Evaluation of disease severity and prediction of severe cases in children hospitalized with influenza A (H1N1) infection during the post-COVID-19 era: a multicenter retrospective study

BACKGROUND

The rebound of influenza A (H1N1) infection in post-COVID-19 era recently attracted enormous attention due the rapidly increased number of pediatric hospitalizations and the changed characteristics compared to classical H1N1 infection in pre-COVID-19 era. This study aimed to evaluate the clinical characteristics and severity of children hospitalized with H1N1 infection during post-COVID-19 period, and to construct a novel prediction model for severe H1N1 infection.

METHODS

A total of 757 pediatric H1N1 inpatients from nine tertiary public hospitals in Yunnan and Shanghai, China, were retrospectively included, of which 431 patients diagnosed between February 2023 and July 2023 were divided into post-COVID-19 group, while the remaining 326 patients diagnosed between November 2018 and April 2019 were divided into pre-COVID-19 group. A 1:1 propensity-score matching (PSM) was adopted to balance demographic differences between pre- and post-COVID-19 groups, and then compared the severity across these two groups based on clinical and laboratory indicators. Additionally, a subgroup analysis in the original post-COVID-19 group (without PSM) was performed to investigate the independent risk factors for severe H1N1 infection in post-COIVD-19 era. Specifically, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to select candidate predictors, and logistic regression was used to further identify independent risk factors, thus establishing a prediction model. Receiver operating characteristic (ROC) curve and calibration curve were utilized to assess discriminative capability and accuracy of the model, while decision curve analysis (DCA) was used to determine the clinical usefulness of the model.

RESULTS

After PSM, the post-COVID-19 group showed longer fever duration, higher fever peak, more frequent cough and seizures, as well as higher levels of C-reactive protein (CRP), interleukin 6 (IL-6), IL-10, creatine kinase-MB (CK-MB) and fibrinogen, higher mechanical ventilation rate, longer length of hospital stay (LOS), as well as higher proportion of severe H1N1 infection (all P < 0.05), compared to the pre-COVID-19 group. Moreover, age, BMI, fever duration, leucocyte count, lymphocyte proportion, proportion of CD3+ T cells, tumor necrosis factor α (TNF-α), and IL-10 were confirmed to be independently associated with severe H1N1 infection in post-COVID-19 era. A prediction model integrating these above eight variables was established, and this model had good discrimination, accuracy, and clinical practicability.

CONCLUSIONS

Pediatric H1N1 infection during post-COVID-19 era showed a higher overall disease severity than the classical H1N1 infection in pre-COVID-19 period. Meanwhile, cough and seizures were more prominent in children with H1N1 infection during post-COVID-19 era. Clinicians should be aware of these changes in such patients in clinical work. Furthermore, a simple and practical prediction model was constructed and internally validated here, which showed a good performance for predicting severe H1N1 infection in post-COVID-19 era.

Factors related to the mortality risk of severe hand, foot, and mouth diseases (HFMD): a 5-year hospital-based survey in Guangxi, Southern China

BACKGROUND

To understand the factors influencing clinical outcomes of severe hand, foot, and mouth diseases (HFMD), and to provide scientific evidence for reducing the mortality risk of severe HFMD.

METHODS

From 2014 to 2018, children diagnosed with severe HFMD cases in Guangxi, China, were enrolled in this hospital-based study. The epidemiological data obtained through face-to-face interviews with the parents and guardians. Univariate and multivariate logistics regression models were used to analyze the factors influencing the clinical outcomes of severe HFMD. The impact of the EV-A71 vaccination on inpatient mortality was analyzed by a comparison approach.

RESULTS

A total of 1565 severe HFMD cases were enrolled in this survey, including 1474 (94.19%) survival cases and 91 (5.81%) death cases. The multivariate logistic analysis demonstrated that HFMD history of playmates in the last three months, first visit to the village hospital, time from the first visit to admission less than two days, no correct diagnosis for HFMD at the first visit, and having no rash symptoms were the independent risk factors for severe HFMD cases (all p < 0.05). While EV-A71 vaccination was a protective factor (p < 0.05). The EV-A71 vaccination group versus the non-vaccination group showed 2.23% of death in the vaccination group and 7.24% of death in the non-vaccination group. The EV-A71 vaccination protected 70.80% of the death of severe HFMD cases, with an effective index of 4.79.

CONCLUSIONS

The mortality risk of severe HFMD in Guangxi was related to playmates had HFMD history in last 3 months, hospital grade, EV-A71 vaccination, patients visit hospital previously, and rash symptom. EV-A71 vaccination can significantly reduce mortality among severe HFMD. The findings are of great significance for the effective prevention and control of HFMD in Guangxi, southern China.

Integration of protein context improves protein-based COVID-19 patient stratification

BACKGROUND

Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19.

METHODS

We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort.

RESULTS

Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type.

