Multi-omics analysis reveals overactive inflammation and dysregulated metabolism in severe community-acquired pneumonia patients.

BACKGROUND: Severe community-acquired pneumonia (S-CAP) is a public health threat, making it essential to identify novel biomarkers and investigate the underlying mechanisms of disease severity. METHODS: Here, we profiled host responses to S-CAP through proteomics analysis of plasma samples from a cohort of S-CAP patients, non-severe (NS)-CAP patients, diseases controls (DCs), and healthy controls (HCs). Then, typical differentially expressed proteins were then validated by ELISA in an independent cohort. Metabolomics analysis was further performed on both the cohort 1 and cohort 2. Then, the proteomic and metabolomic signatures were compared between the adult and child cohorts to explore the characteristics of severe pneumonia patients. RESULTS: There were clear differences between CAP patients and controls, as well as substantial differences between the S-CAP and NS-CAP. Pathway analysis of changes revealed excessive inflammation, suppressed immunity, and lipid metabolic disorders in S-CAP cases. Interestingly, comparing these signatures between the adult and child cohorts confirmed that overactive inflammation and dysregulated lipid metabolism were common features of S-CAP patients, independent of age. The change proportion of glycerophospholipids, glycerolipids, and sphingolipids were obviously different in the adult and child S-CAP cases. CONCLUSION: The plasma multi-omics profiling revealed that excessive inflammation, suppressed humoral immunity, and disordered metabolism are involved in S-CAP pathogenesis.

A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia.

BACKGROUND: Community-Acquired Pneumonia (CAP) remains a significant global health concern, with a subset of cases progressing to Severe Community-Acquired Pneumonia (SCAP). This study aims to develop and validate a CT-based radiomics model for the early detection of SCAP to enable timely intervention and improve patient outcomes. METHODS: A retrospective study was conducted on 115 CAP and SCAP patients at Southern Medical University Shunde Hospital from January to December 2021. Using the Pyradiomics package, 107 radiomic features were extracted from CT scans, refined via intra-class and inter-class correlation coefficients, and narrowed down using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The predictive performance of the radiomics-based model was assessed through receiver operating characteristic (ROC) analysis, employing machine learning classifiers such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), trained and validated on datasets split 7:3, with a training set (n = 80) and a validation set (n = 35). RESULTS: The radiomics model exhibited robust predictive performance, with the RF classifier achieving superior precision and accuracy compared to LR, SVM, and KNN classifiers. Specifically, the RF classifier demonstrated a precision of 0.977 (training set) and 0.833 (validation set), as well as an accuracy of 0.925 (training set) and 0.857 (validation set), suggesting its superior performance in both metrics. Decision Curve Analysis (DCA) was utilized to evaluate the clinical efficacy of the RF classifier, demonstrating a favorable net benefit within the threshold ranges of 0.1 to 0.8 for the training set and 0.2 to 0.7 for the validation set. CONCLUSIONS: The radiomics model developed in this study shows promise for early SCAP detection and can improve clinical decision-making.

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study.

BACKGROUND: Severe community-acquired pneumonia is one of the most lethal forms of CAP with high mortality. For rapid and accurate decisions, we developed a mortality prediction model specifically tailored for elderly SCAP patients. METHODS: The retrospective study included 2365 elderly patients. To construct and validate the nomogram, we randomly divided the patients into training and testing cohorts in a 70% versus 30% ratio. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were used in the training cohort to identify independent risk factors. The robustness of this model was assessed using the C index, ROC and AUC. DCA was employed to evaluate the predictive accuracy of the model. RESULTS: Six factors were used as independent risk factors for in-hospital mortality to construct the prediction model, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet, and BUN. The C index was 0.743 (95% CI 0.719-0.768) in the training cohort and 0.731 (95% CI 0.694-0.768) in the testing cohort. The ROC curves and AUC for the training cohort and testing cohort (AUC = 0.742 vs. 0.728) indicated a robust discrimination. And the calibration plots showed a consistency between the prediction model probabilities and observed probabilities. Then, the DCA demonstrated great clinical practicality. CONCLUSIONS: The nomogram incorporated six risk factors, including age, the use of vasopressor, chronic renal disease, neutrophil, platelet and BUN, which had great predictive accuracy and robustness, while also demonstrating clinical practicality at ICU admission.

Risk factors and predicting nomogram for the clinical deterioration of non-severe community-acquired pneumonia.

