CHF Prognostication

I favor the model described by Lee et al in the following study:

Predicting Mortality Among Patients Hospitalized for Heart Failure

JAMA.2003;290(19):2581-2587.

ACP Journal Club Commentary.

The actual risk prediction tool uses easily obtained variables (e.g. Age • Respiratory rate • Systolic blood pressure • Blood urea nitrogen • Serum sodium • Comorbid conditions: cerebrovascular disease, dementia, chronic obstructive pulmonary disease, cirrhosis, cancer, anemia) and the 30d. and 1 year risk score can be computed online:

http://www.ccort.ca/Research/CHFRiskModel.aspx

It has also been validated in populations in the US. 

Risk categories (score)	30-day mortality rate		1-year mortality rate
	Derivation	Validation	Derivation	Validation
Very low ( 60)	0.4%	0.6%	7.8%	2.7%
Low (61 to 90)	3.4%	4.2%	12.9%	14.4%
Intermediate (91 to 120)	12.2%	13.7%	32.5%	30.2%
High (121 to 150)	32.7%	26.0%	59.3%	55.5%
Very high (> 150)	59.0%	50.0%	78.8%	74.7%

A more recent article uses the same model to provide a median survival based on the risk scores above:

Life expectancy after an index hospitalization for patients with heart failure: a population-based study.

Am Heart J. 2008 Feb;155(2):324-31. PubMed PMID:18215604.

  

Table II. Mortality and life expectancy for all patients with HF according to the EFFECT HF risk score

	No. of patients	1-y mortality, n (%)	5-y mortality, n (%)	Median survival		Median survival for patients who survived first 3 m after hospitalization
				Months	95% CI	Months	95% CI
All patients with HF	9943	3294 (33.1%)	6833 (68.7%)	29	28-30	41	40-43
Baseline risk⁎
Very low	490	41 (8.4%)	117 (23.9%)	NA	NA	NA	NA
Low	3101	502 (16.2%)	1573 (50.7%)	59	55-62	66	63-71
Intermediate	4225	1450 (34.3%)	3185 (75.4%)	25	24-27	33	32-35
High	1770	1034 (58.4%)	1618 (91.4%)	8	7-9	18	17-20
Very high	357	267 (74.5%)	340 (95.2%)	3	2-4	12	10-15

NA, Not available.

Table IV. Mortality and life expectancy for patients with HF who had LVEF of ≤30% according to the EFFECT HF risk score

	No. of patients, n	1-y mortality, n (%)	5-y mortality, n (%)	Median survival		Median survival for patients who survived first 3 m after hospitalization
				Months	95% CI	Months	95% CI
All patients	1,467	489 (33.3%)	966 (65.8%)	31	27-35	46	42-49
Baseline risk⁎
Very low	131	16 (12.2%)	32 (24.4%)	NA	NA	NA	NA
Low	498	89 (17.9%)	252 (50.6%)	59	51-67	66	60-NA
Intermediate	559	207 (37.0%)	423 (75.7%)	22	19-27	32	28-36
High	225	140 (62.2%)	210 (93.3%)	6	4-9	18	14-21
Very high	54	37 (68.5%)	49 (90.7%)	3	2-8	14	7-24

 

Also useful is the EPERC FAST FACTS on CHF Prognostication and the Readmission Risk Calculator for CHF (also includes MI and Pneumonia).

The Central Role of Prognosis in Clinical Decision Making

JAMA Network | JAMA: The Journal of the American Medical Association | The Central Role of Prognosis in Clinical Decision Making

Physicians should be trained to consider prognosis in their clinical decision making. As a starting point, age-, sex-, and race-specific life expectancies (median and interquartile range) can be calculated using data from standard life tables.
Physicians could then make qualitative judgments, based on information from the medical record or clinical assessment, about whether a patient is likely to live substantially longer or shorter than an average person in his or her age and race cohort. The strongest and most consistent predictors of mortality in older persons include comorbidity and functional status. Lung disease requiring regular use of corticosteroids or supplemental oxygen, New York Heart Association class III or IV congestive heart failure, renal disease requiring dialysis, advanced dementia, inability to walk more than a block, and need for personal assistance with bathing are examples of factors that would reduce life expectancy substantially below the average.
The absence of significant comorbid conditions or functional limitations would identify older persons who are likely to live longer than average.