lucille travis phd, rn, ne-bc professor, unc-charlotte son sonya r. hardin phd, rn, np-c

UNC Charlotte School of Nursing

Change in Quality of Life in Heart Failure Patients who utilize a Nurse Managed

Population Based (PBMC) Heart Failure Clinic

Lucille Travis PhD, RN, NE-BCProfessor, UNC-Charlotte SON

Sonya R. Hardin PhD, RN, NP-CProfessor, UNC-Charlotte SON

Zeleka Benton MSN, RNNovant Health, Charlotte, NC

Leigh Austin MSN, RN, ANPNovant Health

Funding for this research was supported in part by aUNC-Charlotte Faculty Research Grant


Background

Heart Failure affects over 5.7 million Americans. Heart Failure is among leading indication for

hospitalization and the discharge diagnosis for patients over 65.

39.2 billion dollars spent on heart failure related care in 2010.

Patients diagnosed with heart failure report poor quality of life, including physical, emotional and economic burden.

Research shows that early intervention after heart failure diagnosis improves survival rates, reduces readmissions, improves patient compliance with medication and diet, all leading to an improved quality of life.


Purpose

Purpose of the study was to examine the change over time of quality of life (QOL) in heart failure patients utilizing population based managed care (PBMC) in an urban nurse-managed heart failure clinic in NC.


AIMS

To examine quality of life in HF patients at: baseline6 months12 months

To correlate physiological and demographics with quality of life in HF patients.


IRB Approval

IRB approval was obtained from: UNC Charlotte Novant Health

Upon IRB approval, investigators began recruiting HF patients.


Setting

Study conducted in a nurse-managed disease management program.

Program provides education, treatment and management of heart failure patients.

Clinic team consists of a nurse practitioner, expert nurse clinician, dietitian, social worker and resource specialist.


Participant Enrollment Criteria

Criteria to participate included:18 years of age or olderAbility to speak and read EnglishNew York Heart Association (NYHA) stage

of II, III, or IVNew referral to Heart Failure Clinic

Data was collected from a convenient sample of HF patients enrolled in a nurse-managed HF outpatient clinic.


Demographics of Sample (n=80)

Characteristics

Gender

Male

Female

N

48

32

Percent

60.0%

40.0%

Race

Caucasian

African-American

Unknown

46

33

1

57.5%

41.3%

1.3%

New York Heart Failure

Classification (NYHA)

I

II

III

IV

0

15

52

13

0%

18.8%

65.0%

16.3%


Methods

This was a descriptive correlational repeated measure design study.

Subjects completed the Quality of Life Survey (SF-12).It was part of the regular patient work up at

each visit.Each subject completed 3 surveys:

baseline, 6 months and 12 months.Demographic data was collected.


Instrument

SF-12 – a short form health survey with 12 questions was used to assess quality of life.

Survey includes questions related to:Physical functioningRole functioning physicalBodily painGeneral health VitalitySocial functioningRole functioning emotional Mental health


Instrument

SF-12 can be reported as a total score, Physical component score (PCS) and Mental component score (MCS)

Validity and reliability of the SF-12 are well establishedReliability scores range from 0.91 to 0.92Validity scores for the PCS range from 0.43 to 0.93

(median=0.67)Validity scores for the MCS range from 0.60-1.07

(median=0.97)SF-12 has been found to be a satisfactory tool for

monitoring overall physical and mental health outcomes.


Understanding Results of SF-12

Results expressed in 2 meta-scores:Physical Component Summary (PCS)Mental Component Summary (MCS)

High scores indicate better function and quality of life.

PCS and MCS scores range from 0-100Mean score 50; representing average

health status of the general population.


Mean, Median, Range of PCS and MSC Scores (n=80)

PCS MCS

Mean32.7729 49.9517

Median32.8000 50.4500

Std. Deviation 9.42027 10.31398

Minimum10.60 18.90

Maximum60.60 71.20

PCS and MCS calculated for a total average over 3 time points

These are of interest given the population mean is 50


Mean Difference in PCS Scores and MCS Scores from Time 1 to Time 3

Time N Mean Std. Dev Std. Error Mean T-Test1.00 80 30.8912 8.8160 .98566 Sig .