BACKGROUND: Currently, there remains insufficient focus on non-severe community-acquired pneumonia (CAP) patients who are at risk of clinical deterioration, and there is also a dearth of research on the related risk factors. Early recognition of hospitalized patients at risk of clinical deterioration will be beneficial for their clinical management. METHOD: A retrospective study was conducted in The First Affiliated Hospital of Wenzhou Medical University, China, spanning from January 1, 2018 to April 30, 2022, and involving a total of 1,632 non-severe CAP patients. Based on whether their condition worsened within 72 h of admission, patients were divided into a clinical deterioration group and a non-clinical deterioration group. Additionally, all patients were randomly assigned to a training set containing 75% of patients and a validation set containing 25% of patients. In the training set, risk factors for clinical deterioration in patients with non-severe CAP were identified by using LASSO regression analysis and multivariate logistic regression analysis. A nomogram was developed based on identified risk factors. The effectiveness of the nomogram in both the training and validation sets was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Age, body mass index (BMI), body temperature, cardiovascular comorbidity, respiratory rate, LDH level, lymphocyte count and D-dimer level were identified as risk factors associated with the clinical deterioration of non-severe CAP within 72 h of admission. The area under curve (AUC) value of the nomogram was 0.78 (95% CI: 0.74-0.82) in the training set and 0.75 (95% CI: 0.67-0.83) in the validation set. Furthermore, the calibration curves for both the training and validation sets indicated that the predicted probability of clinical deterioration aligned with the actual probability. Additionally, DCA revealed clinical utility for the nomogram at a specific threshold probability. CONCLUSION: The study successfully identified the risk factors linked to the clinical deterioration of non-severe CAP and constructed a nomogram for predicting the probability of deterioration. The nomogram demonstrated favorable predictive performance and has the potential to aid in the early identification and management of non-severe CAP patients at elevated risk of deterioration.

Low thoracic skeletal muscle is a risk factor for 6-month mortality of severe community-acquired pneumonia in older men in intensive care unit.

BACKGROUND: Patients with severe community-acquired pneumonia (sCAP) admitted to the intensive care unit (ICU) often exhibit muscle catabolism, muscle weakness, and/or atrophy, all related to an increased morbidity and mortality. However, the relationship between thoracic skeletal muscle mass and sCAP-related mortality has not been well-studied. Early recognition of sarcopenia in ICU patients with sCAP would benefit their prognosis. METHODS: A retrospective study was conducted in Taizhou Hospital of Zhejiang Province, involving 101 patients with sCAP admitted in the ICU between December 2022 and February 2023. We measured the cross-sectional aera of the pectoralis, intercostal, paraspinal, serratus, and latissimus muscles at the T4 vertebral level (T4CSA) using chest computed tomography. Discriminatory thresholds were established by performing receiver operating characteristic curve analysis, with a designated cutoff value of 96.75 cm2 for male patients. This cohort was classified into mortality and survival groups based on a 6-month post-admission outcome. Univariate and multifactorial logistic regression analyses were performed to validate the correlation between low thoracic skeletal muscle area and prognostic outcomes. RESULTS: The mean age of the patients was 75.39 ± 12.09 years, with an overall 6-month mortality of 73.27%. T4CSA of the 6-month survival group was significantly larger than that in the mortality group for overall cohort. The T4CSA in the survival group was significantly larger than that in the mortality group (104.29 ± 23.98cm2 vs. 87.44 ± 23.0cm2, p = 0.008). T4CSA predicted the 6-month mortality from sCAP in males with an AUC of 0.722 (95% confidence interval (CI), 0.582-0.861). The specificity and sensitivity were 71.4% and 71.1%, respectively, (p < 0.05). No significant difference was observed between the two groups in terms of T4CSA. CONCLUSIONS: This study revealed that low thoracic skeletal muscle mass increased the risk of all-cause 6-month mortality in ICU patients with sCAP, particularly among male patients.

Stress hyperglycemia ratio and its influence on mortality in elderly patients with severe community-acquired pneumonia: a retrospective study.

