Prospective external validation of the FluScore risk score for influenza in outpatients.

BACKGROUND: Testing for influenza in patients with acute lower respiratory tract infection (LRTI) is common and in some cases is performed for all patients with LRTI. A more selective approach to testing could be more efficient. METHODS: We used data from two prospective studies in the US primary and urgent care settings that enrolled patients with acute LRTI or influenza-like illness. Data were collected in the 2016, 2019, 2021, and 2022 flu seasons. All patients underwent polymerase chain reaction (PCR) testing for influenza and the FluScore was calculated based on patient-reported symptoms at their initial visit. The probability of influenza in each risk group was reported, as well as stratum-specific likelihood ratios (SSLRs) for each risk level. RESULTS: The prevalence of influenza within risk groups varied based on overall differences in flu seasons and populations. However, the FluScore exhibited consistent performance across various seasons and populations based on the SSLRs. The FluScore had a consistent SSLR range of 0.20 to 0.23 for the low-risk group, 0.63 to 0.99 for the moderate-risk group, and 1.46 to 1.67 for the high-risk group. The diagnostic odds ratio based on the midpoints of these ranges was 7.25. CONCLUSIONS: The FluScore could streamline patient categorization, identifying patients who could be exempted from testing, while identifying candidates for rapid influenza tests. This has the potential to be more efficient than a "one size fits all" test strategy, as it strategically targets the use of tests on patients most likely to benefit. It is potentially usable in a telehealth setting.

Identifying the Best Initial Oral Antibiotics for Adults with Community-Acquired Pneumonia: A Network Meta-Analysis.

BACKGROUND: The objective of this network meta-analysis was to compare rates of clinical response and mortality for empiric oral antibiotic regimens in adults with mild-moderate community-acquired pneumonia (CAP). METHODS: We searched PubMed, Cochrane, and the reference lists of systematic reviews and clinical guidelines. We included randomized trials of adults with radiologically confirmed mild to moderate CAP initially treated orally and reporting clinical cure or mortality. Abstracts and studies were reviewed in parallel for inclusion in the analysis and for data abstraction. We performed separate analyses by antibiotic medications and antibiotic classes and present the results through network diagrams and forest plots sorted by p-scores. We assessed the quality of each study using the Cochrane Risk of Bias framework, as well as global and local inconsistency. RESULTS: We identified 24 studies with 9361 patients: six at low risk of bias, six at unclear risk, and 12 at high risk. Nemonoxacin, levofloxacin, and telithromycin were most likely to achieve clinical response (p-score 0.79, 0.71, and 0.69 respectively), while penicillin and amoxicillin were least likely to achieve clinical response. Levofloxacin, nemonoxacin, azithromycin, and amoxicillin-clavulanate were most likely to be associated with lower mortality (p-score 0.85, 0.75, 0.74, and 0.68 respectively). By antibiotic class, quinolones and macrolides were most effective for clinical response (0.71 and 0.70 respectively), with amoxicillin-clavulanate plus macrolides and beta-lactams being less effective (p-score 0.11 and 0.22). Quinolones were most likely to be associated with lower mortality (0.63). All confidence intervals were broad and partially overlapping. CONCLUSION: We observed trends toward a better clinical response and lower mortality for quinolones as empiric antibiotics for CAP, but found no conclusive evidence of any antibiotic being clearly more effective than another. More trials are needed to inform guideline recommendations on the most effective antibiotic regimens for outpatients with mild to moderate CAP.

High Prevalence of Both Previous Infection with SARS-CoV-2 and Persistent Symptoms.

INTRODUCTION: Universities are unique settings with large populations, congregate housing, and frequent attendance of events in large groups. However, the current prevalence of previous COVID-19 infection in university students, including symptomatic and asymptomatic disease, is unknown. Our goal therefore was to determine the prevalence of previous infection, risk factors for infection, and the prevalence of persistent symptoms following infection among university students. METHODS: This was a cross-sectional study set in a large public university between January 22 and March 22, 2021. We surveyed students about demographics, risk factors, and symptoms, and simultaneously tested their saliva for IgA antibodies to SARS-CoV-2. To estimate the prevalence of previous infection we adjusted our intentional sample of a diverse student population for year in school and age to resemble the composition of the entire student body and adjusted for the imperfect sensitivity and specificity of the antibody test. Univariate and multiple regression analysis was used to identify independent risk factors for infection, and the proportion of students with persistent symptoms following acute infection was determined. RESULTS: A total of 488 students completed the survey, 432 had a valid antibody result, and 428 had both. The estimated prevalence of previous infection for 432 participants with valid antibody results was 41%. Of 145 students in our sample with a positive antibody test, 41.4% denied having a previous positive polymerase chain reaction (PCR) test for SARS-CoV-2 and presumably had an asymptomatic infection; in our adjusted analysis we estimate that approximately 2-thirds of students had asymptomatic infections. Independent risk factors for infection included male sex, having a roommate with a known symptomatic infection, and having two or fewer roommates. More frequent attendance of parties and bars was a univariate risk factor, but not in the multiple regression analysis. Of 122 students reporting a previous symptomatic infection, 14 (11.4%) reported persistent symptoms consistent with postacute COVID-19 a median of 132 days later. CONCLUSIONS AND RELEVANCE: Previous COVID-19 infection, both symptomatic and asymptomatic, was common at a large university. Measures that could prevent resurgence of the infection when students return to campus include mandatory vaccination policies, mass surveillance testing, and testing of sewage for antigen to SARS-CoV-2.

