Post kidney transplant quality of life prediction models

Donna Hathaway, Rebecca P. Winsett, Cheryl Johnson, Elizabeth Tolley, Mary Hartwig, Jean Milstead, Mona Wicks, A. Osama Gaber

Research output: Contribution to journalArticle

68 Citations (Scopus)

Abstract

Quality of life (QoL) is generally found to improve for renal transplant recipients, although some patients continue to experience health-related problems. It was within this context that we undertook our investigation which focused on identifying the factors predictive of QoL following kidney transplantation. Methods. The sample included 91 non-diabetic patients of which 69 provided 6-month data and 68 provided 12-month data. Three QoL questionnaires were administered to capture as many QoL dimensions as possible. Repeated measure analyses of variance with multiple post hoc comparisons of LS means was conducted to determine how QoL outcomes differed over time. Correlational analyses were performed on the 12-month dataset to determine which variables to include in the modeling process. Multiple stepwise regression with forward and backward entry were used in the prediction modeling. Results: Essentially all patients experienced a significant improvement in QoL and the improvement occurred early and appeared to be sustained. Five separate prediction models were constructed, each including number of hospital days in first 6 months, employment, and social support. Conclusions. The similarity of the five models is of note. It is not necessarily these specific variables per se that predict QoL outcomes, but rather what they conceptually represent. These findings provide direction for interventions designed to enhance post-transplant QoL.

Original languageEnglish (US)
Pages (from-to)168-174
Number of pages7
JournalClinical Transplantation
Volume12
Issue number3
StatePublished - Jun 1 1998

Fingerprint

Quality of Life
Transplants
Kidney
Quality Improvement
Social Support
Kidney Transplantation
Analysis of Variance
Health

All Science Journal Classification (ASJC) codes

  • Transplantation

Cite this

Hathaway, D., Winsett, R. P., Johnson, C., Tolley, E., Hartwig, M., Milstead, J., ... Gaber, A. O. (1998). Post kidney transplant quality of life prediction models. Clinical Transplantation, 12(3), 168-174.

Post kidney transplant quality of life prediction models. / Hathaway, Donna; Winsett, Rebecca P.; Johnson, Cheryl; Tolley, Elizabeth; Hartwig, Mary; Milstead, Jean; Wicks, Mona; Gaber, A. Osama.

In: Clinical Transplantation, Vol. 12, No. 3, 01.06.1998, p. 168-174.

Research output: Contribution to journalArticle

Hathaway, D, Winsett, RP, Johnson, C, Tolley, E, Hartwig, M, Milstead, J, Wicks, M & Gaber, AO 1998, 'Post kidney transplant quality of life prediction models', Clinical Transplantation, vol. 12, no. 3, pp. 168-174.
Hathaway D, Winsett RP, Johnson C, Tolley E, Hartwig M, Milstead J et al. Post kidney transplant quality of life prediction models. Clinical Transplantation. 1998 Jun 1;12(3):168-174.
Hathaway, Donna ; Winsett, Rebecca P. ; Johnson, Cheryl ; Tolley, Elizabeth ; Hartwig, Mary ; Milstead, Jean ; Wicks, Mona ; Gaber, A. Osama. / Post kidney transplant quality of life prediction models. In: Clinical Transplantation. 1998 ; Vol. 12, No. 3. pp. 168-174.
@article{250c4a31bb614f18bdd575e2a670eefe,
title = "Post kidney transplant quality of life prediction models",
abstract = "Quality of life (QoL) is generally found to improve for renal transplant recipients, although some patients continue to experience health-related problems. It was within this context that we undertook our investigation which focused on identifying the factors predictive of QoL following kidney transplantation. Methods. The sample included 91 non-diabetic patients of which 69 provided 6-month data and 68 provided 12-month data. Three QoL questionnaires were administered to capture as many QoL dimensions as possible. Repeated measure analyses of variance with multiple post hoc comparisons of LS means was conducted to determine how QoL outcomes differed over time. Correlational analyses were performed on the 12-month dataset to determine which variables to include in the modeling process. Multiple stepwise regression with forward and backward entry were used in the prediction modeling. Results: Essentially all patients experienced a significant improvement in QoL and the improvement occurred early and appeared to be sustained. Five separate prediction models were constructed, each including number of hospital days in first 6 months, employment, and social support. Conclusions. The similarity of the five models is of note. It is not necessarily these specific variables per se that predict QoL outcomes, but rather what they conceptually represent. These findings provide direction for interventions designed to enhance post-transplant QoL.",
author = "Donna Hathaway and Winsett, {Rebecca P.} and Cheryl Johnson and Elizabeth Tolley and Mary Hartwig and Jean Milstead and Mona Wicks and Gaber, {A. Osama}",
year = "1998",
month = "6",
day = "1",
language = "English (US)",
volume = "12",
pages = "168--174",
journal = "Clinical Transplantation",
issn = "0902-0063",
publisher = "Wiley-Blackwell",
number = "3",

