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Trends in Heart Failure Hospitalization Rate and Outcome in the United States
The 11th Annual Scientific Meeting 18031) versus 13% (629 of 4,946) for INO with PROC. Mean total hospital costs for the four groups in the table were: $32,697, $62,793, $43,967 and $54,466; average time to death following PROC: 5.9, 11.0, 5.5, and 14.3 days respectively. Conclusions: High mortality and costs are associated with implant/revision of a device in patients who require INO. Risk stratification methodology for patients selected for device therapy that incorporates ongoing use of or anticipates need for INO are required given cost considerations and early post-PROC mortality.
Inotropic Therapy, Device Procedures and Mortality INO on day of PROC (n 5 1331)
INO after day of PROC (n 5 728)
PROC, No INO (n 5 18031)
INO prior to PROC (n 5 2887)
Day of PROC
%Pts / %Mort
%Pts / %Mort
%Pts / %Mort
%Pts / %Mort
1 2-7 8þ
34.3 / 0.7 50.3 / 1.1 15.4 / 2.2
e 40.2 / 8.0 59.8 / 7.9
48.8 / 14.5 41.8 / 20.8 9.4 / 22.4
42.6 / 20.6 44.6 / 20.6 12.8 / 33.3
INO: by day of initiation relative to PROC.
293 Trends in Heart Failure Hospitalization Rate and Outcome in the United States Paul A. Heidenreich1, Anju Sahay1, Martin M. Costa1, Barry M. Massie2; 1VA Palo Alto Health Care System, Palo Alto, CA; 2San Francisco VA Medical Center, San Francisco, CA Background: Therapies for heart failure known to reduce admission rates are increasingly used; however, recent trends in U.S. hospitalization rates are unclear. Methods: We used data from the National Hospital Discharge Survey from 2000 to 2004, a random sample of all discharges from non-federal hospitals. There were 51,670 hospitalizations with heart failure as the primary reason for admission. Rates of admission per age group were calculated using U.S. census data. Trends in in-hospital survival, length of stay, and comorbidity were determined. Results: From 2000 to 2004, the mean age remained unchanged at 72.3 years while the fraction of males increased (43% to 46%, p ! 0.0001). Increases were seen for comorbid COPD (30% to 34%, p ! 0.0001) and renal disease (3.3% to 2.3%, p ! 0.0001) while decreases were seen for cerebrovascular disease (3.3% to 2.3%, p ! 0.0001) and diabetes (34% to 31%, p ! 0.0001). The overall Charlson comorbidity score increased slightly from 2.24 to 2.28 (p 5 0.008). The age-adjusted admission rate per 1000 increased from 21.4 in 2000 to 23 in 2004. This was due to increases in admission rates for the oldest age groups (Figure). Mortality decreased (but not significantly) overall (4.0% to 3.4%, p 5 0.14) and for different age groups (Figure). However, length of stay decreased from 5.8 days to 5.4 days (p 5 0.002) and the death rate adjusted for length of stay was unchanged. Conclusion: Age-specific admission rates for heart failure continue to increase in the United States. A non-significant trend toward improved survival was also noted in the older age groups.
