Weigle K A, Escobar M, Arias A L, Martinez F, Rojas C
Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill 27599-7400.
Int J Epidemiol. 1993 Jun;22(3):548-58. doi: 10.1093/ije/22.3.548.
Neither parasitological nor molecular diagnosis of leishmaniasis is widely available in clinical settings where American cutaneous leishmaniasis (ACL) is endemic. Therefore four clinical prediction rules for ACL were developed which incorporated physical examination findings (clinical rule), physical examination and leishmanin skin test (LST) (clinical-LST rule), physical examination and historical information (clinical-historical rule), or physical examination, historical information and LST (clinical-historical-LST rule). One hundred parasitologically diagnosed ACL cases and 38 cases of chronic skin lesions of other aetiologies comprised the derivation set. The validation set consisted of 124 ACL cases and 35 patients with lesions of other aetiologies. Components of each rule were selected by bivariate analysis, then step-wise logistic regression. Sensitivity, specificity and efficiency were calculated for each score threshold; the threshold achieving greatest efficiency was selected for each rule. When these rules were applied to the validity set the sensitivity, specificity and efficiency were respectively: clinical 93%, 31%, 79%; clinical-LST 90%, 73%, 85.9%; clinical-historical 97%, 51%, 87%; clinical-historical-LST 92%, 70%, 87%. Inclusion of LST skin test consistently improved the specificity of the rules. Should a given clinical setting warrant optimizing either sensitivity or specificity alone, the rule thresholds can be adjusted. These and other prediction rules, once evaluated in other settings, should be incorporated into leishmaniasis control programmes.
在美洲皮肤利什曼病(ACL)流行的临床环境中,利什曼病的寄生虫学诊断和分子诊断都未广泛应用。因此,开发了四种ACL临床预测规则,分别纳入了体格检查结果(临床规则)、体格检查和利什曼原虫皮肤试验(LST)(临床-LST规则)、体格检查和病史信息(临床-病史规则),或体格检查、病史信息和LST(临床-病史-LST规则)。100例经寄生虫学诊断的ACL病例和38例其他病因的慢性皮肤病变病例组成了推导集。验证集由124例ACL病例和35例其他病因的病变患者组成。通过双变量分析,然后逐步进行逻辑回归,选择每条规则的组成部分。计算每个评分阈值的敏感性、特异性和效率;为每条规则选择效率最高的阈值。当将这些规则应用于验证集时,敏感性、特异性和效率分别为:临床规则93%、31%、79%;临床-LST规则90%、73%、85.9%;临床-病史规则97%、51%、87%;临床-病史-LST规则92%、70%、87%。纳入LST皮肤试验始终能提高规则的特异性。如果特定的临床环境需要单独优化敏感性或特异性,则可以调整规则阈值。这些及其他预测规则一旦在其他环境中得到评估,应纳入利什曼病控制计划。