Background Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. Methods We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature. Results After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857–0.887). External validations show AUROCs of 0.805 (95% CI, 0.787–0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837–0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836–0.877) than PCT alone (0.790; 95% CI, 0.759–0.821; P < 0.001). Conclusions Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety.

中文翻译：

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将机器学习应用于血细胞计数数据可预测 ICU 入院时的脓毒症

背景 及时诊断对于脓毒症治疗至关重要。当前的机器学习（ML）模型复杂性高且适用性有限。因此，我们仅使用全血细胞计数 (CBC) 诊断创建了一个 ML 模型。方法 我们收集了德国三级护理中心的非重症监护病房 (non-ICU) 数据（2014 年 1 月至 2021 年 12 月）。使用患者年龄、性别和 CBC 参数（血红蛋白、血小板、平均红细胞体积、白细胞和红细胞），我们训练了一个增强随机森林，该森林可预测 ICU 入院时的脓毒症。使用来自另一个德国三级护理中心和重症监护 IV 医疗信息市场数据库 (MIMIC-IV) 的数据进行了两次外部验证。使用还包括降钙素原 (PCT) 的实验室订单子集，使用 PCT 作为附加功能来训练类似模型。结果 排除后，可获得 1 381 358 个实验室请求（2016 年来自脓毒症病例）。CBC 模型显示接收者操作特征下面积 (AUROC) 为 0.872（95% CI，0.857–0.887）。外部验证显示，格赖夫斯瓦尔德大学医学院的 AUROC 为 0.805（95% CI，0.787–0.824），MIMIC-IV 的 AUROC 为 0.845（95% CI，0.837–0.852）。包含 PCT 的模型显示 AUROC（0.857；95% CI，0.836-0.877）显着高于单独 PCT（0.790；95% CI，0.759-0.821；P < 0.001）。结论 我们的结果表明，常规 CBC 结果与 ML 相结合可以显着改善脓毒症的诊断。CBC 模型可以促进非 ICU 患者的早期脓毒症预测，并且在外部验证中具有很高的稳健性。它在临床决策支持系统中的实施具有提供重要时间优势并提高患者安全的强大潜力。