Efficacy of cardiopulmonary rehabilitation nursing in enhancing pulmonary function, sleep quality, and living ability in COVID-19 patients.

OBJECTIVE: The purpose of this study was to elucidate the impact of cardiopulmonary rehabilitation nursing on the pulmonary function, sleep quality, and living ability of patients afflicted with Coronavirus Disease 2019 (COVID-19). METHODS: A total of 98 patients with COVID-19 treated at The People's Hospital of Guang'an between September 2021 and January 2023 were retrospectively collected as the research subjects. Among them, 48 patients who received standard nursing care from September 2021 to September 2022 were set as the control group, and 50 patients who underwent cardiopulmonary rehabilitation nursing from October 2022 to January 2023 were set as the research group. The pulmonary function indicators [including Forced Expiratory Volume in 1 second (FEV1) and Left Ventricular Ejection Fraction (LVEF)], sleep quality [evaluated using the Pittsburgh Sleep Quality Index (PSQI)], and living ability [assessed by the 36-Item Short Form Survey (SF-36) scale] pre- and post-intervention were compared between the two groups. RESULTS: Pre-intervention, FEV1, LVEF, PSQI scores, inflammatory factor levels [C-reactive protein (CRP), procalcitonin (PCT)], and SF-36 scores showed no significant differences between the two groups (P>0.05). Post-intervention, the research group exhibited notably enhanced FEV1 and LVEF, lower PSQI scores, lower CRP and PCT, and higher SF-36 scores compared with the control group, with statistical significance (P<0.05). Multifactorial logistic regression analysis showed that non-receipt of cardiopulmonary rehabilitation, age ≥60 years, concurrent respiratory failure, coexistent heart failure, and acid-base imbalance were independent risk factors of adverse outcomes in COVID-19 patients (P<0.05). CONCLUSION: Application of cardiopulmonary rehabilitation nursing in COVID-19 patients can significantly improve pulmonary function, sleep quality, and overall quality of life, and relieve the inflammatory state of the patients, thereby enhancing prognosis. This approach has certain value of popularization and application.

Immune checkpoint inhibitor plus chemotherapy as first-line treatment for non-small cell lung cancer with malignant pleural effusion: a retrospective multicenter study.

BACKGROUND: Immune checkpoint inhibitors (ICI) combined with chemotherapy are efficacious for treating advanced non-small cell lung cancer (NSCLC); however, the effectiveness of this approach in the malignant pleural effusion (MPE) population is unclear. This study evaluated ICI plus chemotherapy in NSCLC patients with MPE. METHODS: Patients from 3 centers in China with NSCLC and MPE who received ICI plus chemotherapy (ICI Plus Chemo) or chemotherapy alone (Chemo) between December 2014 and June 2023 were enrolled. Clinical outcomes and adverse events (AEs) were compared. RESULTS: Of 155 eligible patients, the median age was 61.0 years old. Males and never-smokers accounted for 73.5% and 39.4%, respectively. Fifty-seven and 98 patients received ICI Plus Chemo or Chemo, respectively. With a median study follow-up of 10.8 months, progression-free survival (PFS) was significantly longer with ICI Plus Chemo than with Chemo (median PFS: 7.4 versus 5.7 months; HR = 0.594 [95% CI: 0.403-0.874], P = 0.008). Median overall survival (OS) did not differ between groups (ICI Plus Chemo: 34.2 versus Chemo: 28.3 months; HR = 0.746 [95% CI: 0.420-1.325], P = 0.317). The most common grade 3 or worse AEs included decreased neutrophil count (3 [5.3%] patients in the ICI Plus Chemo group vs. 5 [5.1%] patients in the Chemo group) and decreased hemoglobin (3 [5.3%] versus 10 [10.2%]). CONCLUSIONS: In patients with untreated NSCLC with MPE, ICI plus chemotherapy resulted in significantly longer PFS than chemotherapy and had a manageable tolerability profile, but the effect on OS may be limited.

Developing a multi-institutional nomogram for assessing lung cancer risk in patients with 5-30 mm pulmonary nodules: a retrospective analysis.

