Pre-Treatment Neutrophil Count as a Predictor of Antituberculosis Therapy Outcomes: A Multicenter Prospective Cohort Study.

Pre-Treatment Neutrophil Count as a Predictor of Antituberculosis Therapy Outcomes: A Multicenter Prospective Cohort Study.
复制标题

DOI:
10.3389/fimmu.2021.661934
复制
发表时间:
2021
影响因子:
7.3
通讯作者:
RePORT Brazil consortium
RePORT Brazil consortium
中科院分区:
医学2区
文献类型:
--
作者:
Carvalho ACC;Amorim G;Melo MGM;Silveira AKA;Vargas PHL;Moreira ASR;Rocha MS;Souza AB;Arriaga MB;Araújo-Pereira M;Figueiredo MC;Durovni B;Lapa-E-Silva JR;Cavalcante S;Rolla VC;Sterling TR;Cordeiro-Santos M;Andrade BB;Silva EC;Kritski AL;RePORT Brazil consortium

文献摘要

参考文献

被引文献

相似文献

中性粒细胞与许多疾病中的肺组织损伤有关,包括结核病(TB)。中性粒细胞计数是否可以作为不良治疗结果的预测因子尚不清楚。我们前瞻性地评估了936例(172例HIV血清阳性)经培养确诊的肺结核患者,这些患者参加了2015年6月至2019年6月在巴西不同地区进行的多中心前瞻性队列研究,并随访了两年。TB患者在治疗前(第0个月)进行基线访视,并在第2个月和第6个月(或TB治疗结束时)进行访视。在结核病诊断和随访期间进行了肺结核显微镜检查和结核分枝杆菌(MTB)培养。在基线时测量全血细胞计数。治疗结果定义为不利(死亡、治疗失败或TB复发)或有利(治愈或治疗完成)。我们进行了多变量逻辑回归,倾向评分回归调整,以估计中性粒细胞计数与MTB培养结果在第2个月和不利的治疗结果之间的关联。由于结局数量相对较少,我们使用倾向评分调整而不是完全调整的回归模型。在第2个月有MTB培养结果的682例患者中,40例(5.9%)结果呈阳性。经倾向评分校正回归后,在HIV血清阴性(OR = 1.06,95%CI = [0.95;1.19])或HIV血清阳性患者(OR = 0.77,95%CI = [0.51; 1.20])中,未发现基线中性粒细胞计数(103/mm 3)与第2个月时MTB培养阳性之间存在显著相关性。在691例随访至少18个月至24个月的结核病患者中,635例(91.9%)治愈或完成治疗,56例(8.1%)治疗结果不利。采用倾向评分调整的多变量回归分析发现,基线中性粒细胞计数较高(103/mm 3)与HIV血清阴性患者的不良结局相关[OR= 1.17(95% CI= [1.06;1.30])。此外,校正的考克斯回归发现,基线中性粒细胞计数较高(103/mm 3)与总体和HIV血清阴性患者的不良治疗结局相关(HR= 1.16(95% CI = [1.05;1.27])。抗结核治疗开始前中性粒细胞计数增加与不利的治疗结果相关,特别是在HIV血清阴性患者中。需要进一步的前瞻性研究来评估中性粒细胞计数对药物治疗的反应以及与结核病治疗结果的相关性。
Neutrophils have been associated with lung tissue damage in many diseases, including tuberculosis (TB). Whether neutrophil count can serve as a predictor of adverse treatment outcomes is unknown. We prospectively assessed 936 patients (172 HIV-seropositive) with culture-confirmed pulmonary TB, enrolled in a multicenter prospective cohort study from different regions in Brazil, from June 2015 to June 2019, and were followed up to two years. TB patients had a baseline visit before treatment (month 0) and visits at month 2 and 6 (or at the end of TB treatment). Smear microscopy, and culture for Mycobacterium tuberculosis (MTB) were performed at TB diagnosis and during follow-up. Complete blood counts were measured at baseline. Treatment outcome was defined as either unfavorable (death, treatment failure or TB recurrence) or favorable (cure or treatment completion). We performed multivariable logistic regression, with propensity score regression adjustment, to estimate the association between neutrophil count with MTB culture result at month 2 and unfavorable treatment outcome. We used a propensity score adjustment instead of a fully adjusted regression model due to the relatively low number of outcomes. Among 682 patients who had MTB culture results at month 2, 40 (5.9%) had a positive result. After regression with propensity score adjustment, no significant association between baseline neutrophil count (103/mm3) and positive MTB culture at month 2 was found among either HIV-seronegative (OR = 1.06, 95% CI = [0.95;1.19] or HIV-seropositive patients (OR = 0.77, 95% CI = [0.51; 1.20]). Of 691 TB patients followed up for at least 18 months and up to 24 months, 635 (91.9%) were either cured or completed treatment, and 56 (8.1%) had an unfavorable treatment outcome. A multivariable regression with propensity score adjustment found an association between higher neutrophil count (103/mm3) at baseline and unfavorable outcome among HIV-seronegative patients [OR= 1.17 (95% CI= [1.06;1.30]). In addition, adjusted Cox regression found that higher baseline neutrophil count (103/mm3) was associated with unfavorable treatment outcomes overall and among HIV-seronegative patients (HR= 1.16 (95% CI = [1.05;1.27]). Increased neutrophil count prior to anti-TB treatment initiation was associated with unfavorable treatment outcomes, particularly among HIV-seronegative patients. Further prospective studies evaluating neutrophil count in response to drug treatment and association with TB treatment outcomes are warranted.
DOI: 10.1155/2017/8619307
发表时间: 2017
影响因子: 4.6
作者:
Lyadova IV
通讯作者: Lyadova IV
DOI: 10.1093/infdis/158.2.366
发表时间: 1988-08-01
影响因子: 6.4
作者:
BARNES, PF;LEEDOM, JM;MODLIN, RL
通讯作者: MODLIN, RL
DOI: 10.21037/jtd.2017.12.65
发表时间: 2018-01-01
影响因子: 2.5
作者:
Han, Yeji;Kim, Soo Jung;Lee, Jin Hwa
通讯作者: Lee, Jin Hwa
DOI: 10.1097/ede.0000000000000053
发表时间: 2014-03-01
期刊: EPIDEMIOLOGY
影响因子: 5.4
作者:
Naimi, Ashley I.;Moodie, Erica E. M.;Kaufman, Jay S.
通讯作者: Kaufman, Jay S.
DOI: 10.1371/journal.pone.0070630
发表时间: 2013
期刊: PloS one
影响因子: 3.7
作者:
Bloom CI;Graham CM;Berry MP;Rozakeas F;Redford PS;Wang Y;Xu Z;Wilkinson KA;Wilkinson RJ;Kendrick Y;Devouassoux G;Ferry T;Miyara M;Bouvry D;Valeyre D;Gorochov G;Blankenship D;Saadatian M;Vanhems P;Beynon H;Vancheeswaran R;Wickremasinghe M;Chaussabel D;Banchereau J;Pascual V;Ho LP;Lipman M;O'Garra A
通讯作者: O'Garra A