Journal of thoracic disease最新文献

Background: Older lung cancer patients with frailty are of higher risk of therapeutic side effects and mortality. Despite the fact that the estimated prevalence of frailty among older patients with lung cancer is widely reported, these results have not been synthesized. The aim of this review was to systematically assess the prevalence and related factors of frailty in older patients with lung cancer.

Methods: We searched PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), and Wanfang databases for observational studies (published up to January 1, 2025) on the prevalence of frailty in older patients with lung cancer. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of included cohort or case-control studies, the Agency for Healthcare Research and Quality (AHRQ) tool was applied to assess the risk of bias in cross-sectional studies. Pooled estimates, subgroup analyses, meta-regression, and publication bias were conducted using Stata 17.0.

Results: In total, 44 articles comprising 61,587 patients were included in this study. The prevalence of frailty among older patients with lung cancer ranged from 5% to 91%, with an estimated prevalence of 46% [95% confidence interval (CI): 39-54%, I²=99.6%]. Moreover, Egger's regression test suggested no publication bias (P=0.72). Subgroup analyses showed that frailty was more prevalent among female patients, and those older patients ≥70 years old, from developed countries, before radiotherapy, and assessed using the G8 tool.

Conclusions: Frailty is prevalent among older patients with lung cancer, and factors such as age, gender, country, treatment status, and frailty tool were associated with frailty. However, the findings should be interpreted with caution due to high heterogeneity and limited data from regions and subgroups. Early and routine frailty assessment with appropriate management may improve prognosis in this population.

Background: Perioperative hypoalbuminemia is associated with postoperative infection, anastomotic fistula, and a poor prognosis. Compared with the preoperative period, hypoalbuminemia is more prevalent following pulmonary surgery, particularly in the early postoperative phase, which is associated with various postoperative complications. Traditional risk assessment relies on clinical experience and basic laboratory indicators. Currently, no research has been conducted on the application of machine learning (ML) in the prediction of early postoperative hypoalbuminemia (EPH). This study aimed to develop an ML-based predictive model for EPH following pulmonary surgery, offering a novel tool for risk assessment and clinical decision-making in the perioperative management of thoracic surgery.

Methods: The data of patients diagnosed with primary lung cancer who underwent elective pulmonary surgery between January 2022 and December 2024 were retrospectively collected. Based on 1:1 case-control matching, the sample comprised 1,048 cases and 1,048 controls. The outcome variable was binary (the presence or absence of EPH after pulmonary surgery). A logistic regression (LR) model was built with 37 variables; the data were split 8:2 and validated by five-fold stratified cross-validation. Model performance was assessed based on the area under the curve (AUC), accuracy, precision, recall, F1, and Brier score, with SHapley Additive exPlanations (SHAP) used for interpretation.

Results: The model performance metrics were as follows: AUC of the receiver operating characteristic (ROC) curve: 0.8543, precision: 0.7947, recall: 0.7309, F1-score: 0.7606, accuracy: 0.771, and Brier score: 0.1551.

Conclusions: The LR-based ML algorithm demonstrated excellent performance and effectively identified patients at high risk of EPH after pulmonary surgery [serum albumin (ALB) <35 g/L within 5 days of pulmonary surgery].

[This retracts the article DOI: 10.3978/j.issn.2072-1439.2014.09.05.].

Background: Pulmonary nontuberculous mycobacterial (pNTM) infection is increasingly diagnosed, particularly in elderly individuals with impaired immunity or comorbidities. While some patients remain stable without treatment, others experience disease progression despite therapy. This study aimed to investigate the association between radiologic progression of pNTM infection and sarcopenia, along with other clinical factors.

Methods: This single-center cohort included adults diagnosed with pNTM infection between January 1, 2021, and December 31, 2023, from the institutional picture archiving and communication system and electronic medical records using predefined clinical, radiologic, and microbiologic criteria; 64 patients were included (mean age 66.3±10.71 years). Sarcopenia was evaluated by measuring the cross-sectional area (CSA) of the erector spinae muscles (ESMs) at the T12 level on chest computed tomography (CT), normalized for height to calculate the skeletal muscle index at the level of T12 (T12MI, cm²/m²). Radiologic progression was defined as new or worsening lesions on follow-up CT and was verified through multidisciplinary review. Multivariate logistic regression analyzed associations between T12MI, prior tuberculosis (TB) history, pNTM treatment, and other clinical variables.

Results: Ten patients (16%) showed disease progression, including radiologic progression. Sarcopenia (low T12MI) was not significantly associated with progression. In contrast, recent pNTM treatment [odds ratio (OR) =7.167; 95% confidence interval (CI): 1.591-32.291; P=0.01] was significantly associated with progression, and previous TB infection showed a suggestive association (OR =3.500; 95% CI: 0.867-14.133; P=0.08).