CONCLUSION

Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Prototypes virus of hand, foot and mouth disease infections and severe cases in Gansu, China: a spatial and temporal analysis

Background

Little research has been conducted on the spatio-temporal relationship between the severe cases and the enteroviruses infections of hand, foot and mouth disease (HFMD). This study aimed to investigate epidemic features and spatial clusters of HFMD incidence rates and assess the relationship between Enterovirus 71 (EV71) and Coxsackievirus A16 (CoxA16) and severe cases of HMFD in Gansu province, China.

Methods

Weekly county-specific data on HFMD between 1st January and 31st December 2018 were collected from the China Infectious Disease Information System (CIDIS), including enterovirus type (EV71 and CoxA16), severe and non-severe cases in Gansu province, China. Temporal risk [frequency index (α), duration index (β) and intensity index (γ)] and spatial cluster analysis were used to assess epidemic features and identify high-risk areas for HFMD. Time-series cross-correlation function and regression model were used to explore the relationship between the ratios of two types of viruses (i.e. EV71/Cox16) (EC) and severe cases index (i.e. severe cases/non-severe cases) (SI) of HFMD.

Results

Some counties in Dingxi City, Gansu were identified as a hot spot for the temporal risk indices. Time-series cross-correlation analysis showed that SI was significantly associated with EC (r = 0.417, P < 0.05) over a 4-week time lag. The regression analysis showed that SI was positively associated with EC (β = 0.04, 95% confidence interval (CI) 0.02–0.06).

Conclusion

The spatial patterns of HFMD incidence were associated with enteroviruses in Gansu. The research suggested that the EC could be considered a potential early warning sign for predicting severe cases of HFMD in Gansu province.

A comparison of demographic, epidemiological and clinical characteristics of hospital influenza-related viral pneumonia patients

Background

Through the comparison of the demographic, epidemiological, and clinical characteristics of hospital human influenza (influenza A (H1N1) pdm09, H3N2, and B)-related and hospitalized avian-origin influenza A (H7N9)-related viral pneumonia patients, find the different between them.

Methods

A retrospective study was conducted in hospitalized influenza-related viral pneumonia patients.

Results

Human influenza A-related patients in the 35–49-year-old group were more than those with B pneumonia patients (p = 0.027), and relatively less in the ≥ 65-year-old group than B pneumonia patients (p = 0.079). The proportion of comorbid condition to human influenza A pneumonia was 58%, lower than B pneumonia and H7N9 pneumonia patients (78% vs. 77.8%; p = 0.013). The proportion of invasive mechanical ventilation (IMV), lymphocytopenia, elevated lactate dehydrogenase to hospitalized human influenza A-related viral pneumonia patients was higher than B pneumonia patients (p < 0.05), but lower than H7N9 pneumonia patients (p < 0.05). In the multivariate analysis, pulmonary consolidation (odds ratio (OR): 13.67; 95% confidence interval (CI) 1.54–121.12; p = 0.019) and positive bacterial culture (sputum) (OR: 7.71; 95% CI 2.48–24.03; p < 0.001) were independently associated with IMV, while shock (OR: 13.16; 95% CI 2.06–84.07; p = 0.006), white blood cell count > 10,000/mm³ (OR: 7.22; 95% CI 1.47–35.58; p = 0.015) and positive bacterial culture(blood or sputum) (OR: 6.27; 95% CI 1.36–28.85; p = 0.018) were independently associated with death in the three types hospitalized influenza-related viral pneumonia patients.

Conclusions

Hospital influenza B-related viral pneumonia mainly affects the elderly and people with underlying diseases, while human influenza A pneumonia mainly affects the young adults; however, the mortality was similar. The hospitalized human influenza A-related viral pneumonia patients was severer than B pneumonia patients, but milder than H7N9 pneumonia patients. Pulmonary consolidation and positive bacterial culture (sputum) were independently associated with IMV, while shock, white blood cell count > 10,000/mm³, and positive bacterial culture (blood or sputum) were independently associated with death to three types hospitalized influenza-related viral pneumonia patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06485-x.

Potential therapeutic effects of dipyridamole in the severely ill patients with COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can cause acute respiratory distress syndrome, hypercoagulability, hypertension, and multiorgan dysfunction. Effective antivirals with safe clinical profile are urgently needed to improve the overall prognosis. In an analysis of a randomly collected cohort of 124 patients with COVID-19, we found that hypercoagulability as indicated by elevated concentrations of D-dimers was associated with disease severity. By virtual screening of a U.S. FDA approved drug library, we identified an anticoagulation agent dipyridamole (DIP) in silico, which suppressed SARS-CoV-2 replication in vitro. In a proof-of-concept trial involving 31 patients with COVID-19, DIP supplementation was associated with significantly decreased concentrations of D-dimers (P < 0.05), increased lymphocyte and platelet recovery in the circulation, and markedly improved clinical outcomes in comparison to the control patients. In particular, all 8 of the DIP-treated severely ill patients showed remarkable improvement: 7 patients (87.5%) achieved clinical cure and were discharged from the hospitals while the remaining 1 patient (12.5%) was in clinical remission.

