[Palliative Sedation at the End of Life: A Comparative Study of Chronic Obstructive Pulmonary Disease and Lung Cancer Patients]

Differences in the palliative care phase between patients with nonmalignant pulmonary disease and lung cancer: a retrospective study

BACKGROUND

Patients with chronic nonmalignant pulmonary disease and lung cancer both need palliative care, but palliative care services may be better adjusted to serve cancer patients. We compared the timing and clinical practice of palliative care and acute hospital usage during the last year of life in patients with nonmalignant pulmonary disease or lung cancer.

METHODS

This was a retrospective study of all patients in a palliative care phase (palliative goal of care) with nonmalignant pulmonary disease or lung cancer who were treated at Tampere University Hospital, Finland, during the years 2018-2020. The data were collected from the hospital's medical records. Comparisons between the groups were performed by using the Pearson chi-square test, Fisher's exact test, or Mann‒Whitney U test when appropriate. Survival was estimated by using the Kaplan‒Meier method.

RESULTS

The study population consisted of 107 patients with nonmalignant pulmonary disease and 429 patients with lung cancer. Patients with nonmalignant pulmonary disease survived longer in the palliative care phase than patients with lung cancer (115 vs. 59 days, p < 0.001). Compared to lung cancer patients, patients with nonmalignant disease received a palliative care specialist consultation more often during hospitalization (66% vs. 45%, p < 0.001) than during a preplanned outpatient visit (6% vs. 52%, p < 0.001), were less likely to be referred to palliative care pathway (79% vs. 87%, p = 0.033), and spent more days in an acute care hospital during the last year of life (median of 10 vs. 6 days, p = 0.023). Contrary to lung cancer patients, referral to the palliative care pathway was not significantly associated with decreased acute hospital resource usage during the last month of life among patients with nonmalignant pulmonary disease.

CONCLUSIONS

Compared to lung cancer patients, patients with nonmalignant pulmonary disease had longer palliative care phases but fewer visits to the palliative care outpatient clinic and fewer referrals to the palliative care pathways. Palliative care arrangements seemed to have more influence on the end-of-life care of lung cancer patients. There is a need for long-term palliative care services with better abilities to meet the special needs of patients with nonmalignant pulmonary disease.

Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch

Patients with weak or no symptoms accelerate the spread of COVID-19 through various mutations and require more aggressive and active means of validating the COVID-19 infection. More than 30% of patients are reported as asymptomatic infection after the delta mutation spread in Korea. It means that there is a need for a means to more actively and accurately validate the infection of the epidemic via pre-symptomatic detection, besides confirming the infection via the symptoms. Mishara et al. (Nat Biomed Eng 4, 1208–1220, 2020) reported that physiological data collected from smartwatches could be an indicator to suspect COVID-19 infection. It shows that it is possible to identify an abnormal state suspected of COVID-19 by applying an anomaly detection method for the smartwatch’s physiological data and identifying the subject’s abnormal state to be observed. This paper proposes to apply the One Class-Support Vector Machine (OC-SVM) for pre-symptomatic COVID-19 detection. We show that OC-SVM can provide better performance than the Mahalanobis distance-based method used by Mishara et al. (Nat Biomed Eng 4, 1208–1220, 2020) in three aspects: earlier (23.5–40% earlier) and more detection (13.2–19.1% relative better) and fewer false positives. As a result, we could conclude that OC-SVM using Resting Heart Rate (RHR) with 350 and 300 moving average size is the most recommended technique for COVID-19 pre-symptomatic detection based on physiological data from the smartwatch.