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FluA-p score: a novel prediction rule for mortality in influenza A-related pneumonia patients
Liang Chen1, Xiudi Han2, Yan Li Li3
1Department of Infectious Diseases, Beijing Jishuitan Hospital, 4th Medical College of Peking University, Beijing, China. chenliang1995@sina.com.
Background:
The pneumonia severity index (PSI) and the CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥ 65 years) score have been shown to predict mortality in community-acquired pneumonia. Their ability to predict influenza-related pneumonia, however, is less well-established.
Methods:
A total of 693 laboratory-confirmed FluA-p patients diagnosed between Jan 2013 and Dec 2018 and recruited from five teaching hospitals in China were included in the study. The sample included 494 patients in the derivation cohort and 199 patients in the validation cohort. The prediction rule was established based on independent risk factors for 30-day mortality in FluA-p patients from the derivation cohort.
Results:
The 30-day mortality of FluA-p patients was 19.6% (136/693). The FluA-p score was based on a multivariate logistic regression model designed to predict mortality. Results indicated the following significant predictors (regression statistics and point contributions toward total score in parentheses): blood urea nitrogen > 7 mmol/L (OR 1.604, 95% CI 1.150-4.492, p = 0.040; 1 points), pO2/FiO2 ≤ 250 mmHg (OR 2.649, 95% CI 1.103-5.142, p = 0.022; 2 points), cardiovascular disease (OR 3.967, 95% CI 1.269-7.322, p < 0.001; 3 points), arterial PH < 7.35 (OR 3.959, 95% CI 1.393-7.332, p < 0.001; 3 points), smoking history (OR 5.176, 95% CI 2.604-11.838, p = 0.001; 4 points), lymphocytes < 0.8 × 109/L (OR 8.391, 95% CI 3.271-16.212, p < 0.001; 5 points), and early neurominidase inhibitor therapy (OR 0.567, 95% CI 0.202-0.833, p = 0.005; - 2 points). Seven points was used as the cut-off value for mortality risk stratification. The model showed a sensitivity of 0.941, a specificity of 0.762, and overall better predictive performance than the PSI risk class (AUROC = 0.908 vs 0.560, p < 0.001) and the CURB-65 score (AUROC = 0.908 vs 0.777, p < 0.001).
Conclusions:
Our results showed that a FluA-p score was easy to derive and that it served as a reliable prediction rule for 30-day mortality in FluA-p patients. The score could also effectively stratify FluA-p patients into relevant risk categories and thereby help treatment providers to make more rational clinical decisions.
Insights
A new FluA-p score accurately predicts 30-day mortality in influenza A pneumonia patients. This score is more effective than existing methods like PSI and CURB-65 for risk stratification and clinical decision-making.
Area of Science:
- Pulmonology
- Infectious Diseases
- Medical Statistics
Background:
- Community-acquired pneumonia (CAP) mortality is predicted by PSI and CURB-65 scores.
- The predictive accuracy of these scores for influenza A-related pneumonia (FluA-p) is not well-established.
Purpose of the Study:
- To develop and validate a novel prediction score for 30-day mortality in patients with influenza A-related pneumonia (FluA-p).
- To compare the performance of the new FluA-p score against established pneumonia severity indices.
Main Methods:
- A cohort of 693 laboratory-confirmed FluA-p patients was analyzed.
- A multivariate logistic regression model was used to identify independent risk factors for 30-day mortality in a derivation cohort (n=494).
- The prediction rule was validated in a separate cohort (n=199).
Main Results:
- The 30-day mortality rate for FluA-p was 19.6%.
- The developed FluA-p score identified key predictors: elevated BUN, low PaO2/FiO2, cardiovascular disease, low arterial pH, smoking history, low lymphocytes, and early neuraminidase inhibitor therapy.
- The FluA-p score demonstrated superior predictive performance (AUROC=0.908) compared to PSI (AUROC=0.560) and CURB-65 (AUROC=0.777).
Conclusions:
- The FluA-p score is a reliable and easily applicable tool for predicting 30-day mortality in FluA-p patients.
- This score effectively stratifies patients into risk categories, aiding clinicians in making informed treatment decisions.
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