Ningbo Institute for Medicine & Biomedical Engineering Combined Innovation, Ningbo Medical Treatment Centre Lihuili Hospital, Ningbo University, Ningbo, 315040, Zhejiang, China.
School of Mechatronics Engineering, Nanchang University, Nanchang, 330031, Jiangxi, China.
J Transl Med. 2022 Jun 11;20(1):265. doi: 10.1186/s12967-022-03469-6.
Sepsis is a life-threatening syndrome eliciting highly heterogeneous host responses. Current prognostic evaluation methods used in clinical practice are characterized by an inadequate effectiveness in predicting sepsis mortality. Rapid identification of patients with high mortality risk is urgently needed. The phenotyping of patients will assistant invaluably in tailoring treatments.
Machine learning and deep learning technology are used to characterize the patients' phenotype and determine the sepsis severity. The database used in this study is MIMIC-III and MIMIC-IV ('Medical information Mart for intensive care') which is a large, public, and freely available database. The K-means clustering is used to classify the sepsis phenotype. Convolutional neural network (CNN) was used to predict the 28-day survival rate based on 35 blood test variables of the sepsis patients, whereas a double coefficient quadratic multivariate fitting function (DCQMFF) is utilized to predict the 28-day survival rate with only 11 features of sepsis patients.
The patients were grouped into four clusters with a clear survival nomogram. The first cluster (C_1) was characterized by low white blood cell count, low neutrophil, and the highest lymphocyte proportion. C_2 obtained the lowest Sequential Organ Failure Assessment (SOFA) score and the highest survival rate. C_3 was characterized by significantly prolonged PTT, high SIC, and a higher proportion of patients using heparin than the patients in other clusters. The early mortality rate of patients in C_3 was high but with a better long-term survival rate than that in C_4. C_4 contained septic coagulation patients with the worst prognosis, characterized by slightly prolonged partial thromboplastin time (PTT), significantly prolonged prothrombin time (PT), and high septic coagulation disease score (SIC). The survival rate prediction accuracy of CNN and DCQMFF models reached 92% and 82%, respectively. The models were tested on an external dataset (MIMIC-IV) and achieved good performance. A DCQMFF-based application platform was established for fast prediction of the 28-day survival rate.
CNN and DCQMFF accurately predicted the sepsis patients' survival, while K-means successfully identified the phenotype groups. The distinct phenotypes associated with survival, and significant features correlated with mortality were identified. The findings suggest that sepsis patients with abnormal coagulation had poor outcomes, abnormal coagulation increase mortality during sepsis. The anticoagulation effects of appropriate heparin sodium treatment may improve extensive micro thrombosis-caused organ failure.
脓毒症是一种危及生命的综合征，可引发高度异质的宿主反应。目前临床实践中使用的预后评估方法在预测脓毒症死亡率方面效果不佳。迫切需要快速识别高死亡率风险的患者。对患者进行表型分析将有助于制定治疗方案。
使用机器学习和深度学习技术对患者的表型进行特征描述，并确定脓毒症的严重程度。本研究使用的数据库是 MIMIC-III 和 MIMIC-IV（“重症监护医学信息集市”），这是一个大型、公共、免费的数据库。使用 K-均值聚类对脓毒症表型进行分类。基于 35 个脓毒症患者的血液检测变量，使用卷积神经网络（CNN）预测 28 天生存率，而使用仅 11 个脓毒症患者特征的双系数二次多元拟合函数（DCQMFF）预测 28 天生存率。
患者被分为四个具有明确生存预测值的聚类。第一聚类（C_1）的特点是白细胞计数低、中性粒细胞低、淋巴细胞比例最高。C_2 获得的序贯器官衰竭评估（SOFA）评分最低，生存率最高。C_3 的特点是凝血酶时间（PTT）明显延长、SIC 高，且使用肝素的患者比例高于其他聚类的患者。C_3 患者的早期死亡率较高，但长期生存率好于 C_4。C_4 包含预后最差的脓毒症凝血患者，其特点是部分凝血活酶时间（PTT）略有延长、凝血酶原时间（PT）明显延长和高脓毒症凝血疾病评分（SIC）。CNN 和 DCQMFF 模型的生存率预测准确率分别达到 92%和 82%。模型在外部数据集（MIMIC-IV）上进行了测试，表现良好。建立了基于 DCQMFF 的应用平台，用于快速预测 28 天生存率。
CNN 和 DCQMFF 准确预测了脓毒症患者的生存率，而 K-均值聚类成功识别了表型组。确定了与生存相关的不同表型和与死亡率相关的显著特征。研究结果表明，脓毒症患者出现异常凝血时预后不良，脓毒症期间异常凝血会增加死亡率。适当的肝素钠抗凝治疗可能改善广泛微血栓引起的器官衰竭。
