Association of Mycoplasma pneumoniae coinfection with adenovirus pneumonia severity in children

Between the winter of 2018 and the end of 2019, there has been an epidemic of adenovirus infection in southern China, including Zhejiang Province. The number of children suffering from adenovirus pneumonia (AP) has significantly increased. AP can be accompanied by Mycoplasma pneumoniae in children. This study aimed to investigate the association of M. pneumoniae and identify the risk factors for coinfection on hospitalized patients with AP. The patients were classified into two groups by etiologic analysis (single AP and AP with M. pneumoniae coinfection groups). The clinical manifestations, clinical medication, and laboratory and imaging findings of the two groups were compared and analyzed. The coinfection group (n = 125) had a significantly longer duration of fever than the single AP group (n = 171; P = 0.03). Shortness of breath (P = 0.023) and pulmonary imaging findings, such as pulmonary consolidation, atelectasis, pleural effusion, and multilobe lesions (P < 0.05), were more common in the coinfection group. The patients with coinfection had more severe symptoms, significantly longer hospitalization time and an increased proportion of using glucocorticoids and/or immunoglobulin needing oxygen inhalation (P < 0.05). The incidence of AP with M. pneumoniae coinfection is high. The prolonged fever duration and pulmonary imaging findings could be used as prediction factors to predict M. pneumoniae coinfection in children with AP. Patients with AP coinfected with MP may easily develop severe illness. Hence, a reasonable change in the treatment is necessary.

A retrospective observational study to estimate use of biomarkers to predict severity of community acquired pneumonia in children

Purpose: Multiple studies have investigated the role of biomarkers in predicting pneumonia severity in adults but minimal research exists for children. The aim of this study was to determine if the following biomarkers: white blood cell count (WBC), platelet count, C-reactive protein (CRP), procalcitonin (PCT), neutrophil-lymphocyte ratio, neutrophil count, or band count predict community associated pneumonia (CAP) severity in children. Methods: A retrospective chart review was conducted on pediatric patients (aged 60 days to 18 years) diagnosed with CAP, admitted to a regional, tertiary hospital. Patients were stratified into two severity cohorts, mild (no ICU care), and moderate /severe (required ICU care). Biomarker values were then compared between the severity cohorts and area under the curve (AUC), cut-off values, performance characteristics were calculated. Results: A total of 108 patients met inclusion criteria. Among the biomarkers examined, elevated levels of CRP (51.7 mg/L in mild vs. 104.8 mg/L in moderate/severe, p = 0.003, PCT (0.29 ng/ml in mild vs. 4.02 ng/ml in moderate/severe, p = 0.001) and band counts (8% in mild vs. 15% moderate/severe, p = 0.009) were associated with increased pneumonia severity. In predicting moderate/severe CAP, PCT had the highest AUC of 0.77 (p = 0.001) followed by bands AUC of 0.69 (p = 0.009) and CRP AUC of 0.67 (p = 0.003). The cut-off for PCT of 0.55ng/ml had a sensitivity of 83% and a specificity of 65%. A cut-off level of 53.1 mg/L for CRP had a sensitivity of 79% and specificity of 52%. A cut off level of 12.5% bands had a sensitivity of 61% and specificity of 71%. Conclusion: Biomarkers, in particular PCT, obtained early in hospitalization appear to perform as predictors for CAP severity in children and may be beneficial in guiding CAP management

The pneumonia severity index: assessment and comparison to popular machine learning classifiers

Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size. In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC). Machine learning classifiers, such as Random Forest, provided a significant improvement (∼29% in PR AUC and ∼5% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI). There were also statistically significant differences (p<0.05) between Random Forest and PSI among various races/ethnicities. Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.Key MessagesThis work compared the prognostication accuracy performance of patient severity with Community Acquired Pneumonia (CAP) between Pneumonia Severity Index (PSI) and nine machine learning classifiers and found machine learning classifiers provided a significant improvement.

Prognosis of patients hospitalized with a diagnosis of COVID-19 pneumonia in a tertiary hospital in Turkey

BACKGROUND: SARS-CoV2/COVID-19 emerged in China and caused a global pandemic in 2020. The mortality rate has been reported to be between 0% and 14.6% in all patients. In this study, we determined the clinical and laboratory parameters of COVID-19 related morbidity and mortality in our hospital. OBJECTIVES: Investigate the relationship between demographic, clinical, and laboratory parameters on COVID-19-related morbidity and mortality. DESIGN: Retrospective observational study. SETTINGS: Tertiary care hospital. PATIENTS AND METHODS: Patients diagnosed with COVID-19 pneumonia from March until the end of December were included in the study. MAIN OUTCOME MEASURES: The relationship between demographic, clinical, and laboratory parameters and the morbidity and mortality rates of patients diagnosed with COVID-19. SAMPLE SIZE: 124 patients RESULTS: The mortality rate was 9.6% (12/124). Coronary artery disease ( P <.0001) diabetes mellitus ( P =.04) fever (>38.3°C) at presentation ( P =.04) hypertension ( P <.0001), and positive smoking history ( P <.0001) were significantly associated with mortality. Patients who died were older, had a higher comorbid disease index, pneumonia severity index, fasting blood glucose, baseline serum creatinine, D-dimer, and had lower baseline haemoglobin, SaO 2 , percentage of lymphocyte counts and diastolic blood pressure. Patients admitted to the ICU were older, had a higher comorbidity disease index, pneumonia severity index, C-reactive protein, WBC, D-dimer, creatinine, number of antibiotics used, longer O 2 support duration, lower hemoglobin, lymphocyte (%), and baseline SaO 2 (%). CONCLUSIONS: Our results were consistent with much of the reported data. We suggest that the frequency, dosage, and duration of steroid treatment should be limited. LIMITATIONS: Low patient number, uncertain reason of mortality, no standard treatment regimen, limited treatment options, like ECMO. CONFLICT OF INTEREST: None.

