Background Palliative Performance Scale (PPS) has been frequently used to estimate the survival time of palliative care patients. The objective was to determine the associations between the PPS and survival time among cancer and non-cancer patients in Thailand. Methods This is a retrospective cohort study. All in-patient adults who received a palliative care consultation at Chiang Mai University Hospital between 1 July 2018 to 31 July 2019 were included in the study and were followed-up until 26 June 2020. The Palliative Performance Scale was assessed using the validated Thai-Palliative Performance Scale for Adults. Survival analysis was used to determine the association between the Palliative Performance Scale and survival time among cancer and non-cancer patients. Results Out of 407 patients, 220 were male (54.1%). There were 307 cancer patients (75.4%) and 100 non-cancer patients (24.6%). The PPS and survival time in cancer patients were significantly correlated. Cancer patients with PPS 10, 20, 30, 40–60, and 70–80% had a median survival time of 2, 6, 13, 39, and 95 days, respectively. Non-cancer patients with PPS 10, 20, and 30% had a median survival time of 8, 6, and 24 days, respectively. Conclusions While useful for estimating survival time for cancer patients, other factors should be taken into account in estimating the survival time for non-cancer patients.

Background: Multimorbidity, defined as the coexistence of two or more chronic conditions in the same individual, is becoming a crucial health issue in primary care. Patients with multimorbidity utilize health care at a higher rate and have higher mortality rates and poorer quality of life compared to patients with single diseases. Aims: To explore evidence on how to advance multimorbidity management, with a focus on primary care. Primary care is where a large number of patients with multimorbidity are managed and is considered to be a gatekeeper in many health systems. Methods: A narrative review was conducted using four major electronic databases consisting of PubMed, Cochrane, World Health Organization database, and Google scholar. In the first round of reviews, priority was given to review papers summarizing the current issues and challenges in the management of multimorbidity. Thematic analysis using an inductive approach was used to build a framework on how to advance management. The second round of review focused on original articles providing evidence within the primary care context. Results: The review found that advancing multimorbidity management in primary care requires a health system approach and a patient-centered approach. The health systems approach includes three major areas: (i) improves access to care, (ii) promotes generalism, and (iii) provides a decision support system. For the patient-centered approach, four key aspects are essential for multimorbidity management: (i) promoting doctor-patient relationship, (ii) prioritizing health problems and sharing decision-making, (iii) supporting self-management, and (iv) integrating care. Advancement of multimorbidity management in primary care requires integrating concepts of multimorbidity management guidelines with concepts of patient-centered and chronic care models. This simple integration provides an overarching framework for advancing the health care system, connecting the processes of individualized care plans, and integrating care with other providers, family members, and the community.

No abstract

Individuals with metabolic risks are at high risk of cognitive impairment. We aimed to investigate whether the Thai Cardiovascular Risk (TCVR) score can be used to predict mild cognitive impairment (MCI) in Thai adults with metabolic risks. The study was conducted using secondary data of patients with metabolic risks from Maharaj Nakorn Chiang Mai Hospital. MCI was indicated by an MoCA score of less than 25. Six different TCVR models were used with various combinations of ten different variables for predicting the risk of MCI. The area under the receiver operator characteristic curve (AuROC) and Hosmer–Lemeshow goodness of fit tests were used for determining discriminative performance and model calibration. The sensitivity of the discriminative performance was further evaluated by stratifying by age and gender. From a total of 421 participants, 348 participants had MCI. All six TCVR models showed a similar AuROC, varying between 0.58 and 0.61. The anthropometric-based model showed the best risk prediction performance in the older age group (AuROC 0.69). The laboratory-based model provided the highest discriminative performance for the younger age group (AuROC 0.60). There is potential for the development of an MCI risk model based on values from routine cardiovascular risk assessments among patients with metabolic risks.

Background Pneumonia in cancer patients is often problematic in order to decide whether to admit and administer antibiotics or pursue a comfort care pathway that may avoid in-hospital death. We aimed to identify factors which are easily assessed at admission in Thailand’s healthcare context that could serve as prognostic factors for in-hospital death. Methods Regression analysis was utilized to identify the prognostic factors from clinical factors collected at admission. The primary outcome was in-hospital death. Data was collected from the electronic medical records of Chiang Mai University Hospital, Thailand, from 2016 to 2017. Data on adult cancer patients admitted due to pneumonia were reviewed. Results In total, 245 patients were included, and 146 (59.6%) were male. The median age of the patients was 66 years (IQR: 57–75). A total of 72 (29.4%) patients died during admission. From multivariate logistic regression, prognostic factors for in-hospital death included: Palliative Performance Scale (PPS) ≤ 30 (OR: 8.47, 95% CI: 3.47–20.66), Palliative Performance Scale 40–50% (OR: 2.79, 95% CI: 1.34–5.81), percentage of lymphocytes ≤ 8.0% (OR: 2.10, 95% CI: 1.08–4.08), and pulse oximetry ≤ 90% (OR: 2.01, 95% CI: 1.04–3.87). Conclusion The in-hospital death rate of cancer patients admitted with pneumonia was approximately 30%. The PPS of 10–30%, PPS of 40–50%, percentage of lymphocytes ≤ 8%, and oxygen saturation < 90% could serve as prognostic factors for in-hospital death. Further prospective studies are needed to investigate the usefulness of these factors.