Serum calcium and mortality in pediatric pneumonia patients admitted to the PICU: a retrospective cohort study.

This study investigates serum calcium's prognostic value in pediatric pneumonia, focusing on its correlation with PICU mortality, to enhance understanding and treatment approaches in this field. Data from 414 pediatric pneumonia patients (2010-2019) admitted to the intensive care units at the Children's Hospital, Zhejiang University School of Medicine were analyzed. The study utilized restricted cubic spline analysis, Cox proportional hazard regression, and Kaplan-Meier survival curve analysis to assess the relationship between serum calcium levels at admission and PICU mortality risk. After adjusting for multivariate factors, for each 1 mmol/dL increase in serum calcium, the risk of mortality decreased by 24% (HR: 0.76, 95% CI 0.67-0.87). Among the three levels of serum calcium groups, higher serum calcium levels were linked to a 63% reduction in the mortality rate compared to lower levels (HR: 0.37, 95% CI 0.16-0.84). The cumulative hazard estimates of mortality significantly differed across serum calcium groups (log-rank P = 0.032). This association was consistent across diverse subgroups (P for interaction > 0.05). Higher serum calcium levels are associated with decreased PICU mortality in pediatric pneumonia, highlighting its potential as a prognostic marker.

Microbial signatures predictive of short-term prognosis in severe pneumonia.

Objective: This retrospective cohort study aimed to investigate the composition and diversity of lung microbiota in patients with severe pneumonia and explore its association with short-term prognosis. Methods: A total of 301 patients diagnosed with severe pneumonia underwent bronchoalveolar lavage fluid metagenomic next-generation sequencing (mNGS) testing from February 2022 to January 2024. After applying exclusion criteria, 236 patients were included in the study. Baseline demographic and clinical characteristics were compared between survival and non-survival groups. Microbial composition and diversity were analyzed using alpha and beta diversity metrics. Additionally, LEfSe analysis and machine learning methods were employed to identify key pathogenic microorganism associated with short-term mortality. Microbial interaction modes were assessed through network co-occurrence analysis. Results: The overall 28-day mortality rate was 37.7% in severe pneumonia. Non-survival patients had a higher prevalence of hypertension and exhibited higher APACHE II and SOFA scores, higher procalcitonin (PCT), and shorter hospitalization duration. Microbial α and ß diversity analysis showed no significant differences between the two groups. However, distinct species diversity patterns were observed, with the non-survival group showing a higher abundance of Acinetobacter baumannii, Klebsiella pneumoniae, and Enterococcus faecium, while the survival group had a higher prevalence of Corynebacterium striatum and Enterobacter. LEfSe analysis identified 29 distinct terms, with 10 potential markers in the non-survival group, including Pseudomonas sp. and Enterococcus durans. Machine learning models selected 16 key pathogenic bacteria, such as Klebsiella pneumoniae, significantly contributing to predicting short-term mortality. Network co-occurrence analysis revealed greater complexity in the non-survival group compared to the survival group, with differences in central genera. Conclusion: Our study highlights the potential significance of lung microbiota composition in predicting short-term prognosis in severe pneumonia patients. Differences in microbial diversity and composition, along with distinct microbial interaction modes, may contribute to variations in short-term outcomes. Further research is warranted to elucidate the clinical implications and underlying mechanisms of these findings.

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.

Predictors of mortality in severe pneumonia patients: a systematic review and meta-analysis.

