Current status of advance care planning, palliative care consultation, and end-of-life care in patients with glioblastoma in South Korea.

BACKGROUND: Given the typical trajectory of glioblastoma, many patients lose decision-making capacity over time, which can lead to inadequate advance care planning (ACP) and end-of-life (EOL) care. We aimed to evaluate patients' current ACP and EOL care status. PATIENTS AND METHODS: We conducted a cohort study on 205 patients referred to oncologists at a Korean tertiary hospital between 2017 and 2022. We collected information on sociodemographic factors, cancer treatment, palliative care consultation, ACP, legal documents on life-sustaining treatment (LST) decisions, and aggressiveness of EOL care. RESULTS: With a median follow-up time of 18.3 months: 159 patients died; median overall survival: 20.3 months. Of the 159 patients, 11 (6.9%) and 63 (39.6%) had advance directive (AD) and LST plans, respectively, whereas 85 (53.5%) had neither. Among the 63 with LST plans, 10 (15.9%) and 53 (84.1%) completed their forms through self-determination and family determination, respectively. Of the 159 patients who died, 102 (64.2%) received palliative care consultation (median time: 44 days from the first consultation to death) and 78 (49.1%) received aggressive EOL care. Those receiving palliative care consultations were less likely to receive aggressive EOL care (83.3% vs 32.4%, P < .001), and more likely to use more than 3 days of hospice care at EOL (19.6% vs 68.0%, P < .001). CONCLUSIONS: The right to self-determination remains poorly protected among patients with glioblastoma, with nearly 90% not self-completing AD or LST plan. As palliative care consultation is associated with less aggressive EOL care and longer use of hospice care, physicians should promptly introduce patients to ACP conversations and palliative care consultations.

Skeletal-Related Events in Patients With Multiple Myeloma: A Comprehensive Retrospective Cohort Study.

BACKGROUND: Multiple myeloma (MM) patients are at risk of skeletal-related events (SREs) like spinal cord compression, pathologic fractures, bone surgery, and radiation to bone. Real-world data regarding SREs in MM are limited. METHODS: We conducted a large, retrospective, nationwide cohort study using the Korean Health Insurance Review and Assessment Service (HIRA) database from 2007 to 2018. RESULTS: Over a 12-year study period, we identified 6,717 patients who developed symptomatic MM. After a median follow-up of 35.1 months (interquartile range [IQR], 20.8-58.2 months), 43.6% of these patients experienced SREs, and 39.6% had four or more SREs. One in five patients (20.0%) experienced pathologic fractures within the first year of follow-up. The median time to first SRE was 9.6 months (IQR, 1.2-25.8 months), with 3.0 months in the group with prior SREs and 19.8 months in the group without prior SREs. During follow-up, 78.5% of patients received bisphosphonates. Multiple logistic regression analysis revealed several factors associated with an increased risk of SREs, including being female (odds ratio [OR], 1.44), aged 50 or older (OR, 1.87), having cerebrovascular disease (OR, 1.34), undergoing first-line chemotherapy regimens not containing bortezomib or lenalidomide (OR, 1.49), and being in the group with prior SREs and bisphosphonate use (OR, 5.63), compared to the group without prior SREs and without bisphosphonate use. CONCLUSION: This population-based study is the first to report the incidence and risk factors of SREs in Korean MM patients, which can be used to assess their bone health.

Development of a Prediction Model for Delirium in Hospitalized Patients with Advanced Cancer.

