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Background And Objectives: Cardiac resynchronization therapy (CRT) has been known to improve the outcome of advanced heart failure (HF) but is still underutilized in clinical practice. We investigated the prognosis of patients with advanced HF who were suitable for CRT but were treated with conventional strategies. We also developed a risk model to predict mortality to improve the facilitation of CRT.
Subjects And Methods: Patients with symptomatic HF with left ventricular ejection fraction ≤35% and QRS interval >120 ms were consecutively enrolled at cardiovascular hospital. After excluding those patients who had received device therapy, 239 patients (160 males, mean 67±11 years) were eventually recruited.
Results: During a follow-up of 308±236 days, 56 (23%) patients died. Prior stroke, heart rate >90 bpm, serum Na ≤135 mEq/L, and serum creatinine ≥1.5 mg/dL were identified as independent factors using Cox proportional hazards regression. Based on the risk model, points were assigned to each of the risk factors proportional to the regression coefficient, and patients were stratified into three risk groups: low- (0), intermediate-(1-5), and high-risk (>5 points). The 2-year mortality rates of each risk group were 5, 31, and 64 percent, respectively. The C statistic of the risk model was 0.78, and the model was validated in a cohort from a different institution where the C statistic was 0.80.
Conclusion: The mortality of patients with advanced HF who were managed conventionally was effectively stratified using a risk model. It may be useful for clinicians to be more proactive about adopting CRT to improve patient prognosis.
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http://dx.doi.org/10.4070/kcj.2012.42.10.659 | DOI Listing |
J Orthop Res
September 2025
Institute of Orthopaedic Research and Biomechanics, University Medical Center Ulm, Ulm, Germany.
Osteoporotic hip fractures are a considerable cause of pain and disability particularly among the elderly. Osteoporosis causes loss of bone stability, which in turn leads to an increased risk of fractures especially in metaphyseal bone. Moreover, the body's capacity for healing is diminished, resulting in prolonged recovery times following these fractures.
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Florida Orthopaedic Institute, Gainesville, FL, 32607, USA.
Background: A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e.
View Article and Find Full Text PDFGeroscience
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Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden.
To evaluate a simplified version of the Clinical Frailty Scale (SCFS) among older adults presenting to the emergency department (ED) with acute dyspnea. In this retrospective single-center cohort study, we included patients from the Acute Dyspnea Study (ADYS) cohort. Severity of illness was assessed using the Medical Emergency Triage and Treatment System (METTS).
View Article and Find Full Text PDFGeroscience
September 2025
Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
This study aims to investigate the predictive value of combined phenotypic age and phenotypic age acceleration (PhenoAgeAccel) for benign prostatic hyperplasia (BPH) and develop a machine learning-based risk prediction model to inform precision prevention and clinical management strategies. The study analyzed data from 784 male participants in the US National Health and Nutrition Examination Survey (NHANES, 2001-2008). Phenotypic age was derived from chronological age and nine serum biomarkers.
View Article and Find Full Text PDFBariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.
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