BACKGROUND: Severe pneumonia has consistently been associated with high mortality. We sought to identify risk factors for the mortality of severe pneumonia to assist in reducing mortality for medical treatment. METHODS: Electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, and Scopus were systematically searched till June 1, 2023. All human research were incorporated into the analysis, regardless of language, publication date, or geographical location. To pool the estimate, a mixed-effect model was used. The Newcastle-Ottawa Scale (NOS) was employed for assessing the quality of included studies that were included in the analysis. RESULTS: In total, 22 studies with a total of 3655 severe pneumonia patients and 1107 cases (30.29%) of death were included in the current meta-analysis. Significant associations were found between age [5.76 years, 95% confidence interval [CI] (3.43, 8.09), P < 0.00001], male gender [odds ratio (OR) = 1.47, 95% CI (1.07, 2.02), P = 0.02], and risk of death from severe pneumonia. The comorbidity of neoplasm [OR = 3.37, 95% CI (1.07, 10.57), P = 0.04], besides the presence of complications such as diastolic hypotension [OR = 2.60, 95% CI (1.45, 4.67), P = 0.001], ALI/ARDS [OR = 3.63, 95% CI (1.78, 7.39), P = 0.0004], septic shock [OR = 9.43, 95% CI (4.39, 20.28), P < 0.00001], MOF [OR = 4.34, 95% CI (2.36, 7.95), P < 0.00001], acute kidney injury [OR = 2.45, 95% CI (1.14, 5.26), P = 0.02], and metabolic acidosis [OR = 5.88, 95% CI (1.51, 22.88), P = 0.01] were associated with significantly higher risk of death among patients with severe pneumonia. Those who died, compared with those who survived, differed on multiple biomarkers on admission including serum creatinine [Scr: + 67.77 mmol/L, 95% CI (47.21, 88.34), P < 0.00001], blood urea nitrogen [BUN: + 6.26 mmol/L, 95% CI (1.49, 11.03), P = 0.01], C-reactive protein [CRP: + 33.09 mg/L, 95% CI (3.01, 63.18), P = 0.03], leukopenia [OR = 2.63, 95% CI (1.34, 5.18), P = 0.005], sodium < 136 mEq/L [OR = 2.63, 95% CI (1.34, 5.18), P = 0.005], albumin [- 5.17 g/L, 95% CI (- 7.09, - 3.25), P < 0.00001], PaO2/FiO2 [- 55.05 mmHg, 95% CI (- 60.11, - 50.00), P < 0.00001], arterial blood PH [- 0.09, 95% CI (- 0.15, - 0.04), P = 0.0005], gram-negative microorganism [OR = 2.56, 95% CI (1.17, 5.62), P = 0.02], and multilobar or bilateral involvement [OR = 3.65, 95% CI (2.70, 4.93), P < 0.00001]. CONCLUSIONS: Older age and male gender might face a greater risk of death in severe pneumonia individuals. The mortality of severe pneumonia may also be significantly impacted by complications such diastolic hypotension, ALI/ARDS, septic shock, MOF, acute kidney injury, and metabolic acidosis, as well as the comorbidity of neoplasm, and laboratory indicators involving Scr, BUN, CRP, leukopenia, sodium, albumin, PaO2/FiO2, arterial blood PH, gram-negative microorganism, and multilobar or bilateral involvement. SYSTEMATIC REVIEW REGISTRATION: PROSPERO Protocol Number: CRD 42023430684.

Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.

Background: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators. Methods: A retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients. Results: A total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively. Conclusion: CAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability.

Comparison of NEWS2 and PSI as mortality predictors in patients with community acquired pneumonia.

In the West, National Early Warning Score 2 (NEWS2) is commonly applied to predict the severity of illness using only bedside variables unlike the extensive Pneumonia Severity Index (PSI). The objective of this study was to compare these scores as mortality predictors in patients admitted with community acquired pneumonia (CAP). This cross-sectional study was conducted in Jinnah Postgraduate Medical Centre, Karachi, Pakistan, for six months in 2020 on 116 patients presenting with CAP. Cases of aspiration pneumonia, hospital acquired pneumonia, pulmonary tuberculosis, pulmonary embolism, and pulmonary oedema were excluded. In-hospital mortality was taken as the outcome of this study. The mean age of the participants was 46.9±20.5 years. The in-hospital mortalities were 45(38.8%). NEWS2 was 97.8% sensitive but only 15.5% specific in predicting the outcome, whereas PSI was less sensitive (68.9%) but more specific (50.7%), which showed that in comparison with PSI, NEWS2 is a more sensitive mortality predicting score among hospitalised CAP patients.

