IntroductionIn low-to-middle-income countries (LMIC), the orthogeriatric model of care is still in its early stages of development. This study describes the initial results of the first online fragility hip fracture database to be setup in the Philippines using a modified minimum common dataset to generate outcomes data based on current hospital practices.MethodsA multicentre prospective cohort study among 12 Philippine hospitals was conducted from June 2020 to February 2021. Thirty-day mortality, morbidity and mobility were measured. Significant factors associated with mortality were determined.Results158 elderly patients with fragility hip fractures were included in the study. Nine patients (5.7%) were confirmed or suspected to have COVID-19 infection. Median time of injury to admission was at least 3 days (IQR: 1.0–13.7). Overall, 80% of patients underwent surgical intervention with a median time from admission to surgery of at least 5 days (IQR: 2.5–13.6). Thirty-day mortality and morbidity rates for acute fragility fractures were 3.7%. Factors significantly associated with early mortality were poor prefracture mobility, COVID-19 infection, radiograph of the abnormal chest and conservative treatment. Non-surgical patients had no functional mobility or were wheelchair users and had a significantly higher morbidity rate than surgically treated patients (13.6% vs 1.8%; p=0.031).ConclusionDespite treatment delays unique to an LMIC, short-term outcomes remain favourable for non-COVID-19 fragility hip fracture patients treated with surgery. Prompt admission and multidisciplinary care for elderly hip fracture patients while maintaining protective measures for COVID-19 infection control are recommended. The quality of data collected illustrates how this online database can provide a framework for a sustainable audit or registry as well as provide a platform for the introduction of orthogeriatric concepts at a multiregional scale.

Abstract BACKGROUND: Acute respiratory tract infections (ARI) are the most prevalent illness in people of all ages, and they are a leading cause of hospitalization and death. Molecular testing methods have significantly expanded the ability to diagnose respiratory infections. Rapid viral testing aims to prompt the diagnosis of viral infections that could lead to faster hospital discharge, lower healthcare resource use and clinicians are guided on the judicious use of antibiotics, as well as greater isolation precautions. The objective of this study was to determine the clinical impact of the Film Array (Biofire) Respiratory Panel utilization on the outcomes of pediatric patients with acute viral respiratory infection METHODOLOGY: This is a cross-sectional analytic study, conducted in two private tertiary hospitals. Study population includes admitted patients aged 1-18 years old with acute respiratory infection and then divided into two groups: exposure group (with Biofire taken) and non-exposure group (without Biofire taken). Retrospective chart review was done on the admitted patients and analyzed using descriptive and inferential statistics. RESULTS: A total of 220 samples were included. Majority of patients in both groups were female, aged 1-5 years old, lived in an urban locality and with no influenza vaccination. The most single common virus detected was hRV/hEV(n=29%), while the most common codetection virus is hRV/hEV with influenza B (n=23.5%). For those who underwent the test, patients were frequently admitted in the year 2021 (n=90%) and month of July (n=28.2%). Utilization of the respiratory panel was associated with significant changes in medical management including decreased antibiotic usage (P=0.001) and shorter length of hospital stay (P=0.029),compared to those patients who didn’t undergo the test. CONCLUSION: The Film Array (Biofire) respiratory panel is useful in assisting clinical judgement regarding the usage of antibiotic as well as the length of hospitalization among children affected by acute respiratory infections.

Diabetes is prevalent, and it imposes a substantial public health burden globally and in the Asia-Pacific (APAC) region. The cornerstone for optimizing diabetes management and treatment outcomes is glucose monitoring, the techniques of which have evolved from self-monitoring of blood glucose (SMBG) to glycated hemoglobin (HbA1c), and to continuous glucose monitoring (CGM). Contextual differences with Western populations and limited regionally generated clinical evidence warrant regional standards of diabetes care, including glucose monitoring in APAC. Hence, the APAC Diabetes Care Advisory Board convened to gather insights into clinician-reported CGM utilization for optimized glucose monitoring and diabetes management in the region. We discuss the findings from a pre-meeting survey and an expert panel meeting regarding glucose monitoring patterns and influencing factors, patient profiles for CGM initiation and continuation, CGM benefits, and CGM optimization challenges and potential solutions in APAC. While CGM is becoming the new standard of care and a useful adjunct to HbA1c and SMBG globally, glucose monitoring type, timing, and frequency should be individualized according to local and patient-specific contexts. The results of this APAC survey guide methods for the formulation of future APAC-specific consensus guidelines for the application of CGM in people living with diabetes.

In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan's fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms-random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting-to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country's fertility rate. This study should also be of value to follow-up research.

The outbreak of severe acute respiratory syndrome (SARS) in 2003 in Taiwan impacted Taiwanese society. However, the first case of coronavirus disease 2019 (COVID-19) was reported in Wuhan and spread around the world. During these outbreaks, nursing staff experienced different levels of pressure. Studies have explored the stress and adjustment of nurses during these periods, but studies describing the feelings of nurses during both SARS and COVID-19 outbreaks are lacking. The aim of this study was to explore the experiences of nurses who had cared for both SARS and COVID-19 patients. A qualitative study combined with snowball sampling was applied. Semi-structured questions were used to interview 10 nurses who had experienced both SARS and COVID-19. Two themes and four sub-themes were analyzed, which were: facing the epidemic from the unknown to known; and the experiences from ignorance to proficiency. The sub-themes were: the feeling of frustration and concern; bottlenecks and pressures in my work; my mission and support; and positive energy and camaraderie. The results showed that the media acts as an important resource during disease outbreaks; therefore, government departments have to use their wisdom to make good use of the media. Secondly, understanding the general public's response to the disease is also important for first-line nurses. Finally, on-the-job education and guidelines for first-line nurses are necessary, and support from the administration is also important.