BACKGROUND: Community-acquired pneumonia (CAP) is a significant health issue among the elderly, with severe cases (SCAP) having high mortality rates. This study assesses the predictive significance of the stress hyperglycemia ratio (SHR) in elderly SCAP patients and its impact on outcomes in both diabetic and non-diabetic patients. METHODS AND MATERIALS: This retrospective study included 406 SCAP patients aged 65 or older from the Second People's Hospital of Lianyungang (January 2020 to December 2023). Data collected included demographics, medical history, vital signs, and lab results. SHR was calculated from initial blood glucose and estimated average glucose (HbA1c). Statistical analyses, including Cox regression and Kaplan-Meier analysis, evaluated SHR's impact on mortality. Mediation models explored the effects of neutrophil-lymphocyte ratio (NLR) and SHR. RESULTS: The 28-day mortality rate was 21.67%. Deceased patients had higher age, Charlson Comorbidity Index, procalcitonin, NLR, glucose, and SHR levels compared to survivors (P < 0.05). Both SHR and NLR significantly increased mortality risk, particularly in non-diabetic patients. Combining NLR and SHR improved ROC AUC to 0.898, with 89.80% sensitivity and 81.10% specificity. Kaplan-Meier analysis showed higher cumulative survival for SHR < 1.14, regardless of diabetes status (P < 0.05). NLR mediated 13.02% of the SHR-survival relationship, while SHR mediated 14.06% of the NLR-survival relationship. CONCLUSION: Elevated SHR is a significant mortality risk factor in elderly SCAP patients, independent of diabetes status. Stringent glucose control and careful monitoring of SHR may improve outcomes in elderly patients with acute respiratory conditions.

Genome and proteomic analysis of risk factors for fatal outcome in children with severe community-acquired pneumonia caused by human adenovirus 7.

INTRODUCTION: Human adenovirus 7 (HAdV-7) is an important viral pathogen of severe pneumonia in children and a serious threat to health. METHODS: A cohort of 45 pediatric patients diagnosed with HAdV-7-associated severe pneumonia and admitted to the Pediatric Intensive Care Unit at the Children's Hospital of Chongqing Medical University from May 2018 to January 2020 were included. Risk factors of death were analyzed by the Cox proportional risk mode with Clinical data, serum, and nasopharyngeal aspirate adenovirus load, Genome analysis, Olink proteomics, and cytokine profile between dead and surviving patients were also analyzed. RESULTS: A total of 45 children with a median age of 12.0 months (interquartile range [IQR]: 6.5, 22.0) were included (female 14), including 14 (31.1%) who died. High serum viral load was an independent risk factor for mortality (hazard ratio [HR] = 2.16, 95% confidence interval [CI], 1.04-4.49, p = 0.039). BTB and CNC homology 1 (BACH1), interleukin-5 (IL-5), and IL-9 levels were significantly correlated with serum viral load (p = 0.0400, 0.0499, and 0.0290; r = 0.4663, 0.3339, and -0.3700, respectively), with significant differences between the dead and survival groups (p = 0.021, 0.001, and 0.021). CONCLUSIONS: Severe cytokine storm-associated high serum viral load after HAdV-7 infection may be the main mechanism responsible for poor prognosis in children.

Psittacosis caused severe community-acquired pneumonia accompanied by acute hypoxic respiratory failure: a multicenter retrospective cohort study from China.

INTRODUCTION: Psittacosis can cause severe community-acquired pneumonia (CAP). The clinical manifestations of psittacosis range from subclinical to fulminant psittacosis with multi-organ failure. It is essential to summarize the clinical characteristic of patients with severe psittacosis accompanied by acute hypoxic respiratory failure (AHRF). METHODS: This retrospective study included patients with severe psittacosis caused CAP accompanied by AHRF from 19 tertiary hospitals of China. We recorded the clinical data, antimicrobial therapy, respiratory support, complications, and outcomes. Chlamydia psittaci was detected on the basis of metagenomic next-generation sequencing performed on bronchoalveolar lavage fluid samples. Patient outcomes were compared between the treatment methods. RESULTS: This study included 45 patients with severe CAP and AHRF caused by psittacosis from April 2018 to May 2021. The highest incidence of these infections was between September and April. There was a history of poultry contact in 64.4% of the patients. The median PaO2/FiO2 of the patients was 119.8 (interquartile range, 73.2 to 183.6) mmHg. Four of 45 patients (8.9%) died in the ICU, and the median ICU duration was 12 days (interquartile range, 8 to 21) days. There were no significant differences between patients treated with fluoroquinolone initially and continued after the diagnosis, fluoroquinolone initially followed by tetracycline, and fluoroquinolone combined with tetracycline. CONCLUSION: Psittacosis caused severe CAP seems not rare, especially in the patients with the history of exposure to poultry or birds. Empirical treatment that covers atypical pathogens may benefit such patients, which fluoroquinolones might be considered as an alternative.

A nomogram based on the expression level of angiopoietin-like 4 to predict the severity of community-acquired pneumonia.