Loss-of-function/gain-of-function polymorphisms of the ATP sensitive P2X7R influence sepsis, septic shock, pneumonia, and survival outcomes.

Introduction: Extracellular ATP (eATP) released from damaged cells activates the P2X7 receptor (P2X7R) ion channel on the surface of surrounding cells, resulting in calcium influx, potassium efflux and inflammasome activation. Inherited changes in the P2X7R gene (P2RX7) influence eATP induced responses. Single nucleotide polymorphisms (SNPs) of P2RX7 influence both function and signaling of the receptor, that in addition to ion flux includes pathogen control and immunity. Methods: Subjects (n = 105) were admitted to the ICU at the University Hospital Ulm, Germany between June 2018 and August 2019. Of these, subjects with a diagnosis of sepsis (n = 75), were also diagnosed with septic shock (n = 24), and/or pneumonia (n = 42). Subjects with pneumonia (n = 43) included those without sepsis (n = 1), sepsis without shock (n = 29) and pneumonia with septic shock (n = 13). Out of the 75 sepsis/septic shock patients, 33 patients were not diagnosed with pneumonia. Controls (n = 30) were recruited to the study from trauma patients and surgical patients without sepsis, septic shock, or pneumonia. SNP frequencies were determined for 16 P2RX7 SNPs known to affect P2X7R function, and association studies were performed between frequencies of these SNPs in sepsis, septic shock, and pneumonia compared to controls. Results: The loss-of-function (LOF) SNP rs17525809 (T253C) was found more frequently in patients with septic shock, and non-septic trauma patients when compared to sepsis. The LOF SNP rs2230911 (C1096G) was found to be more frequent in patients with sepsis and septic shock than in non-septic trauma patients. The frequencies of these SNPs were even higher in sepsis and septic patients with pneumonia. The current study also confirmed a previous study by our group that showed a five SNP combination that included the GOF SNPs rs208294 (C489T) and rs2230912 (Q460R) that was designated #21211 was associated with increased odds of survival in severe sepsis. Discussion: The results found an association between expression of LOF P2RX7 SNPs and presentation to the ICU with sepsis, and septic shock compared to control ICU patients. Furthermore, frequencies of LOF SNPs were found to be higher in sepsis patients with pneumonia compared to those without pneumonia. In addition, a five SNP GOF combination was associated with increased odds of survival in severe sepsis. These results suggest that P2RX7 is required to control infection in pneumonia and that inheritance of LOF variants increases the risk of sepsis when associated with pneumonia. This study confirms that P2RX7 genotyping in pneumonia may identify patients at risk of developing sepsis. The study also identifies P2X7R as a target in sepsis associated with an excessive immune response in subjects with GOF SNP combinations.

MALAT1 predicts the prognosis of severe community-acquired pneumonia in pediatric patients.

BACKGROUND: To evaluate the role of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in the prognosis of severe community-acquired pneumonia (CAP) in children. METHODS: According to the median MALAT1 value of 3.2 at baseline, 93 pediatric patients with severe CAP were divided into low (n = 46, median MALAT1 level = 1.9) or high (n = 47, median MALAT1 level = 4.5) MALAT1 groups. Another 93 age-, gender-, and body mass index (BMI)-matched healthy individuals were included in the control group using the propensity-score matching (PSM) method. A multivariate Cox proportional hazards model was used to explore the association of MALAT1 level with the 28-day mortality after controlling for potential confounding factors. RESULTS: The MALAT1 expressions were significantly higher in the patients with severe CAP compared with those in the healthy controls (3.2 vs. 0.9, P < 0.01). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) was 0.927 when the cut-off value of MALAT1 was 1.5. Moreover, the MALAT1 expressions were substantially lower in survivals than non-survivals (3.8 vs. 2.6, P < 0.01), and the multivariate Cox regression analysis indicated a positive association between MALAT1 levels and mortality risk (HR = 3.32; 95% CI: 1.05-10.47; P = 0.04). CONCLUSION: MALAT1 might be a promising marker for predicting the prognosis of severe CAP in pediatric patients.

