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Can the emergency department triage category and clinical presentation predict hospitalization of H1N1 patients?

Authors Alshahrani M , Alsubaie A , Alshamsy A, Alkhliwi B, Alshammari H , Alshammari M, Telmesani N , Alshammari R, Perlas Asonto L 

Received 4 February 2019

Accepted for publication 4 August 2019

Published 17 September 2019 Volume 2019:11 Pages 221—228

DOI https://doi.org/10.2147/OAEM.S204110

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Hans-Christoph Pape



Mohammed Alshahrani,1 Aisha Alsubaie,2 Alaa Alshamsy,3 Bayader Alkhliwi,3 Hind Alshammari,3 Maha Alshammari,3 Nosibah Telmesani,3 Reem Alshammari,3 Laila Perlas Asonto4

1Department of Emergency and Critical Care, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Al-Khobar 31952, Kingdom of Saudi Arabia; 2Department of Emergency, King Hamad University Hospital, Busaiteen, Kingdom of Bahrain; 3College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia; 4Department of Emergency Medicine, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia

Correspondence: Mohammed Alshahrani
Department of Emergency and Critical Care, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, PO Box 40236, Al-Khobar 31952, Kingdom of Saudi Arabia
Tel +966 55 696 6663
Email [email protected]

Background: Human H1N1 Influenza A virus was first reported in 2009 when seasonal outbreaks consistently occurred around the world. H1N1 patients present to the emergency departments (ED) with flu-like symptoms extending up to severe respiratory symptoms that require hospital admission. Developing a prediction model for patient outcomes is important to select patients for hospital admission. To date, there is no available data to guide the hospital admission of H1N1 patients based on their initial presentation.
Objective: The aim of this study was to investigate the predictors of hospital admission of H1N1 patients presenting in the ED.
Methods: We conducted a retrospective review of all laboratory-confirmed H1N1 cases presenting to the ED of a tertiary university hospital in the Eastern region of Saudi Arabia within the period from November 2015 to January 2016. We retrieved data of the initial triage category, vital signs, and presenting symptoms. Multivariate logistic regression analysis was performed to evaluate risk factors for hospital admission among H1N1patients presented to the ED.
Results: We identified 333 patients with laboratory-confirmed H1N1. Patients were classified into two groups: admitted group (n=80; 24%) and non-admitted group (n=253; 76%). Sixty patients (75%) were triaged under category IV. Triage category of level III and less were the most predictive for hospital admission. Multivariate regression analysis showed that of all vital signs, tachypnea was a significant risk factor for hospital admission (OR=1.1; 95% CI 1.02 to 1.13, p<0.01). The association between lower triage category and hospital stay was statistically significant (χ2=6.068, p=0.037). Also, patients with dyspnea were 4.5 times more likely to have longer hospital stay (OR=4.5; 95% CI 1.2 to 17.1, p=0.025).
Conclusion: Lower triage category and increased respiratory rate predict the need for hospital admission of H1N1 infected patients; while patients with dyspnea or bronchial asthma are likely to stay longer in the hospital. Further prospective studies are needed to evaluate the accuracy of using the CTAS and other clinical parameters in predicting hospitalization of H1N1 patients during outbreaks.

Keywords: clinical outcome, H1N1, presentation, respiratory virus, triage category

Introduction

Pandemic swine-origin influenza A/H1N1 virus was first detected in April 2009 in Veracruz, Mexico. It took few months span to spread internationally and become the first pandemic disease of the 21st century.1,2 In May 2009, 11,356 suspected and 822 laboratory-confirmed cases had been reported in Mexico; then in June 2009, the World Health Organization raised the pandemic level from phase 5 to phase 6 announcing that the virus reached 43 countries with 91 reported fatalities.3,4 In October 2009, the first laboratory evidence was detected and virologic detection methods were developed to identify influenza A/H1N1 epidemic agent. One year later, the virus extended worldwide with a tenfold mortality rate of seasonal influenza and more than 17,700 fatalities in confirmed cases.5