HFSA
S159
294 Additional Web-Based Telemedicine Monitoring of Heart Failure Patients Prevents ER and Re-Hospitalization Marie T. Droogan1, Abul Kashem1, William P. Santamore1, Philip T. Berger1, Joyce W. Wald1, Carol J. Homko1, Alfred A. Bove1; 1Cardiology, Temple University School of Medicine, Philadelphia, PA Background: Patients with heart failure (HF) demonstrate improved care with continuous close supervision; Telemedicine (T) allows a clinician to monitor clinical status of HF patients at home. We hypothesize that an additional Internet-based Tcommunication system along with standard HF care will reduce hospitalizations, ER visits, and improve the clinical outcome. Methods: The study was designed to measure the effects of web-based T-sytem on hospitalizations, hospital days, HF exacerbations, and clinic visits. Using an Internet-based T-system, we followed 24 Internet (T) and 24 control (C) patients for one year and 8 Internet and 10 control patients for the second year. T- and C- patients recieved usual care with regular clinic visits. In addition, T-group communicated the practice via the Internet. An advancedpractice nurse reviewed the data with a cardiologist and provided care through web. In two HF groups, we compared hospital admissions and ER visits between 1st and 2nd study years. Results: Mean age was 56.0 6 12.3 years (66.7% male, NYHA class II-III). HF etiology: T- ischemic (55%), idiopathic (36%), and other (9%), Cischemic (27%), idiopathic (55%), and other (18%). During 1st year follow-up (f/ u), HF readmissions (T-5x[ 5 20days], C- 19x[ 5 99days]; p ! 0.01), and cardiac admission (T-8x[ 5 23days], C-8x[ 5 34days]; p 5 ns) were lower in T-group. ER visits due to HF were significantly lower in the T-group (T-2; C-11; p ! 0.01). Practice call from T-group were total 73 during 1st year compared to 51 from control (T:pt call-26, practice call-24, refill call-19; C:- pt call-6; practice call-10; refill call-35). During 2nd year f/u, HF readmissions (T-3x[ 5 31days], C- 2x[ 5 21days]; p 5 ns) and cardiac admission (T-4x[ 5 16days], C-3x[ 5 11days] ; p 5 ns) were similar in both groups. ER visits due to HF trended lower in T-group (T-1; C-4; p 5 ns). Practice call from T-group were total 42 during 2nd year compared to 46 from control (T:pt call-11, practice call-20, refill call-8; C:- pt call-8, practice call-11, refill call-27). Scheduled and unscheduled clinic visits were similar in both groups. Conclusions: ER visits were significantly reduced to zero by close Internet communication to monitor HF patients over two years. HF related admissions were reduced in T compared to C. Patient calls to the practice were higher in T whereas medicine re-fill calls were higher in C. These data suggest that web-based Telemedicine monitoring may enhance standard HF management.
295 Validation of the Seattle Heart Failure Model in Patients Referred for Transplant Evaluation Grigorios Giamouzis, Vasiliki Georgiopoulou, Andreas Kalogeropoulos, Andrew L. Smith, Javed Butler; 1Emory University, Atlanta, GA; 2Onassis Cardiac Surgery Center, Athens, Greece Background: Accurate prognosis determination is critical in order to select appropriate candidates for cardiac transplantation. Recently, Seattle Heart Failure Model (SHFM), a multi-marker model which incorporates the effects of medical therapy, was introduced to assess prognosis in a general population of heart failure (HF) patients. In this study, we sought to assess the accuracy of SHFM among patients with advanced symptoms referred for transplant. Methods: To assess the accuracy of the SHFM, we collected data on 76 patients (51 6 14 years, 74% male, and 68% white) referred for evaluation for cardiac transplantation from 5/2003 to 4/2005. SHFM was tested in this population in a two-step process. First, the overall discriminative value of the model was assessed by calculating the c-statistic. Second, prediction of survival was assessed by plotting actuarial survival time vs. model error (5 predicted-actual). Death and urgent cardiac transplantation (UNOS Status 1-A) and placement of a left ventricular assist device (LVAD) was included as an event. Patients undergoing non-urgent transplantation were censored at the time of transplant. Results: Mean follow-up was 2.7 6 0.8 years. Median ejection fraction was 0.18 (25%-75%: 0.12-0.28), heart rate was 81 6 18 bpm, and systolic blood pressure was 114 6 15 mmHg. Of the 76 patients, 9 (11.8%) died, 1 (1.3%) underwent urgent transplantation, and 1 (1.3%) underwent LVAD placement. Mean survival in these patients was 1.4 6 0.7years. Deployment of the SHFM in this population revealed a c-statistic of 0.78. Residual analysis showed that the SHFM overall overestimated survival in these patients by 5.1 6 2.8 years (p ! 0.001), Fig. 1. All patients lived shorter than projected survival. Conclusion: The overall discriminatory property of the SHFM model in HF patients referred for transplantation is comparable to a general HF population in clinical trials from which the model was originally derived. However, SHFM significantly overestimated survival in this small study. Larger studies are needed to validate the utility of the SHFM in these patients.