Purpose: Delirium is a common neurocognitive disorder in patients with advanced cancer and is associated with poor clinical outcomes. As a potentially reversible phenomenon, early recognition of delirium by identifying the risk factors demands attention. To develop a model to predict the occurrence of delirium in hospitalized patients with advanced cancer. Materials and Methods: This retrospective study included patients with advanced cancer admitted to the oncology ward of four tertiary cancer centers in Korea for supportive cares and excluded those discharged due to death. The primary endpoint was occurrence of delirium. Sociodemographic characteristics, clinical characteristics, laboratory findings, and concomitant medication were investigated for associating variables. The predictive model developed using multivariate logistic regression was internally validated by bootstrapping. Results: From January 2019 to December 2020, 2,152 patients were enrolled. The median age of patients was 64 years, and 58.4% were male. A total of 127 patients (5.9%) developed delirium during hospitalization. In multivariate logistic regression, age, body mass index, hearing impairment, previous delirium history, length of hospitalization, chemotherapy during hospitalization, blood urea nitrogen and calcium levels, and concomitant anti-depressant use were significantly associated with the occurrence of delirium. The predictive model combining all four categorized variables showed the best performance among the developed models (area under the curve 0.831, sensitivity 80.3%, and specificity 72.0%). The calibration plot showed optimal agreement between predicted and actual probabilities through internal validation of the final model. Conclusion: We proposed a successful predictive model for the risk of delirium in hospitalized patients with advanced cancer.

Opioid use and subsequent delirium risk in patients with advanced cancer in palliative care: a multicenter registry study.

The prevalent use of opioids for pain management in patients with advanced cancer underscores the need for research on their neuropsychiatric impacts, particularly delirium. Therefore, we aimed to investigate the potential association between opioid use and the risk of delirium in patients with advanced cancer admitted to the acute palliative care unit. We conducted a retrospective observational study utilizing a multicenter, patient-based registry cohort by collecting the data from January 1, 2019, to December 31, 2020, in South Korea. All data regarding exposures, outcomes, and covariates were obtained through retrospective chart reviews by a team of specialized medical professionals with expertise in oncology. Full unmatched and 1:1 propensity-score matched cohorts were formed, and stratification analysis was conducted. The primary outcome, delirium, was defined and diagnosed by the DSM-IV. Of the 2,066 patients with advanced cancer, we identified 42.8% (mean [SD] age, 64.4 [13.3] years; 60.8% male) non-opioid users and 57.2% (62.8 [12.5] years; 55.9% male) opioid users, respectively. Opioid use was significantly associated with an increased occurrence of delirium in patients with advanced cancer (OR, 2.02 [95% CI 1.22-3.35]). The risk of delirium in patients with advanced cancer showed increasing trends in a dose-dependent manner. High-dose opioid users showed an increased risk of delirium in patients with advanced cancer compared to non-opioid users (low-dose user: OR, 2.21 [95% CI 1.27-3.84]; high-dose user: OR, 5.75 [95% CI 2.81-11.77]; ratio of OR, 2.60 [95% CI 1.05-6.44]). Patients with old age, male sex, absence of chemotherapy during hospitalization, and non-obese status were more susceptible to increased risk of delirium in patients with cancer. In this multicenter patient-based registry cohort study, we found a significant, dose-dependent association between opioid use and increased risk of delirium in patients with advanced cancer. We also identified specific patient groups more susceptible to delirium. These findings highlight the importance of opioid prescription in these patients with advanced cancer, balancing effective doses for pain management and adverse dose-inducing delirium.

Home-based supportive care in advanced cancer: systematic review.

OBJECTIVES: This study systematically reviewed the literature on the effect of home-based supportive care (HbSC) programmes on the quality of life (QoL) of patients with advanced cancer. METHODS: The research question 'Do home-based supportive care programmes for patients with advanced cancer improve their QoL?' was addressed. After registering the plan with PROSPERO (CRD42022341237), literature published from 1 January 1990 to 30 May 2023 was searched on PubMed, Embase, Cochrane database, CINAHL and Web of Science, and reviewed for inclusion based on predefined criteria. This review only included trial studies published in English. RESULTS: Of 5,276 articles identified, 17 studies were judged suitable for inclusion in this review. The components of HbSC programmes included home visits, patient and caregiver education, home nursing, psychotherapy, exercise, telephone consultation, and multidisciplinary team meetings. Nine studies reported improvements in QoL, including social functioning, emotional functioning, and subjective QoL. CONCLUSION: HbSC programmes appear to enable the improvement of the QoL of patients with advanced cancer. The area of QoL that shows improvement could vary depending on the HbSC components. More studies that address HbSC programmes are needed to select patients at the proper time and provide suitable programmes for patients to benefit most.

