Background The role of macrolide/β-lactam combination therapy in community-acquired pneumonia (CAP) of moderate severity is a matter of debate. Macrolides expand the coverage to atypical pathogens and attenuate pulmonary inflammation, but have been associated with cardiovascular toxicity and drug interactions. We developed a decision tree based on aetiological and clinical parameters, which are available ex ante to support a personalised decision for or against macrolides for the best clinical outcome of the individual patient. Methods We employed machine learning in a cross-validation scheme based on a well-balanced selection of 4898 patients after propensity score matching to data available on admission of 6440 hospitalised patients with moderate severity (non-intensive care unit patients) from the observational, prospective, multinational CAPNETZ study. We aimed to improve the primary outcome of 180-day survival. Results We found a simple decision tree of patient characteristics comprising chronic cardiovascular and chronic respiratory comorbidities as well as leukocyte counts in the respiratory secretion at enrolment. Specifically, we found that patients without cardiovascular or patients with respiratory comorbidities and high leukocyte counts in the respiratory secretion benefit from macrolide treatment. Patients identified to be treated in compliance with our treatment suggestion had a lower mortality of 27% (OR 1.83, 95% CI 1.48–2.27; p<0.001) compared to the observed standard of care. Conclusion Stratifying macrolide treatment in patients following a simple treatment rule may lead to considerably reduced mortality in CAP. A future randomised controlled trial confirming our result is necessary before implementing this rule into the clinical routine. A simple decision tree distinguishes patients who benefit from macrolides from those who are harmed. In our model, the rule can lower mortality by 30% in hospitalised CAP patients with moderate disease. However, prospective evaluation is required. http://bit.ly/2kG5xA5

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住院CAP患者的大环内酯联合治疗？机器学习支持的个性化方法

背景 大环内酯/β-内酰胺联合治疗在中等严重程度的社区获得性肺炎 (CAP) 中的作用是一个有争议的问题。大环内酯类将覆盖范围扩大到非典型病原体并减轻肺部炎症，但与心血管毒性和药物相互作用有关。我们开发了一个基于病因和临床参数的决策树，这些参数在事前可用，以支持针对大环内酯类药物的个性化决策，以获得个体患者的最佳临床结果。方法 我们在交叉验证方案中采用机器学习，该方案基于对 4898 名患者的均衡选择，在倾向评分与来自观察性、前瞻性研究的 6440 名中等严重程度住院患者（非重症监护病房患者）的入院数据相匹配后, CAPNETZ 跨国研究。我们旨在改善 180 天生存的主要结果。结果我们发现了一个简单的患者特征决策树，包括慢性心血管和慢性呼吸道合并症以及入组时呼吸道分泌物中的白细胞计数。具体来说，我们发现没有心血管疾病的患者或有呼吸道合并症和呼吸道分泌物中白细胞计数高的患者可以从大环内酯类药物治疗中获益。与观察到的护理标准相比，确定按照我们的治疗建议进行治疗的患者死亡率较低，为 27%（OR 1.83，95% CI 1.48–2.27；p<0.001）。结论 遵循简单治疗规则对患者进行分层大环内酯治疗可能会显着降低 CAP 的死亡率。在将此规则应用于临床常规之前，需要进行未来的随机对照试验来确认我们的结果。一个简单的决策树将受益于大环内酯类药物的患者与受到伤害的患者区分开来。在我们的模型中，该规则可以将住院的中度 CAP 患者的死亡率降低 30%。但是，需要进行前瞻性评估。http://bit.ly/2kG5xA5