Cooper Medical School of Rowan University, Camden, NJ, USA.
Lenox Hill Hospital, New York, NY, USA.
Helicobacter. 2021 Feb;26(1):e12769. doi: 10.1111/hel.12769. Epub 2020 Nov 9.
Regional variation in Helicobacter pylori resistance patterns is a significant contributing factor for the ineffectiveness of traditional treatments. To improve treatment outcomes, we sought to create an individualized, susceptibility-driven therapeutic approach among our patient population, which is one of the poorest in the nation. It is medically underserved, minority-predominant and has high incidence of H pylori infection.
We compiled various factors involved in the antibiotic resistance of H pylori from literature. We then created a predictive model to customize therapies based on analyzed data from 2,014 H pylori patients with respect to several of these factors. The predictions of the model were further tested with analysis of patient stool samples.
A clear pattern of H pylori prevalence and antibiotic resistance was observed in our patients. We observed that majority of H pylori patients were women (62%) and over the age of 40 years (80%). 30% and 36% of the H pylori patients were African American and Hispanic, respectively. A median household income of less than $54,000, past H pylori infection, previous use of certain antibiotics for any infection decreased the chance of eradication. Results of the stool testing were consistent with model predictions (90% accuracy).
This model demonstrates the predictive accuracy of H pylori infection and antibiotic resistance based on patient attributes and previous treatment history. It will be useful to formulate customized treatments with predicted outcomes to minimize failures. Our community attributes may contribute toward broad applicability of model for other similar communities.
幽门螺杆菌耐药模式的地域差异是导致传统治疗无效的一个重要因素。为了提高治疗效果,我们试图在我们的患者群体中创建一种个体化的、基于药敏的治疗方法,因为我们的患者群体是全国最贫困的群体之一。该群体医疗服务不足,以少数族裔为主,且幽门螺杆菌感染率较高。
我们从文献中整理了与幽门螺杆菌抗生素耐药性相关的各种因素。然后,我们创建了一个预测模型,根据 2014 名幽门螺杆菌患者的分析数据,针对其中的一些因素来定制治疗方案。我们还通过对患者粪便样本的分析来进一步测试模型的预测结果。
我们的患者中观察到了明显的幽门螺杆菌流行和抗生素耐药模式。我们发现,大多数幽门螺杆菌患者为女性(62%)且年龄超过 40 岁(80%)。30%和 36%的幽门螺杆菌患者分别为非裔美国人和西班牙裔。家庭收入中位数低于 54000 美元、过去有幽门螺杆菌感染史、过去因任何感染而使用过某些抗生素,这些因素降低了根除的可能性。粪便检测结果与模型预测结果一致(准确率为 90%)。
该模型基于患者特征和既往治疗史,展示了幽门螺杆菌感染和抗生素耐药性的预测准确性。它将有助于制定具有预测结果的个体化治疗方案,以最大限度地减少失败。我们的社区特征可能有助于该模型在其他类似社区的广泛适用性。