In Saudi Arabia, during the Hajj season of the year 2010, more than two million pilgrims assembled in the pilgrimage sites in Mecca and Medina that made a faster transmission of the disease. The virus was detected in 120 pilgrims out of 1600 screened at entry borders using real-time polymerase chain reaction (PCR).6 In Saudi Arabia, the clinical features of the disease include fever (94%) and upper respiratory symptoms as cough (92%), and sore throat (66%). Headache, body aches, fatigue, diarrhea, and vomiting also have been observed. In a study of H1N1 hospital admission rates, one-third of the suspected H1N1 cases admitted to the intensive care unit (ICU) were severe cases while the other two-thirds had no complications or comorbidities.7,8

Hospitalization of influenza patients is associated with substantial costs in some countries.11 Therefore, appropriate selection of patients for safe discharge or hospital admission is important from an economic point of view. The aim of this study was to identify the need for hospital admission in H1N1 patients according to the value of initial triage category, vital signs, and clinical presenting symptoms.

Methods

We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement guidelines when reporting this manuscript.9 This study was approved by the ethics committee of the Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia (IRB-UGS-2016–01-076).

Study design, study setting and duration

We conducted a retrospective case-control study of H1N1 cases presented to the Emergency Department (ED) of a tertiary hospital in the Eastern Region of Saudi Arabia. We retrieved data of patients’ demographic characteristics, clinical presentations, radiological and laboratory investigations, and comorbidities. To minimize bias, investigators analyzing the study data were kept blinded to the clinical and microbiological information.

Eligibility criteria

Patients meeting the following inclusion criteria were included in the study:

(1) Patients with laboratory-confirmed H1N1 infection by PCR

(2) Both male and female genders at any age

(3) Patients whose data of the vital signs, clinical presentation, and Canadian Triage and Acuity Scale (CTAS) category10 were available on the hospital records

We excluded patients with the following conditions:

(1) Patients with no documented initial vital signs during their ED visit

(2) Patients with no documented Canadian Triage and Acuity Scale (CTAS) category10 in the ED visit.

Case definition

Patients with laboratory-confirmed H1N1 infection defined as those with positive H1N1 virus based on nasopharyngeal swab samples. The routine laboratory analysis of these samples had been done; they were collected in a universal transport medium and were analyzed immediately using a real-time PCR test, GeneXpert system (Cepheid, Sunnyvale, CA, USA) for diagnosing H1N1.

CTAS definition

The Canadian Emergency Department Triage and Acuity Scale (CTAS) is a common validated triage system that prioritizes patient care through the severity of their illness.10

Data analysis

Data normality was tested using the Kolmogorov–Smirnov test. Categorical variables were expressed as frequencies and percentages. For continuous variables, we used the mean and standard deviation or the median and range to summarize normally and non-normally distributed data respectively. Continuous variables were compared using the Student t-test or Mann–Whitney U test for normally and non-normally distributed data, respectively. For comparison of categorical variables, the Chi-square test and Fisher’s exact test were used when appropriate. We carried out a univariate analysis to investigate the association between patient status, presenting symptoms, comorbidity, and hospital admission. Statistically significant variables (those with p<0.05) were selected for inclusion in the multivariate logistic regression model. Collinearity was explored with correlation matrix. Unadjusted OR (UOR) and adjusted OR (AOR) with the corresponding 95% Confidence intervals were calculated. All analyses were carried out using the Statistical Package for Social Sciences (SPSS) 15.0 software (SPSS, Inc., Chicago, IL, USA). An alpha level below 0.05 was considered for statistical significance.

Results

Characteristics of the study population

Four hundred patients were diagnosed with laboratory confirmed H1N1 infection. Of them, 67 patients were excluded owing to missing data from the hospital records while 333 patients were included in this study. The flow diagram of study participants, their CTAS classification, and their status (discharged or admitted) is shown in Figure 1.

Figure 1 shows the flow diagram of the study participants, their CTAS classification, and hospitalization status.Abbreviation:  CTAS, CanadianTriage and Acuity Scale.

The demographic characteristics, vital signs at presentations and laboratory findings of the study participants are shown in Table 1.

Table 1 Shows demographics, vital signs at presentation, and laboratory findings of the study population (n=333 patients)

Presenting symptoms and comorbidities among H1N1 patients

Cough was the most common presenting symptom; it was reported in 243 patients (73.2%), followed by fever reported in 161 patients (48.5%) and sore throat in 99 patients (29.8%). The percentages of the presenting symptoms in the study population (n=333) are shown in Figure 2.