Background: The diagnosis of benign and malignant solitary pulmonary nodules based on personal experience has several limitations. Therefore, this study aims to establish a nomogram for the diagnosis of benign and malignant solitary pulmonary nodules using clinical information and computed tomography (CT) results. Methods: Retrospectively, we collected clinical and CT characteristics of 1,160 patients with pulmonary nodules in Guang'an People's Hospital and the hospital affiliated with North Sichuan Medical College between 2019 and 2021. Among these patients, data from 773 patients with pulmonary nodules were used as the training set. We used the least absolute shrinkage and selection operator (LASSO) to optimize clinical and imaging features and performed a multivariate logistic regression to identify features with independent predictive ability to develop the nomogram model. The area under the receiver operating characteristic curve (AUC), C-index, decision curve analysis, and calibration plot were used to evaluate the performance of the nomogram model in terms of predictive ability, discrimination, calibration, and clinical utility. Finally, data from 387 patients with pulmonary nodules were utilized for validation. Results: In the training set, the predictors for the nomogram were gender, density of the nodule, nodule diameter, lobulation, calcification, vacuole, vascular convergence, bronchiole, and pleural traction, selected through LASSO and logistic regression analysis. The resulting model had a C-index of 0.842 (95% CI [0.812-0.872]) and AUCs of 0.842 (95% CI [0.812-0.872]). In the validation set, the C-index was 0.856 (95% CI [0.811-0.901]), and the AUCs were 0.844 (95% CI [0.797-0.891]). Results from the calibration curve and clinical decision curve analyses indicate that the nomogram has a high fit and clinical benefit in both the training and validation sets. Conclusion: The establishment of a nomogram for predicting the benign or malignant diagnosis of solitary pulmonary nodules by this study has shown good efficacy. Such a nomogram may help to guide the diagnosis, follow-up, and treatment of patients.

Enhancing Diagnostic Accuracy for Lung Cancer through Combined Detection of Multiple Tumor Markers.

Objective: To explore the application value of combined detection of multiple tumor markers in diagnosing lung cancer (LC). Methods: A total of 32 small cell lung cancer (SCLC) patients and 107 non-small cell lung cancer (NSCLC) patients, including 68 lung adenocarcinoma (LADC) patients and 39 lung squamous cell carcinoma (LSCC) patients diagnosed in our hospital from January 2019 to December 2021, were enrolled. 102 benign lung disease (BCD) patients (including pneumonia, pulmonary tuberculosis, and chronic obstructive pulmonary disease) were chosen as the control group. Serum tumor markers were detected in all patients, and their positive rates and concentrations were compared. Receiver operating characteristic (ROC) curve analysis was used to calculate the diagnostic performance of individual and combined tests. Results: The positive rate and concentration of carcinoembryonic antigen (CEA) were upregulated in the LADC group (P < .05). The positive rate and concentration of squamous cell carcinoma antigen (SCCAg) were upregulated in the LSCC group (P < .05). The positive rate and concentration of carbohydrate antigen 153 (CA153) were upregulated in the SCLC group (P < .05). The positive rate and concentration of cytokeratin fragment antigen 21-1 (CYFRA21-1) were the highest in the LADC and LSCC groups. The ROC curve demonstrated that CEA exhibited higher diagnostic sensitivity and specificity in LADC patients, SCCAg exhibited higher diagnostic sensitivity and specificity in LSCC patients, and CYFRA21-1 exhibited the highest diagnostic sensitivity in LADC and LSCC patients. In combined detection, the 4-marker combined detection and single-marker combined detection showed statistical significance in patients with different pathological types of LC (P < 0.05). Conclusions: CYFRA21-1 combined with CEA assists in diagnosing LADC, CYFRA21-1 combined with SCCAg assists in diagnosing LSCC, and CA153 assists in diagnosing SCLC. These four serum tumor markers can be used to aid in diagnosing LC and differentiating its pathological types.

Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites.