Risk factors at admission of in-hospital dysglycemia, mortality, and readmissions in patients with type 2 diabetes and pneumonia.

AIMS: In-hospital dysglycemia is associated with adverse outcomes. Identifying patients at risk of in-hospital dysglycemia early on admission may improve patient outcomes. METHODS: We analysed 117 inpatients admitted with pneumonia and type 2 diabetes monitored by continuous glucose monitoring. We assessed potential risk factors for in-hospital dysglycemia and adverse clinical outcomes. RESULTS: Time in range (3.9-10.0 mmol/l) decreased by 2.9 %-points [95 % CI 0.7-5.0] per 5 mmol/mol [2.6 %] increase in admission haemoglobin A1c, 16.2 %-points if admission diabetes therapy included insulin therapy [95 % CI 2.9-29.5], and 2.4 %-points [95 % CI 0.3-4.6] per increase in the Charlson Comorbidity Index (CCI) (integer, as a measure of severity and amount of comorbidities). Thirty-day readmission rate increased with an IRR of 1.24 [95 % CI 1.06-1.45] per increase in CCI. In-hospital mortality risk increased with an OR of 1.41 [95 % CI 1.07-1.87] per increase in Early Warning Score (EWS) (integer, as a measure of acute illness) at admission. CONCLUSIONS: Dysglycemia among hospitalised patients with pneumonia and type 2 diabetes was associated with high haemoglobin A1c, insulin treatment before admission, and the amount and severity of comorbidities (i.e., CCI). Thirty-day readmission rate increased with high CCI. The risk of in-hospital mortality increased with the degree of acute illness (i.e., high EWS) at admission. Clinical outcomes were independent of chronic glycemic status, i.e. HbA1c, and in-hospital glycemic status.

Survival at 3, 6 and 12 months in patients diagnosed with community-acquired pneumonia in Colombia: a retrospective cohort study.

BACKGROUND: The primary aim of this study is to assess the survival rates of individuals diagnosed with Community-Acquired Pneumonia (CAP) post-hospitalization in Colombia. Additionally, explore potential risk factors associated with decreased long-term survival. METHODS: A retrospective cohort study was conducted in a hospital in Colombia, evaluating survival at 3, 6 and 12 months in CAP patients, using the Kaplan-Meier method. Stratifications were made by age, sex, comorbidity, and severity. The comparison of survival curves was performed using the Log-Rank test, a multivariate analysis with Cox regression was performed to study possible risk factors that affected 12-month survival in patients with CAP. RESULTS: 3688 subjects were admitted, with a mortality of 16.3 % per year. Survival at three, six, and twelve months was 92.9 % (95 % CI 92-93 %), 88.8 % (95 % CI 87-90 %), and 84.2 % (95 % CI 82-85 %), respectively. Analysis stratified by pneumonia severity index, 12-month survival was 98.7 % in Class I, 95.6 % in Class II, 87.41 % in Class III, 77.1 % in Class IV, and 65.8 % in class-V (p < 0.001). Cox-regression showed that being male (HR = 1.44; 95 % CI 1.22‒1.70; p < 0.001), an elevated pneumonia severity index (HR = 4.22; 95 % CI 1.89‒9.43; p < 0.001), a high comorbidity index (HR = 2.29; 95 % CI 1.89‒2.84; p < 0.001) and vasopressor requirement (HR = 2.22; 95 % CI < 0.001) were associated with a lower survival at twelve months of follow-up. CONCLUSION: Survival in patients with CAP who require hospitalization decreases at 3, 6, and 12 months of follow-up, being lower in patients older than 65 years, men, high comorbidity, and in subjects with severe presentation of the disease.

Clinical characteristics and prognostic factors of non-malignant pleural effusions in hospitalised patients: a retrospective cohort study.