Exploiting routinely collected severe case data to monitor and predict influenza outbreaks

Background

Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and predict the key transmission features of seasonal and pandemic influenza.

Methods

We propose an epidemic model that links the underlying unobserved influenza transmission process to data on severe influenza cases. Within a Bayesian framework, we infer retrospectively the parameters of the epidemic model for each seasonal outbreak from 2012 to 2015, including: the effective reproduction number; the initial susceptibility; the probability of admission to intensive care given infection; and the effect of school closure on transmission. The model is also implemented in real time to assess whether early forecasting of the number of admissions to intensive care is possible.

Results

Our model of admissions data allows reconstruction of the underlying transmission dynamics revealing: increased transmission during the season 2013/14 and a noticeable effect of the Christmas school holiday on disease spread during seasons 2012/13 and 2014/15. When information on the initial immunity of the population is available, forecasts of the number of admissions to intensive care can be substantially improved.

Conclusion

Readily available severe case data can be effectively used to estimate epidemiological characteristics and to predict the evolution of an epidemic, crucially allowing real-time monitoring of the transmission and severity of the outbreak.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5671-7) contains supplementary material, which is available to authorized users.

[Severe cases with hand, foot and mouth disease: data based on national pilot hand, foot and mouth disease surveillance system]

Objective: To investigate the clinical severity, etiological classification and risk factors of severe cases with hand, foot and mouth disease (HFMD). Methods: A total of 1 489 records on severe and fatal HFMD cases reported to the national pilot surveillance system of HFMD were used to analyze the demographic, medical treatment, etiological classification of the cases. Treatment outcome related risk factors were also studied with multi-variable stepwise logistic regression method. Results: Seven out of the 1 489 severe HFMD cases died of this disease. A total of 960 (72.9%) were under three years old and 62.9% were male and most of the cases (937, 62.9%) resided in rural areas. Among all the cases, 494 (33.2%) went to seek the first medical assistance at the institutions of village or township level. Durations between disease onset and first medical attendance, being diagnosed as the disease or diagnosed as severe cases were 0(0-1) d, 1 (0-2) d and 2 (1-4) d, respectively. In total, 773 (51.9%) of the severe HFMD cases were diagnosed as with aseptic meningitis, 260 (17.5%) with brainstem encephalitis, 377 (25.3%) with non-brainstem encephalitis, 6 (0.4%) with encephalomyelitis, 1 (0.1%) with acute flaccid paralysis, 4 (0.3%) with pulmonary hemorrhage/pulmonary edema and 68 (4.6%) with cardiopulmonary failure. Of the etiologically diagnosed 1 217 severe and fatal HFMD cases, 642 (52.8%) were with EV71, other enterovirus 261 (21.5%), Cox A16 36 (3.0%), 1 (0.1%) with both EV71 and Cox A16. However, 277 (22.8%) showed negative on any pathogenic virus. Complication (Z=3.15, P=0.002) and duration between onset and diagnosed as severe cases (Z=3.95, P<0.001) were shown as key factors related to treatment outcomes. Conclusions: Most severe HFMD cases appeared in boys, especially living in the rural areas. Frequently seen complications would include aseptic meningitis, non-brainstem encephalitis and brainstem encephalitis. EV71 was the dominant etiology for severe and fatal cases. Early diagnosis and complication control were crucial, related to the treatment outcome of HFMD.

Imaging manifestations and pathological analysis of severe pneumonia caused by human infected avian influenza (H7N9)

Objective

To investigate the imaging and pathological findings of severe pneumonia caused by human infected avian influenza (H7N9), and therefore to further understand and improve diagnostic accuracy of severe pneumonia caused by human infected avian influenza (H7N9).

Methods

The relevant clinical and imaging data of 19 cases, including 10 males and 9 females, with pneumonia caused by human infected avian influenza (H7N9) was retrospectively analyzed. One of the cases had received percutaneous lung biopsy, with the clinical, imaging and pathological changes possible to be analyzed.

Results

The lesions were mainly located at lower lobes and dorsal of lungs, involving multiple lobes and segments. Ground-glass opacities and/or pulmonary opacities were the more often imaging manifestations of severe pneumonia caused by human infected avian influenza (H7N9) in early and evolving phases (19/19,100%). By biopsy following percutaneous lung puncture, exudation of slurry, cellulose, RBC and neutrophils, formation of hyaline membrane, squamous metaplasia and organizing exudates were observable at the alveolar space. Some of alveoli collapsed, and some responded to show compensatory emphysema.

Conclusion

The imaging features of severe pneumonia caused by human infected avian influenza (H7N9) include obvious ground-glass opacity and pulmonary consolidation, mainly at lower lobes and dorsal of lungs, with rapid changes. The cross-analysis of imaging and pathology preliminary can elucidate the pathological mechanisms of ground-glass opacities and pulmonary consolidation of severe pneumonia. Such an intensive study is beneficial to prompt clinicians to observe and evaluate the progress of the disease. In addition, it is also in favor of managing the symptoms and reducing the mortality rate.