Performance of Modified Early Warning Score (MEWS) and Circulation, Respiration, Abdomen, Motor, and Speech (CRAMS) score in trauma severity and in-hospital mortality prediction in multiple trauma patients: a comparison study

Ethical statement

This retrospective research project was approved by the research ethics committee of Shanghai Ninth People’s Hospital, affiliated to Shanghai JiaoTong University School of Medicine (approval no.: 2018146-T132). The need for informed consent from study participants was waived. All data were processed anonymously.

Settings and subjects, and study design

Shanghai Ninth People’s Hospital is a non-profit university-affiliated tertiary teaching hospital located in Huangpu and Baoshan district in Shanghai, China. Its ED has 50 beds and is the second largest emergency center in Shanghai, equipped with medical detection devices and advanced emergency treatment instruments (www.9hospital.com.cn). The hospital admits approximately 20,000 trauma patients, 1.5% of which suffer from multiple trauma each year.

All adult medical patients consecutively admitted to the ED at Shanghai Ninth People’s Hospital, who had been conclusively diagnosed with multiple trauma from January 2014 to April 2018, were studied retrospectively to compare MEWS and CRAMS score outcomes for trauma injury severity and in-hospital mortality prediction. Here, imaging examinations, such as computed tomography (CT) scan, were used to diagnose multiple trauma by checking for the presence of two or more separate injuries, and if one or a combination of more than one endangered the patient’s life.

Figure 1 shows the flow diagram of the study. Inclusion criteria were: clear history of trauma, and final diagnosis of multiple injuries examined by imaging; age not younger than 16 years; complete clinical and medical history; no stroke; no dysfunction of the heart, liver, kidney, or other important organs before injuries; and no sepsis, pneumonia, and other histories of systemic infections before injuries. This study focused on the outcomes of multiple trauma patients so that only patients finally diagnosed with multiple trauma were included. The heart rate (HR) and systolic blood pressure (SBP) in the MEWS and SBP in the CRAMS is based on a range of normal adult values, so only patients aged sixteen years or older were included. Missing data with missing values of all vital signs related to MEWS and CRAMS score were excluded because they could not be imputed. Further, patients with stroke; dysfunction of the heart, liver, kidney, or other important organs before injuries; and sepsis, pneumonia, and other history of systemic infections before injuries were excluded, because it was difficult to identify the cause-and-effect of the in-hospital mortality and trauma severity. Further, we aimed to investigate in-hospital mortality, so dead-on-arrival patient data were excluded.

The MEWS and CRAMS score were calculated for each subject based on the corresponding measured variables in their medical record (see sections on “MEWS” and “CRAMS score”). The medical records and calculated scores were saved to the database for further statistical analysis.

To investigate the outcome of in-hospital mortality prediction, the subjects were divided into survival and non-survival groups based on living condition within 28 days of hospitalization. Meanwhile, to investigate the outcome of trauma severity prediction, the same subjects were grouped into minor trauma and severe trauma groups based on the ISS—a “gold standard” among the anatomic injury severity indicators that is widely used in clinical science. The ISS was calculated using electronic medical records based on the diagnosis by imaging detection as well as surgical results. The ISS was only used for trauma classification in this study. Among the subjects, an ISS score < 16 was classified as minor trauma, while an ISS score ≥ 16 was classified as severe trauma (Baker et al., 1974; Gennarelli & Wodzin, 2006; Baker & O’Neill, 1976; Copes et al., 1988).

MEWS

The MEWS is widely used in the clinical setting as a quantified scoring system based on HR (beats per minute), SBP (mmHg), respiratory rate (RR, cycles per minute), temperature (T, °C), and AVPU. As reported previously, the AVPU is estimated from the GCS as follows: A = 14–15, V = 9–13, P = 4–8, U = 3 (Kelly, Upex & Bateman, 2004; McNarry & Goldhill, 2004; Raman et al., 2011; Wasserman et al., 2015; Zadravecz et al., 2015). The corresponding score, ranging from zero to three, for each variable is shown in Table 1 (Subbe et al., 2001; Clemmer et al., 1985).

CRAMS score

The CRAMS score was calculated based on the following five variables: circulation, RR, abdomen, motor, and speech. Among these, circulation, RR, and speech are particularly important parameters for monitoring the health status of trauma patients (Gormican, 1982; Clemmer et al., 1985; Peng et al., 2017). The corresponding score, ranging from zero to two, for each variable is shown in Table 2. In contrast to MEWS, a lower CRAMS score indicates worse state of patients.

Statistical analysis

The performance of MEWS and CRAMS score for in-hospital mortality and trauma severity prediction among multiple trauma patients were compared. Categorical variables were compared using Pearson’s chi-squared test and were described as frequencies (%). Numerical variables were compared by the non-parametric Mann–Whitney’s U-test and reported as median (interquartile range (IQR)).

Modified Early Warning Score and CRAMS score for all eligible subjects were computed based on Tables 1 and 2 and were compared using the non-parametric Mann–Whitney’s U-test and were reported as median (IQR).