Figure 2 Clustered bar chart of the percentages of the presenting symptoms in the study population.

In terms of comorbidities, 62 patients (18.6%) had bronchial asthma, 19 patients (5.7%) had diabetes mellitus (DM), 12 patients (3.6%) had other pulmonary diseases, 10 patients (3%) had cardiac disease, and 7 patients (2.1%) had renal disease.

CTAS triage category and hospital admission

Most patients were in the triage category IV (n=216, 64.8%). Patients with CTAS III had higher rates of hospital admission compared to CTAS IV and V (77.78% vs 27.78% and 12.15%, respectively. The frequencies of hospital admissions and triage categories are shown in Figure 3.

Figure 3 Hospital admission rates of H1N1 patients in different CTAS categories. Abbreviation: CTAS, Canadian Emergency Department Triage and Acuity Scale.

Association between the presenting symptoms and hospital admission

There was a statistically significant association between initial symptoms and patient admission status. Higher admission rates were found in patients with vomiting (p=0.001), sore throat (p=0.001), and seizures (p=0.017). A significantly higher proportion of patients with these symptoms were admitted compared to the discharged group. The results of the chi-square analysis of the association between the presenting symptoms and patient status (admitted vs discharged) are shown in Table 2.

Table 2 Association between initial symptom variables and patient status

Association between comorbidities and hospital admission

The association between comorbidities and hospital admission was statistically significant in cases of pulmonary disease, cardiac disease, hypertension, kidney disease, and DM. Patients with these morbidities were likely to be admitted in the hospital compared to those who were discharged. The results of the chi-square analysis of the association between comorbidities and patient status (admitted vs discharged) are shown in Table 3.

Table 3 Association between comorbidities and patient status

Predictors of hospital admission

The multivariate logistic regression analysis showed that four variables could significantly predict hospital admission of H1N1 patients presenting to ED (Table 4). Predictors of hospital admission were tachypnea (OR 1.08, 95% CI 1.02 to 1.14, p=0.005), CTAS triage of III or less (OR 2.981, 95% CI 1.52 to 5.83, p=0.001), presence of cardiac disease (OR 7.96, 95% CI 2.01 to 31.55, p=0.003), and presence of DM (OR 3.86, 95% CI 2.01 to 31.55, p=0.005).

Table 4 Logistic regression results; Risk factors for admission into hospital among H1N1 patients who report to the ED

Discussion

Summary of main results

Our study showed that predictors of hospital admission of H1N1 patients presenting to the ED are: (1) tachypnea, (2) CTAS triage of III or less, and (3) comorbidities as pulmonary disease, cardiac disease, and DM.

Significance of the study findings

The pandemic H1N1 influenza has been reported in many countries. However, there has been less attention to the criteria of hospital admission of H1N1 patients. Hospitalization of influenza patients is associated with substantial costs in some countries.11 Therefore, appropriate selection of patients for safe discharge or hospital admission is important from an economic point of view.

Previous studies

Morton et al12 found that oxygen exchange and CRP are the most important predictors of safe discharge of H1N1 patients.12 In 2014, a registry-based study of 104 H1N1 patients showed that serum albumin levels and glucose levels at the time of presentation can be used as predictors for intensive respiratory or vasopressor support.13

In 2015, Hlavinkova et al14 reported that cardiovascular diseases, DM, and bronchial asthma are significant predictors of hospital admission. Additionally, patients with high CURB-65 scores were likely to be admitted according to Challen et al.15

High CRP level, the partial pressure of oxygen to fraction of inspired oxygen ratio (PaO2/FiO2<300), and the simple triage scoring system might predict hospital stay and the need for ICU admission and the use of mechanical ventilation.1618 In 2015, Morton et al16 suggested that the use of PaO2/FiO2 ratio is more reliable than simple triage scoring system to predict hospital stay.

