Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model.

CONTEXT A predictive model of mortality in heart failure may be useful for clinicians to improve communication with and care of hospitalized patients. OBJECTIVES To identify predictors of mortality and to develop and to validate a model using information available at hospital presentation. DESIGN, SETTING, AND PARTICIPANTS Retrospective study of 4031 community-based patients presenting with heart failure at multiple hospitals in Ontario, Canada (2624 patients in the derivation cohort from 1999-2001 and 1407 patients in the validation cohort from 1997-1999), who had been identified as part of the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) study. MAIN OUTCOME MEASURES All-cause 30-day and 1-year mortality. RESULTS The mortality rates for the derivation cohort and validation cohort, respectively, were 8.9% and 8.2% in hospital, 10.7% and 10.4% at 30 days, and 32.9% and 30.5% at 1 year. Multivariable predictors of mortality at both 30 days and 1 year included older age, lower systolic blood pressure, higher respiratory rate, higher urea nitrogen level (all P<.001), and hyponatremia (P<.01). Comorbid conditions associated with mortality included cerebrovascular disease (30-day mortality odds ratio [OR], 1.43; 95% confidence interval [CI], 1.03-1.98; P =.03), chronic obstructive pulmonary disease (OR, 1.66; 95% CI, 1.22-2.27; P =.002), hepatic cirrhosis (OR, 3.22; 95% CI, 1.08-9.65; P =.04), dementia (OR, 2.54; 95% CI, 1.77-3.65; P<.001), and cancer (OR, 1.86; 95% CI, 1.28-2.70; P =.001). A risk index stratified the risk of death and identified low- and high-risk individuals. Patients with very low-risk scores (< or =60) had a mortality rate of 0.4% at 30 days and 7.8% at 1 year. Patients with very high-risk scores (>150) had a mortality rate of 59.0% at 30 days and 78.8% at 1 year. Patients with higher 1-year risk scores had reduced survival at all times up to 1 year (log-rank, P<.001). For the derivation cohort, the area under the receiver operating characteristic curve for the model was 0.80 for 30-day mortality and 0.77 for 1-year mortality. Predicted mortality rates in the validation cohort closely matched observed rates across the entire spectrum of risk. CONCLUSIONS Among community-based heart failure patients, factors identifiable within hours of hospital presentation predicted mortality risk at 30 days and 1 year. The externally validated predictive index may assist clinicians in estimating heart failure mortality risk and in providing quantitative guidance for decision making in heart failure care.

[1]  Ewout W Steyerberg,et al.  Should scoring rules be based on odds ratios or regression coefficients? , 2002, Journal of clinical epidemiology.

[2]  D. Baker,et al.  Relationships between in-hospital and 30-day standardized hospital mortality: implications for profiling hospitals. , 2000, Health services research.

[3]  J. Habbema,et al.  Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.

[4]  C D Naylor,et al.  Clinical prediction rules. , 1997, Journal of clinical epidemiology.

[5]  J. C. van Houwelingen,et al.  Predictive value of statistical models , 1990 .

[6]  J. Tu,et al.  CCORT/CCS quality indicators for congestive heart failure care. , 2003, The Canadian journal of cardiology.

[7]  M. Radford,et al.  Correlation of the Agency for Health Care Policy and Research congestive heart failure admission guideline with mortality: peer review organization voluntary hospital association initiative to decrease events (PROVIDE) for congestive heart failure. , 1999, Annals of emergency medicine.

[8]  J. Habbema,et al.  Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. , 2000, Statistics in medicine.

[9]  Eric J. Eichhorn Prognosis determination in heart failure. , 2001, The American journal of medicine.

[10]  D. Levy,et al.  Survival After the Onset of Congestive Heart Failure in Framingham Heart Study Subjects , 1993, Circulation.

[11]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[12]  K. Bailey,et al.  Congestive heart failure in the community: a study of all incident cases in Olmsted County, Minnesota, in 1991. , 1998, Circulation.

[13]  L. E. Rohde,et al.  A new casemix adjustment index for hospital mortality among patients with congestive heart failure. , 1998, Medical care.

[14]  E. Bradley,et al.  What matters to seriously ill older persons making end-of-life treatment decisions?: A qualitative study. , 2003, Journal of palliative medicine.

[15]  K. Bailey,et al.  Congestive Heart Failure in the Community , 1998 .

[16]  F. Mair,et al.  Doctors' perceptions of palliative care for heart failure: focus group study , 2002, BMJ : British Medical Journal.

[17]  L Goldman,et al.  Correlates of early hospital readmission or death in patients with congestive heart failure. , 1997, The American journal of cardiology.

