| 摘要: | 本研究目的為探討影響呼吸器照護病房中呼吸器依賴患者30日死亡率之危險因子，並建立其30日死亡率之預測模型，以作為爾後建立該等病患之疾病嚴重度評估模式之基礎。 本研究以描述性統計、雙變項分析、多變項分析(羅吉斯迴歸)及逐步羅吉斯迴歸等統計方法，以回顧文獻中對於呼吸器依賴患者之脫離呼吸器及影響死亡率之各種因素，針對2002年11月至2003年1月台灣北、中、南共六家呼吸照護病房中237位有效調查之呼吸器依賴患者，進行研究。研究結果如下： 一、 在描述性統計中，本研究案例，死亡病例為81例，佔總研究個案34.18%；人口變異項中，男性有132位，女性105位，分佔55.70%及44.30%；年齡分布自26歲至96歲，平均年齡為75歲，最多年齡層為71至80歲，共107位，佔45.15%，平均住院月數為10.68個月。 二、 在雙變項分析中，和30日死亡率之相關因素中，住院月數、腎功能指數(BUN、Creatinine)、白血球數，具有極顯著相關(p<0.01)；糖尿病、呼吸器使用時段、人工氣道及皮膚感染等，具有顯著相關(p<0.05)； 褥瘡有無，則具有明顯相關(p<0.1)。 三、 在多變項分析中(羅吉斯迴歸)中，和30日死亡率相關因素中，住院月數、人工氣道別、白血球數及皮膚感染等，具極顯著意義(p<0.01)；使用呼吸器原因、Creatinine等，具有顯著意義(p<0.05)；而慢性阻塞性肺炎、糖尿病等，則具明顯意義(p<0.01)。 四、 將羅吉斯迴歸所得到具有明顯相關之危險因子，以逐步羅吉斯迴歸統計建立呼吸器依賴患者30日死亡率之預測模型，其中住院月數、使用呼吸器月數、Creatinine、人工氣道別、白血球數及皮膚感染等六大重要危險因子，皆具有顯著以上相關(p<0.05)。本預測模型預測正確率達90.1%(percent concordant 90.1)。 根據本研究結果，建議呼吸照護病房之經營及醫護團隊，建立呼吸照護病房的監測指標；重新訂定醫療及護理照護流程，並建立呼吸器依賴病患之風險評估。就衛生政策主管機關方面，建議訂定呼吸照護病房之品質監控指標，同時建立疾病嚴重度評估系統及支付標準。對於未來研究者方面，則建議可採前進式資料收集，以求資料完善；可依本研究預測模型，進行準確度校定；可節選不同時間，作為死亡率預測時間；可依本研究，進而建立疾病嚴重度評估系統，將呼吸器依賴患者加以分級；可依本研究，計算出各危險性不同患者所耗成本，作為給付標準；可對整體呼吸照護體系對於總額幾付下合理的佔率，作整體研究；亦可對是否加入整合照護計劃，對死亡率的影響。; This study was designed to investigate the risk factors of 30 days mortality for ventilator dependent patients on respiratory care ward (RCW), and to establish the predictive model. Logistic regression was utilized to analyze data. A retrospective collection data of 237 ventilator dependent patients gathered from 6 RCWs, located at Northern, Central and Southern Taiwan. The principal findings were as follows. 1. Among 237 patients, total mortality were 81 patients (34.18%). 132 of them (55.70%) were male and the rest of them (103, 44.3%) were female. Age ranged from 26 to 96 years old. Average age was 75 years old, with 107 patients (45.15%) age ranging from 71-80 years. Average hospital stay was 10.68 months. 2.With regards to bivariate analysis, the length of hospital stay, Creatinine, and WBC count were statistically significant associated with mortality at the alpha level of 0.01. In addition, history of DM, duration under ventilator support, artificial airway and dermal infection were statistically significant impaction motality at the alpha level of 0.05. 3.Controlling other variables by using Logistic regression, six variables were identified as the risk factors of 30 days mortality for ventilator dependent patients on RCW at the alpha level of 0.05. There were length of hospital stay, WBC, infection of skin, artificial airway, causative factors of respiratory failure, and creatinine level. 4.A predictive model of 30 days motality for ventilator dependent patients on RCW, consisted of six variables including length of hospital stay, length of time under ventilator support, Creatinine level, different artificial airway, WBC count and dermal infection was established by using stepwise Logistic regression. The accuracy of classification for this model is high enough to validate our result (percent concordant 90.1). Our results could futher assist third-payer to establish the index of case-mix and quality assessment tool. In addition, the manager of RCW also could use the result of this study to redesign care procedure to assure good quality of care. Finally, the predictive model obtained from our study could be utilized as a risk evaluation model for health care providers. |