level 0.638

PCS 3.00 80 34.1450 99.5505 11.06779

Time N MeanStd.Dev

Std.ErrorMean T-Test

MCS 1.00 80 48.5925 11.281 1.26136

3.00 80 50.0750 9.1078 1.01829 Sig . level 0.0858

A t-test was performed to look at differences in PCS and MCS scores from Time 1 and Time 3 (no significant change was noted)

No significant decline for PSC or MCS across time Clinically significant as stabilization of scores can be seen over 12 months

p<.05


Mean and SD of PCS and MCS scores at Baseline, 6 and 12 months

PCS Baseline (SD=8.81)

PCS 6 months (SD=9.68)

PCS 12 months (SD=9.55)

MCS Baseline (SD-11.28)

MCS 6 months (SD=10.39)

MCS 12 months (SD=9.10)

0

10

20

30

40

50

60

Mean

Mean

Trends in PCS and MCS scores from baseline to 6 to 12 months


Mean Difference in PCS and MCS between Races

Race N MeanStd.

DeviationStd. Error

Mean

Significance

MCS white 46 49.7891 10.16952 .86569 NS

black 33 50.2162 10.35950 1.04117

PCS white 46 31.5196 8.95351 .76217 NS

black 33 34.4303 9.63990 .96885

p<.05

Average score on the PCS and MCS scores were higher for Black patients

No significant differences between races on the PCS or MCS at baseline, 6 and 12 months


Mean Differences between Men and Woman

Wom

en P

CS Bas

eline

(SD=8.

34)

Men

PCS B

aseli

ne (S

D=9.15

)

Wom

en P

CS 6 m

onth

s (S

D=8.80

)

Men

PCS 6

mon

ths

(SD=9.

68)

Wom

en P

CS 12

mon

ths

(SD=9.

45)

Men

PCS 1

2 m

onth

s (S

D=9.65

)

Wom

en M

CS bas

eline

(SD+11

.20)

Men

MCS B

aseli

ne (

SD=11.4

1)

Wom

en M

CS 6 m

onth

s (S

D=11.0

0)

Men

MCS 6

mon

ths

(SD=9.

72)

Wom

en M

CS 12

Mon

ths

(SD=8.

36)

Men

MCS 1

2 m

onth

s (S

D=9.64

)0

10

20

30

40

50

60

30.06 31.21 31.21 33.28 33.17 34.79

47.69 49.19 48.6552.87

49.57 50.41

Mean

Men

Women

PCS MCS

No significant difference between gender on PCS and MCS at significance of .05


Mean Scores and SD per Heart Failure Stage (NYHA)

Stage 2 (sd) Stage 3 (sd) Stage 4 (sd)

PCS 35.81(8.70) 32.06 (9.34) 32.10 (10.01)

MCS 51.59 (10.62) 49.52 (10.53) 49.75 (9.05)

Table shows the slow decline of MCS and PCS scores between NYHA stages II and III

MCS and PCS scores were stable between NYHA stages III and IV


Effects of Age on Physical and Mental scores at each time period

Controlled for the covariates of NYHA stage and time.

Significant difference in PCS and MCS scores between subjects aged 28-69 and those older than 70.

With small sample size, further studies are needed to examine other confounding variables which could help explain differences in PCS and MCS with age.


Summary of Results

There were positive changes in MCS scores over 12 month period.

There was a significant difference between QOL and HF stage.

There was a significance difference in MCS and PCS score between subjects aged 30-69 and those >70.

The PCS and MCS average scores were higher for black subjects overall.

There was no difference in results between men and women. No statistically significant difference in total QOL scores

between baseline, 6 and 12 months. PCS remained unchanged over time.


Discussion

Data suggest that nurse-manage PBMC heart failure clinics helped to maintain quality of life in HF over a 12 month period.

Participants had PCS scores well below those of the general US health population.

MSC scores were at the average score for the US population.

MSC scores essentially were maintained over 12 months of participation in the HF disease management program.


Limitations

Small sample sizeGeneralization of the study is limitedWhile changes across time were minimal,

a larger sample size could possibility yield significant differences


Nursing Implications

This study results provide evidence to support the use of nurse-managed disease management programs for HF patients to improve both health related outcomes and quality of life.

Similar programs have been found to reduce hospital admissions, emergency room visits, hospital days and improve quality of life.


Thank you for your kind attentionQuestions???


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lucille travis phd, rn, ne-bc professor, unc-charlotte son sonya r. hardin phd, rn, np-c

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