BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

Humans ; Bronchiolitis Obliterans/etiology* ; Early Diagnosis ; Pneumonia, Mycoplasma/complications* ; Consensus ; Child ; Mycoplasma pneumoniae ; China/epidemiology* ; Mass Screening ; Adenoviridae Infections/complications*

Pneumocystis jirovecii pneumonia (PJP) is an opportunistic pulmonary infection that commonly occurs in immunocompromised children. We report a case of infantile leukemia complicated by PJP and review the relevant literature. A summary and analysis of 10 infantile leukemia patients with PJP infection (9 cases reported in the literature and 1 case from our center) showed that PJP mostly occurred in the early stages of chemotherapy (80%, 8/10). The main clinical manifestations were dyspnea (100%, 10/10) and hypoxemia (50%, 5/10), while pulmonary imaging findings lacked specificity. In most cases (50%, 5/10), diagnosis was established by identifying pathogens in bronchoalveolar lavage fluid under microscopy. In our case, diagnosis was confirmed using targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid. Treatment with intravenous sulfamethoxazole complex was administered in 8 patients, all of whom eventually recovered. PJP may occur in the early stages of chemotherapy for infantile leukemia, thus early prevention is necessary. tNGS facilitates early diagnosis of PJP, and sulfamethoxazole complex remains an effective therapeutic option.
Humans ; Infant ; Bronchoalveolar Lavage Fluid/microbiology* ; Immunocompromised Host ; Leukemia/complications* ; Pneumocystis carinii/isolation & purification* ; Pneumonia, Pneumocystis/diagnosis* ; Trimethoprim, Sulfamethoxazole Drug Combination/therapeutic use*

OBJECTIVE: To investigate the interaction between α-amylase (α-AMS) and inflammatory response in patients with ventilator-associated pneumonia (VAP) and their predictive value for prognosis. METHODS: A prospective cohort study was conducted. Patients with mechanical ventilation who were treated in the intensive care unit (ICU) of the Second Hospital of Hebei Medical University from June 2020 to June 2023 were enrolled, and the patients were divided into VAP group and non-VAP group according to whether VAP occurred. VAP patients were stratified into mild [acute physiology and chronic health evaluation II (APACHE II) < 10 scores], moderate (APACHE II were 10-20 scores), and severe (APACHE II > 20 scores) groups based on the APACHE II. All patients were followed up for 28 days. In addition, healthy subjects who underwent health examination in our hospital at the same time were selected as the healthy control group. Baseline data including gender, age, mechanical ventilation mode, mechanical ventilation time, underlying diseases, drug use, and laboratory test indicators were collected. The serum levels of α-AMS, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), C-reactive protein (CRP) and other inflammatory factors were analyzed and compared. Pearson correlation analysis was performed to analyze the correlation between serum α-AMS and inflammatory factors. Logistic regression was used to analyze the influencing factors of poor prognosis in patients with VAP. The receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive value of α-AMS on the poor prognosis of patients with VAP. RESULTS: A total of 100 mechanically ventilated patients were enrolled, including 60 cases in the VAP group and 40 cases in the non-VAP group. Among the patients with VAP, there were 24 cases in the mild group, 20 cases in the moderate group, and 16 cases in the severe group. A total of 44 patients survived at 28 days, while 16 died. Additionally, 100 healthy individuals were included as the healthy control group. Serum levels of α-AMS, IL-6, TNF-α and CRP in the VAP group were significantly higher than those in the non-VAP group and the healthy control group, while the levels of α-AMS, IL-6, TNF-α and CRP in the non-VAP group were significantly higher than those in the healthy control group. There were statistically significant differences in serum α-AMS, IL-6, TNF-α, CRP levels and APACHE II scores among VAP patients with different disease severities, and the levels of the above indicators in the severe group were significantly higher than those in the moderate group and mild group, and the levels of the above indicators in the moderate VAP group were significantly higher than those in the mild group. Pearson correlation analysis showed that serum α-AMS was positively correlated with IL-6, TNF-α, CRP, and APACHE II scores (r values were 0.404, 0.392 and 0.493, 0.493, all P < 0.01). Univariate analysis showed that age, mechanical ventilation, diabetes mellitus, ventilation time, ventilation position, prophylactic use of antimicrobial drugs, and serum α-AMS, IL-6, TNF-α, CRP, and APACHE II scores were correlated with the prognosis of VAP patients (all P < 0.05). Multivariate Logistic regression analysis identified age [odds ratio (OR) = 1.340, 95% confidence interval (95%CI) was 1.119-1.605], tracheostomy (OR = 3.050, 95%CI was 1.016-9.157), diabetes mellitus (OR = 1.379, 95%CI was 1.102-1.724), and ventilation time ≥ 7 days (OR = 2.557, 95%CI was 1.163-5.623) and serum α-AMS (OR = 1.428, 95%CI was 1.098-1.856), IL-6 (OR = 1.543, 95%CI was 1.005-2.371), TNF-α (OR = 2.228, 95%CI was 1.107-4.485), CRP (OR = 1.252, 95%CI was 1.131-1.387), APACHE II scores (OR = 1.422, 95%CI was 1.033-1.957) were independent influencing factors for the 28-day prognosis of patients with VAP (all P < 0.05). ROC curve analysis demonstrated that serum α-AMS, IL-6, TNF-α and CRP exhibited significant predictive performance on the prognosis of patients with VAP. The best cut-off value for α-AMS had a sensitivity of 81.3%, specificity of 75.0%, and an area under the ROC curve (AUC) of 0.791, which was significantly higher than those of inflammatory markers IL-6, TNF-α, and CRP (P < 0.05). The combined parameter diagnostic performance was significantly better than those of individual parameters (P < 0.05), with the highest diagnostic performance when combined, corresponding to an AUC of 0.868 (95%CI was 0.798-0.938), sensitivity of 87.5%, and specificity of 79.5%. CONCLUSIONS VAP in mechanically ventilated patients can lead to an increase in the levels of peripheral blood α-AMS and inflammatory factors, and there is an interaction between α-AMS and inflammatory markers in severe VAP patients. These markers are closely related to the severity of the disease and prognosis and have significant implications for predicting patient outcomes.
Humans ; Pneumonia, Ventilator-Associated/diagnosis* ; Prognosis ; Prospective Studies ; Respiration, Artificial ; alpha-Amylases/blood* ; Interleukin-6/blood* ; Male ; Female ; C-Reactive Protein/metabolism* ; APACHE ; Inflammation ; Middle Aged ; Intensive Care Units ; Tumor Necrosis Factor-alpha/blood* ; Aged

