The study was undertaken to evaluate whether the Physiological Cost Index (PCI) can be used as a reliable index of efficiency of gait and as an outcome measure in cerebral palsy (CP). Physiological Cost Index was calculated in normal subjects by recording the heart rate manually and with electrocardiograph recording, and the values compared. In another group of subjects, PCI was calculated after they walked 3 different distances (50, 100, and 150 m). The PCI of normal children and children with CP was then estimated by manual recording of the pulse, with the children walking 50 m indoors and 50 m on an uneven surface outdoors. The reproducibility of calculation of PCI was evaluated. The PCI value of each patient was compared to the corresponding Functional Mobility Score. In a group of children with CP, PCI was calculated before and after therapeutic intervention. The PCI values were comparable with either method of heart rate measurement and for the 3 distances walked. The reproducibility of measurement of PCI was satisfactory (Intraclass Correlation Coefficients, 0.80-0.88). The PCI of normal children was 0.1 beats per meter, whereas children with CP had 6 times higher values of PCI, with the highest values in children with a crouch gait. In normal children, 10% greater PCI values were noted when they walked outdoors compared to a 100% increase in children with CP. The higher the PCI values, the lower the Functional Mobility Scores. Therapeutic interventions altered PCI values, and interventions that effectively reduced energy consumption could be identified. We conclude that PCI may be used as a reliable outcome measure of gait efficiency in children with CP.
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http://dx.doi.org/10.1097/01.bpb.0000242440.96434.26 | DOI Listing |
Int J Cardiol Heart Vasc
February 2025
Department of Cardiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225000, China.
Background: Thrombolysis in Myocardial Infarction (TIMI) risk score in patients with ST-segment elevation myocardial infarction (STEMI) is associated with major adverse cardiovascular events (MACE). This study aimed to develop a prediction model based on the TIMI risk score for MACE in STEMI patients after percutaneous coronary intervention (PCI).
Methods: We conducted a retrospective data analysis on 290 acute STEMI patients admitted to the Affiliated Hospital of Yangzhou University from January 2022 to June 2023 and met the inclusion criteria.
Rev Cardiovasc Med
January 2025
Department of Cardiology, The Second Affiliated Hospital of Xuzhou Medical University, 221000 Xuzhou, Jiangsu, China.
Background: This study aimed to analyze the metabolic risk factors for microcirculation disorders in patients with unstable angina (UA) after percutaneous coronary intervention (PCI), evaluating their predictive value for developing microcirculation disorders.
Methods: A single-center retrospective study design was used, which included 553 patients with UA who underwent PCI. The angiographic microcirculatory resistance (AMR) index was calculated based on coronary angiography data.
Front Cardiovasc Med
January 2025
Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Department of Cardiology, Beijing Anzhen Hospital, Beijing Institute of Heart Lung and Blood Vessel Disease, Clinical Center for Coronary Heart Disease, Capital Medical University, Beijing, China.
Background: The prognostic value of triglyceride-glucose (TyG) has been well described in patients with coronary artery disease (CAD). Hyperhomocysteinemia (HHcy) promotes insulin resistance and has also been regarded as a potential risk factor for cardiovascular disease. However, the prognostic value of TyG in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) and the interaction between TyG and HHcy remain unclear.
View Article and Find Full Text PDFBMC Cardiovasc Disord
January 2025
Department of Cardiology, 920th Hospital of Joint Logistics Support Force, People's Liberation Army of China (PLA), Kunming, Yunnan, China.
Objective: This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm.
Methods: AMI patients who underwent PCI were recruited and randomly divided into non/ACE groups. Inflammatory and nutritional indices were graded according to the laboratory examination reports.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
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