In another study, H1N1 patients were evaluated in terms of their hospital stay and the presence of two or more risk factors as old age (≥65 years), altered mental status, hypoxia (PaO2/FiO2≤250), and bilateral lung infiltration.19 The severity of H1N1 infection was attributed to obesity, pregnancy, and comorbidities such as chronic obstructive pulmonary disease, CVD, and malignancies.14,20 In a retrospective study of 77 H1N1 patients, the increased duration of dyspnea prior to admission, pneumonia, low PaO2/FiO2 ratio, higher PaCO2 on admission, and higher O2 requirement were associated with a poorer outcome.21 Multiple retrospective case-control studies of H1N1 patients showed that morbid obesity might be associated with hospitalization due to pandemic H1N1 infection.22,23

Strength points and limitations of the study

Our study has several strength points. First, the relatively larger sample size of our study compared to previous reports. Secondly, we performed multivariate logistic regression analysis to identify the predictors of hospital admission among H1N1 patients presenting to the ED. Thirdly, to the best of our knowledge, no previous studies have investigated the predictors of hospital admission among H1N1 patients in Saudi Arabia.

Our study has few limitations: (1) the retrospective study design did not allow to include more clinical variables; we were confined to the available data in the hospital records; data of 67 patients were missing and therefore, they were not included in the study; (2) some community clusters have no access to health care and therefore, might have a high tolerance to the disease; those patients might have lower estimates compared to reported in our study; and (3) inter-rater and intra-rater variations should have been taken into consideration.

Conclusion

Lower triage category and increased respiratory rate predict the need for hospital admission of H1N1 infected patients whereas patients with dyspnea or BA are likely to stay longer in the hospital. Further prospective studies are needed to evaluate the accuracy of using the CTAS and other clinical parameters in predicting hospitalization of H1N1 patients during outbreaks.

Ethics approval and consent to participate

Patient consent was not required, and all patient data were kept confidential. No patients were involved neither in the design, recruitment and conduction of this study nor in the development of outcome measures. We will publish the results of the study in lay language for patient interest groups. We obtained ethics approval from Standing Committee for Research Ethics on Living Creatures (SCRELC) from the Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia with IRB number: IRB-UGS-2016-01-076.

Abbreviations

BA, Bronchial asthma; CI, Confidence interval; CRP, C-reactive protein; CTAS, Canadian triage and acuity scale; CVD, Cardiovascular disease; DM, Diabetes mellitus; ED, Emergency departments; H1N1, Hemagglutinin type 1 and neuraminidase type 1; ICU, Intensive care unit; OR, Odds ratio; PCR, Polymerase chain reaction; STSS, Simple triage scoring system.

Acknowledgment

We wish to acknowledge the King Fahad Hospital of the University administrators and staff for providing much needed assistance throughout the course of this research. We thank our colleagues from the emergency department who provided insight and expertise that greatly assisted the research and for their comments on an earlier version of the manuscript. Dr Mohammed Alshahrani provided funding for NegidaClin® Contract Research Organization to perform professional language editing and revision of the manuscript.

Author contributions

All authors contributed toward data analysis, drafting and revising the paper, gave final approval of the version to be published and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Fraser C, Donnelly CA, Cauchemez S, et al. pandemic potential of a strain of influenza A (H1N1): early findings. Science. 2009;324:1557–1561. doi:10.1126/science.1176062

2. Chang L-Y, Shih S-R, Shao P-L, et al. Novel swine-origin influenza virus A (H1N1): the first pandemic of the 21st century. J Formos Med Assoc. 2009;108:526–532. doi:10.1016/S0929-6646(09)60369-7

3. World health Organization. Influenza A (H1N1): pandemic alert phase 6 declared, of moderate severity [Internet]. World Health Organization; 2019. Available from: https://www.euro.who.int/en/health-topics/communicable-diseases/influenza/pandemic-influenza/past-pandemics/pandemic-h1n1-2009/archive-whoeurope-news-and-updates/influenza-a-h1n1-pandemic-alert-phase-6-declared,-of-moderate-severity. Accessed Feb 28, 2017.

4. Surveillance E. Update: novel influenza A (H1N1) virus infection - Mexico, March-May, 2009. Morb Mortal Wkly Rep. 2009;58:585–589. mm5821a2 [pii].

5. World Health Organization. Pandemic (H1N1) 2009 - update 94 [Internet]. World Health Organization; 2015 [cited 2017 Feb 28]. Available from: https://www.who.int/csr/don/2010_04_01/en/. Accessed Feb 28, 2017.

