Implementation of an automated reporting system improves data collection on heart failure patients in a multi-hospital, integrated healthcare delivery system

Implementation of an automated reporting system improves data collection on heart failure patients in a multi-hospital, integrated healthcare delivery system

The 8th Annual Scientific Meeting • HFSA S99 299 301 Survival after Coronary Revascularization in Patients with Heart Failure Ross T. Tsuyuki,1 ...

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The 8th Annual Scientific Meeting



HFSA

S99

299

301

Survival after Coronary Revascularization in Patients with Heart Failure Ross T. Tsuyuki,1 Fiona M. Shrive,2 Michelle M. Graham,1 P. Diane Galbraith,2 William A. Ghali,2 Merril L. Knudtson2; 1Medicine (Cardiology), University of Alberta, Edmonton, AB, Canada; 2Medicine, University of Calgary, Calgary, AB, Canada

Implementation of an Automated Reporting System Improves Data Collection on Heart Failure Patients in a Multi-hospital, Integrated Healthcare Delivery System Susan E. Pollock,1 Colleen A. Roberts,2 Thomas K. French,2 Jill A. Hall,2 Kismet D. Rasmusson,2 Dale G. Renlund,2,3 Holly L. Rimmasch,2 R. Scott Evans2,4; 1Enterprise Data Warehouse, Intermountain Health Care, Salt Lake City, UT; 2Heart Failure Prevention and Treatment Program, LDS Hospital, Salt Lake City, UT; 3Division of Cardiology, University of Utah School of Medicine, Salt Lake City, UT; 4Department of Medical Informatics, University of Utah School of Medicine, Salt Lake City, UT

Introduction: Practice guidelines recommend coronary revascularization for patients with heart failure and coronary artery disease (CAD), however the evidence for efficacy of this practice is weak. Hypothesis: Coronary revascularization is associated with improved survival in patients with heart failure and CAD. Methods: Data were obtained from APPROACH, a clinical data collection and outcome monitoring initiative capturing all patients undergoing catheterization and/or revascularization in the province of Alberta since 1995. Characteristics and survival of a cohort of heart failure patients with significant CAD (⬎70% stenosis in 1 or more vessels) receiving revascularization were compared to those patients not revascularized over 1–7y of follow-up. Patients with prior bypass surgery were excluded. Propensity scores were performed to account for clinical characteristics that may influence the decision to revascularize. Survival curves adjusted by the corrected group prognosis method (incorporating all clinical variables) were constructed. Results: A total of 2538 patients received revascularization (age 68 ⫾ 11y, 31% female) and 1690 patients did not receive revascularization (age 69 ⫾ 11y, 34% female). Crude mortality was 11.8% in those receving revascularization, compared to 21.6% in those not. Propensityadjusted relative risk of 1y survival in those receiving revascularization ranged from 1.9 in those in the lowest quintile of revascularization probability to 2.8 in those in the highest (p ⬍ 0.05 for all quintiles). Survival curves separated early and continued diverging up to 7y of follow-up (Figure). Conclusions: This analysis of a large cohort of heart failure patients showed a strong association of revascularization with survival, validating the recommendations from practice guidelines, and suggesting that revascularization be considered in all patients with heart failure and CAD.

Introduction: Incomplete data capture is an ongoing issue that can compromise the validity of quality improvement, quality assurance, and clinical studies. Intermountain Health Care, a 20-hospital integrated healthcare delivery system, treats over five thousand heart failure (HF) patients annually. A Discharge Medication Database (DMD) was instituted to track appropriate medication prescription and teaching for cardiac patients. To insure that all appropriate HF patients are identified and that pertinent data are entered into the DMD, we developed a weekly report specific to each facility. Hypothesis: The null hypothesis is that the implementation of this reporting system has no significant impact on the proportion of HF patient encounters with data in the DMD. Methods: The automated report utilizes a merged dataset from the Enterprise Data Warehouse. This dataset is derived from CaseMix data and the DMD. An electronic audit of the CaseMix data creates a table listing patient encounters with a primary diagnosis of heart failure and flags those encounters missing a record in the DMD. A standard reporting tool generates hospital-specific reports that are automatically emailed once a week. Discharge data are then retrospectively collected on the identified patient encounters and are entered into the system-wide database. From the three largest IHC hospitals, 1778 inpatient encounters with a primary ICD-9 code for heart failure and a discharge date between 2002 and 2003 were identified. Exclusionary criteria were aligned with JCAHO’s Heart Failure Core Performance Measures. The primary outcome measurement was the proportion of eligible patient encounters entered into the DMD. Results: In 2002, 38.5 % of the HF patient encounters had data entered into the database. After implementation of the automated reporting system, the percentage had risen to 76.9% in 2003. This non-randomized binomial comparative study showed a significant (p ⫽ 0.0043) increase in the proportion of HF patients with data in the DMD. Conclusions: Significant improvement in data collection can be achieved through the implementation of automated reporting systems. Successful systems require specific records to be identified and reported to the accountable persons in a timely manner.