[18]  D Draper,et al.  Predicting hospital-associated mortality for Medicare patients. A method for patients with stroke, pneumonia, acute myocardial infarction, and congestive heart failure. , 1988, JAMA.

[19]  J. Carline,et al.  Communicating with dying patients within the spectrum of medical care from terminal diagnosis to death. , 2001, Archives of internal medicine.

[20]  J. Schwartz,et al.  Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. , 1996, Circulation.

[21]  W. Kannel,et al.  The natural history of congestive heart failure: the Framingham study. , 1971, The New England journal of medicine.

[22]  N. Sharpe Heart failure in the community. , 1998, Progress in cardiovascular diseases.

[23]  A. Laupacis,et al.  Clinical prediction rules. A review and suggested modifications of methodological standards. , 1997, JAMA.

[24]  C Larizza,et al.  Predictors of prognosis in patients awaiting heart transplantation. , 1993, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[25]  H. Krumholz,et al.  Comparing AMI mortality among hospitals in patients 65 years of age and older: evaluating methods of risk adjustment. , 1999, Circulation.

[26]  J. Tu,et al.  Multicenter validation of a risk index for mortality, intensive care unit stay, and overall hospital length of stay after cardiac surgery. Steering Committee of the Provincial Adult Cardiac Care Network of Ontario. , 1995, Circulation.

[27]  J. Pell,et al.  Evidence of Improving Prognosis in Heart Failure , 2000 .

[28]  J. Goldberger,et al.  Prognostic factors in acute pulmonary edema. , 1986, Archives of internal medicine.

[29]  J V Tu,et al.  Development and validation of the Ontario acute myocardial infarction mortality prediction rules. , 2001, Journal of the American College of Cardiology.

[30]  M A Konstam,et al.  Heart failure: evaluation and care of patients with left ventricular systolic dysfunction. , 1995, Journal of cardiac failure.

[31]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[32]  D. McClish,et al.  Results of Report Cards for Patients with Congestive Heart Failure Depend on the Method Used To Adjust for Severity , 2000, Annals of Internal Medicine.

[33]  D Draper,et al.  Changes in sickness at admission following the introduction of the prospective payment system. , 1990, JAMA.

[34]  D. Levy,et al.  Congestive heart failure in subjects with normal versus reduced left ventricular ejection fraction: prevalence and mortality in a population-based cohort. , 1999, Journal of the American College of Cardiology.

[35]  R. D'Agostino,et al.  A Time‐Insensitive Predictive Instrument for Acute Hospital Mortality Due to Congestive Heart Failure: Development, Testing, and Use for Comparing Hospitals A Multicenter Study , 1994, Medical care.

[36]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[37]  N. Freemantle,et al.  Management of heart failure in primary care (the IMPROVEMENT of Heart Failure Programme): an international survey , 2002, The Lancet.

[38]  M. Fine,et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia. , 1997, The New England journal of medicine.

[39]  F. Zannad,et al.  Incidence, clinical and etiologic features, and outcomes of advanced chronic heart failure: the EPICAL Study. Epidémiologie de l'Insuffisance Cardiaque Avancée en Lorraine. , 1999, Journal of the American College of Cardiology.

[40]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[41]  J. Tu,et al.  Prognosis and determinants of survival in patients newly hospitalized for heart failure: a population-based study. , 2002, Archives of internal medicine.

[42]  J Col,et al.  Predictors of 30-day mortality in the era of reperfusion for acute myocardial infarction. Results from an international trial of 41,021 patients. GUSTO-I Investigators. , 1995, Circulation.

[43]  C D Naylor,et al.  A predictive index for length of stay in the intensive care unit following cardiac surgery. , 1994, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[44]  Inger,et al.  A prediction rule to identify low-risk patients with community-acquired pneumonia. , 1997, The New England journal of medicine.

[45]  P. Southard,et al.  Trauma care documentation: a comprehensive guide. , 1989, Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association.

[46]  R. Roberts,et al.  Impact of a clinical decision rule on hospital triage of patients with suspected acute cardiac ischemia in the emergency department. , 2002, JAMA.

[47]  J. Steiner,et al.  Gender, age, and heart failure with preserved left ventricular systolic function. , 2003, Journal of the American College of Cardiology.

[48]  J. Habbema,et al.  Prognostic Modeling with Logistic Regression Analysis , 2001, Medical decision making : an international journal of the Society for Medical Decision Making.

[49]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.

[50]  S. Lemeshow,et al.  Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. , 1993, JAMA.

[51]  J. Pell,et al.  Evidence of Improving Prognosis in Heart Failure: Trends in Case Fatality in 66 547 Patients Hospitalized Between 1986 and 1995 , 2000, Circulation.

[52]  D. Seamark,et al.  Deaths from heart failure in general practice: implications for palliative care , 2002 .