Hostname: page-component-745bb68f8f-d8cs5 Total loading time: 0 Render date: 2025-02-06T07:16:41.933Z Has data issue: false hasContentIssue false
  • English
  • Français

Injury Assessment of Individuals Wounded in the Lushan Earthquake and the Emergency Department Workload: A Corresponding Correlation Study

Published online by Cambridge University Press:  22 September 2020

Shiyuan Tang ,
Fen Ni ,
Hai Hu ,
Xiaojiong Du ,
Shuheng Zhu ,
Haoyuan Wang ,
Zhendong Niu ,
Yarong He  and
Yu Cao
Shiyuan Tang
Affiliation:
Department of Emergency Medicine, Sichuan University West China Hospital, Chengdu, Sichuan, China
Fen Ni
Affiliation:
Department of Emergency Medicine, Qingdao Municipal Hospital Group, Qingdao, Shandong, China
Hai Hu
Affiliation:
Department of Emergency Medicine, Sichuan University West China Hospital, Chengdu, Sichuan, China
Xiaojiong Du
Affiliation:
Vascular Surgery Department, Sichuan University West China Hospital, Chengdu, Sichuan, China
Shuheng Zhu
Affiliation:
Department of Emergency Medicine, Cheng Du University, Chengdu, Sichuan, China
Haoyuan Wang
Affiliation:
Clinical School of Medicine, Sichuan University, Chengdu, Sichuan, China
Zhendong Niu
Affiliation:
Department of Emergency Medicine, Sichuan University West China Hospital, Chengdu, Sichuan, China
Yarong He
Affiliation:
Department of Emergency Medicine, Sichuan University West China Hospital, Chengdu, Sichuan, China
Yu Cao*
Affiliation:
Department of Emergency Medicine, Sichuan University West China Hospital, Chengdu, Sichuan, China
*
Correspondence and reprint requests to Prof Yu Cao, Department of Emergency Medicine, Sichuan University West China Hospital, Chengdu, Sichuan610041, China (email: ddtt7441@163.com).
Rights & Permissions [Opens in a new window]

Abstract

Objective:

In this study, we aimed to evaluate the correlation between the trauma score of individuals wounded in the Lushan earthquake and emergency workload for treatment. We further created a trauma score-emergency workload calculation model.

Methods:

We included data from patients wounded in the Lushan earthquake and treated at West China Hospital, Sichuan University. We calculated scores per the following models separately: Revised Trauma Score (RTS), Prehospital Index (PHI), Circulation Respiration Abdominal Movement Speech (CRAMS), Therapeutic Intervention Scoring System (TISS-28), and Nursing Activities Score (NAS). We assessed the association between values for CRAMS, PHI, and RTS and those for TISS-28 and NAS. Subsequently, we built a trauma score-emergency workload calculation model to quantitative workload estimation.

Results:

Significant correlations were observed for all pairs of trauma scoring models with emergency workload scoring models. TISS-28 score was significantly associated with PHI score and RTS; however, no significant correlation was observed between the TISS-28 score and CRAMS score.

Conclusions:

CRAMS, PHI, and RTS were consistent in evaluating the injury condition of wounded individuals; TISS-28 and NAS scores were consistent in evaluating the required treatment workload. Dynamic changes in emergency workload in unit time were closely associated with wounded patient visits.

Keywords

earthquakeemergencytrauma scoreworkload scorewounded
Type
Brief Report
Information
Disaster Medicine and Public Health Preparedness , Volume 16 , Issue 1 , February 2022 , pp. 29 - 32
Copyright
Copyright © 2020 Society for Disaster Medicine and Public Health, Inc.

Earthquakes are urgent public emergencies that may cause massive casualties. When earthquakes occur, masses of injured patients flow into the emergency departments of all levels of hospitals in a short period of time, resulting in temporary shortages of medical resources.Reference Xu1 In clinical studies, to reflect and quantify the severity of traumatic injuries, we usually use trauma scores, and some generally accepted scoring systems include Revised Trauma Score (RTS), Prehospital Index (PHI), and Circulation Respiration Abdominal Movement Speech (CRAMS).Reference Yin, Kong and Ying2-Reference Liu4 Accordingly, in this study, we evaluated the correlation between individual trauma scores of each trauma patient and the emergency workload based on the data of Lushan earthquake victims. We further proposed trauma score-emergency workload estimation models to make quantitative estimations of the workload required for each wounded patient in unit time.

METHODS

Information Source

The medical records of Lushan earthquake victims admitted to the Emergency Department of West China Hospital, Sichuan University, from 08:00 on April 20, 2013 to 20:00 on April 26, 2014 were collected. Exclusion criteria included the following: incomplete information from the original record and the inability to complete basic inclusion information or CRAMS, PHI, RTS, Therapeutic Intervention Scoring System (TISS-28), or Nursing Activities Score (NAS).

