2.50
Hdl Handle:
http://hdl.handle.net/10755/153771
Type:
Presentation
Title:
Predicting Limited Life Expectancy in Long-Term Care (LTC)
Abstract:
Predicting Limited Life Expectancy in Long-Term Care (LTC)
Conference Sponsor:Sigma Theta Tau International
Conference Year:2004
Conference Date:July 22-24, 2004
Author:Prevost, Suzanne, RN, PhD
P.I. Institution Name:Middle Tennessee State University
Co-Authors:J. Brandon Wallace, PhD
Objective: To identify factors and develop a model to predict the probability of limited life expectancy (<6 months) following admission to LTC. Design: Correlational, multiple regression Sample: 15,050 residents admitted to LTC Setting: 76 LTC facilities across the U.S. Years: 2001-2003 Variables: The criterion variable was death within six months of LTC admission. Predictor variables were derived from a preliminary analysis of correlations between Minimum Data Set (MDS) admission assessment factors and death within a year. Some of the most significant predictive factors included: male sex, withdrawal from social interactions, resistance to nursing care, being bedfast, diagnoses of: dehydration, resistant infection, cancer, or the presence of an amputation. Methods: Secondary data analysis and logistic regression of MDS admission data, linked with mortality data. Using the model derived from logistic regression, we calculated risk scores for the probability of dying within six months. Findings: 2,210 residents died within 6 months of admission. Only 309 (14.0%) of these residents who died had been identified and coded as terminal by the facility staff. Only 188 (8.5%) of the residents who died were receiving hospice/palliative care. Using our model, 496 (22.4%) of the residents who died could have been identified on admission as being terminal. Using the highest risk category in our model (.75 - .99 probability of death), 173 patients could have been identified on admission as being at the highest risk of death; and 147 (85%) of those residents actually died within six months. Conclusions: MDS admission data, collected by all U.S. LTC facilities, may be useful to increase the sensitivity and specificity of identifying terminal patients. Implications: This information could be used to increase coding accuracy, trigger palliative care interventions, implement advanced directives, and improve patient and family education and preparation for end of life decisions.
Repository Posting Date:
26-Oct-2011
Date of Publication:
22-Jul-2004
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titlePredicting Limited Life Expectancy in Long-Term Care (LTC)en_GB
dc.identifier.urihttp://hdl.handle.net/10755/153771-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Predicting Limited Life Expectancy in Long-Term Care (LTC)</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2004</td></tr><tr class="item-conference-date"><td class="label">Conference Date:</td><td class="value">July 22-24, 2004</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Prevost, Suzanne, RN, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Middle Tennessee State University</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">sprevost@mtsu.edu</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">J. Brandon Wallace, PhD</td></tr><tr><td colspan="2" class="item-abstract">Objective: To identify factors and develop a model to predict the probability of limited life expectancy (&lt;6 months) following admission to LTC. Design: Correlational, multiple regression Sample: 15,050 residents admitted to LTC Setting: 76 LTC facilities across the U.S. Years: 2001-2003 Variables: The criterion variable was death within six months of LTC admission. Predictor variables were derived from a preliminary analysis of correlations between Minimum Data Set (MDS) admission assessment factors and death within a year. Some of the most significant predictive factors included: male sex, withdrawal from social interactions, resistance to nursing care, being bedfast, diagnoses of: dehydration, resistant infection, cancer, or the presence of an amputation. Methods: Secondary data analysis and logistic regression of MDS admission data, linked with mortality data. Using the model derived from logistic regression, we calculated risk scores for the probability of dying within six months. Findings: 2,210 residents died within 6 months of admission. Only 309 (14.0%) of these residents who died had been identified and coded as terminal by the facility staff. Only 188 (8.5%) of the residents who died were receiving hospice/palliative care. Using our model, 496 (22.4%) of the residents who died could have been identified on admission as being terminal. Using the highest risk category in our model (.75 - .99 probability of death), 173 patients could have been identified on admission as being at the highest risk of death; and 147 (85%) of those residents actually died within six months. Conclusions: MDS admission data, collected by all U.S. LTC facilities, may be useful to increase the sensitivity and specificity of identifying terminal patients. Implications: This information could be used to increase coding accuracy, trigger palliative care interventions, implement advanced directives, and improve patient and family education and preparation for end of life decisions.</td></tr></table>en_GB
dc.date.available2011-10-26T12:30:16Z-
dc.date.issued2004-07-22en_GB
dc.date.accessioned2011-10-26T12:30:16Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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