为了在汗液氯化物不存在的情况下开发囊性纤维化(CF)的诊断算法,基于临床特征和基本实验室调查。
在一项前瞻性观察研究中,我们招募了患有复发性/持续性肺炎并伴有吸收不良或生长不良的儿童,接受了汗液氯化物测试,2019年1月至2020年12月。同时评估了他们的手部水性皱纹,粪便脂肪球,细菌培养痰,血气,和血清电解质.计算CF和非CF组之间具有显著差异的参数的灵敏度和特异性。开发了用于CF诊断的评分系统和算法。
在134名儿童中,46(34%)有CF。诊断CF的各种参数的敏感性和特异性为:由于呼吸系统疾病导致的兄弟姐妹死亡(30.43%,96.59%),水性起皱(76.74%,47.67%),代谢性碱中毒(17.78%,94.12%),低钠血症(28.89%,89.41%),粪便脂肪球(38.46%,81.18%),痰培养中存在假单胞菌(23.68%,98.80%)。在逐步逻辑回归中使用重要参数的系数,CF诊断的综合评分计算为:3X因呼吸道疾病导致的兄弟姐妹死亡+1.5X低钠血症+1.5X代谢性碱中毒+1.5X水生性皱纹+1X粪便脂肪球+痰培养中存在2.5X假单胞菌(每个变量的缺失和存在评分为0或1,分别)。≥2.5的临界值的敏感性和特异性分别为81.82%和76.83%,分别。
在资源有限的设置中,所提出的诊断算法可用于推定CF的诊断,具有相当的敏感性和特异性。
To develop a diagnostic algorithm for cystic fibrosis (CF) in the setting of unavailability of sweat chloride, based on clinical features and basic laboratory investigations.
In a prospective observational study, we enrolled children with recurrent/persistent pneumonia with either malabsorption or poor growth, undergoing a sweat chloride test, between January 2019 and December 2020. They were simultaneously evaluated for aquagenic wrinkling of hands, stool fat globules, sputum for bacterial culture, blood gas, and serum electrolytes. Sensitivity and specificity were calculated for parameters having a significant difference between CF and non-CF groups. Scoring systems and algorithms for the diagnosis of CF were developed.
Of 134 children enrolled, 46 (34%) had CF. The sensitivity and specificity of various parameters to diagnose CF was: sibling death due to respiratory illness (30.43%, 96.59%), aquagenic wrinkling (76.74%, 47.67%), metabolic alkalosis (17.78%, 94.12%), hyponatremia (28.89%, 89.41%), stool fat globules (38.46%, 81.18%), and presence of Pseudomonas in sputum culture (23.68%, 98.80%). Using coefficients of significant parameters on stepwise logistic regression, the composite score for diagnosis of CF was calculated as: 3X sibling death due to respiratory illness + 1.5X hyponatremia + 1.5X metabolic alkalosis + 1.5X aquagenic wrinkling + 1X stool fat globules + 2.5X presence of Pseudomonas in sputum culture (each of the variables scores 0 or 1 for absence and presence, respectively). The cut-off of ≥2.5 had sensitivity and specificity of 81.82% and 76.83%, respectively.
In resource-limited settings, the proposed diagnostic algorithm can be used for the diagnosis of presumptive CF with fair sensitivity and specificity.