A Systematic Review of Clinical Prediction Rules for the Diagnosis of Influenza.

BACKGROUND: Clinical prediction rules (CPRs) can assist clinicians by focusing their clinical evaluation on the most important signs and symptoms, and if used properly can reduce the need for diagnostic testing. This study aims to perform an updated systematic review of clinical prediction rules and classification and regression tree (CART) models for the diagnosis of influenza. METHODS: We searched PubMed, CINAHL, and EMBASE databases. We identified prospective studies of patients presenting with suspected influenza or respiratory infection and that reported a CPR in the form of a risk score or CART-based algorithm. Studies had to report at a minimum the percentage of patients in each risk group with influenza. Studies were evaluated for inclusion and data were extracted by reviewers working in parallel. Accuracy was summarized descriptively; where not reported by the authors the area under the receiver operating characteristic curve (AUROCC), predictive values, and likelihood ratios were calculated. RESULTS: We identified 10 studies that presented 14 CPRs. The most commonly included predictor variables were cough, fever, chills and/or sweats, myalgias, and acute onset, all which can be ascertained by phone or telehealth visit. Most CPRs had an AUROCC between 0.7 and 0.8, indicating good discrimination. However, only 1 rule has undergone prospective external validation, with limited success. Data reporting by the original studies was in some cases inadequate to determine measures of accuracy. CONCLUSIONS: Well-designed validation studies, studies of interrater reliability between telehealth an in-person assessment, and studies using novel data mining and artificial intelligence strategies are needed to improve diagnosis of this common and important infection.

Primary Care Relevant Risk Factors for Adverse Outcomes in Patients With COVID-19 Infection: A Systematic Review.

BACKGROUND: The aim of this systematic review is to summarize the best available evidence regarding individual risk factors, simple risk scores, and multivariate models that use patient characteristics, vital signs, comorbidities, and laboratory tests relevant to outpatient and primary care settings. METHODS: Medline, WHO COVID-19, and MedRxIV databases were searched; studies meeting inclusion criteria were reviewed in parallel, and variables describing study characteristics, study quality, and risk factor data were abstracted. Study quality was assessed using the Quality in Prognostic Studies tool. Random effects meta-analysis of relative risks (categorical variables) and unstandardized mean differences (continuous variables) was performed; multivariate models and clinical prediction rules were summarized qualitatively. RESULTS: A total of 551 studies were identified and 22 studies were included. The median or mean age ranged from 38 to 68 years. All studies included only inpatients, and mortality rates ranged from 3.2% to 50.5%. Individual risk factors most strongly associated with mortality included increased age, c-reactive protein (CRP), d-dimer, heart rate, respiratory rate, lactate dehydrogenase, and procalcitonin as well as decreased oxygen saturation, the presence of dyspnea, and comorbid coronary heart and chronic kidney disease. Independent predictors of adverse outcomes reported most frequently by multivariate models include increasing age, increased CRP, decreased lymphocyte count, increased lactate dehydrogenase, elevated temperature, and the presence of any comorbidity. Simple risk scores and multivariate models have been proposed but are often complex, and most have not been validated. CONCLUSIONS: Our systematic review identifies several risk factors for adverse outcomes in COVID-19-infected inpatients that are often available in the outpatient and primary care settings: increasing age, increased CRP or procalcitonin, decreased lymphocyte count, decreased oxygen saturation, dyspnea on presentation, and the presence of comorbidities. Future research to develop clinical prediction models and rules should include these predictors as part of their core data set to develop and validate pragmatic outpatient risk scores.

Cancer-Specific Mortality, All-Cause Mortality, and Overdiagnosis in Lung Cancer Screening Trials: A Meta-Analysis.

