2.50
Hdl Handle:
http://hdl.handle.net/10755/157725
Type:
Presentation
Title:
Mortality Prediction in Nursing Homes: Screening for Palliative Care
Abstract:
Mortality Prediction in Nursing Homes: Screening for Palliative Care
Conference Sponsor:Western Institute of Nursing
Conference Year:2009
Author:Etland, Caroline, PhD, RN
P.I. Institution Name:Palomar Pomerado Health
Title:Palliative Care CNS
Contact Address:7110 Colony Rd., La Mesa, CA, 91941, USA
Contact Telephone:760-739-3581
Purpose: This study is undertaken to retrospectively determine survival of a cohort of nursing home (NH) residents and to determine whether specific assessments accurately predict mortality within six months. Background/Conceptual Basis: Improving EOL care of the elderly is a problem of immense proportions. More than 2.4 million Americans die each year of various causes; 1% of the GNP is spent on ICU care and 23% die in nursing homes (Esserman, Belkora, & Lenert, 1995). Upon admission to a NH, 35% will die within one year (van Dijk, et al., 2000). Predicting decline and death in NH residents can be difficult and complex, and delays in referral to appropriate supportive care results in undesired medical intervention and unnecessary suffering in advanced illness. The Quality Health Outcomes Model (Mitchell, et al., 1998) reflects a dynamic, interrelated quality system that flows in many directions, and whose elements all affect each other, rather than structure and process producing outcomes in a linear fashion. This quality model is useful with chronic illness because of the many exacerbations and remissions prior to death. Methods: A retrospective correlational cohort study was conducted to examine mortality in residents of two nursing homes (NH) in southern California. The sample of 191 NH residents was taken from the Minimum Data Set (MDS) federal database of nursing homes that receive Medicare reimbursement. Nine MDS items were analyzed using correlational statistics to determine relationships between the IVs and DV. Logistic regression statistics provided information about the predictive model and how well it fit with the sample. Multicollinearity was analyzed to ensure that variables were measuring different constructs. Results: Analysis was performed on a final sample of 81 subjects who died during the evaluation period. Regression coefficients were generally low (range 0.09-0.46) and none demonstrated significance in the likelihood ratio test. Odds ratios were uniformly low (range 0.52-2.26) as well. Differences between the 2 NH were negligible in the correlational analysis, as well as the logistic regression statistics and overall mortality. IMPLICATIONS: Nurses are affected by the professional norms regarding prognostication that exist among physicians. Assessing for, correctly identifying and intervening when prognostic signs appear over time can be considered the prevention of harm and undesirable outcomes. Normalizing discussions regarding predictions of future health outcomes can enhance communication between patients, families and healthcare providers. Future research should explore how best to access and incorporate prognostic information into treatment decision making. Pilot studies of prognostic scales used in screening for palliative care services can determine utility for interdisciplinary teams in NH.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleMortality Prediction in Nursing Homes: Screening for Palliative Careen_GB
dc.identifier.urihttp://hdl.handle.net/10755/157725-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Mortality Prediction in Nursing Homes: Screening for Palliative Care</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2009</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Etland, Caroline, PhD, RN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Palomar Pomerado Health</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Palliative Care CNS</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">7110 Colony Rd., La Mesa, CA, 91941, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">760-739-3581</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">caroline.etland@pph.org, caroline.etland@cox.net</td></tr><tr><td colspan="2" class="item-abstract">Purpose: This study is undertaken to retrospectively determine survival of a cohort of nursing home (NH) residents and to determine whether specific assessments accurately predict mortality within six months. Background/Conceptual Basis: Improving EOL care of the elderly is a problem of immense proportions. More than 2.4 million Americans die each year of various causes; 1% of the GNP is spent on ICU care and 23% die in nursing homes (Esserman, Belkora, &amp; Lenert, 1995). Upon admission to a NH, 35% will die within one year (van Dijk, et al., 2000). Predicting decline and death in NH residents can be difficult and complex, and delays in referral to appropriate supportive care results in undesired medical intervention and unnecessary suffering in advanced illness. The Quality Health Outcomes Model (Mitchell, et al., 1998) reflects a dynamic, interrelated quality system that flows in many directions, and whose elements all affect each other, rather than structure and process producing outcomes in a linear fashion. This quality model is useful with chronic illness because of the many exacerbations and remissions prior to death. Methods: A retrospective correlational cohort study was conducted to examine mortality in residents of two nursing homes (NH) in southern California. The sample of 191 NH residents was taken from the Minimum Data Set (MDS) federal database of nursing homes that receive Medicare reimbursement. Nine MDS items were analyzed using correlational statistics to determine relationships between the IVs and DV. Logistic regression statistics provided information about the predictive model and how well it fit with the sample. Multicollinearity was analyzed to ensure that variables were measuring different constructs. Results: Analysis was performed on a final sample of 81 subjects who died during the evaluation period. Regression coefficients were generally low (range 0.09-0.46) and none demonstrated significance in the likelihood ratio test. Odds ratios were uniformly low (range 0.52-2.26) as well. Differences between the 2 NH were negligible in the correlational analysis, as well as the logistic regression statistics and overall mortality. IMPLICATIONS: Nurses are affected by the professional norms regarding prognostication that exist among physicians. Assessing for, correctly identifying and intervening when prognostic signs appear over time can be considered the prevention of harm and undesirable outcomes. Normalizing discussions regarding predictions of future health outcomes can enhance communication between patients, families and healthcare providers. Future research should explore how best to access and incorporate prognostic information into treatment decision making. Pilot studies of prognostic scales used in screening for palliative care services can determine utility for interdisciplinary teams in NH.</td></tr></table>en_GB
dc.date.available2011-10-26T20:08:41Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:08:41Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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