Clinical Research Directory

Prediction Model for Postoperative Headache After Transsphenoidal Pituitary Surgery

Primary Objective: This observational study aims to construct a predictive model for short-term headache following endonasal pituitary adenoma surgery and to identify risk factors associated with postoperative headache after endonasal surgery. Secondary Objectives: First, to investigate the relationship between the severity of short-term postoperative headache and long-term life burden (at 1 and 3 months postoperatively), as well as its correlation with quality of life. Second, to elucidate the clinical characteristics and evolutionary patterns of short-term postoperative headache. Third, to explore key aspects of perioperative management, including changes in nasal cavity status and postoperative mobilization, which may optimize the management of short-term postoperative headache. Primary outcome measure1: VAS scale（0-10） Primary outcome measure2: feature of postoperative headache including (location, type, length, accompany symptom, and factors that elevate or sharpen headache, analgesics usage, analgesics frequency, analgesics effect) Secondary outcome measure 1: HIT-6 test Secondary outcome measure 2: Postoperative Olfaction Secondary outcome measure 3: Postoperative Massive Epistaxis Participants will undergo daily assessments postoperatively, which include evaluations of headache, nasal cavity status, and analgesic drug usage. These assessments will continue until two consecutive Visual Analog Scale (VAS) scores are less than 4. Additionally, participants will complete questionnaires, including the Headache Impact (HIT-6) test at 4 weeks and 12 weeks postoperatively.

Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery

The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is: Can a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery? Patients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.

The Fit With Us Study

The purpose of this 32-week study is to use an innovative experimental design known as SMART (Sequential Multiple Assignment Randomized Trial), which will allow us to determine the best way to sequence the delivery of teleexercise (referred to as an adaptive intervention), combined with predictive analytics on participant adherence in a stepped program of physical activity interventions. All 257 participants will have access to a library of recorded video exercise content, and a weekly wellness article. Some participants will receive health coaching calls (1st randomization). Analytic data will be used to determine which participants are responding or not responding to the intervention. Participants not responding after 4 weeks will receive either live one-on-one or group exercise training (2nd randomization). After 8 weeks, the participant will receive only pre-recorded exercise content and articles for a 24-week maintenance phase (weeks 9-32). The study outcomes are: The effectiveness of the adaptive interventions; Exploring mediating and moderating variables; Sensitivity analysis of the predictive analytics.

Clinical Decision Support to Identify Pediatric Patients With Undiagnosed Genetic Disease

This study will evaluate the effectiveness of SIGHT as a clinical support system to prompt provider/patient discussion and shared decision making regarding the need for genetic testing in the form of a chromosomal microarray. Identifying patients at high predicted probability of needing a test in clinical settings will be examined to determine if it decreases the duration of time to testing and increases diagnostic yield. SIGHT requires only data already collected in routine clinical encounters and is calculated prior to a clinical visit at VUMC.