Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)

BMC Emergency Medicine

Jun Lyu¹  Shuai Zheng²  Fengshuo Xu³  Shaojin Li⁴  Tao Huang⁵  Haiyan Yin⁵  Luming Zhang⁶ 

[1] Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China;Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China;School of Public Health, Shannxi University of Chinese Medicine, Xianyang, Shaanxi Province, China;Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China;School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi Province, China;Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China;Intensive Care Unit, The First Affiliated Hospital of Jinan University, 510630, Guangzhou, People’s Republic of China;Intensive Care Unit, The First Affiliated Hospital of Jinan University, 510630, Guangzhou, People’s Republic of China;Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China;

关键词: Machine learning;    Random survival forest;    Elderly;    Sepsis;    Prognosis;   

DOI  :  10.1186/s12873-022-00582-z

来源: Springer

【 摘 要 】

BackgroundElderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model’s predictive value for these patients.MethodsClinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the model was evaluated by C index and calibration curve.ResultsA total of 6,503 patients were enrolled in the study. The top 30 important variables screened by RSF were used to construct the final RSF model. The new model provided a better C-index (0.731 in the validation cohort). The calibration curve described the agreement between the predicted probability of RSF model and the observed 30-day survival.ConclusionsWe constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.

【 授权许可】

CC BY