OBJECTIVE: Non-malignant pleural effusions (NMPE) are common in hospitalised patients. Data on NMPE inpatients are scarce and the factors influencing the prognosis are unknown. DESIGN: This was a retrospective cohort study. SETTING AND PARTICIPANTS: We conducted a retrospective cohort of inpatients (n=86 645) admitted to the Chinese PLA General Hospital from 2018 to 2021, based on electronic medical records. The observations of 4934 subjects with effusions confirmed by chest radiological tests (CT or X-ray) without a diagnosis of malignancy were followed during admission. Logistic regression was used to analyse organ damage and other factors associated with in-hospital death. Patients were clustered according to their laboratory indicators, and the association between the clustering results and outcomes was studied. OUTCOME: The outcome of this study was in-hospital mortality. RESULTS: Among 4934 patients, heart failure + pneumonia + renal dysfunction was the most common (15.12%) among 100 different diagnostic groups. 318 (6.4%) patients died during hospitalisation. Lung (OR 3.70, 95% CI 2.42 to 5.89), kidney (OR 2.88, 95% CI 2.14 to 3.90) and heart (1.80, 95% CI 1.29 to 2.55) damage were associated with in-hospital mortality. Hierarchical clustering of laboratory indicators (estimated glomerular filtration rate, white blood cell count, platelet count, haemoglobin, N-terminal pro-B-type natriuretic peptide, serum albumin) demonstrated the ability to discriminate patients at high risk of in-hospital death. CONCLUSION: Comorbidities and multiorgan failure are the prominent characteristics of NMPE patients, which increase the risk of in-hospital mortality, and comprehensive intervention for specific comorbidity patterns is suggested.

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning. METHODS: The clinical data of 1415 patients diagnosed with CHF complicated by lung infections were obtained from the MIMIC-IV database. According to the pathogen type, the patients were categorized into bacterial pneumonia and non-bacterial pneumonia groups, and their risks of in-hospital death were compared using Kaplan-Meier survival curves. Univariate analysis and LASSO regression were used to select the features for constructing LR, AdaBoost, XGBoost, and LightGBM models, and their performance was compared in terms of accuracy, precision, F1 value, and AUC. External validation of the models was performed using the data from eICU-CRD database. SHAP algorithm was applied for interpretive analysis of XGBoost model. RESULTS: Among the 4 constructed models, the XGBoost model showed the highest accuracy and F1 value for predicting the risk of in-hospital death in CHF patients with lung infections in the training set. In the external test set, the XGBoost model had an AUC of 0.691 (95% CI: 0.654-0.720) in bacterial pneumonia group and an AUC of 0.725 (95% CI: 0.577-0.782) in non-bacterial pneumonia group, and showed better predictive ability and stability than the other models. CONCLUSION: The overall performance of the XGBoost model is superior to the other 3 models for predicting the risk of in-hospital death in CHF patients with lung infections. The SHAP algorithm provides a clear interpretation of the model to facilitate decision-making in clinical settings.

Two-year survival after scheduled extubation in patients with pneumonia or ARDS: a prospective observational study.

PURPOSE: To report two-year survival after scheduled extubation in patients with pneumonia or acute respiratory distress syndrome (ARDS). METHODS: This was a prospective observational study performed in a respiratory ICU of a teaching hospital. Pneumonia or ARDS patients who successfully completed a spontaneous breathing trial were enrolled. Data were collected before extubation. Patients were followed up to two years by phone every 3 months. RESULTS: A total of 230 patients were enrolled in final analysis. One-, 3-, 6-, 12-, and 24-month survival was 77.4%, 63.8%, 61.3%, 57.8%, and 47.8%, respectively. Cox regression shows that Charlson comorbidity index (hazard ratio: 1.20, 95% confidence interval: 1.10-1.32), APACHE II score before extubation (1.11, 1.05-1.17), cough peak flow before extubation (0.993, 0.986-0.999), and extubation failure (3.96, 2.51-6.24) were associated with two-year mortality. To predict death within two years, the area under the curve of receiver operating characteristic was 0.79 tested by Charlson comorbidity index, 0.75 tested by APACHE II score, and 0.75 tested by cough peak flow. Two-year survival was 31% and 77% in patients with Charlson comorbidity index ≥ 1 and < 1, 28% and 62% in patients with APACHE II score ≥ 12 and < 12, and 64% and 17% in patients with cough peak flow > 58 and ≤ 58 L/min, respectively. CONCLUSIONS: Comorbidity, disease severity, weak cough and extubation failure were associated with increased two-year mortality in pneumonia or ARDS patients who experienced scheduled extubation. It provides objective information to caregivers to improve decision-making process during hospitalization and post discharge.