BACKGROUND: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability, especially in resource-limited areas. The combination of mobile CT and deep learning technique has inspired innovations in the routine clinical practice. METHODS: This study recruited participants prospectively in two rural sites of western China. A deep learning system was developed to assist clinicians to identify the nodules and evaluate the malignancy with state-of-the-art performance assessed by recall, free-response receiver operating characteristic curve (FROC), accuracy (ACC), area under the receiver operating characteristic curve (AUC). RESULTS: This study enrolled 12,360 participants scanned by mobile CT vehicle, and detected 9511 (76.95%) patients with pulmonary nodules. Majority of participants were female (8169, 66.09%), and never-smokers (9784, 79.16%). After 1-year follow-up, 86 patients were diagnosed with lung cancer, with 80 (93.03%) of adenocarcinoma, and 73 (84.88%) at stage I. This deep learning system was developed to detect nodules (recall of 0.9507; FROC of 0.6470) and stratify the risk (ACC of 0.8696; macro-AUC of 0.8516) automatically. CONCLUSIONS: A novel model for lung cancer screening, the integration mobile CT with deep learning, was proposed. It enabled specialists to increase the accuracy and consistency of workflow and has potential to assist clinicians in detecting early-stage lung cancer effectively.

Clinical characteristics of inpatients with coronavirus disease 2019 (COVID-19) in Sichuan province.

BACKGROUND: The outbreak of COVID-19 has resulted in serious concerns in China and abroad. To investigate clinical features of confirmed and suspected patients with COVID-19 in west China, and to examine differences between severe versus non-severe patients. METHODS: Patients admitted for COVID-19 between January 21 and February 11 from fifteen hospitals in Sichuan Province, China were included. Experienced clinicians trained with methods abstracted data from medical records using pre-defined, pilot-tested forms. Clinical characteristics between severe and non-severe patients were compared. RESULTS: Of the 169 patients included, 147 were laboratory-confirmed, 22 were suspected. For confirmed cases, the most common symptoms from onset to admission were cough (70·7%), fever (70·5%) and sputum (33·3%), and the most common chest CT patterns were patchy or stripes shadowing (78·0%); throughout the course of disease, 19·0% had no fever, and 12·4% had no radiologic abnormality; twelve (8·2%) received mechanical ventilation, four (2·7%) were transferred to ICU, and no death occurred. Compared to non-severe cases, severe ones were more likely to have underlying comorbidities (62·5% vs 26·2%, P = 0·001), to present with cough (92·0% vs 66·4%, P = 0·02), sputum (60·0% vs 27·9%, P = 0·004) and shortness of breath (40·0% vs 8·2%, P <  0·0001), and to have more frequent lymphopenia (79·2% vs 43·7%, P = 0·003) and eosinopenia (84·2% vs 57·0%, P = 0·046). CONCLUSIONS: The symptoms of patients in west China were relatively mild, and an appreciable proportion of infected cases had no fever, warranting special attention.

Diminishing microbiome richness and distinction in the lower respiratory tract of lung cancer patients: A multiple comparative study design with independent validation.

OBJECTIVES: Current evidence suggests that microorganisms are associated with neoplastic diseases; however, the role of the airway microbiome in lung cancer remains unknown. To investigate the taxonomic profiles of the lower respiratory tract (LRT) microbiome in patients with lung cancer. MATERIALS AND METHODS: BALF samples were collected in a discovery set comprising 150 individuals, including 91 patients with lung cancer, 29 patients with nonmalignant pulmonary diseases and 30 healthy subjects, and an independent validation set including 85 participants. The samples were assessed by metagenomics analysis. Random forest regression analysis was performed to select a diagnostic panel. RESULTS: In the discovery set, richness was reduced in lung cancer patients compared with that in healthy subjects, and the microbiome of patients with nonmalignant diseases resembled that of patients with lung cancer. Interestingly, Bradyrhizobium japonicum was only found in patients with lung cancer, whereas Acidovorax was found in patients with cancer and nonmalignant pulmonary diseases. A microbiota-related diagnostic model consisting of age, pack year of smoking and eleven types of bacteria was built, and the area under the curve (AUC) for discriminating the patients with cancer was 0.882 (95%CI: 0.807-0.957) in the training set and 0.796 (95%CI: 0.673-0.920) in the independent validation set. CONCLUSION: Our study demonstrates that the LRT microbiome richness is diminished in lung cancer patients compared with that in healthy subjects and that microbiota-specific biomarkers may be useful for diagnosing patients for whom lung biopsy is not feasible.

