Predicting influenza A and 2009 H1N1 influenza in patients admitted to hospital with acute respiratory illness

Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review

OBJECTIVES

To systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children.

DESIGN

Systematic review.

DATA SOURCES

PubMed and Embase were searched from 1 January 1975 to 3 February 2022.

ELIGIBILITY CRITERIA

We included diagnostic models predicting viral ARIs in children (<18 years) who sought medical attention from a healthcare setting and were written in English. Prediction model studies specific to SARS-CoV-2, COVID-19 or multisystem inflammatory syndrome in children were excluded.

DATA EXTRACTION AND SYNTHESIS

Study screening, data extraction and quality assessment were performed by two independent reviewers. Study characteristics, including population, methods and results, were extracted and evaluated for bias and applicability using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST (Prediction model Risk Of Bias Assessment Tool).

RESULTS

Of 7049 unique studies screened, 196 underwent full text review and 18 were included. The most common outcome was viral-specific influenza (n=7; 58%). Internal validation was performed in 8 studies (44%), 10 studies (56%) reported discrimination measures, 4 studies (22%) reported calibration measures and none performed external validation. According to PROBAST, a high risk of bias was identified in the analytic aspects in all studies. However, the existing studies had minimal bias concerns related to the study populations, inclusion and modelling of predictors, and outcome ascertainment.

CONCLUSIONS

Diagnostic prediction can aid clinicians in aetiological diagnoses of viral ARIs. External validation should be performed on rigorously internally validated models with populations intended for model application.

PROSPERO REGISTRATION NUMBER

CRD42022308917.

Reducing Antibiotic Use in Ambulatory Care Through Influenza Vaccination

BACKGROUND

Improving appropriate antibiotic use is crucial for combating antibiotic resistance and unnecessary adverse drug reactions. Acute respiratory illness (ARI) commonly causes outpatient visits and accounts for ~41% of antibiotics used in the United States. We examined the influence of influenza vaccination on reducing antibiotic prescriptions among outpatients with ARI.

METHODS

We enrolled outpatients aged ≥6 months with ARI from 50-60 US clinics during 5 winters (2013-2018) and tested for influenza with RT-PCR; results were unavailable for clinical decision making and clinical influenza testing was infrequent. We collected antibiotic prescriptions and diagnosis codes for ARI syndromes. We calculated vaccine effectiveness (VE) by comparing vaccination odds among influenza-positive cases with test-negative controls. We estimated ARI visits and antibiotic prescriptions averted by influenza vaccination using estimates of VE, coverage, and prevalence of antibiotic prescriptions and influenza.

RESULTS

Among 37 487 ARI outpatients, 9659 (26%) were influenza positive. Overall, 36% of ARI and 26% of influenza-positive patients were prescribed antibiotics. The top 3 prevalent ARI syndromes included: viral upper respiratory tract infection (47%), pharyngitis (18%), and allergy or asthma (11%). Among patients testing positive for influenza, 77% did not receive an ICD-CM diagnostic code for influenza. Overall, VE against influenza-associated ARI was 35% (95% CI, 32-39%). Vaccination prevented 5.6% of all ARI syndromes, ranging from 2.8% (sinusitis) to 11% (clinical influenza). Influenza vaccination averted 1 in 25 (3.8%; 95% CI, 3.6-4.1%) antibiotic prescriptions among ARI outpatients during influenza seasons.

CONCLUSIONS

Vaccination and accurate influenza diagnosis may curb unnecessary antibiotic use and reduce the global threat of antibiotic resistance.

Clinical Practice Guidelines by the Infectious Diseases Society of America: 2018 Update on Diagnosis, Treatment, Chemoprophylaxis, and Institutional Outbreak Management of Seasonal Influenzaa

These clinical practice guidelines are an update of the guidelines published by the Infectious Diseases Society of America (IDSA) in 2009, prior to the 2009 H1N1 influenza pandemic. This document addresses new information regarding diagnostic testing, treatment and chemoprophylaxis with antiviral medications, and issues related to institutional outbreak management for seasonal influenza. It is intended for use by primary care clinicians, obstetricians, emergency medicine providers, hospitalists, laboratorians, and infectious disease specialists, as well as other clinicians managing patients with suspected or laboratory-confirmed influenza. The guidelines consider the care of children and adults, including special populations such as pregnant and postpartum women and immunocompromised patients.

Clinical Practice Guidelines by the Infectious Diseases Society of America: 2018 Update on Diagnosis, Treatment, Chemoprophylaxis, and Institutional Outbreak Management of Seasonal Influenzaa

These clinical practice guidelines are an update of the guidelines published by the Infectious Diseases Society of America (IDSA) in 2009, prior to the 2009 H1N1 influenza pandemic. This document addresses new information regarding diagnostic testing, treatment and chemoprophylaxis with antiviral medications, and issues related to institutional outbreak management for seasonal influenza. It is intended for use by primary care clinicians, obstetricians, emergency medicine providers, hospitalists, laboratorians, and infectious disease specialists, as well as other clinicians managing patients with suspected or laboratory-confirmed influenza. The guidelines consider the care of children and adults, including special populations such as pregnant and postpartum women and immunocompromised patients.

