Risk Assessment

This is a single-center prospective observational cohort study enrolling adult patients scheduled for cardiac surgery. Multi-timepoint clinical indicators, stool samples for gut microbiota and metabolomics profiling and artery blood will be collected before and after surgery. We aim to construct and validate a multi-dimensional preoperative predictive model for CSA-AKI based on clinical, microbial and metabolic signatures. Meanwhile, this research will explore the potential mediating role of the gut-kidney axis in the pathogenesis of cardiac surgery-associated acute kidney injury, and provide novel mechanistic evidence for early risk stratification and intervention of CSA-AKI.

This study is a pilot trial that builds on findings from the validation of prostate cancer risk calculators in Nigerian men. The goal of the overall study is to improve the early detection of prostate cancer in a high-risk population. The main questions the validation study aims to answer are: 1. How accurately do existing prostate cancer risk calculators identify Nigerian men with clinically significant prostate cancer? 2. Will a new risk calculator designed for Nigerian men more accurately identify those with clinically significant prostate cancer? The main questions the intervention study aims to answer is: Will the primary care provider-facing risk calculator be feasible and acceptable for primary care providers to implement? The intervention trial will be piloted among participants in community-level hospitals in order to primarily assess implementation outcomes

This prospective cohort study aims to develop and validate a personalized disease risk prediction model for adults by integrating multiple sources of health data. The study will recruit community-dwelling adults aged 18 years and older in Taiwan. After providing informed consent, participants will complete a structured questionnaire, undergo pure tone hearing testing, and wear a smartwatch for 2 weeks to collect continuous physiological data, including heart rate and physical activity. With participant authorization, the study will also collect data from personal health records and national health insurance databases to allow longer-term follow-up of health outcomes. The main goals of the study are to examine the relationships among hearing, lifestyle factors, and wearable device data; to identify combinations of risk factors associated with progression from health to subclinical or chronic disease states; and to develop analytical methods for integrating heterogeneous health data from questionnaires, physiological monitoring, hearing tests, and medical databases. Machine learning methods will be used to identify important predictors and build risk prediction models. The study hypothesis is that combining hearing measures, lifestyle information, wearable physiological data, and longitudinal medical record data will improve the ability to identify individuals at higher risk of future disease compared with using a single source of information alone. The long-term objective is to support early risk identification, personalized health management, and prevention strategies in community adults.

Dilated cardiomyopathy (DCM) is a common and serious heart disease characterized by left ventricular enlargement and impaired pumping function, with adverse prognosis (including heart failure, arrhythmia, heart-related hospitalization, and death) being a major concern for patients. Currently, a critical gap exists in accurately predicting which DCM patients are at high risk of these severe outcomes, limiting targeted clinical care. This observational, non-invasive study aims to develop and validate a clinical prediction model for early risk warning of adverse prognosis in DCM patients. The model integrates multi-parameter stress perfusion cardiac magnetic resonance (MP stress perfusion CMR)-a safe, high-resolution imaging technique that assesses cardiac structure, function, blood perfusion, and tissue damage under mild stress-and standard clinical data (e.g., age, gender, blood pressure, and routine heart test results). The model will be trained and tested using follow-up data from hundreds of DCM patients, with the analysis identifying patterns in CMR and clinical data associated with adverse outcomes. Once validated for accuracy, the model will provide doctors with personalized risk scores to prioritize care for high-risk patients (e.g., early intervention, close monitoring) and avoid over-treatment for lower-risk individuals. Beyond clinical application, the study will enhance understanding of DCM progression, laying the groundwork for improved diagnostic tools, more effective treatments, and better strategies to prevent DCM-related complications, ultimately improving patient quality of life and reducing mortality.

The objective of this study is to determine whether healthcare data and remotely collected patient data can accurately predict asthma attacks in children and young people aged 5-17 years. The main outcome is: when using this new system, is there a reduction in asthma attacks compared with a historic average. The whole population of children and young people with asthma will have routine healthcare data monitored, with a subset of people with high risk asthma asked to participate in a more detail study involving remotely monitored data.

With the global rates of gynecologic cancers on the rise, optimizing perioperative care is imperative. Accurate risk prediction is essential for enhancing patient care, directing preoperative interventions, and facilitating informed decision-making in oncology. This research compares two widely-used risk assessment tools: the Charlson Comorbidity Index (CCI) and the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM), in predicting perioperative outcomes. The CCI predominantly addresses comorbidities, providing simplicity and broad applicability, while POSSUM incorporates both physiological and operative factors for a more comprehensive risk assessment. Despite their application across various surgical specialties, the specific utility of these tools in onco-gynecologic surgery remains insufficiently explored. The study aims to evaluate the effectiveness of CCI and POSSUM in predicting perioperative complications, with a focus on the incidence of these complications, length of hospital stay, and 30-day mortality. The implementation of these risk tools may enhance multidisciplinary risk management, thus improving patient outcomes in gynecologic oncology surgery.

Cranial defects often result from brain injuries, hemorrhages, strokes, or brain tumors. These conditions can increase pressure inside the skull, and if left untreated, may lead to dangerous complications like brain herniation. To manage this, a common procedure called decompressive craniectomy is performed to reduce intracranial pressure. While this surgery often stabilizes the patient's condition, it leaves a cranial defect that exposes the brain to external risks, including pressure fluctuations and potential damage. In severe cases, patients with larger defects may develop complications such as sinking skin flap syndrome. Cranial reconstruction, also known as cranioplasty, is an important procedure to restore the skull's structure and protect the brain. This surgery can improve brain function, stabilize intracranial pressure, and enhance the patient's appearance. While cranioplasty is a standard neurosurgical procedure, it has a relatively high risk of complications compared to other brain surgeries. Common complications include infections, bleeding, hydrocephalus, and seizures. In severe cases, complications may lead to the failure of the reconstruction. Understanding the factors that contribute to complications after cranioplasty is crucial for neurosurgeons to improve outcomes and reduce risks. This study aims to identify these factors and develop predictive models for postoperative complications of cranioplasty.

The primary objective of this study is to investigate the impact of preoperative focused transthoracic ultrasound (FOCUS) on intraoperative hypotension and postoperative complications in hip fracture surgery. Our hypothesis is that a preoperative FOCUS along with a hemodynamic optimization protocol will reduce the occurrence of intraoperative drops in blood pressure and post-operative complications.