BMC Pulmonary Medicine最新文献

Objective: This study aimed to identify short-term mortality risk factors for patients with acute respiratory failure (ARF) the MIMIC-IV database, construct a prognostic nomogram and evaluate its predictive performance compared to conventional scoring systems.

Methods: Clinical data from patients diagnosed with ARF were retrospectively collected from the MIMIC-IV database and randomly divided into training and validation groups. The variables were selected via the Lasson regression, and a nomogram was constructed. The nomogram was compared with acute physiology score III (APSIII), simplified acute physiology scores II (SAPS II) and oxford acute severity of illness score (OASIS) model via the C-index, area under the receiver operating characteristic curve (ROC), net reclassification index (NRI), integrated discrimination improvement index (IDI), decision curve analysis (DCA).

Results: A total of 559 patients were included. The study identified nine independent risk factors: age (HR: 1.022, 95% CI: 1.008-1.036, P = 0.002), WBC (HR: 1.060, 95% CI:1.033-1.086, P < 0.001), glucose levels (HR:1.002, 95% CI: 1.001-1.004, P = 0.003), temperature (HR: 0.544, 95% CI: 0.430-0.689, P < 0.001), metastatic solid tumor (HR: 2.138, 95% CI: 1.045-4.372, P = 0.037), malignant cancer (HR: 2.455, 95% CI: 1.456-4.138, P < 0.001), diabetes without chronic complications (HR: 0.288, 95% CI: 0.157-0.807, P < 0.001), cerebrovascular disease (HR: 2.156, 95% CI: 1.180-3.940, P = 0.012), dementia (HR: 2.23, 95% CI: 1.132-4.392, P = 0.020). The nomogram demonstrated strong discriminative performance with C-indices of 0.782 and 0.749 in the training and validation sets, respectively. The AUC for the Training and Validation cohorts were 0.811 (APS III: 0.652; SAPS II: 0.672; OASIS: 0.624) and 0.790 (APS III: 0.634; SAPS II: 0.652; OASIS: 0.609), respectively. The nomogram also significantly outperformed traditional scoring systems, as evidenced by positive NRI and IDI values.

Conclusion: The newly developed nomogram exhibits superior predictive capability to traditional scoring systems (APS III, SAPS II and OASIS scores), offering clinicians a practical and reliable tool for accurately assessing short-term mortality risks in ICU patients with ARF.

Background: Current understanding indicates that primary spontaneous pneumothorax (PSP) typically occurs in younger individuals, whereas secondary spontaneous pneumothorax (SSP) is more common in older patients. However, the specific age distribution patterns distinguishing these two types of spontaneous pneumothorax (SP) remain poorly characterized. Furthermore, while a low partial pressure of oxygen (PaO₂) is a recognized clinical feature of pneumothorax, limited research has explored whether lower PaO₂ levels are specifically indicative of underlying lung disease in patients with SSP.

Methods: In this observational cohort study, we enrolled 473 male SP patients over a six-year period. We use frequency distribution plots to observe the distribution differences of continuous variables between SSP and PSP patients. Receiver operating characteristic (ROC) curve analysis and logistic regression modeling were employed to quantify the association between age, PaO₂, and SSP.

Results: The frequency distributions of age and PaO₂ were bimodal in patients with PSP and SSP. Multivariate logistic regression analysis identified age (using a cutoff of > 50 vs. ≤50 years) and PaO₂ (using a cutoff of > 90 vs. ≤90 mmHg) as independent factors associated with SSP, with odds ratios (ORs) of 10.58 (95% CI: 6.15-18.20) and 0.45 (95% CI: 0.27-0.74), respectively. While alternative cutoffs of age (> 40 vs. ≤40 years) and PaO₂ (> 85 vs. ≤85 mmHg) were also significant, with ORs of 7.74 (95% CI: 4.46-13.41) and 0.32 (95% CI: 0.19-0.55), the OR for age was lower (a decrease of 2.84 from the > 50-year cutoff). ROC curve analysis showed that the sensitivity and specificity for distinguishing SSP were 0.835 (95%CI: 0.812-0.853) and 0.789 (95%CI: 0.779-0.801), respectively, for the 50-year age cutoff, and 0.746 (95% CI: 0.721-0.762) and 0.679 (95%CI: 0.652-0.698) for the 90 mmHg PaO₂ cutoff.

Conclusion: Among male patients with SP, an age of 50 years offers higher sensitivity and specificity than an age of 40 years in distinguishing SSP from PSP. Furthermore, even after oxygen administration, PaO₂ levels in SSP patients remain lower than those in PSP patients. A PaO₂ threshold of 90 mmHg also demonstrates high sensitivity and specificity in differentiating SSP from PSP.

Objective: To develop and validate a prediction model for metastasis risk in lung cancer patients based on circulating biomarkers and clinical factors, thereby facilitating early risk assessment.

Methods: A total of 511 lung cancer patients who received treatment in the hospital from January 2020 to December 2024 were selected. Their clinical data and laboratory test indicators were collected and divided into a training set (n = 358) and a validation set (n = 153) at a ratio of 7:3. In the training set, risk factors were screened by univariate and multivariate Logistic regression to construct a nomogram model. The receiver operating characteristic curve (ROC) and calibration curve were drawn to evaluate the model's efficacy, and the model was validated in the validation set. Decision curve analysis (DCA) was used to evaluate the clinical value.

