Factors Contributing to Non-Concordance Between End-of-Life Care and Advance Care Planning.

CONTEXT: Despite making do-not-resuscitate or comfort care decisions during advance care planning, terminally ill patients sometimes receive life-sustaining treatments as they approach end of life. OBJECTIVES: To examine factors contributing to nonconcordance between end-of-life care and advance care planning. METHODS: In this longitudinal retrospective cohort study, terminally ill patients with a life expectancy shorter than six months, who had previously expressed a preference for do-not-resuscitate or comfort care, were followed up after palliative shared care intervention. An instrument with eight items contributing to non-concordant care, developed through literature review and experts' consensus, was employed. An expert panel reviewed electronic medical records to determine factors associated with non-concordant care for each patient. Statistical analysis, including descriptive statistics and the chi-square test, examines demographic characteristics, and associations. RESULTS: Among the enrolled 7871 patients, 97 (1.2%) received non-concordant care. The most prevalent factor was "families being too distressed about the patient's deteriorating condition and therefore being unable to let go" (84.5%) followed by "limited understanding of medical interventions among patients and surrogates" (38.1%), and "lack of patient participation in the decision-making process" (25.8%). CONCLUSIONS: This study reveals that factors related to relational autonomy, emotional support, and health literacy may contribute to non-concordance between advance care planning and end-of-life care. In the future, developing an advance care planning model emphasizes respecting relational autonomy, providing emotional support, and enhancing health literacy could help patients receiving a goal concordant and holistic end-of-life care.

Improved Symptom Change Enhances Quality of Dying in Patients With Advanced Cancer: An East Asian Cross-Cultural Study.

BACKGROUND: Symptom burdens tend to increase for patients with cancer and their families over the disease trajectory. There is still a lack of evidence on the associations between symptom changes and the quality of dying and death. In this context, this research investigated how symptom changes influence the quality of dying and death. METHODS: This international prospective cohort study (the East Asian Collaborative Cross-Cultural Study to Elucidate the Dying Process (EASED), 2017-2019) included 22, 11, and 4 palliative care units across Japan, South Korea, and Taiwan. Eligible participants were adults (Japan and Korea, ≥18 years; Taiwan, ≥20 years) with locally advanced or metastatic cancer. Physical and psychological symptoms were assessed by physicians upon admission and within 3 days before death. Death quality was assessed using the Good Death Scale (GDS), developed in Taiwan. Univariate and multivariate regression analyses were used to identify correlations between symptom severity changes and GDS scores. RESULTS: Among 998 patients (542 [54.3%] men and 456 [45.7%] women; mean [SD] age = 70.1 [± 12.5] years), persistent dyspnea was associated with lower GDS scores when compared to stable dyspnea (ß = -0.427, 95% CI = -0.783 to -0.071). Worsened (-1.381, -1.932 to -0.831) and persistent (-1.680, -2.701 to -0.659) delirium were also significantly associated with lower GDS scores. CONCLUSIONS: Better quality of dying and death was associated with improved symptom control, especially for dyspnea and delirium. Integrating an outcome measurement for the quality of dying and death is important in the management of symptoms across the disease trajectory in a goal-concordant manner.

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.

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.

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis.

The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

Survival and characteristics of older adults receiving home-based medical care: A nationwide analysis in Taiwan.

BACKGROUND: In Taiwan, the National Health Insurance Administration initiated the integrated home-based medical care (iHBMC) program in 2016 to improve accessibility to health care for homebound patients. This study aimed to describe the characteristics of older people receiving iHBMC services in Taiwan as well as the relationship between patient characteristics and survival. METHODS: All older adults registered in the iHBMC application dataset were enrolled between March 1, 2016, and December 31, 2018. Data on social determinants of health (income level, residential area), functional status, consciousness status, nasogastric tube or urinary catheter placement, and major diseases were retrieved from the database. Data on the frequency of multidisciplinary team members' visits were collected. The survival rate was investigated using the Kaplan-Meier method. A Cox proportional hazards univariate regression was conducted to analyze factors influencing survival rates. RESULTS: A total of 41,079 patients aged ≥65 years were enrolled in iHBMC services. The results showed that the one-year survival rates were 72.1%, 67.4%, and 14.7% in the home-based primary care (HBPC), home-based primary care plus (HBPC-Plus), and home-based palliative care (HBPalC), respectively. Nearly two-thirds of the HBPC-Plus patients underwent nasogastric tube placement. The Cox proportional hazards univariate regression analysis showed that a low urbanization level, a low income level, a low functional status, and an impaired consciousness status were significant predictors of poor survival after adjustment for confounding variables. CONCLUSIONS: Older adults receiving iHBMC services had a high mortality rate. The high rate of feeding tube use indicated that education and support for both clinical practitioners and family caregivers regarding careful hand feeding are warranted. There was a relationship between low income levels and poor survival in rural areas. Further research on whether social care could impact prognosis should be considered.

