Predicting Hospital Readmission Elderly Equation | Medical Algorithm

Description

Pacala et al identified comorbid conditions that could predict the probability of repeated hospital admission in an elderly patient. This can help identify those patients who may require closer monitoring or more aggressive therapy. The authors are from the University of Minnesota.

Parameters:

(1) self-rated health

(2) coronary artery disease (including angina pectoris, myocardial infarction)

(3) diabetes mellitus in past year

(4) hospitalization in past year

(5) number of doctor visits in past year

(6) presence of an informal caregiver able to care for the patient for several days

(7) age

(8) gender

Parameter	Finding	Points
self rated health	poor	0.770
	fair	0.552
	good	0.340
	very good	0.327
coronary artery disease	absent	0
	present	0.390
diabetes mellitus in past year	absent	0
	present	0.319
hospitalized within past year	no	0
	yes	0.545
number of doctor visits in past year	<= 6 visits	0
	> 6 visits	0.318
informal caregiver available (friend, relative, neighbor)	none	-0.738
	present	0
age in years	< 75 years	0
	75 – 79 years	0.255
	80 – 84 years	0.327
	>= 85 years	0.559
gender	male	0.257
	female	0

where:

• The points assigned are the regression coefficients for each parameter from Appendix B.

• I am confused by the point assignment for informal caregiver. I would think the presence of a friend, relative or neighbor able to care for the patient would reduce the hospital admissions. However, as the table in Appendix B is written, an informal caregiver increases the risk of admission. Perhaps a person without a caregiver dies without being admitted.

X =

= SUM(points for all 8 parameters) – 1.802

probability of being repeatedly admitted to the hospital within the next 4 years =

= EXP(X) / (1 + EXP(X))

To read more or access our algorithms and calculators, please log in or register.

Description

Objective

ICD-10

Predicting Probability of Hospital Readmission for Elderly Patients

Purpose: To identify an elderly patient at risk for 2 or more hospital admissions during the next 4 years using the logistic regression equation of Pacala et al.

Description

Objective

ICD-10