6. Ashshi A, Azhar E, Johargy A, et al. Demographic distribution and transmission potential of influenza A and 2009 pandemic influenza A H1N1 in pilgrims. J Infect Dev Ctries. 2014;8:1169–1175. doi:10.3855/jidc.4204

7. Webb SAR, Petilla V, Seppelt I, /bellomo R, Bailey M, Cooper DJ, et al. Critical care services and 2009 H1N1 influenza in Australia and New Zealand. N Engl J Med [Internet]. 2009;361 (20):1925–1934. Available from: doi.org/10.1056/NEJMoa0908481.

8. Kumar A. Critically Ill patients with 2009 influenza A(H1N1) infection in Canada. JAMA. 2009;302:1872. doi:10.1001/jama.2009.1496

9. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–349. doi:10.1016/j.jclinepi.2007.11.008

10. Elkum NB, Barrett C, Al-Omran H. Canadian emergency department triage and acuity scale: implementation in a tertiary care center in Saudi Arabia. BMC Emerg Med. 2011;11:3. doi:10.1186/1471-227X-11-3

11. Chan Y-K, Wong RY, Ip M, et al. Economic outcomes of influenza in hospitalized elderly with and without ICU admission. Antivir Ther. 2017;22:173–177. doi:10.3851/IMP3102

12. Morton B, Nweze K, O’Connor J, et al. Oxygen exchange and C-reactive protein predict safe discharge in patients with H1N1 influenza. QJM. 2017;110:227–232. doi:10.1093/qjmed/hcw176

13. Wi YM, Kim JM, Peck KR. Serum albumin level as a predictor of intensive respiratory or vasopressor support in influenza A (H1N1) virus infection. Int J Clin Pract. 2014;68:222–229. doi:10.1111/ijcp.12249

14. Hlavinkova L, Kristufkova Z, Mikas J. Risk factors for severe outcome of cases with pandemic influenza A(H1N1)pdm09. Bratisl Lek Listy. 2015;116:389–393.

15. Challen K, Goodacre SW, Wilson R, et al. Evaluation of triage methods used to select patients with suspected pandemic influenza for hospital admission. Emerg Med J. 2012;29:383–388. doi:10.1136/emj.2010.104380

16. Morton B, Tang L, Gale R, et al. Performance of influenza-specific triage tools in an H1N1-positive cohort: P/F ratio better predicts the need for mechanical ventilation and critical care admission † †This article is accompanied by Editorial Aev141. Br J Anaesth. 2015;114:927–933. doi:10.1093/bja/aev042

17. Adeniji KA, Cusack R. The Simple Triage Scoring System (STSS) successfully predicts mortality and critical care resource utilization in H1N1 pandemic flu: a retrospective analysis. Crit Care. 2011;15:R39. doi:10.1186/cc10001

18. Zimmerman O, Rogowski O, Aviram G, et al. C-reactive protein serum levels as an early predictor of outcome in patients with pandemic H1N1 influenza A virus infection. BMC Infect Dis. 2010;10. doi:10.1186/1471-2334-10-288.

19. Oh WS, Lee S-J, Lee C-S, et al. A prediction rule to identify severe cases among adult patients hospitalized with pandemic influenza A (H1N1) 2009. J Korean Med Sci. 2011;26:499. doi:10.3346/jkms.2011.26.4.499

20. Singanayagam A, Singanayagam A, Wood V, et al. Factors associated with severe illness in pandemic 2009 influenza a (H1N1) infection: Implications for triage in primary and secondary care. J Infect. 2011;63:243–251. doi:10.1016/j.jinf.2011.07.014

21. Chawla R, Kansal S, Chauhan M, et al. Predictors of mortality and length of stay in hospitalized cases of 2009 influenza A (H1N1): Experiences of a tertiary care center. Indian J Crit Care Med. 2013;17:275–282. doi:10.4103/0972-5229.120318

22. Morgan OW, Bramley A, Fowlkes A, et al. Morbid obesity as a risk factor for hospitalization and death due to 2009 pandemic influenza A(H1N1) disease. PLoS One. 2010;5:e9694. doi:10.1371/journal.pone.0009694

23. Gilca R, De Serres G, Boulianne N, et al. Risk factors for hospitalization and severe outcomes of 2009 pandemic H1N1 influenza in Quebec, Canada. Influenza Other Respi Viruses. 2011;5:247–255. doi:10.1111/j.1750-2659.2011.00204.x

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