BACKGROUND: The morbidity and mortality of community-acquired pneumonia (CAP) remain high among infectious diseases. It was reported that angiopoietin-like 4 (ANGPTL4) could be a diagnostic biomarker and a therapeutic target for pneumonia. This study aimed to develop a more objective, specific, accurate, and individualized scoring system to predict the severity of CAP. METHODS: Totally, 31 non-severe community-acquired pneumonia (nsCAP) patients and 14 severe community-acquired pneumonia (sCAP) patients were enrolled in this study. The CURB-65 and pneumonia severity index (PSI) scores were calculated from the clinical data. Serum ANGPTL4 level was measured by enzyme-linked immunosorbent assay (ELISA). After screening factors by univariate analysis and receiver operating characteristic (ROC) curve analysis, multivariate logistic regression analysis of ANGPTL4 expression level and other risk factors was performed, and a nomogram was developed to predict the severity of CAP. This nomogram was further internally validated by bootstrap resampling with 1000 replications through the area under the ROC curve (AUC), the calibration curve, and the decision curve analysis (DCA). Finally, the prediction performance of the new nomogram model, CURB-65 score, and PSI score was compared by AUC, net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A nomogram for predicting the severity of CAP was developed using three factors (C-reactive protein (CRP), procalcitonin (PCT), and ANGPTL4). According to the internal validation, the nomogram showed a great discrimination capability with an AUC of 0.910. The Hosmer-Lemeshow test and the approximately fitting calibration curve suggested a satisfactory accuracy of prediction. The results of DCA exhibited a great net benefit. The AUC values of CURB-65 score, PSI score, and the new prediction model were 0.857, 0.912, and 0.940, respectively. NRI comparing the new model with CURB-65 score was found to be statistically significant (NRI = 0.834, P < 0.05). CONCLUSION: A robust model for predicting the severity of CAP was developed based on the serum ANGPTL4 level. This may provide new insights into accurate assessment of the severity of CAP and its targeted therapy, particularly in the early-stage of the disease.

Prediction accuracy of commonly used pneumonia severity scores in Aboriginal patients with severe community-acquired pneumonia: a retrospective study.

BACKGROUND: Severe community-acquired pneumonia (SCAP) is highly prevalent in the Aboriginal population. Few pneumonia severity scores are validated in this population. AIMS: To assess the prediction accuracy of pneumonia severity scores in Aboriginal patients with SCAP and to identify risk factors for poor prognosis. METHODS: Retrospective cohort study examining Aboriginal patients admitted to the intensive care unit with confirmed SCAP between January 2011 and December 2014. Severity scores were calculated for SMARTCOP (systolic blood pressure, multi-lobar, albumin, respiratory rate, tachycardia, confusion, oxygenation and arterial pH), SMARTACOP (systolic blood pressure, multi-lobar, albumin, respiratory rate, tachycardia, Aboriginal status, confusion, oxygenation and arterial pH), CURB-65 (confusion, urea, respiratory rate, blood pressure and age ≥65 years), pneumonia severity index, Infectious Diseases Society of America and American Thoracic Society SCAP, and Acute Physiology and Chronic Health Evaluation (APACHE) II/III using medical records. Prediction accuracy of 30-day mortality and requirement for intensive respiratory and/or vasoactive support (IRVS) were assessed using logistic regression and the area under the receiver operating characteristic curve (AUROC). Multivariate analysis was used to test associations between poor prognosis and demographic/clinical variables. RESULTS: A total of 203 cases (49% women) was identified. Thirty-day mortality was 6.4% (n = 13), and 53% (n = 107) required IRVS. None of the tested pneumonia severity scores accurately predicted mortality. SMARTCOP and SMARTACOP predicted IRVS requirement with the highest diagnostic accuracy, but only achieved acceptable discrimination (P <0.001 and <0.001; AUROC = 0.74 and 0.75 respectively). APACHE II/III predicted both mortality (P = 0.003 and 0.001; AUROC = 0.74 and 0.73 respectively) and IRVS requirement (P <0.001 and <0.001; AUROC = 0.72 and 0.73 respectively). Multivariate analysis associated mortality with male gender, cirrhosis, immunosuppression and acidaemia, and IRVS requirement with multi-lobar pneumonia, hypotension and tachypnoea. Multivariate analysis for mortality and IRVS requirement achieved an AUROC of 0.93 and 0.87 respectively. CONCLUSION: None of the pneumonia severity scores accurately predicted mortality. We recommend SMARTACOP to predict IRVS requirement in Aboriginal patients with SCAP. Given Aboriginal patients are over-represented in Australian intensive care units, a new score is warranted for this understudied population.

Association of admission lactate with mortality in adult patients with severe community-acquired pneumonia.

