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ObjectiveAs patient life expectancy has increased and people are living longer than before, the rate of mechanical ventilation among elderly patients in the intensive care unit has increased. Older patients who receive mechanical ventilation and have multiple comorbidities are more likely to have a do not resuscitate order than are younger patients with fewer comorbidities. The aim of our study was to describe the patient characteristics and predictive factors of do not resuscitate orders during hospitalization among elderly patients who received ventilation in the intensive care unit.MethodsThis was a retrospective review of the electronic medical records of patients in the intensive care unit of a teaching hospital in southern Taiwan. We enrolled patients admitted to the general intensive care unit from January 1, 2018, to September 31, 2020, and patients older than 80 years who experienced respiratory failure, were intubated and received mechanical ventilation. We analyzed patient demographics, disease severity during hospitalization and comorbidities. If a patient had multiple admissions to the intensive care unit, only the first admission was recorded.ResultsOf the 305 patients over 80 years of age with respiratory failure who were intubated and placed on a ventilator, 66 were excluded because of incomplete data, and 13 were excluded because they had already signed a do not resuscitate order prior to admission to the hospital. Ultimately, 226 patients were included in this study. A higher acute physiology and chronic health evaluation II score (>30) was also associated with an increased likelihood of a do not resuscitate order (odds ratio (OR) = 3.85, 95% CI = 1.09–13.62, p = 0.0362). Patients who had acute kidney injury or cerebrovascular accident were more likely to have a do not resuscitate order (OR = 2.74, 95% CI = 1.03–7.28, p = 0.0428 and OR = 7.32, 95% CI = 2.02–26.49, p = 0.0024, respectively).ConclusionOur study showed that older age, greater disease severity, and certain critical interventions were associated with a greater propensity for do not resuscitate orders, which is crucial for understanding patient preferences and guiding end-of-life care discussions. These findings highlight the importance of clinical severity and specific health events in predicting end-of-life care preferences in older patient groups.

Abstract Miliary lung metastasis is a unique feature of lung metastasis in non-small cell lung cancer (NSCLC), indicating hematogenous dissemination. Some studies reported more frequency of epidermal growth factor receptor (EGFR) mutation and worse prognosis in these patients. Cases were identified from Chi-Mei medical center cancer registry for the period 2015–2019. Inclusion criteria were NSCLC with contra-lateral lung metastasis harboring EGFR mutation, under tyrosine kinase inhibitor (TKI) prescription. Patients with miliary or non-miliary lung metastasis were enrolled for survival analysis. 182 NSCLC patients were enrolled for assessing time to discontinuation of TKI (TD-TKI), progression-free survival (PFS) and overall survival (OS). 54 patients with miliary lung metastasis had average 13.2 months [95% confidence interval (CI) 10.7–15.6] of TD-TKI, 11.4 months (95% CI 9.3–13.6) of PFS, and 21.3 months (95% CI 16.8–25.8) of OS, which were shorter than non-miliary group with marginally statistical significance. In multivariate analysis, miliary lung metastasis had no statistical significance, and other strong prognostic indicators were found including performance status, liver metastasis, EGFR type, and generation of TKI. In NSCLC patients harboring EGRF mutation under TKI prescription, miliary lung metastasis was not a dominant indicator for outcomes evaluation.

Since the fall was often occurred in elders daily, this paper focused on gait analysis with fall detection to develop a wearable device. To ensure that the mobile application, APP, could be used in different platform of mobile phone, such Android or iOS, the designed wearable device also could be used in cross-platform in mobile phone. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed. Since CPGAFDWD APP was used in web browser without limiting to platform, it could be used for different platforms of mobile phone. The gait analysis could be detected at home. The fall detection also could be executed in any place immediately. The patients and medical staff all could query the status of rehabilitation in any place and any time via the Internet. The experimental results showed that the correct rate of gait analysis and fall detection could be up to 90% in cross-platform of mobile phone. In the future, CPGAFDWD will be planned to be verified by Institutional Review Board, IRB, for clinical treatment.

The grey Riccati model (GRM) is a generalization of the grey Verhulst model (GVM). Both the GRM and GVM generally perform well in simulating and forecasting the raw sequences with a bell-shaped or single peak feature. Although there are several methods to solve the Riccati differential equation, the existing time response functions of the GRM are generally complicated. In order to reduce the computational complexity of the time response function, this study attempts to transform the Riccati equation into a Bernoulli equation with the help of a known particular solution. Then, a unified time response function of the GRM is obtained by the proposed methodology. The simulation results demonstrate that the proposed unified grey Riccati model performs the same as the grey generalized Verhulst model (a kind of grey Riccati model) and is better than the traditional grey Verhulst model. The fact also reveals that the newly developed grey Riccati model is reasonable and effective.