Evaluation of triage methods used to select patients with suspected pandemic influenza for hospital admission

Challen, Kirsty; Goodacre, Steve; Wilson, Richard W.; Bentley, Andrew; Campbell, Mike; Fitzsimmons, Christopher; Walter, Darren

doi:10.1136/emj.2010.104380

Cited by 16 publications

(16 citation statements)

References 27 publications

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“…A recent systematic review on prediction models for diagnosis and prognosis of COVID-19 reported that proposed models were poorly described, had a high risk of bias, and may be unreliable when used in situ [109]. Most triage protocols were developed with experts (e.g., ethicists, lawyers, healthcare professionals, decision-makers and, in some cases, members of the public) in advance of a pandemic (e.g., CHES T consensus statements) [11,88,91,96,100,110] or were validated using a non-representative population (e.g., patients with COVID-19, seasonal influenza or ARDS) [111][112][113][114][115][116][117][118][119]. As such, most triage criteria are not validated (and it may not be possible to validate them) prior to their use.…”

Section: Discussionmentioning

confidence: 99%

Allocation of intensive care resources during an infectious disease outbreak: a rapid review to inform practice

Fiest

Krewulak

Plotnikoff

et al. 2020

BMC Med

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Background The COVID-19 pandemic has placed sustained demand on health systems globally, and the capacity to provide critical care has been overwhelmed in some jurisdictions. It is unknown which triage criteria for allocation of resources perform best to inform health system decision-making. We sought to summarize and describe existing triage tools and ethical frameworks to aid healthcare decision-making during infectious disease outbreaks. Methods We conducted a rapid review of triage criteria and ethical frameworks for the allocation of critical care resources during epidemics and pandemics. We searched Medline, EMBASE, and SCOPUS from inception to November 3, 2020. Full-text screening and data abstraction were conducted independently and in duplicate by three reviewers. Articles were included if they were primary research, an adult critical care setting, and the framework described was related to an infectious disease outbreak. We summarized each triage tool and ethical guidelines or framework including their elements and operating characteristics using descriptive statistics. We assessed the quality of each article with applicable checklists tailored to each study design. Results From 11,539 unique citations, 697 full-text articles were reviewed and 83 articles were included. Fifty-nine described critical care triage protocols and 25 described ethical frameworks. Of these, four articles described both a protocol and ethical framework. Sixty articles described 52 unique triage criteria (29 algorithm-based, 23 point-based). Few algorithmic- or point-based triage protocols were good predictors of mortality with AUCs ranging from 0.51 (PMEWS) to 0.85 (admitting SOFA > 11). Most published triage protocols included the substantive values of duty to provide care, equity, stewardship and trust, and the procedural value of reason. Conclusions This review summarizes available triage protocols and ethical guidelines to provide decision-makers with data to help select and tailor triage tools. Given the uncertainty about how the COVID-19 pandemic will progress and any future pandemics, jurisdictions should prepare by selecting and adapting a triage tool that works best for their circumstances.

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Section: Discussionmentioning

confidence: 99%

Allocation of intensive care resources during an infectious disease outbreak: a rapid review to inform practice

Fiest

Krewulak

Plotnikoff

et al. 2020

BMC Med

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“…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 al15…”

Section: Discussionmentioning

confidence: 99%

<p>Can the emergency department triage category and clinical presentation predict hospitalization of H1N1 patients?</p>

Alshahrani

Alsubaie

Alshamsy

et al. 2019

OAEM

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BackgroundHuman 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.ObjectiveThe aim of this study was to investigate the predictors of hospital admission of H1N1 patients presenting in the ED.MethodsWe 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.ResultsWe 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).ConclusionLower 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.

