Bacteremia, or bloodstream infection, is a serious side-effect of chemotherapy and a leading cause of death in cancer patients. It is especially common in patients receiving high-dose chemotherapy to prepare forhematopoietic stem cellor bone marrow transplantation to treat hematological diseases.
Bacteria are thought to enter the bloodstream through intestinal lesions due to chemotherapy-induced inflammation of the membrane lining the digestive tract. Once the infection begins, patients’ own immune systems are depleted and are often unable to fight off the pathogens.
Patients with a bloodstream infection have little recourse other than to take various types of antibiotics in an attempt to control the infection. Unfortunately the antibiotics do not always work.
Infection rates are typically between 20-40% or higher in these populations, with subsequent mortality rates as high as 15-30%. Sadly, many people die from the infections that they get as a result of cancer treatment for certain types of cancer.
最危险的是谁?
目前没有很好的方法可以预测哪些患者将获得血液感染，因此在诊所之间，预防方案差异很大。
目前没有很好的方法可以预测哪些患者将获得血液感染，因此在诊所之间，预防方案差异很大。在某些诊所中，所有患者在整个化疗中均应给予预防性抗生素。
在其他诊所中，很少有或没有给予预防性抗生素，因为抗生素会导致患者菌群的抗生素耐药性升高。一些患者还接受了非常昂贵的药物来刺激嗜中性粒细胞的生长，攻击和吃传染性细菌的白细胞的生长，但医生没有定量的方法来知道哪些患者应该接受这些药物以及何时何时接受这些药物。
Therefore there is an unmet need to predict which patients are most likely to acquire bloodstream infections so that the right patients can receive appropriate therapies to reduce the risk of bloodstream infection and sepsis.
我们做了什么，我们发现了什么？
In这项研究我们开始了解the starting configuration of the gut microbiota, before a patient begins treatment, relates to risk of bloodstream infection. We began by collecting fecal samples from patients withnon-Hodgkin’s lymphoma在他们开始化疗之前。我们对细菌DNA进行了测序，以测量每个患者肠道中细菌生态系统的健康。
然后使用机器学习工具，然后创建了一种算法，可以通过研究一组患者来学习哪些细菌是好是坏的
关于40% of our subjects acquired a bloodstream infection. But interestingly we found that those patients may have had more than bad luck going against them. They had significantly different mixtures of gut bacteria than the patients that did not get infections.
然后，我们使用机器学习工具创建了一种算法，可以通过研究一组患者来学习哪些细菌是好是坏的，然后可以预测其以前从未见过的新患者是否会感染，精度约为85％。
未来的工作
Although our predictive model was quite robust, our findings are still based on a limited number of patients with a single type of chemotherapy at a single clinic. The next step is to validate our approach in a much larger cohort including patients with different cancer types, different treatment types, and from multiple treatment centers.
我们也不知道这些细菌是否在感染中起着任何因果作用，还是仅仅是患者中其他诱发疾病的生物标志物。
Nonetheless, we expect that our results will interest clinicians involved in management of cancer patients, and will break ground for clinical research into the development of similar microbiome-based diagnostic and prognostic models in other diseases.
注释