Performance of risk scores in predicting mortality at 3, 6, and 12 months in patients diagnosed with community-acquired pneumonia.

BACKGROUND: Risk scores (RS) evaluate the likelihood of short-term mortality in patients diagnosed with community-acquired pneumonia (CAP). However, there is a scarcity of evidence to determine the risk of long-term mortality. This article aims to compare the effectiveness of 16 scores in predicting mortality at three, six, and twelve months in adult patients with CAP. METHODS: A retrospective cohort study on individuals diagnosed with CAP was conducted across two hospitals in Colombia. Receiver Operating Characteristic (ROC) curves were constructed at 3, 6, and 12 months to assess the predictive ability of death for the following scoring systems: CURB-65, CRB-65, SCAP, CORB, ADROP, NEWS, Pneumonia Shock, REA-ICU, PSI, SMART-COP, SMRT-CO, SOAR, qSOFA, SIRS, CAPSI, and Charlson Comorbidity Index (CCI). RESULTS: A total of 3688 patients were included in the final analysis. Mortality at 3, 6, and 12 months was 5.2%, 8.3%, and 16.3% respectively. At 3 months, PSI, CCI, and CRB-65 scores showed ROC curves of 0.74 (95% CI: 0.71-0.77), 0.71 (95% CI: 0.67-0.74), and 0.70 (95% CI: 0.66-0.74). At 6 months, PSI and CCI scores showed performances of 0.74 (95% CI: 0.72-0.77) and 0.72 (95% CI: 0.69-0.74), respectively. Finally at 12 months, all evaluated scores showed poor discriminatory capacity, including PSI, which decreased from acceptable to poor with an ROC curve of 0.64 (95% CI: 0.61-0.66). CONCLUSION: When predicting mortality in patients with CAP, at 3 months, PSI, CCI, and CRB-65 showed acceptable predictive performances. At 6 months, only PSI and CCI maintained acceptable levels of accuracy. For the 12-month period, all evaluated scores exhibited very limited discriminatory ability, ranging from poor to almost negligible.

Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.

Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing electronic medical records and chest CT scans of 778 ICU patients with severe community-acquired pneumonia from January 2016 to December 2021, AI-enhanced 3D segmentation was used to assess thoracic muscle mass. Patients were categorized into clusters based on muscle mass profiles derived from CT scans, and their effects on clinical outcomes such as extubation success and in-hospital mortality were assessed. The study identified three clusters, showing that higher muscle mass (Cluster 1) correlated with lower in-hospital mortality (8% vs. 29% in Cluster 3) and improved clinical outcomes like extubation success. The model integrating muscle mass metrics outperformed conventional scores, with an AUC of 0.844 for predicting extubation success and 0.696 for predicting mortality. These findings highlight the strong predictive capacity of muscle mass evaluation over indices such as APACHE II and SOFA. Using AI to analyze thoracic muscle mass via chest CT provides a promising prognostic approach in severe pneumonia, advocating for its integration into clinical practice for better outcome predictions and personalized patient management.

Development and verification of a prediction model for outcomes of elderly patients with nursing home-acquired pneumonia.