Comparison of clinical features and prognosis in patients with cryptogenic and secondary organizing pneumonia

Background Organizing pneumonia (OP) can be diagnosed pathologically, and cryptogenic OP (COP) and secondary OP (SOP) have been classified by cause and particular underlying context. Because it is clinically difficult to differentiate between COP and SOP, this study investigated characteristics that could distinguish between COP and SOP. Methods The medical records of patients who underwent lung biopsy for a diagnosis of OP at a single tertiary hospital from January 2016 to December 2018 were retrospectively reviewed. Results Eighty-five patients had pathologically proven OP, including 16 diagnosed with COP and 69 diagnosed with SOP. The most common cause of SOP was infectious pneumonia, observed in 57 (82.6%) of the 69 patients, followed by cancer and radiation pneumonitis. The pathogens causing infectious pneumonia were identified in 45 (65.2%) patients. There were no differences in age, sex, and lung function between the COP and SOP groups. Median body mass index was significantly lower (P = 0.030), and median time from symptom onset to hospital admission significantly shorter (P = 0.006), in the SOP than in the COP group. Fever was more common in the SOP group (P = 0.024), and CURB 65, an index of pneumonia severity, tended to be higher in the SOP group (P = 0.017). Some laboratory results differed significantly between the two groups. Lymphocyte counts in bronchoalveolar lavage (BAL) fluid were significantly higher in the COP than in the SOP group (P = 0.012). Radiologic findings showed that effusion was more common in the SOP group (P = 0.036). There were no between-group differences in steroid use, 30 day and in-hospital mortality rates, and rates of OP outcomes and recurrences. Pneumonia recurrence rate was significantly higher in SOP patients who were than were not treated with steroids (P = 0.035). Conclusions Infection is the main cause of SOP. Symptom onset is more rapid in patients with SOP than with COP. Some blood and BAL fluid test results differed significantly in the COP and SOP groups. Pleural effusion was more common in the SOP group but there were no differences in clinical course. Recurrence in patients with SOP was more common in those who were than were not treated with steroids.

Validity of Pneumonia Severity Assessment Scores in Africa and South Asia: A Systematic Review and Meta-Analysis

Background: Although community-acquired pneumonia (CAP) severity assessment scores are widely used, their validity in low- and middle-income countries (LMICs) is not well defined. We aimed to investigate the validity and performance of the existing scores among adults in LMICs (Africa and South Asia). Methods: Medline, Embase, Cochrane Central Register of Controlled Trials, Scopus and Web of Science were searched to 21 May 2020. Studies evaluating a pneumonia severity score/tool among adults in these countries were included. A bivariate random-effects meta-analysis was performed to examine the scores’ performance in predicting mortality. Results: Of 9900 records, 11 studies were eligible, covering 12 tools. Only CURB-65 (Confusion, Urea, Respiratory Rate, Blood Pressure, Age ≥ 65 years) and CRB-65 (Confusion, Respiratory Rate, Blood Pressure, Age ≥ 65 years) were included in the meta-analysis. Both scores were effective in predicting mortality risk. Performance characteristics (with 95% Confidence Interval (CI)) at high (CURB-65 ≥ 3, CRB-65 ≥ 3) and intermediate-risk (CURB-65 ≥ 2, CRB-65 ≥ 1) cut-offs were as follows: pooled sensitivity, for CURB-65, 0.70 (95% CI = 0.25–0.94) and 0.96 (95% CI = 0.49–1.00), and for CRB-65, 0.09 (95% CI = 0.01–0.48) and 0.93 (95% CI = 0.50–0.99); pooled specificity, for CURB-65, 0.90 (95% CI = 0.73–0.96) and 0.64 (95% CI = 0.45–0.79), and for CRB-65, 0.99 (95% CI = 0.95–1.00) and 0.43 (95% CI = 0.24–0.64). Conclusions: CURB-65 and CRB-65 appear to be valid for predicting mortality in LMICs. CRB-65 may be employed where urea levels are unavailable. There is a lack of robust evidence regarding other scores, including the Pneumonia Severity Index (PSI).