Conclusions: Sarcopenia was not a significant predictor of radiologic progression in pNTM infection. Instead, treatment history and prior TB were more closely related to progression, suggesting that these clinical factors may be more relevant indicators for radiologic surveillance and management decisions.

Background: Retrospective studies indicate that morning chemotherapy enhances efficacy and reduces side effects in non-small cell lung cancer (NSCLC). However, the role of infusion timing for pemetrexed plus platinum (AP) chronotherapy remains unclear. This study evaluates the impact of AP administration time on efficacy and safety in advanced NSCLC.

Methods: We retrospectively analyzed 132 advanced NSCLC patients receiving AP chemotherapy at Guangdong Second Provincial General Hospital from 2018 to 2023. Based on previous research, patients were grouped into morning (AM; infusion before 2:00 PM, n=58) and afternoon (PM; n=74) groups. Treatment response was evaluated using the Response Evaluation Criteria in Solid Tumors Criteria V.1.1. The primary endpoint was progression-free survival (PFS), with safety profile serving as the secondary endpoint. All adverse events (AEs) were identified and graded according to the National Cancer Institute-Common Terminology Criteria for Adverse Events version 5.0.

Results: The AM group showed significantly longer PFS than the PM group (24.0 vs. 14.0 months, P=0.04). Subsequent subgroup analysis in the AP cohort favored the AM group across all major subgroups for PFS treatment effect. Furthermore, the analysis of adverse reactions revealed similar incidences of any treatment-emergent adverse events (TEAEs) in both AM and PM (AM 86.21% vs. PM 86.49% in AP cohort), and grade ≥3 TEAEs (AM 31.03% vs. PM 21.62% in AP cohort). The most common AEs were anemia, leukopenia, and neutropenia. Univariate and multivariate analyses indicated that the infusion time of AP chemotherapy (P=0.03) was an independent prognostic factor for NSCLC.

Conclusions: AP treatment administered in the morning may enhance PFS in advanced NSCLC. This suggests that chrono-chemotherapy (CCT) could potentially enhance the efficacy of individualized chemotherapy in advanced NSCLC.

Background and objective: Fluid management remains central to critical care, requiring a careful balance between early resuscitation and the prevention or reversal of pulmonary and systemic congestion. Thoracic ultrasound (TUS) offers real-time, organ-specific assessment of extravascular lung water (EVLW) and pleural effusion, helping clinicians recognize both fluid responsiveness and fluid intolerance-an increasingly relevant distinction in acute respiratory distress syndrome (ARDS), acute heart failure, kidney replacement therapy, and shock. This review synthesizes current evidence on TUS-guided fluid administration and removal and introduces the Fluid Responsiveness & Tolerance, Lung Congestion, Ultrafiltration Optimization, Individualized Therapy, Differentiating Shock (FLUID) framework as a practical bedside reasoning tool emphasizing repeated reassessment rather than prescriptive thresholds.

Methods: Narrative review of PubMed, Scopus, and Embase (January 2015 to January 2025), supplemented by landmark earlier articles when clinically relevant. Eligible studies included adult human research, systematic reviews, randomized trials, and consensus statements. Editorials, pediatric, and veterinary studies were excluded. Selection and full-text appraisal were performed independently by two reviewers.

Key content and findings: TUS provides bedside visualization of pulmonary congestion through B-lines, lung ultrasound scoring, and effusion monitoring, improving detection of EVLW compared with physical examination or central venous pressure. Integrated with focused cardiac and venous Doppler evaluation, TUS supports decisions regarding resuscitation, diuretic escalation, ultrafiltration, or fluid removal in ARDS, heart failure, and shock. Evidence suggests TUS-guided strategies may reduce cumulative fluid balance and rehospitalization in heart failure and may facilitate ventilator liberation, though definitive outcome effects remain under investigation. The FLUID framework structures iterative bedside reasoning without functioning as a prescriptive protocol.

Conclusions: TUS is a practical and repeatable tool that enhances individualized fluid management by identifying evolving pulmonary congestion and estimating fluid tolerance. The FLUID framework supports structured clinical integration of ultrasound findings but requires further prospective validation. Future trials and artificial intelligence (AI)-assisted quantification tools may help standardize practice and clarify outcome benefits.

Background: With the widespread use of computerized tomography (CT) screening, early-stage small-sized (≤2 cm) non-small cell lung cancer (NSCLC) is increasingly detected. However, the optimal extent of lymph node (LN) dissection for these patients remains controversial. This study aimed to comprehensively investigate the patterns and risk factors of LN metastasis in NSCLC ≤2 cm to guide more precise and individualized surgical management.

Methods: We conducted a retrospective analysis of 1,878 patients with clinical N0 NSCLC ≤2 cm who underwent surgical resection with adequate LN dissection at our institution between December 2009 and November 2019. Univariate and multivariate logistic regression were used to identify risk factors for LN metastasis.