PURPOSE: The present study was conducted to investigate the association of admission lactate with mortality in severe community-acquired pneumonia (SCAP). METHODS: We performed a retrospective, observational, cohort study on adult SCAP patients admitted to intensive care unit (ICU) in West China Hospital of Sichuan University between December 2011 and December 2018. The primary outcome was hospital mortality. Univariate and then multivariate analysis were performed to identify independent risk factors for hospital mortality. The association of admission lactate categories with hospital mortality was examined in three logistic regression models and Kaplan-Meier plots. We also applied restricted cubic splines to estimate the potential non-linear associations. RESULTS: In total, 2275 SCAP patients were included. Admission lactate remained a significant factor for mortality after multivariate regression (OR: 1.085; 95% CI: 1.033,1.141; by continuous variable). After lactate was categorized into quartiles and the confounders were fully adjusted, compared with the quartile 1, ORs (95% CIs) of hospital mortality for quartile 2, quartile 3 and quartile 4 were 1.001 (0.759-1.321), 1.153 (0.877-1.516) and 1.593 (1.202-2.109), respectively (P for trend =0.001). Survival curves indicated that elevated lactate was associated with poor prognosis (P < 0.001). Moreover, this association was non-linear, indicating that increased lactate has the most notable impact on mortality within the range of 1.5 to 4 mmol/L (P non-linear: 0.029 for hospital mortality; 0.004 for ICU mortality). CONCLUSION: Elevated admission lactate has a significant, independent, and potentially non-linear association with increased mortality in SCAP patients.

Erector spinae muscle-based nomogram for predicting in-hospital mortality among older patients with severe community-acquired pneumonia.

BACKGROUND: No multivariable model incorporating erector spinae muscle (ESM) has been developed to predict clinical outcomes in older patients with severe community-acquired pneumonia (SCAP). This study aimed to construct a nomogram based on ESM to predict in-hospital mortality in patients with SCAP. METHODS: Patients aged ≥ 65 years with SCAP were enrolled in this prospective observational study. Least absolute selection and shrinkage operator and multivariable logistic regression analyses were used to identify risk factors for in-hospital mortality. A nomogram prediction model was constructed. The predictive performance was evaluated using the concordance index (C-index), calibration curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. RESULTS: A total of 490 patients were included, and the in-hospital mortality rate was 36.1%. The nomogram included the following independent risk factors: mean arterial pressure, peripheral capillary oxygen saturation, Glasgow Coma Scale score (GCS), lactate, lactate dehydrogenase, blood urea nitrogen levels, and ESM cross-sectional area. Incorporating ESM into the base model with other risk factors significantly improved the C-index from 0.803 (95% confidence interval [CI], 0.761-0.845) to 0.836 (95% CI, 0.798-0.873), and these improvements were confirmed by category-free NRI and IDI. The ESM-based nomogram demonstrated a high level of discrimination, good calibration, and overall net benefits for predicting in-hospital mortality compared with the combination of confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years (CURB-65), Pneumonia Severity Index (PSI), Acute Physiology and Chronic Health Evaluation II (APACHEII), and Sequential Organ Failure Assessment (SOFA). CONCLUSIONS: The proposed ESM-based nomogram for predicting in-hospital mortality among older patients with SCAP may help physicians to promptly identify patients prone to adverse outcomes. TRIAL REGISTRATION: This study was registered at www.chictr.org.cn (registration number Chi CTR-2300070377).

Development and validation of an in-hospital mortality risk prediction model for patients with severe community-acquired pneumonia in the intensive care unit.

BACKGROUND: A high mortality rate has always been observed in patients with severe community-acquired pneumonia (SCAP) admitted to the intensive care unit (ICU); however, there are few reported predictive models regarding the prognosis of this group of patients. This study aimed to screen for risk factors and assign a useful nomogram to predict mortality in these patients. METHODS: As a developmental cohort, we used 455 patients with SCAP admitted to ICU. Logistic regression analyses were used to identify independent risk factors for death. A mortality prediction model was built based on statistically significant risk factors. Furthermore, the model was visualized using a nomogram. As a validation cohort, we used 88 patients with SCAP admitted to ICU of another hospital. The performance of the nomogram was evaluated by analysis of the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, and decision curve analysis (DCA). RESULTS: Lymphocytes, PaO2/FiO2, shock, and APACHE II score were independent risk factors for in-hospital mortality in the development cohort. External validation results showed a C-index of 0.903 (95% CI 0.838-0.968). The AUC of model for the development cohort was 0.85, which was better than APACHE II score 0.795 and SOFA score 0.69. The AUC for the validation cohort was 0.893, which was better than APACHE II score 0.746 and SOFA score 0.742. Calibration curves for both cohorts showed agreement between predicted and actual probabilities. The results of the DCA curves for both cohorts indicated that the model had a high clinical application in comparison to APACHE II and SOFA scoring systems. CONCLUSIONS: We developed a predictive model based on lymphocytes, PaO2/FiO2, shock, and APACHE II scores to predict in-hospital mortality in patients with SCAP admitted to the ICU. The model has the potential to help physicians assess the prognosis of this group of patients.

