93). Statistically significant risk factors of prosthesis failure were younger age (compared with 60-64 years group, the HRs were 0.84, 0.68, 0.58, and 0.55 for the 65-69, 70-74, 75-79, and ?80 years age groups, respectively), male (compared with female, the HR was 1.39 for the male), and the ?2 comorbidity number patient was associated with significantly higher risk of prosthesis failure than no comorbidity patient (HR=1.21, 95% CI=1.03-1.43). Furthermore, using the Fine and Gray regression model which treated death as the competing risk, the comorbidity numbers become insignificant for assessing the risk factor of prosthesis failure. Conclusions: Patients with younger age, male gender, and higher comorbidity numbers had higher risk of prosthesis failure after primary TKR surgery from the Cox regression model results. However, using the Fine and Gray regression model which treated death as the competing risk, the comorbidity numbers become insignificant for assessing the risk factor of prosthesis failure. The Fine and Gray model may obtain a more accurate result than the Cox regression model in estimating the cumulative incident and testing the risk factors for prosthesis failure.
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