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.

This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website ( http://ai-wm.khu.ac.kr/Delirium/ ) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.

Longitudinal Comparative Analysis of Circulating Tumor DNA and Matched Tumor Tissue DNA in Patients with Metastatic Colorectal Cancer Receiving Palliative First-Line Systemic Anti-Cancer Therapy.

Purpose: This study aimed to compare tumor tissue DNA (ttDNA) and circulating tumor DNA (ctDNA) to explore the clinical applicability of ctDNA and to better understand clonal evolution in patients with metastatic colorectal cancer undergoing palliative first-line systemic therapy. Materials and Methods: We performed targeted sequencing analysis of 88 cancer-associated genes using germline DNA, ctDNA at baseline (baseline-ctDNA), and ctDNA at progressive disease (PD-ctDNA). The results were compared with ttDNA data. Results: Among 208 consecutively enrolled patients, we selected 84 (41 males; median age 59, range 35 to 90) with all four sample types available. A total of 202 driver mutations were found in 34 genes. ttDNA exhibited the highest mutation frequency (n=232), followed by baseline-ctDNA (n=155) and PD-ctDNA (n=117). Sequencing ctDNA alongside ttDNA revealed additional mutations in 40 patients (47.6%). PD-ctDNA detected 13 novel mutations in 10 patients (11.9%) compared to ttDNA and baseline-ctDNA. Notably, 7 mutations in 5 patients (6.0%) were missense or nonsense mutations in APC, TP53, SMAD4, and CDH1 genes. In baseline-ctDNA, higher maximal variant allele frequency (VAF) values (p=0.010) and higher VAF values of APC (p=0.012), TP53 (p=0.012), and KRAS (p=0.005) mutations were significantly associated with worse overall survival. Conclusion: While ttDNA remains more sensitive than ctDNA, our ctDNA platform demonstrated validity and potential value when ttDNA was unavailable. Post-treatment analysis of PD-ctDNA unveiled new pathogenic mutations, signifying cancer's clonal evolution. Additionally, baseline-ctDNA's VAF values were prognostic after treatment.

Association of TP53 Mutation Status and Sex with Clinical Outcome in NSCLC Treated with Immune Checkpoint Inhibitors: A Retrospective Cohort Study.

Purpose: Some studies suggest that TP53 mutations are associated with the response to immune checkpoint inhibitors (ICI) in patients with non-small cell lung cancer (NSCLC) and also contribute to sex disparities in several cancers. Thus, we hypothesized that TP53 mutations might serve as sex-dependent genomic biomarkers of ICI treatment response in patients with NSCLC. Materials and Methods: Clinical data of 100 patients with metastatic NSCLC treated with ICI monotherapy at Seoul National University Bundang Hospital (SNUBH) were retrospectively reviewed. Genomic and clinical datasets of TCGA and an ICI-treated lung cancer cohort (cBioPortal) were also analyzed. Results: In SNUBH cohort, no statistically significant difference was observed in disease control rate per the TP53 mutation status (p=0.503); however, female patients with TP53 mutated (MT) had a significantly prolonged median progression-free survival (PFS) compared to wild-type (WT) (6.1 months in TP53 MT vs. 2.6 months in TP53 WT; p=0.021). PD-L1 high (≥50%) expression was significantly enriched in female patients with TP53 MT (p=0.001). The analysis from publicly available dataset also revealed that females with NSCLC with TP53 MT showed significantly longer PFS than those with TP53 WT (p<0.001). In TCGA analysis, expression of immune-related genes, and TMB score in TP53 MT females were higher than in males without TP53 MT. Conclusion: Female patients with NSCLC with TP53 mutations had high PD-L1 expression and showed favorable clinical outcomes following ICI therapy, suggesting a need for further research to explore the role of TP53 mutations for sex disparities in response to ICI therapy.