Palliative Care in Lung Cancer

Prognosis and risk factor assessment of patients with advanced lung cancer with low socioeconomic status: model development and validation

BACKGROUND

Lung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES.

DESIGN

A retrospective study.

METHOD

The 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods and four machine learning algorithms (DT, RF, SVM, and XGBoost) were used to construct prognostic risk prediction models.

RESULTS

The CoxPH regression-based nomogram demonstrated reliable predictive accuracy for survival at 60, 90, and 120 days. Among the machine learning models, XGBoost showed the best performance, whereas RF had the lowest accuracy at 60 days, DT at 90 days, and SVM at 120 days. Key predictors across all models included Karnofsky Performance Status (KPS) score, quality of life (QOL) score, and cough symptoms.

CONCLUSIONS

CoxPH, DT, RF, SVM, and XGBoost models are effective in predicting mortality risk over 60-120 days in patients with advanced lung cancer with low SES. Monitoring KPS, QOL, and cough symptoms is crucial for identifying high-risk patients who may require intensified care. Clinicians should select models tailored to individual patient needs and preferences due to varying prediction accuracies.

REPORTING METHOD

This study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

The Effect of Simulation-Based Training on the Hand Hygiene Knowledge and Practices of Palliative Caregivers: A Double-Blind, Randomized, Controlled Single-Center Study

This study aimed to investigate the effect of simulation-based training on hand hygiene knowledge and practices among palliative caregivers. The study was conducted with 60 caregivers in a palliative care clinic between December 2022 and September 2023. The participants were divided into two groups by simple randomization. The intervention and control groups received the same hand hygiene theoretical education and demonstration. The intervention group also received additional simulation-based hand hygiene practices recommended by the World Health Organization. A pretest-posttest design was used to assess hand hygiene knowledge and practices. Data were collected with personal information, hand hygiene knowledge, and hand hygiene practice forms. Analysis of covariance was performed to compare posttest scores between the groups. Simulation-based hand hygiene training programs offer an effective and feasible strategy to improve the hand hygiene knowledge and practices of caregivers. It should be integrated into clinical areas to increase palliative caregivers' hand hygiene knowledge and practices. Evidence-based practices can be improved by increasing randomized controlled studies on the effectiveness of simulation-based hand hygiene training for caregivers. Trial Registration: The study was registered at ClinicalTrials.gov with registration number NCT05848596.

Timing of Palliative Care Consultation Impacts End of Life Care Outcomes in Metastatic Non-Small Cell Lung Cancer

CONTEXT

Early specialist palliative care (PC) involvement in metastatic non-small cell lung cancer (mNSCLC) is associated with improved quality of life, less aggressive end of life (EoL) care, and longer survival. As treatment paradigms for NSCLC have evolved, PC utilization remains low.

OBJECTIVES

This work examines how the timing and extent of PC involvement impacts outcomes and the patient experience in mNSCLC in the era of immunotherapy.

METHODS

This retrospective review analyzed patients with mNSCLC who initiated first-line treatment with chemotherapy, immunotherapy, or combined chemoimmunotherapy at Duke University between March 2015 and July 2019. PC consultation and outcomes data were abstracted through November 2022. EoL care variables were analyzed using descriptive statistics.

RESULTS

152 patients were stratified based on whether PC was consulted during their disease course. 80 patients (53%) never saw PC, while the 72 patients (47%) who saw PC were further stratified by time to first PC encounter and total number of PC visits. 31% were seen within two months of diagnosis (early), 33% between two and six months (intermediate), and 36% after 6 months (late). Patients who received early PC had longer median time on hospice (35 days), had lower rates of aggressive EoL care (43%), and experienced less frequent in-hospital death (14%) compared to other groups.

CONCLUSION

This real-world study reveals that referrals to PC still occur late or not at all in mNSCLC despite demonstrated benefits of early PC integration. Early outpatient PC referrals resulted in longer time on hospice, lower frequency of aggressive EoL care, and lower rates of in-hospital death.

Lung Cancer Supportive Care and Symptom Management

Lung cancer carries significant mortality and morbidity. In addition to treatment advances, supportive care may provide significant benefit for patients and their caregivers. A multidisciplinary approach is critical in addressing complications of lung cancer, including disease- and treatment-related complications, oncologic emergencies, symptom management and supportive care, and addressing the psychosocial needs of affected patients.