RLC Circuit Forecast in Analog IC Packaging and Testing by Machine Learning Techniques.

For electronic products, printed circuit boards are employed to fix integrated circuits (ICs) and connect all ICs and electronic components. This allows for the smooth transmission of electronic signals among electronic components. Machine learning (ML) techniques are popular and employed in various fields. To capture the nonlinear data patterns and input-output electrical relationships of analog circuits, this study aims to employ ML techniques to improve operations from modeling to testing in the analog IC packaging and testing industry. The simulation calculation of the resistance, inductance, and capacitance of the pin count corresponding to the target electrical specification is a complex process. Tasks include converting a two-dimensional circuit into a three-dimensional one in simulation and modeling-buried structure operations. In this study, circuit datasets are employed for training the ML model to predict resistance (R), inductance (L), and capacitance (C). The least squares support vector regression (LSSVR) with Genetic Algorithms (GA) (LSSVR-GA) serves as an ML model for forecasting RLC values. Genetic algorithms are used to select parameters of LSSVR models. To demonstrate the performance of LSSVR models in forecasting RLC values, three other ML models with genetic algorithms, including backpropagation neural networks (BPNN-GA), random forest (RF-GA), and eXtreme gradient boosting (XGBoost-GA), were employed to cope with the same data. Numerical results illustrated that the LSSVR-GA outperformed the three other forecasting models by around 14.84% averagely in terms of mean absolute percentage error (MAPE), weighted absolute percent error measure (WAPE), and normalized mean absolute error (NMAE). This study collected data from an IC packaging and testing firm in Taiwan. The innovation and advantage of the proposed method is using a machine approach to forecast RLC values instead of through simulation ways, which generates accurate results. Numerical results revealed that the developed ML model is effective and efficient in RLC circuit forecasting for the analog IC packaging and testing industry.

Upregulation of cluster of differentiation 36 mRNA expression in peripheral blood mononuclear cells correlates with frailty severity in older adults.

BACKGROUND: Aging-associated frailty has been connected to low-grade chronic inflammation and also to progressive monocytic activation. CD36 (cluster of differentiation 36, platelet glycoprotein 4 or fatty acid translocase) has been shown to induce the expression of pro-inflammatory cytokines and to activate macrophage connected inflammation. This study aims to examine whether the expression of CD36 is up-regulated among frail older adults. METHODS: The demographic data, Fried Frailty Index, metabolic and inflammatory parameters of our observational study were obtained from the comprehensive geriatric assessment programme of a hospital-based outpatient department. The mRNA isolated from the peripheral blood mononuclear cells (PBMCs) was used to determine the levels of CD36, tumour necrosis factor alpha (TNF-α), and CXC chemokine ligand-10 (CXCL10) mRNAs with real-time polymerase chain reaction (PCR). RESULTS: A total of 189 older adults (58% female) were included in the analysis, and the mean age was 77.19 ± 6.12 years. The numbers of participants who fitted in the groups of robust, pre-frail, and frail were 46, 106, and 37, respectively. Our data showed that CD36 mRNA expression levels in PBMCs were the highest in the frail group (1.25 ± 0.53 in robust, 2.13 ± 1.02 in pre-frail, and 2.78 ± 1.15 in frail group, P < 0.001). Further regression analyses revealed that CD36 mRNA levels were positively correlated with both the pre-frail and frailty status in the univariate analysis (both P's < 0.001). What might suggest something worthy of further investigation is that, with potential confounders being adjusted for, CD36 remained as an independent factor that positively correlated with the pre-frail and frailty status in the multivariable analysis (P < 0.001). CONCLUSIONS: CD36 mRNA levels in PBMCs in robust older adults are significantly lower than in pre-frail and in frail. Our findings suggest that CD36 mRNA levels in PBMCs may be considered a potential biomarker for frail severity.

[The Promotion of Resources Integration in Long-Term Care Service: The Experience of Taipei City Hospital].

The home-based medical care integrated plan under Taiwan National Health Insurance has changed from paying for home-based medical care, home-based nursing, home-based respiratory treatment, and palliative care to paying for a single, continuous home-based care service package. Formerly, physician-visit regulations limited home visits for home-based nursing to providing medical related assessments only. This limitation not only did not provide practical assistance to the public but also caused additional problems for those with mobility problems or who faced difficulties in making visits hospital. This 2016 change in regulations opens the door for doctors to step out their 'ivory tower', while offering the public more options to seek medical assistance in the hope that patients may change their health-seeking behavior. The home-based concept that underlies the medical service system is rooted deeply in the community in order to set up a sound, integrated model of community medical care. It is a critical issue to proceed with timely job handover confirmation with the connecting team and to provide patients with continuous-care services prior to discharge through the discharge-planning service and the connection with the connecting team. This is currently believed to be the only continuous home-based medical care integrated service model in the world. This model not only connects services such as health literacy, rehabilitation, home-based medical care, home-based nursing, community palliative care, and death but also integrates community resources, builds community resources networks, and provides high quality community care services.

