Predictors of In-Hospital Death in Patients with Lung Cancer Admitted for Acute Radiation Pneumonitis: A Healthcare Cost and Utilization Project (HCUP) Analysis

Are comorbidities associated with differences in healthcare charges among lung cancer patients in US hospitals? Focusing on variances by patient and socioeconomic factors

OBJECTIVE

The clinical aspects of lung cancer patients are well-studied. However, healthcare charge patterns have yet to be explored through a large-scale representative population-based sample investigating differences by socioeconomic factors and comorbidities.

AIM

To identify how comorbidities associated with healthcare charges among lung cancer patients.

METHODS

We examined the characteristics of the patient sample and the association between comorbidity status (diabetes, hypertension, or both) and healthcare charge. Multivariate survey linear regression models were used to estimate the association. We also investigated sub-group association through various patient and socioeconomic factors.

RESULTS

Of 212,745 lung cancer patients, 68.5% had diabetes and/or hypertension. Hospital charges were higher in the population with comorbidities. The results showed that lung cancer patients with comorbidities had 9.4%, 5.1%, and 12.0% (with diabetes, hypertension, and both, respectively) higher hospital charges than those without comorbidities. In sub-group analysis, Black patients also showed a similar trend across socioeconomic (i.e. household income and primary payer) and racial (i.e. White, Black, Hispanic, and Asian/Pacific Islander) factors.

DISCUSSION

Black patients may be significantly financially burdened because of the prevalence of comorbidities and low-income status. More work is required to ensure healthcare equality and promote access to care for the uninsured, low-income, and minority populations because comorbidities common in these populations can create more significant financial barriers.

Characterisation of quantitative imaging biomarkers for inflammatory and fibrotic radiation-induced lung injuries using preclinical radiomics

BACKGROUND AND PURPOSE

Radiomics is a rapidly evolving area of research that uses medical images to develop prognostic and predictive imaging biomarkers. In this study, we aimed to identify radiomics features correlated with longitudinal biomarkers in preclinical models of acute inflammatory and late fibrotic phenotypes following irradiation.

MATERIALS AND METHODS

Female C3H/HeN and C57BL6 mice were irradiated with 20 Gy targeting the upper lobe of the right lung under cone-beam computed tomography (CBCT) image-guidance. Blood samples and lung tissue were collected at baseline, weeks 1, 10 & 30 to assess changes in serum cytokines and histological biomarkers. The right lung was segmented on longitudinal CBCT scans using ITK-SNAP. Unfiltered and filtered (wavelet) radiomics features (n = 842) were extracted using PyRadiomics. Longitudinal changes were assessed by delta analysis and principal component analysis (PCA) was used to remove redundancy and identify clustering. Prediction of acute (week 1) and late responses (weeks 20 & 30) was performed through deep learning using the Random Forest Classifier (RFC) model.

RESULTS

Radiomics features were identified that correlated with inflammatory and fibrotic phenotypes. Predictive features for fibrosis were detected from PCA at 10 weeks yet overt tissue density was not detectable until 30 weeks. RFC prediction models trained on 5 features were created for inflammation (AUC 0.88), early-detection of fibrosis (AUC 0.79) and established fibrosis (AUC 0.96).

CONCLUSIONS

This study demonstrates the application of deep learning radiomics to establish predictive models of acute and late lung injury. This approach supports the wider application of radiomics as a non-invasive tool for detection of radiation-induced lung complications.

Lung cancer mortality and associated predictors: systematic review using 32 scientific research findings

Background

Cancer is a chronic disease brought on by mutations to the genes that control our cells' functions and become the most common cause of mortality and comorbidities. Thus, this study aimed to assess the comprehensive and common mortality-related risk factors of lung cancer using more than thirty scientific research papers.

Methods

Possible risk factors contributing to lung cancer mortality were assessed across 201 studies sourced from electronic databases, including Google Scholar, Cochrane Library, Web of Science (WOS), EMBASE, Medline/PubMed, the Lung Cancer Open Research Dataset Challenge, and Scopus. Out of these, 32 studies meeting the eligibility criteria for meta-analysis were included. Due to the heterogeneous nature of the studies, a random-effects model was applied to estimate the pooled effects of covariates.

Results

The overall prevalence of mortality rate was 10% with a 95% confidence interval of 6 and 16%. Twenty studies (62.50%) studies included in this study considered the ages of lung cancer patients as the risk factors for mortality. Whereas, eighteen (56.25%) and thirteen (40.63%) studies incorporated the gender and smoking status of patients respectively. The comorbidities of lung cancer mortality such as cardiovascular disease, hypertension, diabetes, and pneumonia were also involved in 7 (21.90%), 6 (18.75%), 5 (15.63%), and 2 (6.25%) studies, respectively. Patients of older age are more likely to die as compared to patients of younger age. Similarly, lung patients who had smoking practice were more likely to die as compared to patients who hadn't practiced smoking.

Conclusion

The mortality rate of lung cancer patients is considerably high. Older age, gender, stage, and comorbidities such as cardiovascular, hypertension, and diabetes have a significant positive effect on lung cancer mortality. The study results will contribute to future research, management, and prevention strategies for lung cancer.

Considerations of Medical Preparedness to Assess and Treat Various Populations During a Radiation Public Health Emergency

During a radiological or nuclear public health emergency, given the heterogeneity of civilian populations, it is incumbent on medical response planners to understand and prepare for a potentially high degree of interindividual variability in the biological effects of radiation exposure. A part of advanced planning should include a comprehensive approach, in which the range of possible human responses in relation to the type of radiation expected from an incident has been thoughtfully considered. Although there are several reports addressing the radiation response for special populations (as compared to the standard 18-45-year-old male), the current review surveys published literature to assess the level of consideration given to differences in acute radiation responses in certain sub-groups. The authors attempt to bring clarity to the complex nature of human biology in the context of radiation to facilitate a path forward for radiation medical countermeasure (MCM) development that may be appropriate and effective in special populations. Consequently, the focus is on the medical (as opposed to logistical) aspects of preparedness and response. Populations identified for consideration include obstetric, pediatric, geriatric, males, females, individuals of different race/ethnicity, and people with comorbidities. Relevant animal models, biomarkers of radiation injury, and MCMs are highlighted, in addition to underscoring gaps in knowledge and the need for consistent and early inclusion of these populations in research. The inclusion of special populations in preclinical and clinical studies is essential to address shortcomings and is an important consideration for radiation public health emergency response planning. Pursuing this goal will benefit the population at large by considering those at greatest risk of health consequences after a radiological or nuclear mass casualty incident.

Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Radiation-induced lung injury (RILI) is one of the main dose-limiting side effects in patients with thoracic cancer during radiotherapy. No reliable predictors or accurate risk models are currently available in clinical practice. Severe radiation pneumonitis (RP) or pulmonary fibrosis (PF) will reduce the quality of life, even when the anti-tumor treatment is effective for patients. Thus, precise prediction and early diagnosis of lung toxicity are critical to overcome this longstanding problem. This review summarizes the primary mechanisms and preclinical animal models of RILI reported in recent decades, and analyzes the most promising biomarkers for the early detection of lung complications. In general, ideal integrated models considering individual genetic susceptibility, clinical background parameters, and biological variations are encouraged to be built up, and more prospective investigations are still required to disclose the molecular mechanisms of RILI as well as to discover valuable intervention strategies.