PURPOSE: Benefit of lung cancer screening using low-dose computed tomography (LDCT) in reducing lung cancer-specific and all-cause mortality is unclear. We undertook a meta-analysis to assess its associations with outcomes. METHODS: We searched the literature and previous systematic reviews to identify randomized controlled trials comparing LDCT screening with usual care or chest radiography. We performed meta-analysis using a random effects model. The primary outcomes were lung cancer-specific mortality, all-cause mortality, and the cumulative incidence ratio of lung cancer between screened and unscreened groups as a measure of overdiagnosis. RESULTS: Meta-analysis was based on 8 trials with 90,475 patients that had a low risk of bias. There was a significant reduction in lung cancer-specific mortality with LDCT screening (relative risk = 0.81; 95% CI, 0.74-0.89); the estimated absolute risk reduction was 0.4% (number needed to screen = 250). The reduction in all-cause mortality was not statistically significant (relative risk = 0.96; 95% CI, 0.92-1.01), but the absolute reduction was consistent with that for lung cancer-specific mortality (0.34%; number needed to screen = 294). In the studies with the longest duration of follow-up, the incidence of lung cancer was 25% higher in the screened group, corresponding to a 20% rate of overdiagnosis. CONCLUSIONS: This meta-analysis showing a significant reduction in lung cancer-specific mortality, albeit with a tradeoff of likely overdiagnosis, supports recommendations to screen individuals at elevated risk for lung cancer with LDCT.

Accuracy of Signs and Symptoms for the Diagnosis of Community-acquired Pneumonia: A Meta-analysis.

BACKGROUND: Community-acquired pneumonia (CAP) is an important source of morbidity and mortality. However, overtreatment of acute cough illness with antibiotics is an important problem, so improved diagnosis of CAP could help reduce inappropriate antibiotic use. METHODS: This was a meta-analysis of prospective cohort studies of patients with clinically suspected pneumonia or acute cough that used imaging as the reference standard. All studies were reviewed in parallel by two researchers and quality was assessed using the QUADAS-2 criteria. Summary measures of accuracy included sensitivity, specificity, likelihood ratios, the diagnostic odds ratio, and the area under the receiver operating characteristic curve (AUROCC) and were calculated using bivariate meta-analysis. RESULTS: We identified 17 studies, of which 12 were judged to be at low risk of bias and the remainder at moderate risk of bias. The prevalence of CAP was 10% in nine primary care studies and was 20% in seven emergency department studies. The probability of CAP is increased most by an abnormal overall clinical impression suggesting CAP (positive likelihood ratio [LR+] = 6.32, 95% CI = 3.58 to 10.5), egophony (LR+ = 6.17, 95% CI = 1.34 to 18.0), dullness to percussion (LR+ = 2.62, 95% CI = 1.14 to 5.30), and measured temperature (LR+ = 2.52, 95% CI = 2.02 to 3.20), while it is decreased most by the absence of abnormal vital signs (LR- = 0.25, 95% CI = 0.11 to 0.48). The overall clinical impression also had the highest AUROCC at 0.741. CONCLUSIONS: While most individual signs and symptoms were unhelpful, selected signs and symptoms are of value for diagnosing CAP. Teaching and performing these high value elements of the physical examination should be prioritized, with the goal of better targeting chest radiographs and ultimately antibiotics.

Accuracy of Biomarkers for the Diagnosis of Adult Community-acquired Pneumonia: A Meta-analysis.

BACKGROUND: Biomarkers such as C-reactive protein (CRP) and procalcitonin may help distinguish community-acquired pneumonia (CAP) from other causes of lower respiratory tract infection. METHODS: We performed a systematic review of the literature to identify prospective studies evaluating the accuracy of a biomarker in patients with acute cough or suspected CAP. We performed parallel abstraction of data regarding study inclusion, characteristics, quality, and test accuracy. Study quality was evaluated using QUADAS-2. Bivariate meta-analysis was performed using the mada package in R, and summary receiver operating characteristic (ROC) curves were created. RESULTS: Fourteen studies met our inclusion and exclusion criteria; three were at low risk of bias and four at moderate risk of bias, largely due to failure to prespecify diagnostic thresholds. Considering all studies regardless of the cutoff used, CRP was most accurate (area under the ROC curve = 0.802), followed by leukocytosis (0.777) and procalcitonin (0.771). Lipopolysaccharide-binding protein and fibrinogen are promising, but were only studied in a single report. For CRP and procalcitonin, the positive and negative likelihood ratios (LR+ and LR-, respectively) varied inversely based on the cutoff. For CRP, LR+ and LR- were 2.08 and 0.32 for a cutoff of 20 mg/L, 3.64 and 0.36 for a cutoff of 50 mg/L, and 5.89 and 0.47 for a cutoff of 100 mg/L. For procalcitonin, LR+ and LR- were 2.50 and 0.39 for a cutoff of 0.10 µg/L, 5.43 and 0.62 for a cutoff of 0.25 µg/L, and 8.25 and 0.76 for a cutoff of 0.50 µg/L. The combination of CRP >49.5 mg/L and procalcitonin >0.1 µg/L had LR+ of 2.24 and LR- of 0.44. CONCLUSIONS: The best evidence supports CRP as the preferred biomarker for diagnosis of outpatient CAP given its accuracy, low cost, and point-of-care availability.