Half-Managed Care: A Preliminary Assessment of a Capitation Program in a Health Care System Without Gatekeepers.

In 2011, a novel capitation program was launched in Taiwan under its universal health insurance plan. This study aimed to assess the short-term impact of the program. Two hospitals in the greater Taipei area, one participating in the "loyal patient" model (13,319 enrollees) and one in the "regional resident" model (13,768 enrollees), were analyzed. Two comparison groups were selected by propensity score matching. Generalized estimating equation models with differences-in-differences analysis were used to examine the net effects of the capitation program on health care utilization, expenses, and outcomes. Enrollees in the loyal patient model had fewer physician visits in the host hospital, but more physician visits outside that hospital during the program year than they had the year before. Compared with non-enrollees, the loyal patient model enrollees incurred fewer physician visits (ß = -0.042, p < .001), fewer emergency department visits, (ß = -0.140, p < .001), and similar total expenses and outcome. For the regional resident model, no differences were found in the number of physician visits, expenses, or outcomes between enrollees and non-enrollees. The novel capitation models in Taiwan had minimal impact on health care utilization after 1 year of implementation and the health care outcome was not compromised.

Establishment of a renal supportive care program: Experience from a rural community hospital in Taiwan.

Renal supportive care (RSC) denotes a care program dedicated for patients with acute, chronic renal failure, and end-stage renal disease (ESRD), aiming to offer maximal symptom relief and optimize patients' quality of life. The uncertainty of prognosis for patients with chronic kidney disease and ESRD, the sociocultural issues inherent to the Taiwanese society, and the void of structured and practical RSC pathway, contributes to the underrecognition and poor utilization of RSC. Taiwanese patients rarely receive information regarding RSC as part of a standardized care and are not commonly offered this option. In National Taiwan University Hospital Jinshan branch, we started a RSC subprogram, supported by the community-based palliative/hospice care main program. We focused on understanding the need and providing the choice of RSC to suitable candidates. A three-step and four-phase protocol was designed and implemented to identify appropriate patients and to enhance the applicability of the RSC. We harnessed family visit and home-based family meeting as a vehicle to understand the patients' preferences, to discover what ESRD patients and their family value most, and to introduce the option of RSC. In the current review, we described our pilot experience of establishing a RSC program in Taiwan, and discuss its potential advantage.

Patient Preferences versus Family Physicians' Perceptions Regarding the Place of End-of-Life Care and Death: A Nationwide Study in Taiwan.

BACKGROUND: Enabling people to die in their preferred place is important for providing high-quality end-of-life care. OBJECTIVE: The study objective was to explore patients' preferences regarding the place of end-of-life care and death and to compare these preferences with the perceptions of their family physicians. METHODS: This cross-sectional study used stratified random sampling, surveying 400 registered patients and 200 of their family physicians nationwide, with a five-part, structured, self-report questionnaire. RESULTS: Of the selected population, 310 patients (response rate 77.5%) and 169 physicians (response rate 84.5%) responded. Regarding the preferred place for end-of-life care, most of the patients would choose to receive care at home (60.6%) if home care services were available. Additionally, home was the most frequently preferred (66.5%) place of death. The family physicians' survey showed that a higher proportion of physicians selected home as the preferred place for end-of-life care and death (71.6% and 87.2%, respectively). The results of logistic regression analysis showed that patients younger than 50 years of age who believed in Chinese folk religion and who resided in a rural area were more likely to prefer to die at home. CONCLUSIONS: The most commonly preferred place for end-of-life care and death is the patient's home. Establishing a community-based palliative care system should be encouraged to allow more individuals to die in their preferred locations. There were discrepancies in the preferred place of end-of-life care and death between the patients' preferences and their family physicians' perceptions. More effective physician-patient communication regarding end-of-life care is needed.

[The present and future of community/home-based palliative care in Taiwan].

