This study involved longitudinal evaluations of symptom severity and describes the symptom patterns of 77 terminal cancer patients (median age: 62 years; 61% female), selected from 537 consecutive patients admitted to the Palliative Care Unit of the National Taiwan University Hospital. The most common primary cancer sites in these patients were lung (23.4%), liver (15.6%), and stomach (13%). Nineteen physical and psychological symptoms were assessed using different scales. The median number of symptoms was 11 (range: 1-18) on admission, among which weakness, fatigue, anorexia, pain, and depression were the most common. A comparison of the initial symptom severity scores with those at one week after admission and two days before death suggested six symptom change patterns: A: continuous static (restless/heat, abdominal fullness, constipation, dizziness, and insomnia); B: static-increase (fatigue, weakness, nausea/vomiting, taste alteration, dysphagia, diarrhea, dry mouth, and night sweats); C: decrease-static (pain and depression); D: decrease-increase (anorexia and dyspnea); E: static-decrease (aggression); and F: gradually decrease (anxiety). These six symptom patterns can be divided into two categories on the basis of the relative severity of symptoms between one week after admission and two days before death. The first category included patterns A, C, E and F, and the symptoms improved with palliative care. However, the symptoms in the second category (patterns B and D), which were associated with the anorexia-cachexia syndrome and dyspnea, did not show improvement. As symptom management is an essential component of palliative care, holistic care, which encompasses physical, psychosocial and spiritual aspects, represents a rational approach for the relief of these incurable symptoms at the end stage of life for these patients.

Smoking is a major preventable cause of morbidity and premature death worldwide. Both varenicline and nicotine replacement therapy (NRT) help achieve smoking cessation. However, limited evidence exists regarding whether combination of varenicline and NRT is more effective than either alone. The aim of this research was to investigate the efficacy and safety of varenicline combined with NRT. A systematic search of MEDLINE, EMBASE, ClinicalTrial.gov, and Cochrane Library was conducted in November 2014. Two authors independently reviewed and selected randomized controlled trials. The quality of the studies was evaluated by the Jadad score. We carried out meta-analysis of both early (abstinence rate assessed before or at the end of treatment) and late (assessed after the end of the treatment) outcomes. Three randomized controlled trials with 904 participants were included in this meta-analysis. All three were comparing combination therapy with varenicline therapy alone. The late outcomes were assessed in 2 of the 3 trials. Both the early and late outcomes were favorable for combination therapy (OR = 1.50, 95 % CI 1.14 to 1.97; OR = 1.62, 95 % CI 1.18 to 2.23, respectively). However, this significance diminished after eliminating a study with pre-cessation treatment using nicotine patch. The most common adverse events were nausea, insomnia, abnormal dreams, and headache. One study reported more skin reactions (14.4 % vs 7.8 %; p = 0.03) associated with combination therapy. Combination therapy is more effective than varenicline alone, especially if pre-cessation treatment of nicotine patch is administrated. Adverse events of combination therapy are similar to mono-therapy except for skin reactions.

Background: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. Objective: This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. Methods: This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning–based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. Results: From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. Conclusions: We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. Trial Registration: ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907