}

TY - JOUR

T1 - Post kidney transplant quality of life prediction models

AU - Hathaway, Donna

AU - Winsett, Rebecca P.

AU - Johnson, Cheryl

AU - Tolley, Elizabeth

AU - Hartwig, Mary

AU - Milstead, Jean

AU - Wicks, Mona

AU - Gaber, A. Osama

PY - 1998/6/1

Y1 - 1998/6/1

N2 - Quality of life (QoL) is generally found to improve for renal transplant recipients, although some patients continue to experience health-related problems. It was within this context that we undertook our investigation which focused on identifying the factors predictive of QoL following kidney transplantation. Methods. The sample included 91 non-diabetic patients of which 69 provided 6-month data and 68 provided 12-month data. Three QoL questionnaires were administered to capture as many QoL dimensions as possible. Repeated measure analyses of variance with multiple post hoc comparisons of LS means was conducted to determine how QoL outcomes differed over time. Correlational analyses were performed on the 12-month dataset to determine which variables to include in the modeling process. Multiple stepwise regression with forward and backward entry were used in the prediction modeling. Results: Essentially all patients experienced a significant improvement in QoL and the improvement occurred early and appeared to be sustained. Five separate prediction models were constructed, each including number of hospital days in first 6 months, employment, and social support. Conclusions. The similarity of the five models is of note. It is not necessarily these specific variables per se that predict QoL outcomes, but rather what they conceptually represent. These findings provide direction for interventions designed to enhance post-transplant QoL.

AB - Quality of life (QoL) is generally found to improve for renal transplant recipients, although some patients continue to experience health-related problems. It was within this context that we undertook our investigation which focused on identifying the factors predictive of QoL following kidney transplantation. Methods. The sample included 91 non-diabetic patients of which 69 provided 6-month data and 68 provided 12-month data. Three QoL questionnaires were administered to capture as many QoL dimensions as possible. Repeated measure analyses of variance with multiple post hoc comparisons of LS means was conducted to determine how QoL outcomes differed over time. Correlational analyses were performed on the 12-month dataset to determine which variables to include in the modeling process. Multiple stepwise regression with forward and backward entry were used in the prediction modeling. Results: Essentially all patients experienced a significant improvement in QoL and the improvement occurred early and appeared to be sustained. Five separate prediction models were constructed, each including number of hospital days in first 6 months, employment, and social support. Conclusions. The similarity of the five models is of note. It is not necessarily these specific variables per se that predict QoL outcomes, but rather what they conceptually represent. These findings provide direction for interventions designed to enhance post-transplant QoL.

UR - http://www.scopus.com/inward/record.url?scp=0031809334&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031809334&partnerID=8YFLogxK

M3 - Article

VL - 12

SP - 168

EP - 174

JO - Clinical Transplantation

JF - Clinical Transplantation

SN - 0902-0063

IS - 3

ER -