Inotropic Therapy, Device Procedures and Mortality INO on day of PROC (n 5 1331)
INO after day of PROC (n 5 728)
PROC, No INO (n 5 18031)
INO prior to PROC (n 5 2887)
Day of PROC
%Pts / %Mort
%Pts / %Mort
%Pts / %Mort
%Pts / %Mort
1 2-7 8þ
34.3 / 0.7 50.3 / 1.1 15.4 / 2.2
e 40.2 / 8.0 59.8 / 7.9
48.8 / 14.5 41.8 / 20.8 9.4 / 22.4
42.6 / 20.6 44.6 / 20.6 12.8 / 33.3
INO: by day of initiation relative to PROC.
293 Trends in Heart Failure Hospitalization Rate and Outcome in the United States Paul A. Heidenreich1, Anju Sahay1, Martin M. Costa1, Barry M. Massie2; 1VA Palo Alto Health Care System, Palo Alto, CA; 2San Francisco VA Medical Center, San Francisco, CA Background: Therapies for heart failure known to reduce admission rates are increasingly used; however, recent trends in U.S. hospitalization rates are unclear. Methods: We used data from the National Hospital Discharge Survey from 2000 to 2004, a random sample of all discharges from non-federal hospitals. There were 51,670 hospitalizations with heart failure as the primary reason for admission. Rates of admission per age group were calculated using U.S. census data. Trends in in-hospital survival, length of stay, and comorbidity were determined. Results: From 2000 to 2004, the mean age remained unchanged at 72.3 years while the fraction of males increased (43% to 46%, p ! 0.0001). Increases were seen for comorbid COPD (30% to 34%, p ! 0.0001) and renal disease (3.3% to 2.3%, p ! 0.0001) while decreases were seen for cerebrovascular disease (3.3% to 2.3%, p ! 0.0001) and diabetes (34% to 31%, p ! 0.0001). The overall Charlson comorbidity score increased slightly from 2.24 to 2.28 (p 5 0.008). The age-adjusted admission rate per 1000 increased from 21.4 in 2000 to 23 in 2004. This was due to increases in admission rates for the oldest age groups (Figure). Mortality decreased (but not significantly) overall (4.0% to 3.4%, p 5 0.14) and for different age groups (Figure). However, length of stay decreased from 5.8 days to 5.4 days (p 5 0.002) and the death rate adjusted for length of stay was unchanged. Conclusion: Age-specific admission rates for heart failure continue to increase in the United States. A non-significant trend toward improved survival was also noted in the older age groups.