Oxygen exchange and C-reactive protein predict safe discharge in patients with H1N1 influenza

BACKGROUND

: Pandemic influenza has potential to overwhelm healthcare resources. There is uncertainty over performance of existing triage tools for hospital admission and discharge decisions.

AIM

: Our aim was to identify clinical criteria that predict safe discharge from hospital and develop a pragmatic triage tool to guide physician decision-making.

DESIGN

: We retrospectively examined an existing database of patients who presented to the Royal Liverpool University Hospital during the 2010-11 influenza pandemic.

METHODS

Inclusion criteria: patients ≥18 years, with PCR confirmed H1N1 influenza. Exclusion criteria: died in the emergency department or case notes unavailable. Successful discharge was defined as discharge within 24 h of presentation and no readmission within 7 days.

RESULTS

Eighty-six patients were included and 16 were successfully discharged. Estimated P/F ratio and C-reactive protein predicted safe discharge in a multivariable logistic regression model (AUC 0.883). A composite univariate predictor (estimated P/F minus C-reactive protein, AUC 0.877) was created to calculate specific cut off points for sensitivity and specificity. A pragmatic decision tool was created to incorporate these thresholds and relevant guidelines. Discharge: SpO 2 (in air)   ≥ 94% and CRP <50. Observe: SpO 2  ≥   94% and CRP >50 or SpO 2  ≤   93% and CRP <50. Admit: SpO 2  ≤   93% and CRP >50.

CONCLUSIONS

We identified that oxygen exchange and CRP, a marker of acute inflammation, were the most important predictors of safe discharge. Our proposed simple triage model requires validation but has the potential to aid clinical decisions in the event of a future pandemic, and potentially for seasonal influenza.

Respiratory viruses identified in an urban children's hospital emergency department during the 2009 influenza A(H1N1) pandemic

OBJECTIVES

Two surges in influenza-like illness (ILI) visits to Children's Medical Center Emergency Departments, Dallas and Legacy, occurred in late spring (wave 1) and late summer 2009 (wave 2). This study describes respiratory viruses identified during the first weeks of waves 1 and 2 of the 2009 influenza A(H1N1) pandemic (pH1N1) and compares patients infected with pH1N1 with those infected with other respiratory viruses during wave 1.

METHODS

From April 27 to May 7 and August 23 to September 7, 2009, nasopharyngeal swab specimens from all patients with temperature 38.2°C or higher plus 2 or more symptoms of ILI were tested by rapid antigen, direct fluorescent antibody, or multiplex polymerase chain reaction assays. Patients with pH1N1 during wave 1 were classified as cases and 3 age- and sex-matched controls were randomly selected from patients with 1 respiratory virus other than pH1N1. Odds ratios (ORs) and associated 95% confidence intervals (95% CIs) of characteristics associated with patients with pH1N1 were estimated using conditional logistic regression models.

RESULTS

During wave 1, single viruses identified in 1023 symptomatic children were confirmed pH1N1 (55, 5.4%), rhinovirus (505, 49.4%), parainfluenza 3 (199, 19.5%), and human metapneumovirus (169, 16.5%). By multivariable analysis, duration of fever (OR, 1.49; 95% CI, 1.02-2.20) and myalgia at presentation (OR, 3.09; 95% CI, 1.09-8.76) were independent predictors associated with pH1N1. During wave 2, 114 (59.7%) of single viruses were pH1N1.

CONCLUSIONS

During the epidemic of ILI in Spring 2009, other respiratory viruses were identified more frequently than pH1N1 influenza in children with ILIs. Clinical presentation was similar for all respiratory viruses. Molecular diagnostic testing can define the prevalent viruses during community outbreaks and provide guidance to physicians making treatment decisions in emergency departments.

Rethinking approaches to improve the utilization of nucleic acid amplification tests for detection and characterization of influenza A in diagnostic and reference laboratories

Influenza A virus (IFVA) is a significant cause of respiratory infections worldwide and was also responsible for a recent pandemic in 2009. Laboratory identification of IFVA can guide antiviral therapy, assist in cohorting of patients and prevent antibiotic use. Characterization of the virus can track the emergence of novel strains, identify resistance and determine how circulating strains match with vaccine components. The gold standard for detection and characterization of IFVA is nucleic acid amplification technology (e.g., reverse transcriptase PCR [RT-PCR]), which must contend with a constantly evolving viral genome. Although molecular technology has been available for over two decades, there is still an operational gap between assay design and utilization of these tests for the diagnosis and characterization of IFVA. This review will discuss issues surrounding the implementation and use of RT-PCR for the identification and characterization of IFVA, and speculate on why RT-PCR has not been used more widely in clinical laboratories or moved closer to the patient. Newer, less widely used technologies that may change our laboratory practices will be identified and the authors will close with an attempt to identify some future applications of RT-PCR-based technologies for the detection and characterization of IFVA.