BACKGROUND: Among all infections in nursing homes, pneumonia has the highest mortality. Nurses have a 24-h relationship with patients and have a key role in identifying and preventing adverse outcomes. However, tools to engage nurses in pneumonia patient outcomes evaluation have not occurred. PURPOSE: This study aimed to develop and validate a prediction model to predict the outcome of elderly patients with nursing home-acquired pneumonia (NHAP). METHODOLOGY: A retrospective observational study was conducted with 219 elderly NHAP patients. Baseline characteristics, health history, and treatment/nursing status were collected. Variables for constructing nomograms were screened by univariate and multivariate analysis. The nomogram model was evaluated using the concordance index (C-index), decision curve analysis (DCA) curves, and receiver operating characteristic (ROC) curves. RESULTS: 9 independent risk factors were identified and assembled into the nomogram. The nomogram exhibited reasonably accurate discrimination (area under the receiver operating characteristic curve (AUC-ROC): 0.931, P < 0.05) and calibration (C-index: 0.931, 95 % CI: 0.898-0.964) in the validation cohort. DCA and clinical impact curves demonstrated that the nomogram was clinically beneficial. CONCLUSIONS: A visualization nomogram model was successfully established for predicting the outcome of the NHAP elderly patients. The model has extremely high reliability, extremely high predictive ability, and good clinical applicability.

Clinical characteristics and risk factors for mortality in pneumonia-associated acute respiratory distress syndrome patients: a single center retrospective cohort study.

Background: Pathogenic diversity may have contributed to the high mortality of pneumonia-associated acute respiratory distress syndrome (p-ARDS). Metagenomics next-generation sequencing (mNGS) serves as a valuable diagnostic tool for early pathogen identification. However, its clinical utility in p-ARDS remains understudied. There are still limited researches on the etiology, clinical characteristics and risk factors for 28-day mortality in p-ARDS patients. Methods: A single center retrospective cohort study of 75 p-ARDS patients was conducted. Patients were categorized into survival and deceased groups based on their 28-day outcomes. A comprehensive clinical evaluation was conducted, including baseline characteristics, laboratory indicators, outcomes and pathogen identification by mNGS and traditional microbiological testing. We then evaluated the diagnostic value of mNGS and identified clinical characteristics and risk factors for 28-day mortality in p-ARDS. Result: The overall ICU mortality was 26.67%, and the 28-day mortality was 57.33%, with 32 cases (42.67%) in the survival group, and 43 cases (57.33%) in the deceased group. Patients in the deceased group were older than those in the survival group (68(59,73) years vs. 59(44,67) years, P=0.04). The average lengths of ICU and hospital stay were 9(5,13) days and 14(7,21) days, respectively. The survival group had longer lengths of ICU and hospital stay (ICU: 11(7,17) days and hospital: 17(9,27) days) compared to the deceased group (ICU: 8(4,11) days and hospital: 12(6,19) days) (P<0.05). Survival patients exhibited lower Acute Physiology and Chronic Health Evaluation (APACHE) II score on the 3rd and 7th days, higher lymphocyte counts, higher CD3+ and CD8+ T cell counts compared to deceased patients (P<0.05). Multivariate logistic regression analysis identified age, APACHE II scores on 3rd and 7th days, CD8+ T cell count and length of ICU as independent risk factors for 28-day mortality in p-ARDS patients. mNGS demonstrated a significantly higher overall pathogen detection rate (70/75, 93.33%) compared to the traditional method (50/75, 66.67%, P=0.022). The average turnaround time (TAT) for mNGS was significantly shorter at 1(1,1) day compared to 4(3,5) days for the traditional method (P<0.001). Conclusion: Metagenome next-generation sequencing can be used as a valuable tool for identifying pathogens in p-ARDS, reducing diagnostic time and improving accuracy. Early application of mNGS alongside traditional methods is recommended for p-ARDS. Furthermore, older age, higher APACHE II scores, lower lymphocyte counts and lymphocyte subset counts were associated with increased mortality in p-ARDS patients, highlighting the importance of timely assessment of immune status and disease severity, especially in elderly.

