Rural and urban characteristics impact cardiovascular risk reduction.

PURPOSE: Our aim was to provide a descriptive analysis of specific differences between rural and urban residents and the interaction between these differences and those who reduced cardiovascular disease (CVD) risk in response to intervention versus those who did not. METHODS: This study is a descriptive analysis comparing rural groups with urban groups and those who decreased CVD risk with those who did not. Two hundred five rural (median age = 64.0 years [interquartile = 57.0, 71.0], 56% men) and 183 urban (median age = 58.0 years [interquartile = 50.0, 65.0], 53% men) residents were included. RESULTS: Rural and urban groups differed (P < .05) for demographic, anthropometric, physiological, and health-related variables. Those who decreased CVD risk, regardless of rural or urban, had greater blood pressure, greater low-density lipoprotein cholesterol, lower walking distance, greater CVD risk score, greater metabolic syndrome score, and greater internal health locus of control (all P < .05). Interestingly, there were differences between those who decreased risk and those who did not within the rural and urban groups. Triglycerides, C-reactive protein, diabetes knowledge, risk perception, and outcome expectations were greater for the rural group who decreased their CVD risk versus those who did not (all P < .05). For the urban group, there was a greater powerful others locus of control for those who decreased CVD risk (P < .05). CONCLUSIONS: To maximize the likelihood of success, risk reduction intervention and educational strategies for urban and rural groups must be tailored to address unique demographic, physiological, and health-related characteristics.

Web-based Internet telemedicine management of patients with heart failure.

An Internet-based store-and-retrieval telemedicine system to communicate between patients and their healthcare provider was tested. The system requires no specialized equipment, is Web-based, and allows frequent surveillance of the health status of the patients with heart failure (HF). Thirty six patients were recruited to evaluate a Web-based telemedicine system for reducing care encounters. Eighteen patients were randomized to the telemedicine arm (group T), and 18 were given usual clinical care (group C) in our HF center. Patients in group T reported three times weekly via a secure Internet site for telemedicine intervention. We studied patients with HF with New York Heart Association (NYHA) class 2 to 4 with hospitalization within past 6 months. Mean age was 56.1 +/- 12.6 years (66.7% male; 66.7% Caucasian, 27.8% African American, and 5.6% Hispanic). Mean ejection fraction (EF) was 23.9% +/- 17.6% in group T and 26.6% +/- 16.4% in group C. Over an 8-month period, unscheduled (group T-3; group C-5), and scheduled clinic visits (group T-11, group C-7) were similar (p = NS); one group T patient was transplanted, one group C patient died. Total hospital days were lower with group T (44 days) compared to group C (133 days), p < 0.05. An Internet-based telemedicine system was able to closely monitor patients with HF. Surveillance through Internet-based telemedicine resulted in less hospitalization compared to control patients. This system may be helpful in reducing the cost of HF patient care.

Factors affecting survival after heart transplantation: comparison of pre- and post-1999 listing protocols.

BACKGROUND: Listing status for heart transplantation (Tx) patients was changed in 1999 from Status 1 and 2 to Status 1A, 1B and 2. Because the selection process was modified in favor of seriously ill patients, it was not clear whether this change would affect survival or other aspects of transplant management. METHODS: We examined outcomes in 551 patients transplanted at our institution between 1986 and 2002 (pre-1999: n = 419; post-1999: n = 132) to determine the effects of change in listing protocol on transplant outcome. RESULTS: Using Cox proportional hazard analysis, survival was not different between pre-1999 (pre) and post-1999 (post). Overall waiting-list times were longer post-1999 (pre: 134 +/- 10.5 days, post: 172 +/- 15.6 days; p = 0.044), and were longer post-1999 for blood groups A (177 vs 123 days), B (96 vs 84 days) and O (229 vs 172 days), but were shorter post for blood group AB (42 vs 68 days). Survival was not affected by age (pre: 53.7 +/- 0.52 years, post: 53.1 +/- 1.04 years; hazard ratio [HR] 1.00; 95% confidence interval [CI] 0.996 to 1.023; p = 0.181), male gender (HR 1.132; 95% CI 0.822 to 1.56; p = 0.447) or waiting-list time. Serum creatinine was similar between the 2 groups (pre: 1.25 +/- 0.02, post: 1.26 +/- 0.04; p = 0.794), whereas pulmonary artery (PA) diastolic pressure was increased post-1999 (pre: 24.9 +/- 0.46, post: 27.0 +/- 0.74; p = 0.023). Survival was not affected by PA pressure (HR 1.00; 95% CI 0.986 to 1.014; p = 0.976), but an elevated pre-transplant creatinine reduced survival (HR 1.484; 95% CI 1.139 to 1.933; p = 0.003). CONCLUSIONS: The change in listing status implemented in 1999 caused an increase in wait times for patients with blood types A, B and O, and shortened wait time for type AB; however, no differences occurred in overall post-transplant survival after the change in listing protocol. Age, gender and PA pressure had no effect on survival in either time period, whereas pre-transplant serum creatinine decreased survival in both patient groups.

Management of heart failure patients using telemedicine communication systems.

Heart failure (HF) continues to place significant demands on health care resources because of the large number of hospital admissions for HF, the growth of the elderly population with HF, and the improved survival of patients with chronic heart disease who develop HF that requires continuous care. Because HF is best managed using a disease management approach, frequent communication is an important component of care. A variety of studies using the telephone to maintain communication have demonstrated reduced hospital admissions and improved morbidity rate. Hardware monitoring systems that can record vital signs and transmit information from the home to a data center have also demonstrated their value in HF care, but such systems become expensive when considered for large populations of HF patients. Most HF patients can transmit their vital signs, weight, and symptoms to a practice data center using the Internet with no specialized hardware other than a sphygmomanometer and a scale. We have used such a system to monitor HF patients and have provided care instructions using the same system. With use of an Internet communication system, it is possible to reduce hospitalizations and maintain a stable HF status without frequent office visits.