Literature DB >> 32888979

Initial MEWS score to predict ICU admission or transfer of hospitalized patients with COVID-19: A retrospective study.

William R Barnett1, Muthukumar Radhakrishnan2, John Macko3, Bryan T Hinch4, Nezam Altorok4, Ragheb Assaly2.   

Abstract

Entities:  

Keywords:  COVID-19; Early warning systems; Intensive care; SARS-CoV-2; Sequential organ failure assessment

Year:  2020        PMID: 32888979      PMCID: PMC7462753          DOI: 10.1016/j.jinf.2020.08.047

Source DB:  PubMed          Journal:  J Infect        ISSN: 0163-4453            Impact factor:   6.072


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Dear Editor, Early warning scores (EWS) were introduced in early 2001 to identify patients at risk of deterioration in a busy clinical environment as a track-and-trigger system where an increasing score produced an escalated response. EWS have demonstrated better capability in identifying deteriorating patients leading to improved clinical outcomes. Furthermore, an EWS at admission can be used to determine in-hospital mortality and intensive care unit (ICU) transfer.2, 3, 4 Our academic medical center developed a modified early warning score (MEWS) system in 2015 and it was rolled out hospital-wide the following year. Since serious adverse events in hospitalized patients are often preceded by signs of clinical deterioration, we believed MEWS scores could be used to predict events, such as cardiopulmonary arrest. As the coronavirus pandemic continues, could MEWS provide any usefulness in predicting ICU level care among hospitalized COVID-19 patients? A retrospective study was conducted at the University of Toledo Medical Center (Toledo, OH) on COVID-19 positive patients hospitalized from late March to the end of May 2020. All patients were confirmed for COVID-19 by real-time reverse transcription polymerase chain reaction. Patient demographics, biometrics, and comorbidities were gathered from the electronic medical record. The highest level of hospital disposition was used to create the medical floor and ICU groups. The MEWS scores, which are calculated hourly using the criteria in Fig. 1 , were retrieved from an electronic database. Initial MEWS scores were considered either at admission or prior to ICU transfer. Sequential organ failure assessment (SOFA)
Fig. 1

MEWS scoring system.

MEWS scoring system. scores were manually calculated and included as a point of comparison. As a validated scoring system, SOFA scores are used to determine a patient's prognosis and have been considered for use in hospitalized COVID-19 patients. Overall, 142 COVID-19 positive patients were identified in the hospital during the study period. The baseline characteristics of each group are represented in Table 1 . Ninety-eight patients were admitted to the medical floor, while 14 of those patients were subsequently transferred to the ICU. The median (interquartile range [IQR]) initial MEWS score of the 14 ICU transfer patients at admission was 3.5 (5.0), while the median score at ICU transfer was 7.0 (2.0). The median change in score was 3.5 (4.3). The comparative SOFA values were 3.0 (2.5), 7.0 (3.5), and 3.0 (5.3), respectively. Forty-four patients were directly admitted to the ICU.
Table 1

Baseline characteristics of hospitalized COVID-19 patients.

VariableMedical floorICUp-value
N = 84N = 58
Mean initial MEWS score, (SD)1.8 (1.5)6.3 (2.3)0.000
Mean initial SOFA score, (SD)2.3 (1.8)7.8 (3.2)0.000
Mean age, years (SD)61.4 (16.1)66.2 (14.1)0.067
Sex, male (%)47 (56.0)29 (50.0)0.598
Race (%)0.511
 White or Caucasian33 (39.3)27 (46.6)
 Black or African-American50 (59.5)31 (53.4)
 Unknown1 (1.2)0 (0.0)
Mean body mass index, kg/m2 (SD)32.0 (8.3)32.2 (9.4)0.898
Comorbidities
 Diabetes mellitus (%)34 (40.5)32 (55.2)0.120
 Hypertension (%)54 (64.3)35 (60.3)0.764
 Coronary artery disease (%)12 (14.3)12 (20.7)0.439
 Congestive heart failure (%)7 (8.3)11 (19.0)0.106
 Chronic obstructive pulmonary disease (%)8 (9.5)12 (20.7)0.102
 Chronic kidney disease (%)15 (17.9)13 (22.4)0.648
Outcomes
 Median length of stay, days [IQR]6.0 [7.0]14.0 [10.0]0.000
 In-hospital mortality (%)5 (6.0)28 (48.3)0.000

Definitions of Abbreviations.