Association between glucose levels at admission and outcomes of pneumonia: a systematic review and meta-analysis.

BACKGROUND: Elevated blood glucose at hospital admission is frequently observed and has been associated with adverse outcomes in various patient populations. This meta-analysis sought to consolidate existing evidence to assess the association between elevated blood glucose at admission and clinical outcomes amongst pneumonia patients. METHODS: We searched PubMed, Medline, Cochrane library, Web of Science (WoS), and Scopus databases for studies, published up to 31 August 2023, and reporting on the clinical outcomes and the blood glucose levels at admission. Data were extracted by two independent reviewers. Random-effects meta-analyses were used to pool odds ratios (ORs) with 95% confidence intervals (CI) for dichotomous outcomes and weighted mean differences (WMDs) for continuous outcomes. RESULTS: A total of 23 studies with 34,000 participants were included. Elevated blood glucose at admission was significantly associated with increased short-term (pooled OR: 2.67; 95%CI: 1.73-4.12) and long-term mortality (pooled OR: 1.70; 95%CI: 1.20-2.42). Patients with raised glucose levels were more likely to require ICU admission (pooled OR: 1.86; 95%CI: 1.31-2.64). Trends also suggested increased risks for hospital readmission and mechanical ventilation, though these were not statistically significant. Elevated blood glucose was linked with approximately 0.72 days longer duration of hospital stay. CONCLUSION: Elevated blood glucose level at the time of hospital admission is associated with several adverse clinical outcomes, especially mortality, in patients with pneumonia. These findings underscore the importance of recognizing hyperglycemia as significant prognostic marker in pneumonia patients. Further research is needed to determine whether targeted interventions to control glucose levels can improve these outcomes.

Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.

This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001-1.031). The model that combined CURB-65, initial O2 requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628-0.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644-0.809) compared to the conventional CURB-65 and PSI (p < 0.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice.

The relationship between comprehensive geriatric assessment on the pneumonia prognosis of older adults: a cross-sectional study.

BACKGROUND: The mortality of pneumonia in older adults surpasses that of other populations, especially with the prevalence of coronavirus disease 2019 (COVID-19). Under the influence of multiple factors, a series of geriatric syndromes brought on by age is one of the main reasons for the poor prognosis of pneumonia. This study attempts to analyze the impact of geriatric syndrome on the prognosis of pneumonia. METHODS: This is a prospective cross-sectional study. Patients over 65 years old with COVID-19 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-negative community-acquired pneumonia (SN-CAP) were included in the research. General characteristics, laboratory tests, length of stay (LOS), and comprehensive geriatric assessment (CGA) were collected. Multivariate regression analysis to determine the independent predictors of the severity, mortality, and LOS of COVID-19. At the same time, the enrolled subjects were divided into three categories by clustering analysis of 10 CGA indicators, and their clinical characteristics and prognoses were analyzed. RESULTS: A total of 792 subjects were included in the study, including 204 subjects of SN-CAP (25.8%) and 588 subjects (74.2%) of COVID-19. There was no significant difference between non-severe COVID-19 and SN-CAP regarding mortality, LOS, and CGA (P > 0.05), while severe COVID-19 is significantly higher than both (P < 0.05). The Barthel Index used to assess the activities of daily living was an independent risk factor for the severity and mortality of COVID-19 and linearly correlated with the LOS (P < 0.05). The cluster analysis based on the CGA indicators divided the geriatric pneumonia patients into three groups: Cluster 1 (n = 276), named low ability group, with the worst CGA, laboratory tests, severity, mortality, and LOS; Cluster 3 (n = 228), called high ability group with the best above indicators; Cluster 2 (n = 288), named medium ability group, falls between the two. CONCLUSION: The Barthel Index indicates that decreased activities of daily living are an independent risk factor for the severity, mortality, and LOS of geriatric COVID-19. Geriatric syndrome can help judge the prognosis of pneumonia in older adults.