The prognostic value of C-reactive protein to albumin ratio in patients with lung cancer.

BACKGROUND: To perform a meta-analysis of retrospective studies exploring the association of C-reactive protein to albumin (CAR) with overall survival (OS) in patients with lung cancer. METHODS: Relevant studies were enrolled by searching databases of PubMed, Cochrane Library, Web of Science, and Embase were searched until July 16, 2017. We combined the hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the correlation between CAR and OS in patients with lung cancer RESULTS:: Four studies involving 1257 participants from several countries were involved in the meta-analysis. In a pooled analysis of all studies, elevated CAR predicted poor OS (HR: 2.13; 95% CI: 1.52-2.97; P < .05). Subgroup analysis showed that high level of CAR predicted poor OS in patients with lung cancer though multivariate analyses on 1092 participants (HR: 1.63; 95% CI: 1.24-2.51; P < .001) and the heterogeneity decreased to 45.4%. Moreover, a similar trend was observed in patients receiving surgery (HR: 2.64; 95% CI: 2.08-3.35; P < .001) and chemotherapy (HR: 1.75; 95% CI: 1.93-2.57; P = .004). And the HRs for patients receiving surgery was moderately higher than that for patients receiving chemotherapy. CONCLUSION: Our findings indicate that CAR may have a prognostic value in lung cancer as we detected a significant association between elevated CAR and poorer OS. However, further studies are warranted to draw firm conclusions.

Higher pretreatment lactate dehydrogenase concentration predicts worse overall survival in patients with lung cancer.

BACKGROUND: The aim of this study was to systematically evaluate the prognostic role of pretreatment lactate dehydrogenase (LDH) concentration for survival in patients with lung cancer through performing a meta-analysis. METHODS: PubMed, EMBASE, Cochrane Library, Web of Science, and China National Knowledge Infrastructure were searched for potentially relevant literature. The study and patients' characteristics were extracted. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were pooled to estimate the prognostic role of LDH in patients with lung cancer. RESULTS: Fourteen studies with 4084 patients were included. Higher pretreatment LDH concentration was significantly associated with an increased risk of overall mortality in patients with lung cancer (HR = 1.49, 95% CI, 1.38-1.59). Subgroup analysis of studies also resulted in a significantly increased risk of mortality in patients with small cell lung cancer (SCLC, HR = 1.54, 95% CI, 1.43-1.67) or nonsmall cell lung cancer (NSCLC, HR = 1.25, 95% CI, 1.06-1.46), with high pretreatment LDH concentration. No significant between-study heterogeneity was observed (I = 12.0%, P = .321). No significant publication bias was found (P = .352) in the meta-analysis. CONCLUSION: The results suggested that higher pretreatment LDH concentration was associated with worse overall survival in patients with lung cancer. The findings may assist future research on anticancer therapy by targeting LDH and help predict prognosis in lung cancer patients. However, high-quality studies are required to further research and support these associations. Moreover, confounding factors such as patient ethnicity and tumor type should be considered in future studies.

[Co-expression and prognostic significance of hnRNP B1 and CD44 in non-small cell lung cancers].

OBJECTIVE: To study the co-expression and prognostic significance of hnRNP B1 and CD44 in non-small cell lung cancers (NSCLC). METHODS: The co-expressions of CD44 and hnRNP B1 in the tissues from 88 cases of NSCLC were measured by immunohistochemistry. The relationship between the expressions and the prognosis of NSCLC was analysed. RESULTS: The NSCLC had a high expression of hnRNP B1 (68.18%) and CD44 (52.27%). The cells had the longest average life [(59.607 +/- 4.092) months] in the conditions of high expressed hnRNP B1 and low expressed CD44, and shortest average life [(21.357 +/- 3.545) months] in the conditions of low expressed hnRNP B1 and high expressed CD44. CONCLUSION: Combined detection of hnRNP B1 and CD44 can help forecast the prognosis of NSCLC.