干旱区生态环境知识资源中心(Arid): Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database

Arid

DOI	10.1186/s12871-024-02467-z
	Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database
	Shi, Jincun; Chen, Fujin; Zheng, Kaihui; Su, Tong; Wang, Xiaobo; Wu, Jianhua; Ni, Bukao; Pan, Yujie
通讯作者	Pan, YJ
来源期刊	BMC ANESTHESIOLOGY
ISSN	1471-2253
出版年	2024
卷号	24 期号:1
英文摘要	BackgroundThe duration of hospitalization, especially in the intensive care unit (ICU), for patients with diabetic ketoacidosis (DKA) is influenced by patient prognosis and treatment costs. Reducing ICU length of stay (LOS) in patients with DKA is crucial for optimising healthcare resources utilization. This study aimed to establish a nomogram prediction model to identify the risk factors influencing prolonged LOS in ICU-managed patients with DKA, which will serve as a basis for clinical treatment, healthcare safety, and quality management research.MethodsIn this single-centre retrospective cohort study, we performed a retrospective analysis using relevant data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Clinical data from 669 patients with DKA requiring ICU treatment were included. Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. Subsequently, the selected variables were subjected to a multifactorial logistic regression analysis to determine independent risk factors for prolonged ICU LOS in patients with DKA. A nomogram prediction model was constructed based on the identified predictors. The multivariate variables included in this nomogram prediction model were the Oxford acute severity of illness score (OASIS), Glasgow coma scale (GCS), acute kidney injury (AKI) stage, vasoactive agents, and myocardial infarction.ResultsThe prediction model had a high predictive efficacy, with an area under the curve value of 0.870 (95% confidence interval [CI], 0.831-0.908) in the training cohort and 0.858 (95% CI, 0.799-0.916) in the validation cohort. A highly accurate predictive model was depicted in both cohorts using the Hosmer-Lemeshow (H-L) test and calibration plots.ConclusionThe nomogram prediction model proposed in this study has a high clinical application value for predicting prolonged ICU LOS in patients with DKA. This model can help clinicians identify patients with DKA at risk of prolonged ICU LOS, thereby enhancing prompt intervention and improving prognosis.
英文关键词	Diabetic ketoacidosis Intensive care unit Length of stay Nomogram prediction model MIMIC-IV database
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001178615600002
WOS关键词	HOSPITALIZATIONS ; HYPERGLYCEMIA ; MORTALITY
WOS类目	Anesthesiology
WOS研究方向	Anesthesiology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403041
推荐引用方式 GB/T 7714	Shi, Jincun,Chen, Fujin,Zheng, Kaihui,et al. Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database[J],2024,24(1).
APA	Shi, Jincun.,Chen, Fujin.,Zheng, Kaihui.,Su, Tong.,Wang, Xiaobo.,...&Pan, Yujie.(2024).Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database.BMC ANESTHESIOLOGY,24(1).
MLA	Shi, Jincun,et al."Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database".BMC ANESTHESIOLOGY 24.1(2024).