302 Impact of Supervised Cardiac Rehabilitation on Clinical Outcomes in Patients with Chronic LV Systolic Dysfunction Robert M. Siegel,2 Ambika Bhaskaran,1 Greta Koehnemann,1 John Johnson,1 Kim Wisowaty,1 Jennifer Barton,2 James Romo1; 1Division of Cardiovascular Research, Advanced Cardiac Specialists, Gilbert, AZ; 2Department of Cardiololgy, Mesa General Hospital, Mesa, AZ

300 Effect of Poorly Controlled Diabetes on Hospital Stay in Exacerbation of Congestive Heart Failure Vishal Bhatia, Boban Mathew, Ruchi Bhatia; Internal Medicine, State University of New York, Buffalo, NY; Internal Medicine, State University of New York, Buffalo, NY; Internal Medicine, State University of New York, Buffalo, NY Background: Diabetes is a known risk factor for CHF, increasing both the prevalence as well as the incidence of CHF. It is independently associated with clinical and subclinical left ventricular systolic and diastolic dysfunction. Hyperglycemia with or without diabetes has been shown to be associated with worse symptomatic status in patients with CHF. Aims and Objectives: The aim of this study was to correlate glycemic control in diabetic patients who are admitted with acute exacerbation of congestive heart failure (CHF) with the number of hospital days of admission. Methodology: One hundred patients (42 males & 58 females) who carried an admission diagnosis of CHF and diabetes were included in the study by retrospective chart review. Patients who developed any new condition while in hospital that could have prolonged the hospitalization were excluded. Results: The mean age of patients was 76.5 years (range 49–94 years). The average stay in hospital was 4.8 days. There was a strong correlation between HbA1C levels and number of days of hospitalization (r⫽0.669, ⬍0.001). Uncontrolled diabetics (n ⫽ 48; HbA1C ⬎7) stayed for 6.3 days in hospital as compared to diabetics with good glycemic control (n ⫽ 52; HbA1C ⬍ ⫽ 7) who stayed for 3.2 days. Diabetics with uncontrolled baseline glycemic status also had worse glycemic control in hospital. There was also a strong correlation between average fasting blood sugar while in hospital and number of days of hospitalization (r ⫽ 0.499; p ⬍ 0.001). Higher blood sugar values at admission also correlated very well with in-hospital days (r ⫽ 0.587; p ⬍ 0.001). Conclusion: Poorly controlled diabetics who are admitted with CHF exacerbation stay longer in hospital. The admission blood sugar values can be helpful in identifying this subset of patients who might have poor in-hospital glycemic control and may stay longer.

Cardiac rehabilitation is increasingly being recognized as an important factor in reducing morbidity and mortality in patients with chronic LV systolic dysfunction (LVSD) due to cardiovascular disease. It is credited with improving functional capacity, reducing ischemic burden, modifying LV geometry and reducing risk of future cardiac events. We evaluated the effects of a 12-week aerobic training program in patients with significant chronic LVSD (LVEF40%). From 1/00-12/03, 606 such patients (mean age 67; 23% female) were enrolled in a supervised exercise program. The exercise prescription used a preset protocol designed by a group of exercise specialists and consisted of ECG-monitored, symptom-limited cycle ergometry and treadmill walking up to 60 minutes per session, 3 days a week. Exercise intensity was set at 60-70% of heart rate reserve. Multiple indices of exercise tolerance, LVEF and metabolic parameters were recorded at entry and at completion of cardiac rehabilitation. There were no significant complications. Conclusions: (1) Our experience demonstrates the benefits of a supervised cardiac rehabilitation program on patients with chronic LVSD. (2)There is significant improvement in LV systolic and diastolic performance, HDL and FBG levels. (3) This translates into significant improvement in functional class, resting heart rate and exercise times and intensity. Longer follow-up will determine if these promising results are sustained in the longterm and their impact on long-term survival. Results Variable Weight (lb) NYHA Class Resting HR Exercise HR Treadmill Time (min) TM Workload (METS) Cycle Ergometry Time (min) GXT Workload (METS) GXT Double Product LVEF (%) Diastolic dysfx (%) Total Cholesterol (mg/dL) HDL (mg/dL) LDL (mg/dL) FBS (mg/dL)

Pre-Rehab

Post-Rehab

‘P’ Value

182.21 2.9 84.93 98.11 16.59 ⫾ 8.24 2.45 ⫾ 0.75 6.58 ⫾ 3.82 3.78 ⫾ 1.46 15,225 ⫾ 4,566 31.27 ⫾ 7.90 46.1 198.1 31.95 106.75 122.33

179.45 2.1 76.12 90.93 28.87 ⫾ 10.77 3.72 ⫾ 1.75 20.90 ⫾ 3.55 8.44 ⫾ 5.02 20,533 ⫾ 4,245 39.34 ⫾ 11.80 11.8 167.8 40.89 99.82 115.06

0.000* 0.003** 0.000* 0.561 0.000* 0.000* 0.0034** 0.003** 0.000* 0.010** 0.001** 0.282 0.000* 0.515 0.010**

*‘P’ value significant at ⱕ0.0001; **‘P’ value significant at ⱕ0.05; FBS:fasting blood sugar