OBJECTIVE: To construct a risk prediction model for elderly severe patients with pneumonia infection, and analyze the prevention effect of 1M3S nursing plan under early warning mode. METHODS: Firstly, 180 elderly severe patients admitted to the department of intensive care unit (ICU) of the Second Affiliated Hospital of Xingtai Medical College from September 2020 to September 2021 were enrolled. Their clinical data were collected and retrospectively analyzed, and they were divided into infected group and non-infected group according to whether they developed severe pneumonia. The risk factors affecting severe pneumonia in elderly severe patients were screened by univariate and multifactorial analysis methods, and the risk prediction model was constructed. The predictive efficiency of the model was analyzed by receiver operator characteristic curve (ROC curve). Then the risk prediction model was applied to prospectively include 60 high-risk elderly patients with severe pneumonia admitted from December 2021 to August 2022. The patients were randomly divided into study group and control group by envelope method, with 30 cases in each group. Both groups were given routine nursing. On this basis, the study group adopted 1M3S nursing scheme [standardized nursing management (1M), improving nursing skills (S1), optimizing nursing service (S2), ensuring nursing safety (S3)] in the early warning mode for intervention. Acute physiology and chronic health evaluation II (APACHE II) and Murray lung injury score were compared between the two groups before intervention and 7 days after intervention. RESULTS: Among 180 elderly severe patients, 34 cases were infected with pneumonia (18.89%). The proportion of patients with Glasgow coma scale (GCS) ≤ 8, duration of mechanical ventilation > 7 days, use of antibiotics, poor oral hygiene, hospital stay > 15 days and albumin ≤ 30 g/L in the infected group were significantly higher than those in the non-infected group. Multivariate Logistic regression analysis showed that duration of mechanical ventilation > 7 days, use of antibiotics, GCS score≤ 8, hospital stay > 15 days, albumin ≤ 30 g/L and poor oral hygiene were all independent risk factors for severe pneumonia in elderly severe patients. The odds ratio (OR) values were 3.180, 3.394, 1.108, 1.881, 1.517 and 2.512 (all P < 0.05). ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model to predict severe pneumonia in elderly severe patients was 0.838, 95% confidence interval was 0.748-0.927, sensitivity and specificity were 81.25% and 72.57%, respectively, and the Youden index was 0.538. (2) There was no significantly difference in general data between the study group and the control group, which was comparable. After intervention, the APACHE II score and Murray lung injury score of the two groups were significantly decreased, and the APACHE II score and Murray lung injury score of the study group were significantly lower than those of the control group (APACHE II score: 3.15±1.02 vs. 3.81±0.25, Murray lung injury score: 5.01±1.12 vs. 6.55±0.21, both P < 0.01). CONCLUSIONS There are many risk factors affecting the development of severe pneumonia in elderly severe patients. The risk prediction model based on duration of mechanical ventilation > 7 days, hospital stay > 15 days, GCS score≤ 8, albumin ≤ 30 g/L, poor oral hygiene and history of combined antibacterial use has high predictive efficacy. The intervention of 1M3S nursing scheme in the early warning mode can effectively reduce the risk of severe pneumonia in elderly severe patients, and significantly improve the pathophysiological status.
Humans ; Pneumonia/diagnosis* ; Aged ; Risk Factors ; Retrospective Studies ; Intensive Care Units ; Female ; Male ; ROC Curve ; Risk Assessment/methods*

