Identifying a Clinical Risk Triage Score for Adult Emergency Department

Zhu, Aiqun; Liu, Xiao; Zhang, Jingping

doi:10.1177/10547738211003273

Cited by 1 publication

(1 citation statement)

References 40 publications

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“…At present, in the research on the related factors of early identi cation of critical emergency patients, the factors involved in identi cation include: temperature, HR, R, SBP, SpO2, pain score, consciousness, pupil, mental state, blood sugar, blood routine, creatinine, blood potassium, etc., but there is no consensus yet [3,8,[13][14] . In this study, through multivariate logistic regression analysis, it is found that the risk factors for early judgment of critical risk of emergency patients are gender, age,HR, R, SBP, SpO2, consciousness, pupil, mental state and pain score, while temperature, tip, urine volume and hyperhidrosis are not independent risk factors.…”

Section: Risk Factors Of Prediction Modelmentioning

confidence: 99%

Discussion on the prediction model of early judgment of critical risk in emergency patients

Li,

Lv,

et al. 2024

Preprint

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Objective: To establish a predictive model for early assessment of critical risk in emergency patients and evaluate its clinical clinical benefits. Method: Clinical data of 3859 patients who visited the emergency department at Hebei General Hospital from November 2021 to December 2021 were selected. The enrolled patients were randomly divided into a training set (2703 cases) and a validation set (1156 cases) in a 7:3 ratio. In the training set, a predictive model was established based on the results of multivariate logistic stepwise regression analysis. At the same time, risk levels were divided, and the predictive efficacy and clinical benefits of the predictive model were verified. Result: There was no statistically significant difference in clinical data between the training and validation set (P>0.05), indicating comparability between the two groups. Multivariate logistic stepwise regression analysis showed that Gender, age, HR, R, SBP, SPO2, consciousness, pupil, mental state, and pain score were independent risk factors for early assessment of critical risk (P<0.05), and a predictive model was established based on this. Using conditional inference tree, critical risks are classified into low risk (P≤0.075), medium risk (0.075＜P≤0.656), and high risk (P>0.656). Furthermore, the prediction model was internally validated in both the training and validation sets, with a training set AUC of 0.926 [95% CI (0.912-0.939), P<0.001] and a validation set AUC of 0.911 [95% CI (0.886-0.936), P<0.001], indicating good discrimination of the prediction model; The calibration curve of the training set fits the standard curve, while the calibration curve of the validation set model slightly deviates from the standard curve. In the Hosmer Limeshow test, the results of the two groups were P=0.180 and P=0.546, respectively, indicating good calibration of the predicted model; The DCA curve suggests that both groups of people can achieve good clinical benefits. Conclusion: Establishing a predictive model for early assessment of emergency critical risk is helpful for early identification and intervention of emergency critical patients.

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Section: Risk Factors Of Prediction Modelmentioning

confidence: 99%