Tangkiatkumjai Mayuree, Vadcharavivad Somratai, Mahachai Varocha
Faculty of Pharmacy, Srinakharinwirot University, Nakhonnayok 26120, Thailand.
J Med Assoc Thai. 2005 May;88(5):672-7.
The purpose of this study was to create a predicting tool for UGIB event in NSAID users. The patients of this case-control study were NSAID users who had received NSAIDs for at least 3 days and were gastroscoped The patients with a history of gastrointestinal varices, gastrointestinal cancer, chronic renal failure, coagulopathy, or Mallory-Weiss tear were excluded. The data was collected between July 2001 and January 2002 by patient interviewing and medical record reviewing. One hundred and fifty four NSAID users were identified (89 in the UGIB group, 65 in the non-bleeding group). Most patients were elderly (mean age +/- SD: 60.9 +/- 12.6 years). Age and the number of current NSAID users were significantly higher in UGIB patients than in non-bleeding patients (p < 0.05 and p < 0.01, respectively). The number of antiulceration drug users in non-bleeding patients was higher than in UGIB patients (p < 0. 01). An equation for prediction of UGIB probability in NSAID users was generated by using enter logistic regression. The best model of predicting the risk of UGIB event in NSAID users was logit (UGIB) = 0.33 + 2.09 Multiple NSAID use + 1.43 H. pylori infection + 0.34 Current NSAID use + 0.12 (Age x Sex) - 8.53 Sex - 2.41 Antiulceration drugs - 0. 000048 Age. The model had 80.2% of the overall rate of correct classification. The positive and negative predictive values were 80.8% and 78.9% respectively. The probability of UGIB = e((logit(UGIB)) /1 + e(logit(UGlB)). If the value of the probability of UGIB is more than 0. 5, the patient has a high risk of UGIB. Multiple NSAID use is the strongest factor that affects the probability of UGIB in NSAID users. H. pylori infection is another strong risk factor of NSAID-related UGIB. Antiulceration drug usage reduced the risk of UGIB in this group of patients. The developed model can be used as a guide for pharmacotherapeutic planning in clinical practices.
本研究的目的是创建一种针对非甾体抗炎药(NSAID)使用者上消化道大出血(UGIB)事件的预测工具。本病例对照研究的患者为使用NSAIDs至少3天且接受过胃镜检查的NSAID使用者。排除有胃肠道静脉曲张、胃肠道癌症、慢性肾衰竭、凝血功能障碍或马-魏斯撕裂病史的患者。2001年7月至2002年1月期间,通过患者访谈和病历审查收集数据。共确定了154名NSAID使用者(UGIB组89名,非出血组65名)。大多数患者为老年人(平均年龄±标准差:60.9±12.6岁)。UGIB患者的年龄和当前NSAID使用者数量显著高于非出血患者(分别为p<0.05和p<0.01)。非出血患者中使用抗溃疡药物的人数高于UGIB患者(p<0.01)。通过使用逐步逻辑回归生成了一个预测NSAID使用者UGIB概率的方程。预测NSAID使用者UGIB事件风险的最佳模型为:logit(UGIB)=0.33+2.09多次使用NSAID+1.43幽门螺杆菌感染+0.34当前使用NSAID+0.12(年龄×性别)-8.53性别-2.41抗溃疡药物-0.000048年龄。该模型的总体正确分类率为80.2%。阳性和阴性预测值分别为80.8%和78.9%。UGIB的概率=e^((logit(UGIB))/(1 + e^(logit(UGIB))))。如果UGIB概率值大于0.5,则患者发生UGIB的风险较高。多次使用NSAID是影响NSAID使用者UGIB概率的最强因素。幽门螺杆菌感染是NSAID相关UGIB的另一个强风险因素。使用抗溃疡药物可降低该组患者发生UGIB的风险。所开发的模型可作为临床实践中药物治疗规划的指导。