Community-acquired pneumonia (CAP) is the third leading cause of death worldwide and one of the most commonly infectious diseases. Its epidemiological characteristics vary with host and immune status, and corresponding pathogen spectrums migrate over time and space distribution. Meanwhile, with the outbreak of COVID-19, some unconventional treatment strategies are on the rise. This article reviewed the epidemiological characteristics, pathogen spectrum and treatment direction of CAP in China over the years, and aimed to provide guidance for the diagnosis and treatment of CAP in clinical practice.
Humans ; COVID-19 ; Pneumonia/diagnosis* ; Community-Acquired Infections/drug therapy* ; Causality ; Risk Factors

Humans ; Pneumonia, Mycoplasma/diagnosis*

OBJECTIVES: To investigate the risk factors for performing bronchoalveolar lavage (BAL) in children with Mycoplasma pneumoniae pneumonia (MPP) and pulmonary consolidation, and to construct a predictive model for performing BAL in these children. METHODS: A retrospective analysis was performed for the clinical data of 202 children with MPP who were hospitalized in the Department of Pediatrics, Changzhou No. 2 People's Hospital Affiliated to Nanjing Medical University, from August 2019 to September 2022. According to whether BAL was performed, they were divided into BAL group with 100 children and non-BAL group with 102 children. A multivariate logistic regression analysis was used to identify the risk factors for performing BAL in MPP children with pulmonary consolidation. Rstudio software (R4.2.3) was used to establish a predictive model for performing BAL, and the receiver operator characteristic (ROC) curve, C-index, and calibration curve were used to assess the predictive performance of the model. RESULTS: The multivariate logistic regression analysis demonstrated that the fever duration, C-reactive protein levels, D-dimer levels, and presence of pleural effusion were risk factors for performing BAL in MPP children with pulmonary consolidation (P<0.05). A nomogram predictive model was established based on the results of the multivariate logistic regression analysis. In the training set, this model had an area under the ROC curve of 0.915 (95%CI: 0.827-0.938), with a sensitivity of 0.826 and a specificity of 0.875, while in the validation set, it had an area under the ROC curve of 0.983 (95%CI: 0.912-0.996), with a sensitivity of 0.879 and a specificity of 1.000. The Bootstrap-corrected C-index was 0.952 (95%CI: 0.901-0.986), and the calibration curve demonstrated good consistency between the predicted probability of the model and the actual probability of occurrence. CONCLUSIONS The predictive model established in this study can be used to assess the likelihood of performing BAL in MPP children with pulmonary consolidation, based on factors such as fever duration, C-reactive protein levels, D-dimer levels, and the presence of pleural effusion. Additionally, the model demonstrates good predictive performance.
Child ; Humans ; Mycoplasma pneumoniae ; Retrospective Studies ; C-Reactive Protein/analysis* ; Pneumonia, Mycoplasma/diagnosis* ; Bronchoalveolar Lavage ; Pleural Effusion

