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고해원,오재훈,강형구,임태호,고벽성 대한응급의학회 2022 대한응급의학회지 Vol.33 No.4
Objective: Lactate clearance is reportedly associated with the outcomes of various critical illnesses. However, few studies have examined the association between lactate clearance and outcomes in patients with gastrointestinal bleeding (GIB). Methods: A single-center retrospective observational study between 2016 and 2020 was conducted. Consecutive adult patients with GIB symptoms (melena, hematemesis, and hematochezia) presenting to the emergency department were included. Lactate clearance was calculated as ([initial lactate-subsequent lactate]/initial lactate)×100. The association between lactate clearance and outcomes was examined by multivariable logistic regression analysis. The primary outcome was the in-hospital mortality. The area under the curve (AUC) of lactate clearance for in-hospital mortality was calculated. The sensitivity and specificity with optimal cutoff values were computed. The AUC of lactate clearance was compared with the Glasgow-Blatchford score and AIMS65 for predicting in-hospital mortality. Results: Three hundred and fifty-one patients were included in the final analysis, and the in-hospital mortality rate was 12%. Lactate clearance was significantly associated with lower in-hospital mortality (odds ratio, 0.991; P=0.016). The AUC of lactate clearance for in-hospital mortality was 0.64. The AUC of Glasgow-Blatchford score and AIMS65 for inhospital mortality was not significantly different from lactate clearance (P=0.759 and P=0.442, respectively). A cutoff lactate clearance of less than 10% had 45.2% sensitivity, 30.7% specificity, 8.2% positive predictive value, and 80.5% negative predictive value for predicting in-hospital mortality. Conclusion: Lactate clearance was independently associated with in-hospital mortality in GIB patients. Further prospective studies will be needed to address the prognostic value of lactate clearance in GIB.
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고해원,김동재,Ko, Hae-Won,Kim, Dong-Jae 한국데이터정보과학회 2010 한국데이터정보과학회지 Vol.21 No.6
임상시험을 시행하는 경우 위약을 신약과 비교하는 경우가 대다수이다. 기존에 독립인 두 모 집단의 표본수를 계산하는 방법으로 모수적 방법에서는 t검정을 이용하였고, 비모수적 방법에서는 Wilcoxon 순위합검정 (Wilcoxon, 1945)을 이용하였다. 본 논문에서는 Orban과 Wolfe (1982)가 제안한 선형위치통계량의 검정법과, Kim (1994)이 선형위치통계량에 기초하여 계산한 검정력의 결과를 이용하여 표본수 구하는 방법을 제안한다. 또한 앞서 제안한 방법의 표본수를 기존의 Wilcoxon 순위합검정을 이용하여 Wang 등 (2003)이 제안한 공식을 이용한 표본수, 그리고 모수적 방법을 이용한 t검정의 표본수와 비교하였다. For clinical trials, it is common to compare the placebo and new drug. The method of calculating a sample size for two independent populations are the t-test that is used for parametric methods, and the Wilcoxon rank-sum test that is used in the non-parametric methods. In this paper, we propose a method that is using Kim's (1994) statistic power based on the linear placement statistic, which was proposed by Orban and Wolfe (1982). We also compare the sample size for the proposed method with that for using Wang et al. (2003)'s sample size formula which is based on Wilcoxon rank-sum test, and with that of t-test for parametric methods.
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