Page 70 - 中国全科医学2022-20

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【Abstract】 Background Severe multiple trauma prevalence has been increasing recently，which has become the
leading cause of labor force loss. Early and rapid assessment of patients' conditions will greatly affect their prognosis，which could
be significantly supported by a concise and effective visual scoring system. Objective To identify and Screen the prognostic
factors of severe multiple trauma，and use them to develop two nomograms，then improve them，and verify their clinical
application values. Methods Patients with severe multiple trauma were recruited from the general ICU and EICU，Suzhou
Ninth People's Hospital，including 321 treated during December 2015 to December 2020（model group），and 136 treated
during January to August 2021（validation group）. General data at admission and clinical data within 24 hours of admission were
retrospectively collected. Prognosis（successful or unsuccessful treatment result） was assessed at discharge. Prognostic factors
of severe multiple trauma were Screened using univariate and LASSO regression，and used to develop models using multivariate
Logistic regression with restricted cubic splines，then based on this，two nomograms were developed，and their calibration
accuracies were estimated using the bootstrap approach and decision curve analysis（DCA）. The receiver operating characteristic
（ROC） analysis with associated AUC values was used to estimate the prognostic value of two nomograms in severe multiple
trauma. External verification of the nomograms was carried out in the validation group to evaluate their clinical application values.
Results （1） In the model group，successful and unsuccessful treatment results occurred in 244 and 77 cases，respectively.
LASSO regression with multivariate Logistic regression analyses showed that age （OR=1.028），Glasgow Coma Score （GCS）
（OR=0.616），arterial lactate（OR=1.202），platelet count （OR=3.888） and Injury Severity Score （ISS） （OR=1.104）
were associated with the prognosis of severe multiple trauma（P<0.05）. Hosmer-Lemeshow test indicated that this model fitted
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the data well（χ =2.717，P=0.951），and was appropriate for developing a static and network-based dynamic nomogram （nomogram
1）. LASSO plus multivariate regression analyses with restricted cubic splines revealed that age and GCS had nonlinear correlation
with treatment results（P=0.027，0.001），and the fit of this model was satisfactory assessed using Hosmer-Lemeshow test
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（χ =2.468，P=0.932），and was appropriate for developing a static and network-based dynamic nomogram （nomogram 2）.
Calibration charts showed that the standard curve fitted well with the probability calibration curves of nomograms 1 and 2（absolute
error=0.010，and 0.019），indicating that the calibration accuracies of both models were good. The AUC of nomogram 1 in
predicting the prognosis of severe multiple trauma was 0.963〔95%CI（0.936，0.981）〕with 0.414 was the optimal cut-off
value，and that of nomogram 2 was 0.974〔95%CI（0.949，0.988）〕 with 0.261 as the optimal cut-off value. Nomogram 2
had a larger AUC value than nomogram 1 （Z=-2.400，P=0.016）. The DCA results showed that under any threshold probability
（0-100%），the net benefit rate of nomogram 2 was higher than that of nomogram 1. （2） In the validation group，successful
and unsuccessful treatment results occurred in 104 and 32 cases，respectively. The AUC of nomogram 2 predicting the prognosis
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of severe multiple trauma was 0.949〔95%CI （0.898，0.979）〕. And the model fitted well （χ =5.813，P=0.668）revealed
by Hosmer-Lemeshow test. The AUC of nomogram 2 in predicting the prognosis of severe multiple trauma in model and validation
groups had insignificant changes （Z=1.124，P=0.263）. Conclusion Age，GCS，arterial lactate，platelet count and ISS
were prognostic factors of severe multiple trauma，and the two nomograms in this study based on these five factors had good
prognosis predictive value. In particular，the optimized nomogram 2 had higher accuracy （the network-based dynamic version
is available at https://yinfxyz.shinyapps.io/dynnomapp2/），which was rapid，and easy-to-use，and it can help clinicians to
identify patients early and improve the prognosis of patients.
【Key words】 Multiple trauma；Wounds and injuries；Prognosis；Least absolute shrinkage and selection operator
regression；Restricted cubic spline；Nomogram；Models，statistical

《中国统计年鉴 2020》报告，2019 年我国部分地 可以用来评估多发伤患者的病情严重程度及预后，包括
区城乡居民主要疾病死亡率及死因构成中损伤和中毒居 基于生理学因素的改良创伤评分（RTS）、急性生理与
第 5 位，城市居民损伤和中毒的死亡率为 36.06/10 万， 慢性健康评分（APACHE），基于解剖因素的简明损伤
而在农村居民中高达 51.08/10 万 ［1］ 。在发达国家，创 定级标准（AIS）、损伤严重程度评分（ISS）和两者兼
伤已成为中青年人群第 1 位致死原因，因车祸、生产安 有的创伤严重程度评分（TRISS）、创伤严重程度特征
全事故等造成的人体损害事件逐年攀升，其造成的社会 评分（ASCOT）等 ［3-5］ 。DE MUNTER 等 ［6］ 在分析了
危害和劳动力损失远大于任何一类疾病 ［2］ 。对于创伤 1990—2015 年发表的 90 篇文献后发现，基于双重因素
患者，尤其是严重多发伤患者病情变化迅速、病死率 的 TRISS、ASCOT 较仅有生理或解剖单一因素的评分系
高，如何早期判断伤情严重程度将直接影响临床医师的 统在评估预后方面具有更高的准确性，但其仍需较多的
诊疗和患者的预后。近年来越来越多的评分系统被证实 变量，一定程度上增加了数据遗漏或不可靠的可能，并