In Taiwan, the Department of Health (DOH) has implemented regulations and policies related to hospice and palliative care since 1995. Taiwan is the first country in Asia to have a Natural Death Act, promulgated in 2000. Although recognition of the need for palliative care in non-cancer terminally ill patients is increasing, at present, the needs of these patients are often not met. Moreover, while a majority of the population prefers to die at home, the percentage of patients who die in the home setting remains small. The palliative care system should be adjusted to improve the accessibility and continuity of care based on the needs of patients. Therefore, the Jin-Shan Branch of the National Taiwan University Hospital has run a pilot community palliative care service model since 2012. National Health Insurance reimbursement was introduced in 2014 for community-based palliative care services. Establishing a formal system of community-based palliative care should be encouraged in order to improve the quality of care at the end of life and to allow more patients to receive end-of-life care and die in their own communities. This system will require that skilled nurses provide discharge planning, symptoms control, end-of-life communications, social-resources integration, and social-support networks in order to achieve a high quality of end-of-life care.

Incidence, life expectancy and prognostic factors in cancer patients under prolonged mechanical ventilation: a nationwide analysis of 5,138 cases during 1998-2007.

INTRODUCTION: This study is aimed at determining the incidence, survival rate, life expectancy, quality-adjusted life expectancy (QALE) and prognostic factors in patients with cancer in different organ systems undergoing prolonged mechanical ventilation (PMV). METHODS: We used data from the National Health Insurance Research Database of Taiwan from 1998 to 2007 and linked it with the National Mortality Registry to ascertain mortality. Subjects who received PMV, defined as having undergone mechanical ventilation continuously for longer than 21 days, were enrolled. The incidence of cancer patients requiring PMV was calculated, with the exception of patients with multiple cancers. The life expectancies and QALE of patients with different types of cancer were estimated. Quality-of-life data were taken from a sample of 142 patients who received PMV. A multivariable proportional hazards model was constructed to assess the effect of different prognostic factors, including age, gender, type of cancer, metastasis, comorbidities and hospital levels. RESULTS: Among 9,011 cancer patients receiving mechanical ventilation for more than 7 days, 5,138 undergoing PMV had a median survival of 1.37 months (interquartile range [IQR], 0.50 to 4.57) and a 1-yr survival rate of 14.3% (95% confidence interval [CI], 13.3% to 15.3%). The incidence of PMV was 10.4 per 100 ICU admissions. Head and neck cancer patients seemed to survive the longest. The overall life expectancy was 1.21 years, with estimated QALE ranging from 0.17 to 0.37 quality-adjusted life years for patients with poor and partial cognition, respectively. Cancer of liver (hazard ratio [HR], 1.55; 95% CI, 1.34 to 1.78), lung (HR, 1.45; 95% CI, 1.30 to 1.41) and metastasis (HR, 1.53; 95% CI, 1.42 to 1.65) were found to predict shorter survival independently. CONCLUSIONS: Cancer patients requiring PMV had poor long-term outcomes. Palliative care should be considered early in these patients, especially when metastasis has occurred.

Effect of a compassion-focused training program in palliative care education for medical students.

BACKGROUND: Compassion is the key value of humanities perspective. Little is known, however, concerning the impact of enhancing compassion on ethical decision making in end-of-life care. METHODS: A total of 251 preclinical medical students were enrolled in a palliative care training course. A structured self-report questionnaire was administered before and after training. RESULTS: Experience with caring for patients with terminal cancer was positively related to improvement in the decision of "truth telling is helpful to a good death." In addition, improvement in the perception of "compassionate care" was correlated with higher improvement in the decision of "discharge planning and home care." CONCLUSION: Compassion-focused training program can be helpful to improve medical students' competence in making more appropriate ethical decisions in end-of-life care.

What factors are important in increasing junior doctors' willingness to provide palliative care in Taiwan? An educational intervention study.

PURPOSE: To explore the factors that influence the willingness of junior doctors to provide palliative care in future clinical practice and to evaluate the effect of palliative care education in ameliorating these factors. METHODS: A 2-week clinical training course consisting of clinical patient care, multidisciplinary team meetings, home visits, and lecture series was conducted for interns in a palliative care unit of a university hospital. A structured self-report questionnaire was administered before and after the training. RESULTS: One hundred forty-seven interns were enrolled and completed the questionnaire, which demonstrated significant improvements in knowledge and beliefs toward palliative care after the training (p < 0.001). Although the interns' willingness to provide palliative medical care was influenced by relatives' support before the training (odds ratio [OR] = 3.861, 95% confidence interval [CI] = 1.217-12.243), this relationship vanished after training. A higher willingness to provide home visits was noted in participants who received palliative care education at medical school. In contrast, information received from mass media was shown to be negatively correlated with willingness. After the training, the only independent variable that was positively correlated with interns' willingness to provide home visits was their own beliefs on palliative care (OR = 1.074, 95% CI = 1.058-8.089). CONCLUSIONS: Educating junior doctors to build positive beliefs toward palliative care is strongly encouraged. Palliative care education contributes to increased junior doctors' willingness toward providing palliative care, which suggests that this kind of training course should be emphasized in medical education.