AIMC Topic: Risk Factors

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Incidence and risk factors of inguinal hernia after robot-assisted radical prostatectomy: a retrospective multicenter cohort study in Japan (the MSUG94 group).

Journal of robotic surgery
To investigate the incidence and risk factors of inguinal hernia (IH) after robot-assisted radical prostatectomy (RARP) using a multicentric database. The present study used a multicentric database (the MSUG94) containing data on 3,195 Japanese patie...

Incidence, Determinants, and Outcome of Contrast-induced Acute Kidney Injury following Percutaneous Coronary Intervention at a Tertiary Care Hospital.

Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
Contrast-induced acute kidney injury (CI-AKI) after percutaneous coronary intervention (PCI) is the common cause of in-hospital acquired AKI and is associated with in-hospital mortality and prolonged hospital stay. We studied the incidence of CI-AKI ...

Prevalence of chronic kidney disease and metabolic related indicators in Mianzhu, Sichuan, China.

Frontiers in public health
BACKGROUND: Chronic kidney disease (CKD) is a major public health problem worldwide. Periodic surveys are essential for monitoring the prevalence of CKD and its risk factors. We assessed the prevalence of CKD and its risk factors in Mianzhu City in 2...

Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units.

Revista espanola de cardiologia (English ed.)
INTRODUCTION AND OBJECTIVES: Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predi...

Time-domain heart rate dynamics in the prognosis of progressive atherosclerosis.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIM: The regular uptake of a high-fat diet (HFD) with changing lifestyle causes atherosclerosis leading to cardiovascular diseases and autonomic dysfunction. Therefore, the current study aimed to investigate the correlation of autonomi...

Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

Journal of the American Geriatrics Society
BACKGROUND: While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screen...

Robot-assisted percutaneous vertebroplasty for osteoporotic vertebral compression fracture treatment and risk factor screening for postoperative refracture.

Journal of robotic surgery
Osteoporotic vertebral compression fracture (OVCF) is a serious complication of osteoporosis, and percutaneous vertebroplasty (PVP) is a major therapeutic method for OVCF. This study aimed to evaluate the clinical efficacy and postoperative complicat...

An integrated model incorporating deep learning, hand-crafted radiomics and clinical and US features to diagnose central lymph node metastasis in patients with papillary thyroid cancer.

BMC cancer
OBJECTIVE: To evaluate the value of an integrated model incorporating deep learning (DL), hand-crafted radiomics and clinical and US imaging features for diagnosing central lymph node metastasis (CLNM) in patients with papillary thyroid cancer (PTC).

Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning.

Pediatric research
BACKGROUND: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma.

Predicting low cognitive ability at age 5 years using perinatal data and machine learning.

Pediatric research
BACKGROUND: There are no early, accurate, scalable methods for identifying infants at high risk of poor cognitive outcomes in childhood. We aim to develop an explainable predictive model, using machine learning and population-based cohort data, for t...