Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia.

(1) Background: Biomarker and model development can help physicians adjust the management of patients with community-acquired pneumonia (CAP) by screening for inpatients with a low probability of cure early in their admission; (2) Methods: We conducted a 30-day cohort study of newly admitted adult CAP patients over 20 years of age. Prognosis models to predict the short-term prognosis were developed using random survival forest (RSF) method; (3) Results: A total of 247 adult CAP patients were studied and 208 (84.21%) of them reached clinical stability within 30 days. The soluble form of suppression of tumorigenicity-2 (sST2) was an independent predictor of clinical stability and the addition of sST2 to the prognosis model could improve the performance of the prognosis model. The C-index of the RSF model for predicting clinical stability was 0.8342 (95% CI, 0.8086-0.8598), which is higher than 0.7181 (95% CI, 0.6933-0.7429) of CURB 65 score, 0.8025 (95% CI, 0.7776-8274) of PSI score, and 0.8214 (95% CI, 0.8080-0.8348) of cox regression. In addition, the RSF model was associated with adverse clinical events during hospitalization, ICU admissions, and short-term mortality; (4) Conclusions: The RSF model by incorporating sST2 was more accurate than traditional methods in assessing the short-term prognosis of CAP patients.

Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia.

The soluble form of the suppression of tumorigenicity-2 (sST2) is a biomarker for risk classification and prognosis of heart failure, and its production and secretion in the alveolar epithelium are significantly correlated with the inflammation-inducing in pulmonary diseases. However, the predictive value of sST2 in pulmonary disease had not been widely studied. This study investigated the potential value in prognosis and risk classification of sST2 in patients with community-acquired pneumonia. Clinical data of ninety-three CAP inpatients were retrieved and their sST2 and other clinical indices were studied. Cox regression models were constructed to probe the sST2's predictive value for patients' restoring clinical stability and its additive effect on pneumonia severity index and CURB-65 scores. Patients who did not reach clinical stability within the defined time (30 days from hospitalization) have had significantly higher levels of sST2 at admission (P < 0.05). In univariate and multivariate Cox regression analysis, a high sST2 level (≥72.8 ng/mL) was an independent reverse predictor of clinical stability (P < 0.05). The Cox regression model combined with sST2 and CURB-65 (AUC: 0.96) provided a more accurate risk classification than CURB-65 (AUC:0.89) alone (NRI: 1.18, IDI: 0.16, P < 0.05). The Cox regression model combined with sST2 and pneumonia severity index (AUC: 0.96) also provided a more accurate risk classification than pneumonia severity index (AUC:0.93) alone (NRI: 0.06; IDI: 0.06, P < 0.05). sST2 at admission can be used as an independent early prognostic indicator for CAP patients. Moreover, it can improve the predictive power of CURB-65 and pneumonia severity index score.

Metabolomic profiling of anaerobic and aerobic energy metabolic pathways in chronic obstructive pulmonary disease.

While there is no cure for chronic obstructive pulmonary disease (COPD), its progressive nature and the formidable challenge to manage its symptoms warrant a more extensive study of the pathogenesis and related mechanisms. A new emphasis on COPD study is the change of energy metabolism. For the first time, this study investigated the anaerobic and aerobic energy metabolic pathways in COPD using the metabolomic approach. Metabolomic analysis was used to investigate energy metabolites in 140 COPD patients. The significance of energy metabolism in COPD was comprehensively explored by the Global Initiative for Chronic Obstructive Lung Disease-GOLD grading, acute exacerbation vs. stable phase (either clinical stability or four-week stable phase), age group, smoking index, lung function, and COPD Assessment Test (CAT) score. Through comprehensive evaluation, we found that COPD patients have a significant imbalance in the aerobic and anaerobic energy metabolisms in resting state, and a high tendency of anaerobic energy supply mechanism that correlates positively with disease progression. This study highlighted the significance of anaerobic and low-efficiency energy supply pathways in lung injury and linked it to the energy-inflammation-lung ventilatory function and the motion limitation mechanism in COPD patients, which implies a novel therapeutic direction for this devastating disease.

Krebs Von den Lungen-6 as a predictive indicator for the risk of secondary pulmonary fibrosis and its reversibility in COVID-19 patients.

Dysregulated immune response and abnormal repairment could cause secondary pulmonary fibrosis of varying severity in COVID-19, especially for the elders. The Krebs Von den Lungen-6 (KL-6) as a sensitive marker reflects the degree of fibrosis and this study will focus on analyzing the evaluative efficacy and predictive role of KL-6 in COVID-19 secondary pulmonary fibrosis. The study lasted more than three months and included total 289 COVID-19 patients who were divided into moderate (n=226) and severe groups (n=63) according to the severity of illness. Clinical information such as inflammation indicators, radiological results and lung function tests were collected. The time points of nucleic acid test were also recorded. Furthermore, based on Chest radiology detection, it was identified that 80 (27.7%) patients developed reversible pulmonary fibrosis and 34 (11.8%) patients developed irreversible pulmonary fibrosis. Receiver operating characteristic (ROC) curve analysis shows that KL-6 could diagnose the severity of COVID-19 (AUC=0.862) and predict the occurrence of pulmonary fibrosis (AUC = 0.741) and irreversible pulmonary fibrosis (AUC=0.872). Importantly, the cross-correlation analysis demonstrates that KL-6 rises earlier than the development of lung radiology fibrosis, thus also illuminating the predictive function of KL-6. We set specific values (505U/mL and 674U/mL) for KL-6 in order to assess the risk of pulmonary fibrosis after SARS-CoV-2 infection. The survival curves for days in hospital show that the higher the KL-6 levels, the longer the hospital stay (P<0.0001). In conclusion, KL-6 could be used as an important predictor to evaluate the secondary pulmonary fibrosis degree for COVID-19.

Predictive effects of IgA and IgG combination to assess pulmonary exudation progression in COVID-19 patients.

Our study intended to longitudinally explore the prediction effect of immunoglobulin A (IgA) on pulmonary exudation progression in COVID-19 patients. The serum IgA was tested with chemiluminescence method. Autoregressive moving average model was used to extrapolate the IgA levels before hospital admission. The positive rate of IgA and IgG in our cohort was 97% and 79.0%, respectively. In this study, the IgA levels peaks within 10-15 days after admission, while the IgG levels peaks at admission. We found that the time difference between their peaks was about 10 days. Viral RNA detection results showed that the positive rate in sputum and feces were the highest. Blood gas analysis showed that deterioration of hypoxia with the enlargement of pulmonary exudation area. And alveolar-arterial oxygen difference and oxygenation index were correlated with IgA and IgG. The results of biopsy showed that the epithelium of lung was exfoliated and the mucosa was edematous. In severe COVID-19 patients, the combination of IgA and IgG can predict the progress of pulmonary lesions and is closely related to hypoxemia and both also play an important defense role in invasion and destruction of bronchial and alveolar epithelium by SARS-CoV-2.