Cryptogenic organic pneumonia (COP) refers to organic pneumonia that has not been identified a clear cause by current medical methods. A small proportion of COP can exhibit severe and progressive characteristics, while severe COP can cause systemic inflammatory storms and can be secondary to hemophilia. This article reported a case of acute severe COP secondary to hemophilia. A 67-year-old male patient was admitted to the hospital due to cough, shortness of breath, and fever. At first, he was misdiagnosed as severe pneumonia, but failed to receive anti infection treatments. Sputum pathogenetic examination and Macrogene testing of alveolar lavage fluid were performed, and no etiology was found to explain the patient's condition. The condition was gradually worsened and hemophilia occurred to explain, suggesting that acute severe COP was relevant. After receiving hormone treatment, the condition gradually relieved and the absorption of lung lesions improved. Hemophilia secondary to COP is rare, and the specific mechanism needs further study.
Male ; Humans ; Aged ; Hemophilia A/complications* ; Pneumonia/diagnosis* ; Bronchoalveolar Lavage Fluid ; Cough ; Dyspnea/etiology*

Objective: Based on the diagnostic model established and validated by the machine learning algorithm, to investigate the value of seven tumor-associated autoantibodies (TAABs), namely anti-p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGEA1 and CAGE antibodies in the diagnosis of non-small cell lung cancer (NSCLC) and to differentiate between NSCLC and benign lung nodules. Methods: This was a retrospective study of clinical cases. Model building queue: a total of 227 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from November 2018 to June 2021 were collected as the NSCLC group, and 120 cases of benign lung nodules, 122 cases of pneumonia and 120 healthy individuals were selected as the control groups. External validation queue: a total of 100 primary patients who underwent radical lung cancer surgery in the Department of Thoracic Surgery, Shengjing Hospital of China Medical University, from May 2022 to December 2022 were collected as the NSCLC group, and 36 cases of benign lung nodules, 32 cases of pneumonia and 44 healthy individuals were selected as the control groups. In addition, NSCLC was divided into early (stage 0-ⅠB) and mid-to-late (stage ⅡA-ⅢB) subgroups. The levels of 7-TAABs were detected by enzyme immunoassay, and serum concentrations of CEA and CYFRA21-1 were detected by electrochemiluminescence. Four machine learning algorithms, XGBoost, Lasso logistic regression, Naïve Bayes, and Support Vector Machine are used to establish classification models. And the best performance model was chosen based on evaluation metrics and a multi-indicator combination model was established. In addition, an online risk evaluation tool was generated to assist clinical applications. Results: Except for p53, the levels of rest six TAABs, CEA and CYFRA21-1 were significantly higher in the NSCLC group (P<0.05). Serum levels of anti-SOX2 [1.50 (0.60, 10.85) U/ml vs. 0.8 (0.20, 2.10) U/ml, Z=2.630, P<0.05] and MAGEA1 antibodies [0.20 (0.10, 0.43) U/ml vs. 0.10 (0.10, 0.20) U/ml, Z=2.289, P<0.05], CEA [3.13 (2.12, 5.64) ng/ml vs. 2.11 (1.25, 3.09) ng/ml, Z=3.970, P<0.05] and CYFRA21-1 [4.31(2.37, 7.14) ng/ml vs. 2.53(1.92, 3.48) ng/ml, Z=3.959, P<0.05] were significantly higher in patients with mid-to late-stage NSCLC than in early stages. XGBoost model was used to establish a multi-indicator combined detection model (after removing p53). 6-TAABs combined with CYFRA21-1 was the best combination model for the diagnosis of NSCLC and early NSCLC. The optimal diagnostic thresholds were 0.410, 0.701 and 0.744, and the AUC was 0.828, 0.757 and 0.741, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in model building queue, and the AUC was 0.760, 0.710 and 0.660, respectively (NSCLC vs. control, NSCLC vs. benign lung nodules, early NSCLC vs. benign lung nodules) in external validation queue. Conclusion: In the diagnosis of NSCLC, 6-TAABs is superior to that of traditional tumor markers CEA and CYFRA21-1, and can compensate for the shortcomings of traditional tumor markers. For the differential diagnosis of NSCLC and benign lung nodule, "6-TAABs+CYFRA21-1" is the most cost-effective combination, and plays an important role in prevention and screening for early lung cancer.
Humans ; Carcinoma, Non-Small-Cell Lung/surgery* ; Lung Neoplasms/diagnosis* ; Retrospective Studies ; Autoantibodies ; Bayes Theorem ; Tumor Suppressor Protein p53 ; Carcinoembryonic Antigen ; Antigens, Neoplasm ; Biomarkers, Tumor ; Algorithms ; Pneumonia