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Q In patients presenting with heart failure, does a clinical prediction model adequately stratify the risk of death at 30 days and 1 year?
Clinical ratings GP/FP/Primary care ★★★★★★☆ IM/Ambulatory care ★★★★★★☆ Cardiology ★★★★★☆☆ Critical care ★★★★★☆☆
METHODS
Design:
2 cohort studies, 1 for derivation and 1 for validation.
Setting:
derivation cohort: 34 hospitals; validation cohort: 14 hospitals in Ontario, Canada.
Patients:
newly admitted patients (2624 for derivation [mean age 67 y, 51% women] and 1407 for validation [mean age 75 y, 51% women]) with a primary diagnosis of heart failure meeting modified Framingham heart failure criteria. Exclusion criteria included development of heart failure after admission, transfer from another acute care facility, and age ⩾105 years.
Prediction guide:
potential candidate variables were identified based on literature review, expert opinion, and availability. Data on left ventricular function were collected when available. Score based prediction rules for 30 day and 1 year mortality were developed using a coefficient based scoring method of 30 point increments (very low ⩽60, low 61–90, intermediate 91–120, high 121–150, and very high >150 points).
Outcomes:
Performance of model in predicting all cause 30 day and 1 year mortality.
MAIN RESULTS
Mortality rates at 30 days and 1 year were 10.7% and 32.9%, respectively, in the derivation cohort, and 10.4% and 30.5% in the validation cohort and increased with increasing risk scores (table). Multivariate analysis showed predictors of 30 day and 1 year mortality were increased age, decreased systolic blood pressure, increased respiratory rate, hyponatraemia, and increased urea nitrogen concentrations; and the comorbid conditions of cerebrovascular disease, dementia, chronic obstructive pulmonary disease, cirrhosis, and cancer.
30 day and 1 year mortality rates by risk category in heart failure
CONCLUSION
In patients with heart failure, a prediction model based on clinical data gathered within the initial hours of hospital presentation predicted all cause mortality at 30 days and 1 year.
Abstract and commentary also appear in ACP Journal Club.
Commentary
Prognostic factors have been identified in heart failure patients enrolled in clinical trials. However, unselected admitted heart failure patients may have different prognostic factors. From this group of patients, Lee et al identified those who met modified Framingham heart failure criteria1,2 using the Canadian Institute for Health Information hospital discharge abstract.3 Using presentation features, laboratory values, and pre-existing comorbid conditions, they constructed risk scores to predict 30 day and 1 year mortality.
Risk assessment methods are well known for patients with coronary artery disease but not for heart failure patients. Identifying patients at high risk could be helpful for targeting patients in need of treatment in specialised care units. Furthermore, end of life issues could be dealt with earlier. Identification of low risk patients who could be discharged early to outpatient care is also important in the present era of cost containment.
This study is a retrospective analysis and the amount and management of missing data are not stated. Furthermore, limited information exists about the data abstraction methods and the inter-rater reliability of the abstractions for all variables.4 Also, ventricular function was assessed in only 62% of the derivation cohort and 49% of the validation cohort. The authors state that the model does not significantly change if ventricular function is included. This might be true even in the patients without such an assessment, but this is not shown.
Lee et al have indicated that such initial routine clinical findings as age, vital signs, biochemistry, and comorbid conditions could be used to predict both 1 month and 1 year mortality in unselected heart failure patients. This finding needs to be shown in a prospective validation study.
Footnotes
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For correspondence: Dr J V Tu, Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. tuices.on.ca
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Sources of funding: Ontario Ministry of Health; Canadian Institutes of Health Research; Heart and Stroke Foundation.