Missing data with missed values of all four vital signs (T, HR, RR, and SBP) were excluded because they could not be imputed. Multiple imputation was performed to handle the missing data with missed values containing less than four vital signs.

To investigate the predictive values of MEWS and CRAMS score for trauma severity, logistic regression analysis was performed and the areas under the receiver operating characteristic curve (AUROCs) were evaluated for minor and severe trauma. To investigate the predictive values of MEWS and CRAMS score for in-hospital mortality, the same logistic regression and AUROC analysis framework was used based on survival and non-survival. The following model was employed for logistic regression analysis: (1) $$p=\frac{1}{1+e^{-({\text{β}}_0+{\text{β}}_1{\text{X}}_1)}}$$

where β₀ is the intercept; β₁ is the score coefficient; and X₁ is the score.

The R version 3.5.2 (www.r-project.org), a free software environment for statistical computing and graphics, was used to perform the logistic regression, and the R project package pROC (Robin et al., 2018) was used to compute AUROCs, and sensitivity, specificity, and accuracy rates for the corresponding optimal cut-off points.

Setting and subjects

Figure 2 describes our study population. Medical records of 1,269 patients with multiple trauma were collocated, and 1,127 cases satisfying the inclusion criteria were included. The study population comprised 73.7% males, with a median (IQR) age of 48 (38–59). All patients underwent ground transportation without air transportation. Most patients (91.5%) were transported by ambulance. The median (IQR) length of stay (LOS) of the patients was 12 (4–21) days. Patients in severe trauma group (LOS, 14.5 (3–25) days) were admitted for a longer duration than those in the minor trauma group (LOS, 9 (5–15) days). Majority of patients sustained injuries in motor vehicle crashes (55.5%). The primary injury sites of both survivors and non-survivors were the head and neck (39.7% vs. 70.2%, respectively), while those of minor trauma and severe trauma populations were bony pelvis and extremities (43.1%) and the head and neck (58.3%), respectively. There were 51 (4.5%) patients with missing data, including 30 with missing values of three vital signs, 10 with missing values of two vital signs, and 11 with missing values of one vital sign.

To investigate the outcome of in-hospital mortality prediction, the 1,127 cases were divided into survival and non-survival groups. The 1,127 eligible cases included 946 (83.9%) survivors and 181 (16.1%) non-survivors. Meanwhile, to investigate the outcome of trauma severity prediction, the same 1,127 cases were divided into minor trauma (ISS score < 16) (415 (36.8%) patients) and severe trauma (712 (63.2%)) groups.

Table 3 shows the characteristics of the study population in the survival and non-survival groups. Comparison of the survivors and non-survivors revealed the following factors to be statistically significant (p < 0.05) (Median (IQR))–RR: 20 (18–20) vs. 25 (20–29); temperature: 37.0 (36.8–37.0) vs. 36.5 (36.0–37.0) °C; and AVPU: 0 (0–0) vs. 3 (2–3).

Table 4 shows the characteristics of the study population in minor trauma and severe trauma groups. Upon comparison of minor trauma and severe trauma, the following factors were found to be statistically significant (p < 0.05) (Median (IQR))–SBP: 136 (121–151) vs. 127 (107–145) mmHg; RR: 20 (19–20) vs. 20 (18–21); HR: 82(75–90) vs. 85 (75–100); temperature: 37.0 (36.8–37.0) vs. 36.8 (36.5–37.0) °C; and AVPU: 0 (0–0) vs. 0 (0–2).

MEWS and CRAMS score

Table 3 and 4 show the MEWS and CRAMS score of the study population. Comparing survivor and non-survivor populations, the median MEWS (IQR) was 1 (1–2) vs. 5 (4–8) (p < 0.001); the median CRAMS score (IQR) was 9 (8–10) vs. 5 (4–6) (p < 0.001).

Comparing minor trauma with severe trauma, the median MEWS (IQR) score was 1 (1–2) vs. 2 (1–4), p < 0.001; the median CRAMS (IQR) was 10 (9–10) vs. 8 (6–9), p < 0.001.

Figure 3 shows the receiver operating characteristic curves of the study population. For in-hospital mortality prediction, the AUROCs of MEWS and CRAMS score were 0.90 (95% confidence interval [CI] [0.88–0.92]) and 0.91 (95% CI [0.89–0.94]), respectively, indicating that both MEWS and CRAMS score were good predictors of in-hospital mortality. For multiple trauma severity prediction, the AUROCs of MEWS and CRAMS score were 0.77 (95% CI [0.74–0.79]) and 0.84 (95% CI [0.82–0.87]), respectively, indicating that CRAMS score was a better predictor of trauma severity. Table 5 shows the optimal cut-off values and their corresponding accuracy, sensitivity, and specificity values. For in-hospital mortality prediction, the optimal cut-off values of MEWS and CRAMS score were ≥3 and ≤6, respectively. For multiple trauma severity prediction, optimal cut-off values of MEWS and CRAMS score were ≥2 and ≤8, respectively.