Development of A Plasma Osmolality Prediction Model for the Risk of In-hospital Death in Critically Ill Patients with Acute ST-segment Elevation Myocardial Infarction

Ji JIA; Siming TAO

doi:10.12259/j.issn.2095-610X.S20221212

Volume 43 Issue 12

Dec. 2022

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Ji JIA, Siming TAO. Development of A Plasma Osmolality Prediction Model for the Risk of In-hospital Death in Critically Ill Patients with Acute ST-segment Elevation Myocardial Infarction[J]. Journal of Kunming Medical University, 2022, 43(12): 58-65. doi: 10.12259/j.issn.2095-610X.S20221212

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Development of A Plasma Osmolality Prediction Model for the Risk of In-hospital Death in Critically Ill Patients with Acute ST-segment Elevation Myocardial Infarction

doi: 10.12259/j.issn.2095-610X.S20221212

Ji JIA,
Siming TAO^,

Dept. of Cardiovascular Medicine，Affiliated Hospital of Yunnan University， Kunming Yunnan 650021，China

Received Date: 2022-05-08
Available Online: 2022-12-05
Publish Date: 2022-12-25

Abstract

Abstract

Objective To develop and validate a plasma osmolality prediction model for the risk of in-hospital death in critically ill patients with acute ST-segment elevation myocardial infarction. Methods By retrospective analysis of patients' electronic medical record data, patients with severe STEMI admitted to the Department of Cardiovascular Medicine, Affiliated Hospital of Yunnan University from January 2015 to December 2020 were selected. General information, laboratory examination, comorbid diseases and medications of the patients were extracted to screen the risk factors of in-hospital death of severe STEMI patients and establish a prediction model. Results LASSO regression and multivariate Logistic regression were used and identified albumin （ALB）, leukocyte （WBC）, platelet （PLT）, serum creatinine （Scr）, statins medication, angiotensin-converting enzyme inhibitors （ACEI） medication or angiotensin-converting enzyme receptor antagonists （ARB）, and plasma osmosis Pressure as independent predictor of in-hospital death in severe STEMI patients （P < 0.05）. A nomogram was drawn based on the 7 predictive variables. Model test results showed good differentiation and calibration, and decision curve analysis （DCA） showed a threshold probability of 5%-95%. Conclusion The prediction model has good discrimination and calibration, and can be used as a reference tool for assessing the risk of in-hospital mortality in critically ill patients with STEMI.
- Acute ST-segment elevation myocardial infarction,
- In-hospital mortality,
- Prediction model,
- Nomogram

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