Information Collection

Patient data included visit time, leaving time, gender, age, ethnicity, and 8 indices including the capillary filling time, systolic blood pressure, pulse, respiratory status, chest and abdomen tenderness, athletic ability, speech ability, and state of consciousness measured in the CRAMS score, PHI score, and RTS. In addition, medical orders of original clinical records were collected to determine the emergency workload required by each patient according to the 28 rating points covered by the TISS-28 scale and the 32 scoring points covered by NAS.

Analytical Evaluation

Three trauma scores (CRAMS score, PHI score, and RTS) and 2 workload scores (TISS-28 score and NAS score) were calculated for each earthquake victim. Correlation analysis was performed between the 3 trauma scores and the 2 workload scores. Regression analysis was performed to correlate trauma scores and emergency workload scores for obtaining a corresponding regression model.

Statistical Analyses

Countable data are described as percentiles. Kolmogorov-Smirnov and Shapiro-Wilk tests were used to inspect whether the data conform to normal distribution. The centralized position and dispersion degree of normally distributed measurement data are described as mean ± SD, and the concentration and dispersion degree of nonnormally distributed measurement data are described as medians and size extreme values. For correlation analysis of data, Pearson’s linear correlation analysis was used for normally distributed data. Spearman rank correlation analysis was used for nonnormally distributed data. All statistical analyses were performed using SPSS22.0 software.

RESULTS

General Situation of Case Data

Information Overview

This study collected medical records of Lushan earthquake victims admitted to the emergency department of West China Hospital, Sichuan University, from 08:00 on April 20, 2013 to 20:00 on April 26, 2014. In accordance with the exclusion criteria, 310 cases were eventually included in the study (167 males [53.87%] and 143 females [46.13%]). Of the included patients, 38 were aged 0-14 y (12.6%), 198 were 15-60 y (63.87%), and 74 were >70 y (23.87%) (mean age, 18.76 ± 23.23 y).

Trauma Score

Furthermore, the 310 included patients had a median CRAMS score of 12 (5,12), median PHI score of 0 (0,10), and median RTS of 12 (4,12). Correlation analysis for the 3 trauma scores was performed (described in Table 1), and the correlation coefficient between CRAMS score and PHI score was -0.389 (P < 0.001). Thus, there was a significant correlation between the two. The correlation coefficient between CRAMS score and RTS was 0.25 (P < 0.001), indicating a significant correlation as well. A similar finding was observed between PHI score and RTS, with a correlation coefficient of -0.597 (P < 0.001) (Table 1).

TABLE 1 Correlation Analysis Between the 3 Trauma Scores

N = 310, *Indicates significant correlation (P < 0.005).

Emergency Workload Scores

According to the scoring formula for emergency workload, the included patients showed a TISS-28 score of 9 (5, 43) and NAS score of 27.9 (4.1, 126.4). Correlation analysis was performed for the 2 workload scores, and the correlation coefficient was 0.687 (P < 0.001), indicating a significant correlation between the two (Table 2).

TABLE 2 Correlation Analysis Between the 2 Emergency Workload Scores

N = 310. *Indicates significant correlation (P < 0.005).

Relationship Between Trauma Scores and Emergency Workload Scores of Individual Casualties

Correlation Analysis Between Trauma Score and Emergency Workload Score

There was a significant correlation of TISS-28 workload score with PHI score and RTS (P < 0.05), with the correlation coefficients of 0.159 and -0.126, respectively. No significant correlation between TISS-28 score and CRAMS score was observed (P > 0.05). There was a significant correlation between NAS workload score and RTS (P < 0.05; correlation coefficient, -0.119). There was no significant correlation between NAS score and CRAMS and PHI scores (P > 0.05). The correlation coefficient for TISS-28 score and PHI score was the highest (0.159), with the smallest P value (0.005).

Curve Simulation Analysis of Individual Trauma Scores and Emergency Workload Scores

According to the above-mentioned results of correlation analysis, 3 sets of trauma scores and emergency workload scores with statistical significance were obtained. Accordingly, we performed a curve simulation analysis for each score combination and derived the corresponding optimal equations based on the model’s optimal curve:

  1. (1) Optimal equation between TISS-28 score and PHI score: TISS-28 score = 8.852 − 1.405 × PHI + 0.624 × (PHI)2 − 0.035 × (PHI)3.

  2. (2) Optimal equation between TISS-28 score and RTS: TISS-28 score = 45.030 − 6.826 × RTS + 0.318 × (RTS)2.

  3. (3) Optimal equation between NAS score and RTS: NAS score = 99.61 − 13.943 × RTS + 0.655 × (RTS)2.

DISCUSSION

Rationality for Using Trauma Scores and Emergency Workload Scores

In this study, we conducted a statistical analysis of trauma score outcomes in 310 earthquake victims based on 3 models and found that there was a significant correlation between the 3 scores. Our outcomes were similar to those reported in preliminary studies.Reference Pei, Luo and Liu5 This is because the trauma scoring models selected in this study are all physiological models, focusing on vital sign indicators. The correlation coefficient between PHI score and RTS was observed to be the highest, which may potentially result from the similarity of indexes involved in the 2 models. Therefore, we believe that the 3 scoring systems have similar predictive value for trauma assessment.

