Joshua D Niznik1,2,3, Song Zhang1, Maria K Mor1,4, Xinhua Zhao1, Mary Ersek5,6, Sherrie L Aspinall1,2,7, Walid F Gellad1,8, Joshua M Thorpe1,2, Joseph T Hanlon1,2,3, Loren J Schleiden1,2, Sydney Springer1,2, Carolyn T Thorpe1,2. 1. Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania. 2. School of Pharmacy, Pittsburgh, Pennsylvania. 3. Geriatric Division, School of Medicine, Pittsburgh, Pennsylvania. 4. Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania. 5. National PROMISE Center; Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania. 6. School of Nursing, University of Pennsylvania, Philadelphia, Pennsylvania. 7. Veterans Affairs Center for Medication Safety, Hines, Illinois. 8. Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
Abstract
OBJECTIVES: To evaluate the predictive validity of an adapted version of the Minimum Data Set (MDS) Mortality Risk Index-Revised (MMRI-R) based on MDS version 3.0 assessment items (MMRI-v3) and to compare the predictive validity of the MMRI-v3 with that of a single MDS item indicating limited life expectancy (LLE). DESIGN: Retrospective, cross-sectional study of MDS assessments. Other data sources included the Veterans Affairs (VA) Residential History File and Vital Status File. SETTING: VA nursing homes (NHs). PARTICIPANTS: Veterans aged 65 and older newly admitted to VA NHs between July 1, 2012, and September 30, 2015. MEASUREMENTS: The dependent variable was death within 6 months of admission date. Independent variables included MDS items used to calculate MMRI-v3 scores (renal failure, chronic heart failure, sex, age, dehydration, cancer, unintentional weight loss, shortness of breath, activity of daily living scale, poor appetite, acute change in mental status) and the MDS item indicating LLE. RESULTS: The predictive ability of the MMRI-v3 for 6-month mortality (c-statistic 0.81) is as good as that of the original MMRI-R (c-statistic 0.76). Scores generated using the MMRI-v3 had greater predictive ability than that of the single MDS indicator for LLE (c-statistic 0.76); using the 2 together resulted in greater predictive ability (c-statistic 0.86). CONCLUSION: The MMRI-v3 is a useful tool in research and clinical practice that accurately predicts 6-month mortality in veterans residing in Veterans Affairs NHs. Identification of residents with LLE has great utility for studying palliative care interventions and may be helpful in guiding allocation of these services in clinical practice. J Am Geriatr Soc 66:2353-2359, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
OBJECTIVES: To evaluate the predictive validity of an adapted version of the Minimum Data Set (MDS) Mortality Risk Index-Revised (MMRI-R) based on MDS version 3.0 assessment items (MMRI-v3) and to compare the predictive validity of the MMRI-v3 with that of a single MDS item indicating limited life expectancy (LLE). DESIGN: Retrospective, cross-sectional study of MDS assessments. Other data sources included the Veterans Affairs (VA) Residential History File and Vital Status File. SETTING: VA nursing homes (NHs). PARTICIPANTS: Veterans aged 65 and older newly admitted to VA NHs between July 1, 2012, and September 30, 2015. MEASUREMENTS: The dependent variable was death within 6 months of admission date. Independent variables included MDS items used to calculate MMRI-v3 scores (renal failure, chronic heart failure, sex, age, dehydration, cancer, unintentional weight loss, shortness of breath, activity of daily living scale, poor appetite, acute change in mental status) and the MDS item indicating LLE. RESULTS: The predictive ability of the MMRI-v3 for 6-month mortality (c-statistic 0.81) is as good as that of the original MMRI-R (c-statistic 0.76). Scores generated using the MMRI-v3 had greater predictive ability than that of the single MDS indicator for LLE (c-statistic 0.76); using the 2 together resulted in greater predictive ability (c-statistic 0.86). CONCLUSION: The MMRI-v3 is a useful tool in research and clinical practice that accurately predicts 6-month mortality in veterans residing in Veterans Affairs NHs. Identification of residents with LLE has great utility for studying palliative care interventions and may be helpful in guiding allocation of these services in clinical practice. J Am Geriatr Soc 66:2353-2359, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
Entities:
Keywords:
MDS; mortality; nursing home; prognostic index
Authors: Kali S Thomas; Jessica A Ogarek; Joan M Teno; Pedro L Gozalo; Vincent Mor Journal: J Gerontol A Biol Sci Med Sci Date: 2019-01-16 Impact factor: 6.053
Authors: Jessica A Ogarek; Ellen M McCreedy; Kali S Thomas; Joan M Teno; Pedro L Gozalo Journal: J Am Geriatr Soc Date: 2018-03-02 Impact factor: 5.562
Authors: Joshua D Niznik; Xinhua Zhao; Meiqi He; Sherrie L Aspinall; Joseph T Hanlon; Laura C Hanson; David Nace; Joshua M Thorpe; Carolyn T Thorpe Journal: J Am Geriatr Soc Date: 2019-11-26 Impact factor: 5.562
Authors: Sydney P Springer; Maria K Mor; Florentina Sileanu; Xinhua Zhao; Sherrie L Aspinall; Mary Ersek; Joshua D Niznik; Joseph T Hanlon; Jacob Hunnicutt; Walid F Gellad; Loren J Schleiden; Joshua M Thorpe; Carolyn T Thorpe Journal: J Am Geriatr Soc Date: 2020-02-13 Impact factor: 5.562
Authors: Joshua D Niznik; Xinhua Zhao; Florentina Slieanu; Maria K Mor; Sherrie L Aspinall; Walid F Gellad; Mary Ersek; Ryan P Hickson; Sydney P Springer; Loren J Schleiden; Joseph T Hanlon; Joshua M Thorpe; Carolyn T Thorpe Journal: Diabetes Care Date: 2022-07-07 Impact factor: 17.152
Authors: Joshua D Niznik; Jacob N Hunnicutt; Xinhua Zhao; Maria K Mor; Florentina Sileanu; Sherrie L Aspinall; Sydney P Springer; Mary J Ersek; Walid F Gellad; Loren J Schleiden; Joseph T Hanlon; Joshua M Thorpe; Carolyn T Thorpe Journal: J Am Geriatr Soc Date: 2020-02-17 Impact factor: 5.562
Authors: Carolyn T Thorpe; Florentina E Sileanu; Maria K Mor; Xinhua Zhao; Sherrie Aspinall; Mary Ersek; Sydney Springer; Joshua D Niznik; Michelle Vu; Loren J Schleiden; Walid F Gellad; Jacob Hunnicutt; Joshua M Thorpe; Joseph T Hanlon Journal: J Am Geriatr Soc Date: 2020-08-12 Impact factor: 5.562
Authors: Michelle Vu; Florentina E Sileanu; Sherrie L Aspinall; Joshua D Niznik; Sydney P Springer; Maria K Mor; Xinhua Zhao; Mary Ersek; Joseph T Hanlon; Walid F Gellad; Loren J Schleiden; Joshua M Thorpe; Carolyn T Thorpe Journal: J Am Med Dir Assoc Date: 2020-07-25 Impact factor: 4.669
Authors: Joshua D Niznik; Xintong Li; Meredith A Gilliam; Laura C Hanson; Sherrie L Aspinall; Cathleen Colon-Emeric; Carolyn T Thorpe Journal: J Am Med Dir Assoc Date: 2020-12-13 Impact factor: 7.802