Vera-Cruz P N, Palmes P P, Tonogan Ljm, Troncillo A H
Department of Internal Medicine, West Visayas State University Medical Center, Iloilo City, Philippines.
Department of Orthopaedics, West Visayas State University Medical Center, Iloilo City, Philippines.
Malays Orthop J. 2020 Nov;14(3):114-123. doi: 10.5704/MOJ.2011.018.
Classifications systems are powerful tools that could reduce the length of hospital stay and economic burden. The Would, Ischemia, and Foot Infection (WIFi) classification system was created as a comprehensive system for predicting major amputation but is yet to be compared with other systems. Thus, the objective of this study is to compare the predictive abilities for major lower limb amputation of WIFi, Wagner and the University of Texas Classification Systems among diabetic foot patients admitted in a tertiary hospital through a prospective cohort design.
Sixty-three diabetic foot patients admitted from June 15, 2019 to February 15, 2020. Methods included one-on-one interview for clinico-demographic data, physical examination to determine the classification. Patients were followed-up and outcomes were determined. Pearson Chi-square or Fisher's Exact determined association between clinico-demographic data, the classifications, and outcomes. The receiver operating characteristic (ROC) curve determined predictive abilities of classification systems and paired analysis compared the curves. Area Under the Receiver Operating Characteristic Curve (AUC) values used to compare the prediction accuracy. Analysis was set at 95% CI.
Results showed hypertension, duration of diabetes, and ambulation status were significantly associated with major amputation. WIFi showed the highest AUC of 0.899 (p = 0.000). However, paired analysis showed AUC differences between WIFi, Wagner, and University of Texas classifications by grade were not significantly different from each other.
The WIFi, Wagner, and University of Texas classification systems are good predictors of major amputation with WIFi as the most predictive.
分类系统是强大的工具,可缩短住院时间并减轻经济负担。伤口、缺血和足部感染(WIFi)分类系统是作为预测大截肢的综合系统而创建的,但尚未与其他系统进行比较。因此,本研究的目的是通过前瞻性队列设计,比较三级医院收治的糖尿病足患者中WIFi、瓦格纳和德克萨斯大学分类系统对下肢大截肢的预测能力。
2019年6月15日至2020年2月15日收治的63例糖尿病足患者。方法包括一对一访谈临床人口统计学数据、体格检查以确定分类。对患者进行随访并确定结果。采用Pearson卡方检验或Fisher精确检验确定临床人口统计学数据、分类和结果之间的关联。采用受试者工作特征(ROC)曲线确定分类系统的预测能力,并通过配对分析比较曲线。受试者工作特征曲线下面积(AUC)值用于比较预测准确性。分析设定在95%置信区间。
结果显示高血压、糖尿病病程和行走状态与大截肢显著相关。WIFi的AUC最高,为0.899(p = 0.000)。然而,配对分析显示WIFi、瓦格纳和德克萨斯大学分类在分级上的AUC差异彼此无显著差异。
WIFi、瓦格纳和德克萨斯大学分类系统都是大截肢的良好预测指标,其中WIFi的预测性最强。