HFSA
S159
294 Additional Web-Based Telemedicine Monitoring of Heart Failure Patients Prevents ER and Re-Hospitalization Marie T. Droogan1, Abul Kashem1, William P. Santamore1, Philip T. Berger1, Joyce W. Wald1, Carol J. Homko1, Alfred A. Bove1; 1Cardiology, Temple University School of Medicine, Philadelphia, PA Background: Patients with heart failure (HF) demonstrate improved care with continuous close supervision; Telemedicine (T) allows a clinician to monitor clinical status of HF patients at home. We hypothesize that an additional Internet-based Tcommunication system along with standard HF care will reduce hospitalizations, ER visits, and improve the clinical outcome. Methods: The study was designed to measure the effects of web-based T-sytem on hospitalizations, hospital days, HF exacerbations, and clinic visits. Using an Internet-based T-system, we followed 24 Internet (T) and 24 control (C) patients for one year and 8 Internet and 10 control patients for the second year. T- and C- patients recieved usual care with regular clinic visits. In addition, T-group communicated the practice via the Internet. An advancedpractice nurse reviewed the data with a cardiologist and provided care through web. In two HF groups, we compared hospital admissions and ER visits between 1st and 2nd study years. Results: Mean age was 56.0 6 12.3 years (66.7% male, NYHA class II-III). HF etiology: T- ischemic (55%), idiopathic (36%), and other (9%), Cischemic (27%), idiopathic (55%), and other (18%). During 1st year follow-up (f/ u), HF readmissions (T-5x[ 5 20days], C- 19x[ 5 99days]; p ! 0.01), and cardiac admission (T-8x[ 5 23days], C-8x[ 5 34days]; p 5 ns) were lower in T-group. ER visits due to HF were significantly lower in the T-group (T-2; C-11; p ! 0.01). Practice call from T-group were total 73 during 1st year compared to 51 from control (T:pt call-26, practice call-24, refill call-19; C:- pt call-6; practice call-10; refill call-35). During 2nd year f/u, HF readmissions (T-3x[ 5 31days], C- 2x[ 5 21days]; p 5 ns) and cardiac admission (T-4x[ 5 16days], C-3x[ 5 11days] ; p 5 ns) were similar in both groups. ER visits due to HF trended lower in T-group (T-1; C-4; p 5 ns). Practice call from T-group were total 42 during 2nd year compared to 46 from control (T:pt call-11, practice call-20, refill call-8; C:- pt call-8, practice call-11, refill call-27). Scheduled and unscheduled clinic visits were similar in both groups. Conclusions: ER visits were significantly reduced to zero by close Internet communication to monitor HF patients over two years. HF related admissions were reduced in T compared to C. Patient calls to the practice were higher in T whereas medicine re-fill calls were higher in C. These data suggest that web-based Telemedicine monitoring may enhance standard HF management.
295 Validation of the Seattle Heart Failure Model in Patients Referred for Transplant Evaluation Grigorios Giamouzis, Vasiliki Georgiopoulou, Andreas Kalogeropoulos, Andrew L. Smith, Javed Butler; 1Emory University, Atlanta, GA; 2Onassis Cardiac Surgery Center, Athens, Greece Background: Accurate prognosis determination is critical in order to select appropriate candidates for cardiac transplantation. Recently, Seattle Heart Failure Model (SHFM), a multi-marker model which incorporates the effects of medical therapy, was introduced to assess prognosis in a general population of heart failure (HF) patients. In this study, we sought to assess the accuracy of SHFM among patients with advanced symptoms referred for transplant. Methods: To assess the accuracy of the SHFM, we collected data on 76 patients (51 6 14 years, 74% male, and 68% white) referred for evaluation for cardiac transplantation from 5/2003 to 4/2005. SHFM was tested in this population in a two-step process. First, the overall discriminative value of the model was assessed by calculating the c-statistic. Second, prediction of survival was assessed by plotting actuarial survival time vs. model error (5 predicted-actual). Death and urgent cardiac transplantation (UNOS Status 1-A) and placement of a left ventricular assist device (LVAD) was included as an event. Patients undergoing non-urgent transplantation were censored at the time of transplant. Results: Mean follow-up was 2.7 6 0.8 years. Median ejection fraction was 0.18 (25%-75%: 0.12-0.28), heart rate was 81 6 18 bpm, and systolic blood pressure was 114 6 15 mmHg. Of the 76 patients, 9 (11.8%) died, 1 (1.3%) underwent urgent transplantation, and 1 (1.3%) underwent LVAD placement. Mean survival in these patients was 1.4 6 0.7years. Deployment of the SHFM in this population revealed a c-statistic of 0.78. Residual analysis showed that the SHFM overall overestimated survival in these patients by 5.1 6 2.8 years (p ! 0.001), Fig. 1. All patients lived shorter than projected survival. Conclusion: The overall discriminatory property of the SHFM model in HF patients referred for transplantation is comparable to a general HF population in clinical trials from which the model was originally derived. However, SHFM significantly overestimated survival in this small study. Larger studies are needed to validate the utility of the SHFM in these patients.