Results: In the training set, 143 cases (39.94%) had lung cancer metastasis, and in the validation set, 61 cases (39.87%) had lung cancer metastasis. Multivariate Logistic regression showed that lymph node status, Total Prostate-Specific Antigen (TPSA), Carcinoembryonic Antigen (CEA), tumor size, Carbohydrate Antigen 19 - 9(CA199) and Alpha-Fetoprotein were significantly associated with the risk of lung cancer metastasis (all P < 0.05). The nomogram demonstrated consistent performance across the training and validation sets, with C-indices of 0.714 and 0.710, and AUCs of 0.714 (95% CI: 0.649-0.778), with a sensitivity of 0.571 and a specificity of 0.745 and 0.710 (95% CI: 0.609-0.812), with a sensitivity of 0.622 and a specificity of 0.629, respectively. The P values of the Hosmer - Lemeshow test were 0.183 and 0.075, indicating a good model fit, respectively.

Conclusion: The nomogram model constructed based on circulating biomarkers and clinical factors can effectively predict the metastasis risk of lung cancer patients and has certain clinical application value. However, multi - center and large - sample studies are still needed for further validation.

Background: In patients with primary osseous sacral/pelvic tumors, once pulmonary metastasis has occurred, their prognosis is worrying. It is therefore essential to construct a novel tool to achieve accurate prediction of the probability of pulmonary metastasis from primary osseous sacral/pelvic tumors.

Methods: This study retrospectively analyzed data from 407 patients with primary osseous sacral/pelvic tumors from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate logistic regression analyses were used to identify risk factors. Construction of predictive models based on logistic regression algorithm in R software. The calibration, discrimination, and clinical utility of the models are validated in a validation cohort. The Kaplan-Meier survival curve and log-rank test were used to examine the pulmonary metastasis risk classification system for evaluation.

Results: The total cohort was randomly divided into a training cohort (287 patients) and a validation cohort (120 patients). Five independent risk factors were identified to develop a nomogram model to predict the probability of pulmonary metastasis in patients. The area under the receiver operating characteristic curve (0.860 for the training cohort and 0.895 for the validation cohort) showed that the model showed satisfactory discrimination in both validation cohorts. The calibration curve showed a high predictive accuracy of the model and the Hosmer-Lemeshow test indicated a good model fit (p > 0.05). The decision curve analysis showed that the nomogram is clinically useful and can provide a net benefit to patients within certain limits.

Conclusion: We have successfully developed a nomogram and risk classification system to accurately predict the probability of pulmonary metastasis from primary osseous sacral/pelvic tumors.

Background: Pulmonary hyalinizing granuloma (PHG) is a rare, benign pulmonary disease characterized by dense hyalinized collagen deposition. Diagnosis is often challenging because conventional transbronchial forceps biopsy frequently fails to obtain adequate tissue samples, necessitating surgical lung biopsy. While transbronchial cryobiopsy has been successfully employed in similar sclerotic diseases, its use in PHG has not been reported to date. Herein, we present a case of PHG diagnosed using transbronchial cryobiopsy.

Case presentation: A 61-year-old man was evaluated due to a 5-mm nodular lesion in the right lower pulmonary lobe incidentally detected on chest computed tomography performed as part of a routine health check-up. He was asymptomatic, and physical examination revealed no remarkable clinical findings. Over a 2-year follow-up, the lesion enlarged slightly to 6 mm. Bronchoscopy revealed a firm, whitish endobronchial tumor in the right B10 bronchus. Forceps biopsy was unsuccessful due to the lesion's hardness. Transbronchial cryobiopsy using a 1.7-mm cryoprobe successfully obtained a sufficient tissue sample. Histopathological examination showed dense hyalinized collagen bundles with lymphoplasmacytic infiltration, consistent with PHG. No immediate complications occurred. The lesion remained stable during 18 months of follow-up without intervention.

Conclusions: This case suggests that transbronchial cryobiopsy may be a useful, less-invasive option for diagnosing firm, fibrotic pulmonary lesions, including PHG, and may help reduce the need for surgical procedures in selected patients.

Background: The increasing use of low-dose CT (LDCT) screening has significantly enhanced the detection of pulmonary nodules, particularly in early-stage lung cancer. However, diagnosing peripheral pulmonary nodules (PPNs) presents unique challenges due to their distal location, rendering traditional methods like CT-guided biopsy less effective and associated with higher complication risks. Robotic-assisted bronchoscopy (RAB) has emerged as a promising minimally invasive technology that offers improved diagnostic accuracy and safety.

Methods: This review explores recent advancements in RAB technology for PPN diagnosis, focusing on the integration of multimodal imaging innovations. These include shape-sensing technology, electromagnetic navigation bronchoscopy (ENB), radial endobronchial ultrasound (rEBUS), cone-beam CT (CBCT), and needle-based confocal laser endomicroscopy (nCLE). Additionally, advanced biopsy techniques such as transbronchial cryobiopsy (TBCB) are discussed for their contributions to improving diagnostic yield.

Results: The integration of multimodal imaging technologies has significantly enhanced the precision of navigation and biopsy, reducing the risk of complications associated with traditional methods. Comparative studies show that RAB achieves similar or superior diagnostic outcomes compared to conventional approaches, with improved lesion targeting and tissue sampling. The use of techniques like TBCB has further improved the diagnostic yield and quality of tissue samples.

Conclusions: RAB represents a safer and more accurate alternative to conventional biopsy methods for diagnosing peripheral pulmonary nodules. The combined use of advanced imaging and biopsy techniques has solidified RAB's clinical utility, making it a promising tool for current and future developments in pulmonary nodule diagnosis.