A prediction model for hospital mortality in patients with severe community-acquired pneumonia and chronic obstructive pulmonary disease.

BACKGROUND: No personalized prediction model or standardized algorithm exists to identify those at high risk of death among severe community-acquired pneumonia (SCAP) patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to investigate the risk factors and to develop a useful nomogram for prediction of mortality in those patients. METHODS: We performed a retrospective, observational, cohort study in the intensive care unit (ICU) of West China Hospital, Sichuan University with all consecutive SCAP patients with COPD between December 2011 and December 2018. The clinical data within 24 h of admission to ICU were collected. The primary outcome was hospital mortality. We divided the patients into training and testing cohorts (70% versus 30%) randomly. In the training cohort, univariate and multivariate logistic regression analysis were used to identify independent risk factors applied to develop a nomogram. The prediction model was assessed in both training and testing cohorts. RESULTS: Finally, 873 SCAP patients with COPD were included, among which the hospital mortality was 41.4%. In training cohort, the independent risk factors for hospital mortality were increased age, diabetes, chronic renal diseases, decreased systolic blood pressure (SBP), and elevated fibrinogen, interleukin 6 (IL-6) and blood urea nitrogen (BUN). The C index was 0.840 (95% CI 0.809-0.872) in training cohort and 0.830 (95% CI 0.781-0.878) in testing cohort. Furthermore, the time-dependent AUC, calibration plots, DCA and clinical impact curves indicated the model had good predictive performance. Significant association of risk stratification based on nomogram with mortality was also found (P for trend < 0.001). The restricted cubic splines suggested that estimated associations between these predictors and hospital mortality were all linear relationships. CONCLUSION: We developed a prediction model including seven risk factors for hospital mortality in patients with SCAP and COPD. It can be used for early risk stratification in clinical practice after more external validation.

Nasopharyngeal aspirates in children with severe community-acquired pneumonia collected within 3 days before bronchoscopy can partially reflect the pathogens in bronchoalveolar lavage fluids.

BACKGROUND: There is little evidence about consistency between nasopharyngeal and pulmonary pathogens in children with severe pneumonia. This study aims to compare the difference of pathogens between nasopharyngeal aspirates (NPAs) collected before bronchoscopy and bronchoalveolar lavage fluids (BALFs) in children with severe community-acquired pneumonia (SCAP). METHODS: NPAs and BALFs were collected form pediatric SCAP cases hospitalized from January 2018 to March 2019. NPAs were colleced within 3 days before bronchoscopy. Samples were detected by direct immunofluorescence assay (DFA) for seven respiratory viruses and by routine bacterial culture in the clinical microbiology laboratory. Respiratory syncytial virus (RSV), Adenovirus (ADV), Influenza virus types A, B (IV-A and IV-B), Parainfluenza virus 1-3 (PIV1-3) were detected with a commercial assay. The virological and bacteriological detention results of NPAs were compared with the results of BALFs. RESULTS: In total 204 cases with mean age of 3.4 ± 2.8 years (IQR, 1 month-14 years) were included in the study. Both NPA and BALF were collected from those cases. The positive rates of pathogen in NPAs and BALFs were 25.0% (51/204) and 36.7% (75/204), respectively (x2 = 6.614, P = 0.010). Respiratory viruses were found in 16.1% (33/204) from NPAs and 32.3% (66/204) from BALFs (x2 = 14.524, P < 0.001). RSV and ADV were the two most frequent detected viruses in NPAs and BALFs. High consistentcy of pathogens between NPAs and BALFs was observed, and 96.9% (32/33) viruses detected in NPAs were also found in BALFs. While bacteria were isolated from 12.7% (26/204) and 10.7% (22/204) of the two kinds of samples, respectively (x2 = 0.378, P = 0.539). In addition, Haemophilus influenzae (HI) was the dominant germ in both samples. CONCLUSION: The DFA method used to detect seven respiratory viruses from NPAs collected within 3 days before bronchoscopy can partially reflect the pathogens in the lungs in children with SCAP.

Epidemiology and microbiology of severe community-acquired pneumonia in Central Australia: a retrospective study.

