Literature DB >> 31177131

RAND-36-Item Health Survey: A Comprehensive Test for Long-term Outcome and Health Status Following Surgery.

Iina Saimanen1, Viivi Kuosmanen1, Dina Rahkola1, Tuomas Selander2, Jari Kärkkäinen1, Jukka Harju3, Samuli Aspinen1, Matti Eskelinen4.   

Abstract

BACKGROUND/AIM: The aim of this study was to assess the 3-year health status of cholecystectomy patients by the RAND-36 Survey. PATIENTS AND METHODS: Initially, 110 patients with symptomatic gallstone disease were randomized to undergo either minicholecystectomy (MC) (n=58) or laparoscopic cholecystectomy (LC) (n=52). RAND-36 survey was performed preoperatively, 4 weeks, 6 months and 3 years following surgery.
RESULTS: RAND-36 scores improved in several RAND-36 domains in MC and LC groups with a similar postoperative course over the 3-year study period. In addition, at the 3-year follow-up telephone interview, no significant differences in patient-reported outcome measures between MC and LC patients were shown. The linear mixed effect model was used to test the overall significance of the RAND-36 survey during a 36-month follow-up period and the overall p-values were statistically significant in vitality, mental health (0.03), role physical and bodily pain domains.
CONCLUSION: During the three years following cholecystectomy, four RAND-36 domains remained significantly higher, indicating a significant positive change in quality of life. RAND-36-Item Health Survey is a comprehensive test for analyzing long-term outcome and health status after cholecystectomy. Copyright
© 2019, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Entities:  

Keywords:  RAND–36; cholecystectomy; long-term outcome; surgery

Mesh:

Year:  2019        PMID: 31177131     DOI: 10.21873/anticanres.13422

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


  2 in total

1.  A Finnish Version of RAND-36-Item Health Survey Versus Structured Interview 8 Years Postoperatively.

Authors:  Iina Saimanen; Viivi Kuosmanen; Jukka Harju; Tuomas Selander; Samuli Aspinen; Matti Eskelinen
Journal:  In Vivo       Date:  2021 Mar-Apr       Impact factor: 2.155

2.  Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes.

Authors:  Deepika Verma; Duncan Jansen; Kerstin Bach; Mannes Poel; Paul Jarle Mork; Wendy Oude Nijeweme d'Hollosy
Journal:  BMC Med Inform Decis Mak       Date:  2022-09-01       Impact factor: 3.298

  2 in total

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