Retrospective Prognostic Study of Death at Home or Hospice Versus at a Hospital Among Patients With Advanced Non-Small Cell Lung Cancer

Exploring the effects of acceptable palliative care models on survival time and healthcare expenditure among patients with cancer: a national longitudinal population-based study

OBJECTIVE

Hospice care ensures better end-of-life quality by relieving terminal symptoms. Prior research has indicated that hospice care could prolong survival and reduce end-of-life medical expenditures among patients with cancer. However, the dearth of studies on the effects of hospice care type and use sequence on survival time and end-of-life medical expenditures substantiates the need for investigation.

DATA SOURCES AND STUDY SETTING

Two million random records were obtained from the National Health Insurance Research Database.

STUDY DESIGN

We estimated the effects of the type and sequence of hospice care use on survival time and medical expenditures among advanced cancer patients. This was a cross-sectional study.

DATA COLLECTION/EXTRACTION METHODS

Patient data were collected from 2 million random records provided by the National Health Insurance Research Database of Taiwan. We included people with cancer and excluded patients under 20 years of age; 2860 patients remained after matching.

PRINCIPAL FINDINGS

The results indicated that the average survival time of patients who received inpatient palliative care (1022 days) was significantly shorter than that of patients who did not receive palliative care (P < 0.001), but the health care expenditure during the entire course of cancer therapy was not the lowest. Interestingly, patients who received inpatient palliative care had the lowest health care expenditure at 1 year or month before the end of life (P < 0.001).

CONCLUSION

The type and sequence of palliative care affected the survival time and health care expenditures of cancer patients. Receiving palliative care did not prolong survival but rather reduced health care expenditures. The sequence of receiving palliative care significantly affected health care expenditures.

Quality of End-of-Life in Cancer Patients With Dementia: Using A Nationwide Inpatient Database

CONTEXT

The growing number of older people significantly affects end-of-life care. However, few studies have assessed the quality of end-of-life care among cancer patients with dementia.

OBJECTIVES

To assess the quality of end-of-life care among non-small cell lung cancer patients with or without dementia using a nationwide inpatient database from Japan.

METHODS

This was a retrospective observational study that used a nationwide inpatient database of 366 acute care hospitals from April 2014 to November 2018. Poisson regression models were used where the quality indicator was the dependent variable, dementia status was the independent variable, and the age group and Charlson comorbidity index were covariates. Incidence proportion ratios (IPRs) and confidence intervals (CIs) were obtained from the model.

RESULTS

The study population included 16,758 patients, of whom 4507 (26.9%) had dementia. The incidence proportion of opioid use (61.8% vs. 70.8%; IPR: 0.87, 95% CI: 0.83-0.91), palliative care consultation (2.7% vs. 3.8%; IPR: 0.71, 95% CI: 0.58-0.88), mechanical ventilation (4.0% vs. 5.4%; IPR: 0.74, 95% CI: 0.62-0.87), and cardiopulmonary resuscitation (2.2% vs. 2.8%; IPR: 0.79, 95% CI: 0.63-0.99) was significantly lower in patients with dementia than in those without dementia.

CONCLUSION

Patients with dementia are less likely to receive end-of-life care. This study demonstrates the importance of providing high-quality end-of-life care regardless the cognitive status of patients with cancer.

Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with lung cancer inflammation index, computed tomography and chest radiograph

The purpose of the present study was to evaluate the feasibility of applying the advanced lung cancer inflammation index (ALI) in patients with coronavirus disease 2019 (COVID-19) and to establish a combined ALI and radiologic risk prediction model for disease exacerbation. The present study included patients diagnosed with COVID-19 infection in our single institution from March to October 2020. Patients without clinical information and/or chest computed tomography (CT) upon admission were excluded. A radiologist assessed the CT severity score and abnormality on chest radiograph. The combined ALI and radiologic risk prediction model was developed via random forest classification. Among 79 patients (age, 43±19 years; male/female, 45:34), 72 experienced improvement and seven patients experienced exacerbation after admission. Significant differences were observed between the improved and exacerbated groups in the ALI (median, 47.6 vs. 13.2; P=0.011), frequency of chest radiograph abnormality (24.7 vs. 83.3%; P<0.001), and chest CT score (CCTS; median, 1 vs. 9; P<0.001). For the accuracy of predicting exacerbation, the receiver-operating characteristic curve analysis demonstrated an area under the curve of 0.79 and 0.92 for the ALI and CCTS, respectively. The combined ALI and radiologic risk prediction model had a sensitivity of 1.00 and a specificity of 0.81. Overall, ALI alone and CCTS alone modestly predicted the exacerbation of COVID-19, and the combined ALI and radiologic risk prediction model exhibited decent sensitivity and specificity.