The predictive values of admission characteristics for 28-day all-cause mortality in septic patients with diabetes mellitus: a study from the MIMIC database

被引:1
|
作者
Yang, Chengyu [1 ]
Jiang, Yu [2 ]
Zhang, Cailin [1 ]
Min, Yu [3 ]
Huang, Xin [1 ]
机构
[1] Sichuan Univ, West China Hosp 4, West China Sch Publ Hlth, Chengdu, Sichuan, Peoples R China
[2] Chinese Peoples Liberat Army China PLA Med Sch, Dept Cardiol, Beijing, Peoples R China
[3] Sichuan Univ, West China Hosp, Natl Clin Res Ctr Geriatr, Canc Ctr,Dept Biotherapy, Chengdu, Sichuan, Peoples R China
来源
关键词
sepsis; diabetes mellitus; glycosylated hemoglobin; intensive care unit; all-cause mortality; CELL DISTRIBUTION WIDTH; SERUM ANION GAP; IMMUNE DYSFUNCTION; SEPSIS; BICARBONATE; INFECTIONS; ACIDOSIS;
D O I
10.3389/fendo.2023.1237866
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BackgroundSeptic patients with diabetes mellitus (DM) are more venerable to subsequent complications and the resultant increase in associated mortality. Therefore, it is important to make tailored clinical decisions for this subpopulation at admission. MethodData from large-scale real-world databases named the Medical Information Mart for Intensive Care Database (MIMIC) were reviewed. The least absolute selection and shrinkage operator (LASSO) was performed with 10 times cross-validation methods to select the optimal prognostic factors. Multivariate COX regression analysis was conducted to identify the independent prognostic factors and nomogram construction. The nomogram was internally validated via the bootstrapping method and externally validated by the MIMIC III database with receiver operating characteristic (ROC), calibration curves, decision curve analysis (DCA), and Kaplan-Meier curves for robustness check. ResultsA total of 3,291 septic patients with DM were included in this study, 2,227 in the MIMIC IV database and 1,064 in the MIMIC III database, respectively. In the training cohort, the 28-day all-cause mortality rate is 23.9% septic patients with DM. The multivariate Cox regression analysis reveals age (hazard ratio (HR)=1.023, 95%CI: 1.016-1.031, p<0.001), respiratory failure (HR=1.872, 95%CI: 1.554-2.254, p<0.001), Sequential Organ Failure Assessment score (HR=1.056, 95%CI: 1.018-1.094, p=0.004); base excess (HR=0.980, 95%CI: 0.967-0.992, p=0.002), anion gap (HR=1.100, 95%CI: 1.080-1.120, p<0.001), albumin (HR=0.679, 95%CI: 0.574-0.802, p<0.001), international normalized ratio (HR=1.087, 95%CI: 1.027-1.150, p=0.004), red cell distribution width (HR=1.056, 95%CI: 1.021-1.092, p=0.001), temperature (HR=0.857, 95%CI: 0.789-0.932, p<0.001), and glycosylated hemoglobin (HR=1.358, 95%CI: 1.320-1.401, p<0.001) at admission are independent prognostic factors for 28-day all-cause mortality of septic patients with DM. The established nomogram shows satisfied accuracy and clinical utility with AUCs of 0.870 in the internal validation and 0.830 in the external validation cohort as well as 0.820 in the septic shock subpopulation, which is superior to the predictive value of the single SOFA score. ConclusionOur results suggest that admission characteristics show an optimal prediction value for short-term mortality in septic patients with DM. The established model can support intensive care unit physicians in making better initial clinical decisions for this subpopulation.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Looking beyond 28-day all-cause mortality
    Rubenfeld G.
    Critical Care, 6 (4): : 293 - 294
  • [2] Looking beyond 28-day all-cause mortality
    Rubenfeld, G
    CRITICAL CARE, 2002, 6 (04): : 293 - 294
  • [3] Association between serum osmolality and 28-day all-cause mortality in patients with heart failure and reduced ejection fraction: a retrospective cohort study from the MIMIC-IV database
    Zou, Qi
    Li, Jiazheng
    Lin, Pengyang
    Ma, Jialiang
    Wei, Zhiliang
    Tao, Ting
    Han, Guodong
    Sun, Shougang
    FRONTIERS IN ENDOCRINOLOGY, 2024, 15
  • [4] Predictive value of the serum anion gap for 28-day in-hospital all-cause mortality in sepsis patients with acute kidney injury: a retrospective analysis of the MIMIC-IV database
    Jiang, Long
    Wang, Zhigao
    Wang, Lu
    Liu, Yan
    Chen, Dong
    Zhang, Daquan
    Shi, Xiaohui
    Xiao, Dong
    ANNALS OF TRANSLATIONAL MEDICINE, 2022, 10 (24)
  • [5] Correlation of the triglyceride-glucose index and heart rate with 28-day all-cause mortality in severely ill patients: analysis of the MIMIC-IV database
    Shao, Yuekai
    Gan, Zhikun
    Wang, Taishan
    Shao, Zhiqiang
    Yu, Hong
    Qin, Song
    Mei, Hong
    Chen, Tao
    Fu, Xiaoyun
    Liu, Guoyue
    Chen, Miao
    LIPIDS IN HEALTH AND DISEASE, 2024, 23 (01)
  • [6] PREDICTIVE VALUE OF NEUTROPHIL EXTRACELLULAR TRAP COMPONENTS FOR 28-DAY ALL-CAUSE MORTALITY IN PATIENTS WITH CARDIAC ARREST: A PILOT OBSERVATIONAL STUDY
    Li, Peijuan
    Liang, Shuangshuang
    Wang, Ling
    Guan, Xiaolan
    Wang, Jin
    Gong, Ping
    SHOCK, 2023, 60 (05): : 664 - 670
  • [7] Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database
    Shi, Caiping
    Jie, Qiong
    Zhang, Hongsong
    Zhang, Xinying
    Chu, Weijuan
    Chen, Chen
    Zhang, Qian
    Hu, Zhen
    CARDIOLOGY, 2024,
  • [8] Association between lactate dehydrogenase and 28-day all-cause mortality in patients with non-traumatic intracerebral hemorrhage: A retrospective analysis of the MIMIC-IV database
    Feng, Jia Hui
    Liu, Ren Jie
    Chen, Xuan
    BIOMOLECULES AND BIOMEDICINE, 2025, 25 (03): : 663 - 671
  • [9] Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database
    Zhengqiu Yu
    Lexin Fang
    Yueping Ding
    European Journal of Medical Research, 30 (1)
  • [10] The correlation of hemoglobin and 28-day mortality in septic patients: secondary data mining using the MIMIC-IV database
    Chen, Yu
    Chen, Lu
    Meng, Zengping
    Li, Yi
    Tang, Juan
    Liu, Shaowen
    Li, Li
    Zhang, Peisheng
    Chen, Qian
    Liu, Yongmei
    BMC INFECTIOUS DISEASES, 2023, 23 (01)