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“…ILI, according to the Centers for Disease Control and Prevention (CDC), was defined as a history of fever or sudden onset of fever (>37.8°C, within 7 days) and a cough and/or sore throat without a known acute respiratory infection other than influenza. The following SARI case definition was used: (1) history of fever or sudden onset of fever (>37.8°C, within 7 days), (2) one or more respiratory symptom (cough, sore throat, rhinorrhea), (3) shortness of breath or difficulty breathing, and (4) admission [11]. However, the HIMM system uses “modified SARI,” which is defined as either (A) classical SARI by WHO or (B) non-ILI SARI with laboratory-confirmed influenza-related admission.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Risk Factors for Severe Acute Respiratory Infection and Pneumonia and among Adult Patients with Acute Respiratory Illness during 2011-2014 Influenza Seasons in Korea

et al. 2016

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BackgroundThe World Health Organization recommends the surveillance of influenza-like illness (ILI) and severe acute respiratory infection (SARI) to respond effectively to both seasonal influenza epidemics and pandemics. In Korea, the “Hospital-based Influenza Morbidity and Mortality (HIMM)” surveillance system has been operated to monitor ILI and SARI occurrences.Materials and MethodsA multi-center prospective observational study was conducted. Adult patients with acute respiratory infection (ARI) were enrolled during the 2011-12, 2012-2013, and 2013-2014 influenza seasons at the 10 university hospitals using the HIMM surveillance system. With respect to SARI and pneumonia development, risk profiles were analyzed in patients with ARI in Korea.ResultsA total of 5,459 cases were eligible for this analysis. Among 5,459 cases with ARI, 2,887 cases (52.9%) were identified that they had influenza infection. Among enrolled cases, 750 cases belonged to the SARI group, while 4,709 cases belonged to the non-SARI group. With respect to pneumonia development, 317 cases were accompanied by pneumonia, and 5,142 cases were not. Multivariate analyses revealed that the following factors were associated with an increased risk of SARI: Old age (≥65 years) (odds ratio [OR] 2.69, 95% confidence interval [CI] 2.2-3.32), chronic heart disease (CHD) (OR 2.24, 95% CI 1.68-2.98), cerebrovascular disease (CVD) (OR 1.49, 95% CI 1.05-2.10), chronic obstructive pulmonary disease (COPD) (OR 2.34, 95% CI 1.48-3.69), asthma (OR 2.33, 95% CI 1.62-3.36), chronic kidney disease (CKD) (OR 2.62, 95% CI 1.73-3.99), chronic liver disease (OR 1.71, 95% CI 1.04-2.81), and autoimmune diseases (OR 2.53, 1.57-4.08). Multivariate analyses revealed that the following factors were independent risk factors for pneumonia development: Old age (≥65 years) (OR 5.71, 95% CI 4.10-7.94), CHD (OR 1.54, 95% CI 1.07-2.22), COPD (OR 2.34, 95% CI 1.48-3.69), asthma (OR 2.33, 95% CI 1.62-3.36), CKD (OR 2.62, 95% CI 1.73-3.99), immunocompromised conditions (OR 3.12, 95% CI 1.47-6.62), and autoimmune diseases (OR 3.35, 95% CI 1.79-6.27). The risk of SARI and pneumonia was increased by the number of concurrent chronic medical conditions.ConclusionThe risk of SARI and pneumonia development among adult patient with ARI was significantly increased by the presence or number of concurrent chronic medical conditions in Korea.

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Evaluation of triage methods used to select patients with suspected pandemic influenza for hospital admission

Abstract: Although limited by a paucity of cases, this research shows that current triage methods for suspected pandemic influenza did not reliably discriminate between patients with good and poor outcomes.

Cited by 16 publications

References 27 publications

Allocation of intensive care resources during an infectious disease outbreak: a rapid review to inform practice

Allocation of intensive care resources during an infectious disease outbreak: a rapid review to inform practice

<p>Can the emergency department triage category and clinical presentation predict hospitalization of H1N1 patients?</p>

Analysis of Risk Factors for Severe Acute Respiratory Infection and Pneumonia and among Adult Patients with Acute Respiratory Illness during 2011-2014 Influenza Seasons in Korea

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