SBP: systolic blood pressure; HR: heart rate; RR: respiratory rate; FiO2: fraction of inspired oxygen; ICU: intensive care unit; MEWS: modified early warning system; SOFA: sequential organ failure assessment; SD: standard deviation; IQR: interquartile range.

Baseline characteristics of hospitalized COVID-19 patients. Definitions of Abbreviations. SBP: systolic blood pressure; HR: heart rate; RR: respiratory rate; FiO2: fraction of inspired oxygen; ICU: intensive care unit; MEWS: modified early warning system; SOFA: sequential organ failure assessment; SD: standard deviation; IQR: interquartile range. According to the largest Youden's index, the optimal cutoff value for predicting ICU admission or transfer was a MEWS score of 5. The area under the curve of the receiver operating characteristic (AUC) was 0.935 (95% confidence interval [CI], 0.892–0.979). With regards to SOFA, the score was also 5 with an AUC of 0.924 (95% CI, 0.874−0.973). When the MEWS and SOFA optimal cutoff values are used as predictors of ICU admission or transfer, there is no difference in the two models as assessed by DeLong's test (Z = −1.061, p-value = 0.289). The study suggests that MEWS could be used as a surrogate measure alongside other parameters to determine ICU admission or transfer when the SOFA score is unavailable. A downside to using the SOFA score at admission is the requirement for lab values, such as arterial blood gas, creatinine, platelets, and bilirubin for score accuracy. Most of the time these values are absent early in a patient's hospital course. Among COVID-19 patients, MEWS may have another slight advantage over SOFA considering the latter does not take into consideration temperature, respiratory rate, oxygen therapy, and oxygen saturation. We questioned whether these nuances in the MEWS scoring system would be able to detect COVID-19 patients at risk for clinical deterioration especially in the context of ‘silent hypoxia’. For example, based on the available information as presented in Tobin et al., these patients would have quite low SOFA scores. Even quick SOFA scoring would not sufficiently capture silent hypoxia considering most patients do not present with altered mental status or tachypnea. In comparison, their MEWS scores would extend well beyond our threshold of 5 considering fever is a prominent feature of COVID-19, use of supplemental oxygen, and oxygen saturation below 90%. There are several limitations to these study findings, which should be addressed. First, the study suffers from a small sample size from a single institution and should be replicated with a larger cohort of patients. Secondly, the scoring systems mentioned in this study have been developed for different reasons other than predicting hospital disposition. Thirdly, we did not explore other options, such as scoring trends or using the highest score in the preceding 24-h period in our prediction model. Considering MEWS is a less burdensome scoring system, hospitals should consider adopting a method to calculate MEWS scores on admission with a plan to periodically monitor their patients for increasing scores. Additionally, an optimal cutoff value can aid in decisions to either admit or transfer patients with known or suspected COVID-19 to higher levels of care.

Declarations

Ethics approvals and consent to participate. The study was approved by the University of Toledo Biomedical Institutional Review Board.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Author's contributions

WB conceived and designed the study. MR and JM participated in the acquisition of data. WB analyzed the data. WB drafted the manuscript, and WB, NA, BH, and RA revised the manuscript. All authors read and approved the final manuscript. WB is guarantor of this work and had full access to all the data in the study and takes responsibility for its integrity and the accuracy of the data analysis.

Declaration of Competing Interest

The authors declare that they have no competing interests.
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