BACKGROUND: Severe community-acquired pneumonia (SCAP) has high mortality and morbidity. AIMS: To describe the epidemiology and microbiology of SCAP in Central Australia. METHODS: A retrospective epidemiological study describing the characteristics, incidence rates (IR) and microbiological aetiology of SCAP in Central Australia. Adult patients admitted to Alice Springs Hospital Intensive Care Unit (ICU) between 2011 and 2014 that fitted the Infectious Diseases Society of America and American Thoracic Society definition of SCAP were included. Medical records were reviewed and compared between indigenous and non-indigenous patients. Primary outcomes were incidence rate and microbiological aetiology of SCAP. Secondary outcomes were 30-day mortality, and ICU and hospital length of stay (LoS). RESULTS: A total of 185 patents were included (156 indigenous; 29 non-indigenous). The overall SCAP IR per 1000 person-years was 3.24 (3.75 indigenous; 1.87 non-indigenous) with an IR difference of 2.71 after adjustment (P < 0.001). Those aged ≥50 years had an IR 74.8% higher than those younger. Male IR was 50% higher than females. There was a significant difference between indigenous and non-indigenous groups for age (48 vs 64 years), but not for 30-day mortality (7.7% vs 10.3%), ICU LoS (4.8 vs 4.6 days) and hospital LoS (10.9 vs 15.1 days) respectively. Likely causative pathogen(s) were identified in 117 patients; Streptococcus pneumoniae was the most common pathogen (28.2%), followed by Haemophilus influenzae (19.7%), Influenza A/B (16.2%) and Staphylococcus aureus (14.5%). CONCLUSION: A high incidence of SCAP was observed in Central Australia, disproportionately affecting the indigenous population. Prevention strategies are imperative, as well as early identification of SCAP and appropriate empiric antibiotic regimens.

Nomogram for prediction of severe community-acquired pneumonia development in diabetic patients: a multicenter study.

BACKGROUND: Diabetic patients with community-acquired pneumonia (CAP) have an increased risk of progressing to severe CAP. It is essential to develop predictive tools at the onset of the disease for early identification and intervention. This study aimed to develop and validate a clinical feature-based nomogram to identify diabetic patients with CAP at risk of developing severe CAP. METHOD: A retrospective cohort study was conducted between January 2019 to December 2020. 1026 patients with CAP admitted in 48 hospitals in Shanghai were enrolled. All included patients were randomly divided into the training and validation samples with a ratio of 7:3. The nomogram for the prediction of severe CAP development was established based on the results of the multivariate logistic regression analysis and other predictors with clinical relevance. The nomogram was then assessed using receiver operating characteristic curves (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: Multivariate analysis showed that chronic kidney dysfunction, malignant tumor, abnormal neutrophil count, abnormal lymphocyte count, decreased serum albumin level, and increased HbA1c level at admission was independently associated with progression to severe CAP in diabetic patients. A nomogram was established based on these above risk factors and other predictors with clinical relevance. The area under the curve (AUC) of the nomogram was 0.87 (95% CI 0.83-0.90) in the training set and 0.84 (95% CI 0.78-0.90). The calibration curve showed excellent agreement between the predicted possibility by the nomogram and the actual observation. The decision curve analysis indicated that the nomogram was applicable with a wide range of threshold probabilities due to the net benefit. CONCLUSION: Our nomogram can be applied to estimate early the probabilities of severe CAP development in diabetic patients with CAP, which has good prediction accuracy and discrimination abilities. Since included biomarkers are common, our findings may be performed well in clinical practice and improve the early management of diabetic patients with CAP.

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients with comorbid cardiovascular disease: a retrospective cohort study.

BACKGROUND: Researchers have linked cardiovascular disease (CVD) with advancing age; however, how it drives disease progression in elderly severe community acquired pneumonia (SCAP) patients is still unclear. This study aims to identify leading risk predictors of in-hospital mortality in elderly SCAP patients with CVD, and construct a comprehensive nomogram for providing personalized prediction. PATIENTS AND METHODS: The study retrospectively enrolled 2365 elderly patients identified SCAP. Among them, 413 patients were found to have CVD. The LASSO regression and multivariate logistic regression analysis were utilized to select potential predictors of in-hospital mortality in elderly SCAP patients with CVD. By incorporating these features, a nomogram was then developed and subjected to internal validations. Discrimination, calibration, and clinical use of the nomogram were assessed via C-index, calibration curve analysis, and decision plot. RESULTS: Compared with patients without CVD, elderly SCAP patients with CVD had a significant poor outcome. Further analysis of the CVD population identified 7 independent risk factors for in-hospital mortality in elderly SCAP patients, including age, the use of vasopressor, numbers of primary symptoms, body temperature, monocyte, CRP and NLR. The nomogram model incorporated these 7 predictors showed sufficient predictive accuracy, with the C-index of 0.800 (95% CI 0.758-0.842). High C-index value of 0.781 was obtained in the internal validation via bootstrapping validation. Moreover, the calibration curve indicative a good consistency of risk prediction, and the decision curve manifested that the nomogram had good overall net benefits. CONCLUSION: An integrated nomogram was developed to facilitate the personalized prediction of in-hospital mortality in elderly SCAP patients with CVD.