The emergency workload scores for earthquake victims were statistically analyzed and the results showed that the TISS-28 score averaged 9 (5,43) points and the NAS score averaged 27.9 (4.1, 126.4) points in trauma patients, which were significantly higher than these scores in ordinary patients.Reference Ye, Lai and Liu6,Reference Su7 In addition, there was a significant correlation between the 2 scores. Although 2 workloads differ in terms of item types and scores for each item, they have been repeatedly verified worldwide and have a high reliability.

Choosing an Appropriate Trauma and Emergency Workload Scoring System

The correlation analysis suggested that the TISS-28 workload score has a significant correlation with PHI score and RTS but not with CRAMS score. Furthermore, there was a significant correlation of NAS score with RTS, but not with CRAMS score and PHI score.

With regard to emergency workload estimation, the PHI-TISS-28 scoring model had the largest correlation coefficient; therefore, we believe that this model is the preferred option. Furthermore, RTS significantly correlated with both the TISS-28 and NAS scores; thus, we believe that RTS is mostly applicable.

This study shows that there is a low correlation between trauma score and workload score (P ≤ 0.005). A higher number of cases will be included in future studies, and other more detailed methods of trauma scoring and workload calculation will be explored.

A previous study reportedReference Guo8 that CRAMS had an advantage in terms of sensitivity, specificity, and accuracy of trauma scoring. However, in that study, the evaluation of workload determined based on patients’ injury was not effective considering that the index of chest and abdomen tenderness is not easy to quantify and that the focus of real hospitals is to deal with life-threatening indicators. The analysis suggested that the TISS-28 score was correlated with PHI score and RTS, with the highest correlation with PHI score.Reference Chen, Xu and Zhao9 The NAS score was only associated with RTS, and the correlation coefficient was not the highest. This may be because NAS scores are assigned to nursing activities, such as administrative work and communication, which are closely associated with the work inside wards.

Significance of Trauma Score-Emergency Workload Estimation Models

We aim to optimize the transport of the injured during emergency medical rescue. Accordingly, if the medical institution receiving injured individuals has received the trauma score information beforehand, appropriate measures can be taken.

Given that the total amount of emergency workload is closely associated with the number and condition of wounded patients, this study screened and constructed 3 trauma score-emergency workload estimation models by identifying the correlation between trauma scores and emergency workload scores in individual patients, thus allowing the estimation of the total amount of emergency workload and the number of staff members required per unit time. This may then confirm the feasibility of using an estimation model. However, currently, there is no specific emergency workload scoring system and this study did not involve selecting an optimal emergency workload scoring system. Therefore, the best trauma score-emergency workload estimation model remains unknown. However, considering the correlation coefficient between the PHI score and TISS-28 score, we believe that the PHI-TISS-28 estimation model may have better prospects for further optimization and application.

This is a preliminary study of the trauma score-emergency workload calculation model. We hope to explore other more detailed scoring methods to improve the evaluation model in the future.

References

REFERENCES

Xu, Ru-xiang, ed. Earthquake Disaster Medicine - Review and Experience Summary of Relief from the Wenchuan Earthquake. Beijing: People’s Military Medical Press; 2009 (in Chinese).Google Scholar
Yin, Ting-ting, Kong, Yue, Ying, Ke-man. Research status of prehospital trauma scoring. Nursing Journal of Chinese People’s Liberation Army. 2011;(1):21–24 (in Chinese).Google Scholar
Lang, Zhi-gang, Xiao, Yao. Trauma score in the application of emergency earthquake evaluation. Sichuan Med J. 2013;34(11):1654–1656.Google Scholar
Liu, Bu-bao. Advances in prehospital trauma score. Chin J Conval Med. 2014,23(6):492–494 (in Chinese).Google Scholar
Pei, Hui, Luo, Zhi-yi, Liu, Bao-chi. Clinic study of four types of pre-hospital trauma scores of emergency trauma patients. Chin J Clin. 2011;(15):4394–4400.Google Scholar
Ye, Hui-li, Lai, Jiang-ming, Liu, Yan-ling. Application of treatment intervention scoring system in ICU nursing workload evaluation. Mod Clin Nurs. 2010;(5):72–73 (in Chinese).Google Scholar
Su, Ruo-lan. Application of nursing activity evaluation scale in survey of nursing workload in IC. Chin Nurs Res. 2008,22(9):2329–2331.Google Scholar
Guo, Xiao-wei. Advances in trauma scoring. Chin J Bone Joint Inj. 2013;28(4):399–400 (in Chinese).Google Scholar
Chen, Liming, Xu, Guoying, Zhao, Fengqin, et al. Research progress on influencing factors of nursing workload. Chin Nurs Res. 2013;27(9A):2696–2698.Google Scholar
Figure 0

TABLE 1 Correlation Analysis Between the 3 Trauma Scores

Figure 1

TABLE 2 Correlation Analysis Between the 2 Emergency Workload Scores