Xing Huanmin, Zhou Wendie, Fan Yuying, Wen Taoxue, Wang Xiaohui, Chang Guangming
Nursing Department, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
Nursing Department, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.
BMJ Open. 2019 Nov 12;9(11):e030733. doi: 10.1136/bmjopen-2019-030733.
We aimed to develop and validate a postoperative delirium (POD) prediction model for patients admitted to the intensive care unit (ICU).
A prospective study was conducted.
The study was conducted in the surgical, cardiovascular surgical and trauma surgical ICUs of an affiliated hospital of a medical university in Heilongjiang Province, China.
This study included 400 patients (≥18 years old) admitted to the ICU after surgery.
The primary outcome measure was POD assessment during ICU stay.
The model was developed using 300 consecutive ICU patients and was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological and Operative Severity Score for the enumeration of Mortality and morbidity; acid-base disturbance and history of coma, diabetes or hypertension. The model had an area under the receiver operating characteristics curve of 0.852 (95% CI 0.802 to 0.902), Youden index of 0.5789, sensitivity of 70.73% and specificity of 87.16%. The Hosmer-Lemeshow goodness of fit was 5.203 (p=0.736). At a cutoff value of 24.5%, the sensitivity and specificity were 71% and 69%, respectively.
The model, which used readily available data, exhibited high predictive value regarding risk of ICU-POD at admission. Use of this model may facilitate better implementation of preventive treatments and nursing measures.
我们旨在为入住重症监护病房(ICU)的患者开发并验证一种术后谵妄(POD)预测模型。
进行了一项前瞻性研究。
该研究在中国黑龙江省一所医科大学附属医院的外科、心血管外科和创伤外科ICU中进行。
本研究纳入了400例术后入住ICU的患者(年龄≥18岁)。
主要结局指标是ICU住院期间的POD评估。
该模型使用300例连续的ICU患者进行开发,并使用来自同一ICU的100例患者进行验证。该模型基于五个风险因素:用于计算死亡率和发病率的生理与手术严重程度评分;酸碱紊乱以及昏迷、糖尿病或高血压病史。该模型的受试者工作特征曲线下面积为0.852(95%可信区间0.802至0.902),约登指数为0.5789,敏感性为70.73%,特异性为87.16%。Hosmer-Lemeshow拟合优度为5.203(p = 0.736)。在临界值为24.5%时,敏感性和特异性分别为71%和69%。
该模型使用易于获取的数据,在入院时对ICU-POD风险具有较高的预测价值。使用该模型可能有助于更好地实施预防性治疗和护理措施。