Predictive factors and outcomes of severe community acquired pneumonia in patients with respiratory failure.

Objectives: To explore the risk factors, pathogens and outcomes of severe community-acquired pneumonia (SCAP) in patients with respiratory failure. Methods: A prospective observational study was conducted at Northwest General Hospital & Research Centre, Peshawar, Pakistan from February 2016 to October 2018. All patients with Community-acquired pneumonia (CAP) who fulfilled the inclusion criteria were recorded consecutively. Diagnosis of SCAP was made following the criteria established by the IDSA/ATS in the consensus guidelines on the management of CAP in adults published in 2007. In-hospital mortality was the main outcome. Results: The final analysis comprised a total of 100 patients with SCAP. The mean age was 60.0±18.01 years, and 54.0% were female patients. Afghani patients represented 22.0% of the total patients. The most common comorbidity associated with SCAP was hypertension (42.0%). The most commonly isolated etiological agents were Acinetobacter baumannii, followed by extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli. In-hospital mortality was 45%. On multivariate analysis, factors associated with in-hospital mortality were age (OR 1.054; 95%Cl 1.01-1.10; p=0.021), presence of two or more complications (OR 4.51; 95%Cl 1.18-17.28; p=0.028), septic shock (OR 6.44; 95%Cl 1.55-26.803; p=0.010), length of mechanical ventilation (OR 1.17; 95%Cl 1.01-1.40; p=0.043), and paO2 (OR 4.51; 95%Cl 1.18-17.28; p=0.004). Conclusion: A high mortality rate was observed in our study. Age, presence of two or more complications, septic shock, length of mechanical ventilation, and low paO2 were identified to be independent predictors of mortality for patients with SCAP.

Clinical characteristics and risk factors associated with mortality in patients with severe community-acquired pneumonia and type 2 diabetes mellitus.

BACKGROUND: The present study was performed to investigate the impacts of type 2 diabetes mellitus (T2DM) on severe community-acquired pneumonia (SCAP) and to develop a novel prediction model for mortality in SCAP patients with T2DM. METHODS: This was a retrospective observational study conducted in consecutive adult patients with SCAP admitted to the intensive care unit (ICU) of West China Hospital, Sichuan University, China, between September 2011 and September 2019. The primary outcome was hospital mortality. A propensity score matching (PSM) analysis model with a 1:2 ratio was used for the comparisons of clinical characteristics and outcomes between T2DM and nondiabetic patients. The independent risk factors were identified via univariate and then multivariable logistic regression analysis and were then used to establish a nomogram. RESULTS: In total, 1262 SCAP patients with T2DM and 2524 matched patients without T2DM were included after PSM. Patients with T2DM had longer ICU length of stay (LOS) (13 vs. 12 days, P = 0.016) and higher 14-day mortality (15% vs. 10.8%, P < 0.001), 30-day mortality (25.7% vs. 22.7%, P = 0.046), ICU mortality (30.8% vs. 26.5%, P = 0.005), and hospital mortality (35.2% vs. 31.0%, P = 0.009) than those without T2DM. In SCAP patients with T2DM, the independent risk factors for hospital mortality were increased numbers of comorbidities and diabetes-related complications; elevated C-reactive protein (CRP), neutrophil to lymphocyte ratio (NLR), brain natriuretic peptide (BNP) and blood lactate; as well as decreased blood pressure on admission. The nomogram had a C index of 0.907 (95% CI: 0.888, 0.927) in the training set and 0.873 (95% CI: 0.836, 0.911) in the testing set, which was superior to the pneumonia severity index (PSI, AUC: 0.809, 95% CI: 0.785, 0.833). The calibration curve and decision curve analysis (DCA) also demonstrated its accuracy and applicability. CONCLUSIONS: SCAP patients with T2DM had worse clinical outcomes than nondiabetic patients. The nomogram has good predictive performance for hospital mortality and might be generally applied after more external validations.