Results: The overall LN metastasis rate was 5.6% (106/1,878). Metastasis rates by lobe were: right upper lobe 4.8% (34/712), right middle lobe 5.3% (10/187), right lower lobe 5.6% (17/305), left upper lobe 6.8% (26/384), and left lower lobe 6.6% (19/290). Upper lobe tumors primarily involved upper mediastinal nodes, while lower lobe tumors mainly metastasized to lower mediastinal nodes. Station 7 metastasis occurred in five upper lobe tumors, all measuring ≥1 cm with pure solid appearance. Multivariate analysis identified tumor size ≥1 cm [odds ratio (OR) =64.41, 95% confidence interval (CI): 9.95-877.52], pure solid radiological appearance (OR =7.88, 95% CI: 4.63-14.37), ≥10 LNs removed (OR =1.64, 95% CI: 1.01-2.76), adenosquamous carcinoma (OR =6.02, 95% CI: 2.27-15.76), and pleural invasion (OR =3.66, 95% CI: 2.17-6.11) as independent risk factors. No LN metastasis occurred in tumors <1 cm or pure ground-glass opacity (GGO) nodules.

Conclusions: Lobe-specific LN dissection is recommended for NSCLC ≤2 cm. For upper lobe tumors ≥1 cm with pure solid appearance, station 7 dissection should be considered. LN dissection may be omitted for pure GGO nodules or tumors ≤1 cm.

Background: Spread through air spaces (STAS) is recognized as a novel invasive mode of lung adenocarcinoma (LADC), linked with poorer prognosis and high risk of recurrence. The aim of this study was to develop and evaluate a radiomics nomogram using computed tomography (CT)-based tumoral and peritumoral radiomics features for preoperatively predicting STAS status in clinical stage I pure-solid LADC.

Methods: This study retrospectively enrolled 308 individuals with stage I LADC appearing as pure-solid nodules on thin-section CT who underwent surgical resection from three institutions. We randomly split the patients at authors' hospital into a training set (n=174) and internal validation set (n=73) in a ratio of 7:3, while the external validation set consisted of 61 patients from the other two hospitals. The radiomics features extracted from the gross tumor volume (GTV), two types of peritumoral tumor volume (PTV) (5 and 10 mm around the tumor), and their corresponding two types of gross peritumoral tumor volume (GPTV) were utilized to construct five radiomics models, respectively. Univariate and multivariate analyses identified the independent predictors of STAS. The radscore of the radiomics model with optimal performance was integrated with clinical predictor to develop a comprehensive nomogram.

Results: The STAS positive status was found in 118 (38.3%) of the 308 patients {female: 54.2%; median [interquartile range (IQR)] age: 65, [57-72] years}. The GPTV10 model achieved the highest area under the curve (AUC) values of 0.741, 0.737 and 0.741 in three cohorts. The multivariate logistic regression (LR) suggested that micropapillary component was the independent risk factor of pathological STAS. The comprehensive model constructed using the GPTV10 radscore and clinical predictor exhibited AUCs of 0.788, 0.748 and 0.783. The decision curve analysis (DCA) revealed that the nomogram had superior capacity for predicting STAS status in LADC. Furthermore, both pathological STAS status and STAS predicted by the combined model stratified patients for prognosis, with 5-year recurrence-free survival (RFS) showing obvious difference between STAS-positive and STAS-negative.

Conclusions: Peritumoral features were significantly correlated with STAS status. The integration of radiomics characteristics and clinical factor provided better performance in the prediction of STAS status.

Background: In patients with traumatic brain injury (TBI) requiring mechanical ventilation, ventilator-associated pneumonia (VAP) is a frequent and serious complication that often leads to prolonged hospitalization and increased mortality. However, reliable predictive tools for this specific patient population remain limited. This study aims to develop and validate an effective prediction model for VAP in TBI patients based on clinical variables.

Methods: We conducted a retrospective study using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Univariate and multivariate logistic regression analyses were applied to identify independent predictors of VAP and to develop a nomogram. Model performance was assessed by receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). External validation was performed with clinical data from 43 TBI patients treated at the Third Affiliated Hospital of Anhui Medical University.

Results: Among 819 TBI patients admitted to the intensive care unit (ICU), 141 developed VAP. Four independent predictors of VAP were identified: sepsis, neuromuscular blocking agent (NMBA) use, ICU length of stay (LOS) and prothrombin time (PT). The nomogram demonstrated strong discriminative ability, with area under the curve (AUC) values of 0.800 [95% confidence interval (CI): 0.617-0.859] in the training cohort, 0.822 (95% CI: 0.621-0.929) in the testing cohort, and 0.711 (95% CI: 0.600-0.957) in the external validation cohort. The calibration curves demonstrated that the predictive model possesses satisfactory discriminative power with excellent model calibration. DCA revealed the nomogram's clinical utility across a probability threshold range of 5-45% for VAP intervention.

Conclusions: We have developed and validated a predictive model for VAP in TBI patients. This high-performance tool can assist clinicians in early identification of high-risk cases and guide prevention strategies.