Development and Validation of the Nursing Home Minimum Data Set 3.0 Mortality Risk Score (MRS3)
To develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments. We developed a training cohort of Medicare beneficiaries newly admitted to United States NHs during 201...
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| Veröffentlicht in: | The journals of gerontology. Series A, Biological sciences and medical sciences Jg. 74; H. 2; S. 219 |
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16.01.2019
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| Abstract | To develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments.
We developed a training cohort of Medicare beneficiaries newly admitted to United States NHs during 2012 (N = 1,426,815) and a testing cohort from 2013 (N = 1,160,964).
Data came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30- and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission.
The 30- and 60-day mortality rates for the testing population were 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 [95% confidence limit (CL) = 0.741, 0.747] and 0.709 [95% CL = 0.706, 0.711], respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 [95% CL = 0.607, 0.615] and 0.608 [95% CL = 0.605, 0.610]) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 [95% CL = 0.542, 0.545] and 0.528 [95% CL = 0.527, 0.529]).
The MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality. |
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| AbstractList | To develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments.BackgroundTo develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments.We developed a training cohort of Medicare beneficiaries newly admitted to United States NHs during 2012 (N = 1,426,815) and a testing cohort from 2013 (N = 1,160,964).ParticipantsWe developed a training cohort of Medicare beneficiaries newly admitted to United States NHs during 2012 (N = 1,426,815) and a testing cohort from 2013 (N = 1,160,964).Data came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30- and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission.MethodsData came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30- and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission.The 30- and 60-day mortality rates for the testing population were 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 [95% confidence limit (CL) = 0.741, 0.747] and 0.709 [95% CL = 0.706, 0.711], respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 [95% CL = 0.607, 0.615] and 0.608 [95% CL = 0.605, 0.610]) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 [95% CL = 0.542, 0.545] and 0.528 [95% CL = 0.527, 0.529]).ResultsThe 30- and 60-day mortality rates for the testing population were 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 [95% confidence limit (CL) = 0.741, 0.747] and 0.709 [95% CL = 0.706, 0.711], respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 [95% CL = 0.607, 0.615] and 0.608 [95% CL = 0.605, 0.610]) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 [95% CL = 0.542, 0.545] and 0.528 [95% CL = 0.527, 0.529]).The MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality.ConclusionsThe MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality. To develop a score to predict mortality using the Minimum Data Set 3.0 (MDS 3.0) that can be readily calculated from items collected during nursing home (NH) residents' admission assessments. We developed a training cohort of Medicare beneficiaries newly admitted to United States NHs during 2012 (N = 1,426,815) and a testing cohort from 2013 (N = 1,160,964). Data came from the MDS 3.0 assessments linked to the Medicare Beneficiary Summary File. Using the training dataset, we developed a composite MDS 3.0 Mortality Risk Score (MRS3) consisting of 17 clinical items and patients' age groups based on their relation to 30-day mortality. We assessed the calibration and discrimination of the MRS3 in predicting 30- and 60-day mortality and compared its performance to the Charlson Comorbidity Index and the clinician's assessment of 6-month prognosis measured at admission. The 30- and 60-day mortality rates for the testing population were 2.8% and 5.6%, respectively. Results from logistic regression models suggest that the MRS3 performed well in predicting death within 30 and 60 days (C-Statistics of 0.744 [95% confidence limit (CL) = 0.741, 0.747] and 0.709 [95% CL = 0.706, 0.711], respectively). The MRS3 was a superior predictor of mortality compared to the Charlson Comorbidity Index (C-statistics of 0.611 [95% CL = 0.607, 0.615] and 0.608 [95% CL = 0.605, 0.610]) and the clinicians' assessments of patients' 6-month prognoses (C-statistics of 0.543 [95% CL = 0.542, 0.545] and 0.528 [95% CL = 0.527, 0.529]). The MRS3 is a good predictor of mortality and can be useful in guiding decision-making, informing plans of care, and adjusting for patients' risk of mortality. |
| Author | Mor, Vincent Ogarek, Jessica A Gozalo, Pedro L Teno, Joan M Thomas, Kali S |
| Author_xml | – sequence: 1 givenname: Kali S surname: Thomas fullname: Thomas, Kali S organization: Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island – sequence: 2 givenname: Jessica A surname: Ogarek fullname: Ogarek, Jessica A organization: Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island – sequence: 3 givenname: Joan M surname: Teno fullname: Teno, Joan M organization: Division of General Internal Medicine and Geriatrics, Oregon Health Science University, Portland, Oregon – sequence: 4 givenname: Pedro L surname: Gozalo fullname: Gozalo, Pedro L organization: Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island – sequence: 5 givenname: Vincent surname: Mor fullname: Mor, Vincent organization: Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29514187$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Aged Aged, 80 and over Cause of Death - trends Female Follow-Up Studies Humans Male Medicare - statistics & numerical data Nursing Homes - statistics & numerical data Prognosis Retrospective Studies Risk Assessment - methods Survival Rate - trends Time Factors United States - epidemiology |
| Title | Development and Validation of the Nursing Home Minimum Data Set 3.0 Mortality Risk Score (MRS3) |
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