Background
Studies on the incidence and predictors of heart failure (HF) are often restricted to elderly persons or identify only inpatient cases.
Methods and Results
We determined the incidence and predictors of new HF diagnosed in either outpatient or inpatient settings, among 359 947 women and men (age ≥18 years) insured by Kaiser Permanente Georgia at any time during calendar years 2000 to 2005. Subjects were free of HF at baseline, and incident HF was identified with ICD-9 codes (1 inpatient or 2 outpatient HF visits). We developed multivariable Cox models to assess the association of antecedent factors (coronary heart disease, hypertension, diabetes mellitus, atrial fibrillation, and valvular heart disease) with incident HF. Separate models were created for each sex and for newly diagnosed HF in outpatient or inpatient settings. There were 4001 incident HF cases (50% women and 48% in subjects <65 years old), during 1 015 794 person-years of follow-up. The incidence rate of HF was greater in men than in women (4.24 versus 3.68 per 1000 person-years) but was stable across the study interval in both sexes. Two thirds of incident HF cases from this population occurred in outpatients. These 5 antecedent factors and age yielded excellent discrimination for incident HF in both outpatients and inpatients and in both sexes (C >0.85 in all models).
Conclusions
Common modifiable risk factors accurately discriminate women and men at risk for HF diagnosed in either outpatient or inpatient settings. Approximately two thirds of new HF cases in our insured population were diagnosed in outpatients; more research is needed to characterize these subjects and their prognosis.
Consistent with animal models and laboratory investigations, higher doses of selective COX-2 inhibitors were more protective against breast cancer than non-specific NSAIDs. With exposure to rofecoxib, a selective COX-2 inhibitor, breast cancer risk reduction was appreciable (46%), suggesting a possible role for selective COX-2 inhibitors in breast cancer prophylaxis.
As it is crucial to protect the transmission line from inevitable faults consequences, intelligent scheme must be employed for immediate fault detection and classification. The application of Artificial Neural Network (ANN) to detect the fault, identify it's section, and classify the fault on transmission lines with improved zone reach setting is presented in this article. The fundamental voltage and current magnitudes obtained through Discrete Fourier Transform (DFT) are specified as the inputs to the ANN. The relay is placed at section-2 which is the prime section to be protected. The ANN was trained and tested using diverse fault datasets; obtained from the simulation of different fault scenarios like different types of fault at varying fault inception angles, fault locations and fault resistances in a 400 kV, 216 km power transmission network of CSEB between Korba-Bhilai of Chhattisgarh state using MATLAB. The simulation outcomes illustrated that the entire shunt faults including forward and reverse fault, it's section and phase can be accurately identified within a half cycle time. The advantage of this scheme is to provide a major protection up to 99.5% of total line length using single end data and furthermore backup protection to the forward and reverse line sections. This routine protection system is properly discriminatory, rapid, robust, enormously reliable and incredibly responsive to isolate targeted fault.
The hyperandrogenic state in PCOS appears to have heterogenous origins. Nonobese patients with PCOS have adrenal hyperandrogenism as the underlying mechanism while their obese/ insulin-resistant counterparts have low SHBG and hence an increased fraction of free testosterone.
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