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Integrating the Surprise Question, Palliative Care Screening Tool, and Clinical Risk Models to Identify Peritoneal Dialysis Patients With High One-Year Mortality
Universal screening to identify vulnerable patients who may receive limited benefits from life-sustaining treatments can facilitate palliative care in dialysis populations. We aimed to develop prediction models for one-year mortality in peritoneal dialysis (PD) patients. This prospective cohort stud...
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| Published in: | Journal of pain and symptom management 2020-09, Vol.60 (3), p.613-621.e6 |
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| container_end_page | 621.e6 |
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| container_title | Journal of pain and symptom management |
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| creator | Lai, Chun-Fu Cheng, Ching-I Chang, Chin-Hao Chen, Yi-Ting Hwang, Hsiau-Chien Lin, Shuei-Liong Huang, Jenq-Wen Huang, Sheng-Jean |
| description | Universal screening to identify vulnerable patients who may receive limited benefits from life-sustaining treatments can facilitate palliative care in dialysis populations.
We aimed to develop prediction models for one-year mortality in peritoneal dialysis (PD) patients.
This prospective cohort study included 401 adult Taiwanese prevalent PD patients (average age 56.2 ± 14 years). In addition to obtaining clinical characteristics and laboratory data, the primary care nurses evaluated the surprise question (SQ) and palliative care screening tool (PCST) for each patient in March 2015. Multivariate logistic regression models were conducted to predict the primary outcome of one-year all-cause mortality.
There were 34 (8.5%) patients who died during the first year of follow-up. Patients allocated to the not surprised group according to the SQ and those who received a score of ≥4 on the PCST had increased odds of death (odds ratio 24.68 [95% CI 10.66–57.13] and 12.18 [95% CI 5.66–26.21], respectively). We also developed a clinical risk model for one-year mortality that included sex, dialysis vintage, coronary artery disease, malignancy, normalized protein nitrogen appearance, white blood cell count, and serum albumin and sodium levels. Integrating the SQ, PCST, and clinical risk model exhibited good discrimination with an area under the receiver operating characteristic curve of 0.95. Kaplan-Meier analysis showed worse survival in high-risk patients predicted by the integrated model (log-rank P |
| doi_str_mv | 10.1016/j.jpainsymman.2020.03.035 |
| format | article |
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We aimed to develop prediction models for one-year mortality in peritoneal dialysis (PD) patients.
This prospective cohort study included 401 adult Taiwanese prevalent PD patients (average age 56.2 ± 14 years). In addition to obtaining clinical characteristics and laboratory data, the primary care nurses evaluated the surprise question (SQ) and palliative care screening tool (PCST) for each patient in March 2015. Multivariate logistic regression models were conducted to predict the primary outcome of one-year all-cause mortality.
There were 34 (8.5%) patients who died during the first year of follow-up. Patients allocated to the not surprised group according to the SQ and those who received a score of ≥4 on the PCST had increased odds of death (odds ratio 24.68 [95% CI 10.66–57.13] and 12.18 [95% CI 5.66–26.21], respectively). We also developed a clinical risk model for one-year mortality that included sex, dialysis vintage, coronary artery disease, malignancy, normalized protein nitrogen appearance, white blood cell count, and serum albumin and sodium levels. Integrating the SQ, PCST, and clinical risk model exhibited good discrimination with an area under the receiver operating characteristic curve of 0.95. Kaplan-Meier analysis showed worse survival in high-risk patients predicted by the integrated model (log-rank P < 0.001).
Screening with the use of the integrated measurement can identify high-risk PD patients. This approach may facilitate palliative care interventions for at-risk subpopulations.</description><identifier>ISSN: 0885-3924</identifier><identifier>EISSN: 1873-6513</identifier><identifier>DOI: 10.1016/j.jpainsymman.2020.03.035</identifier><identifier>PMID: 32278098</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Cardiovascular disease ; chronic kidney failure ; Cohort analysis ; Coronary artery disease ; Dialysis ; Discrimination ; First year ; High risk ; Leukocytes ; Measurement ; Medical screening ; Mortality ; Nurses ; observational study ; Palliative care ; Peritoneal dialysis ; Prediction models ; Primary care ; Serum ; Sodium ; Vulnerability</subject><ispartof>Journal of pain and symptom management, 2020-09, Vol.60 (3), p.613-621.e6</ispartof><rights>2020 American Academy of Hospice and Palliative Medicine</rights><rights>Copyright © 2020 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Sep 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c456t-97cef92c4f200bc53a01d90bd66fdc6000d726afc666b1a8790106b8e24a3e2f3</citedby><cites>FETCH-LOGICAL-c456t-97cef92c4f200bc53a01d90bd66fdc6000d726afc666b1a8790106b8e24a3e2f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925,30999</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32278098$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lai, Chun-Fu</creatorcontrib><creatorcontrib>Cheng, Ching-I</creatorcontrib><creatorcontrib>Chang, Chin-Hao</creatorcontrib><creatorcontrib>Chen, Yi-Ting</creatorcontrib><creatorcontrib>Hwang, Hsiau-Chien</creatorcontrib><creatorcontrib>Lin, Shuei-Liong</creatorcontrib><creatorcontrib>Huang, Jenq-Wen</creatorcontrib><creatorcontrib>Huang, Sheng-Jean</creatorcontrib><title>Integrating the Surprise Question, Palliative Care Screening Tool, and Clinical Risk Models to Identify Peritoneal Dialysis Patients With High One-Year Mortality</title><title>Journal of pain and symptom management</title><addtitle>J Pain Symptom Manage</addtitle><description>Universal screening to identify vulnerable patients who may receive limited benefits from life-sustaining treatments can facilitate palliative care in dialysis populations.
We aimed to develop prediction models for one-year mortality in peritoneal dialysis (PD) patients.
This prospective cohort study included 401 adult Taiwanese prevalent PD patients (average age 56.2 ± 14 years). In addition to obtaining clinical characteristics and laboratory data, the primary care nurses evaluated the surprise question (SQ) and palliative care screening tool (PCST) for each patient in March 2015. Multivariate logistic regression models were conducted to predict the primary outcome of one-year all-cause mortality.
There were 34 (8.5%) patients who died during the first year of follow-up. Patients allocated to the not surprised group according to the SQ and those who received a score of ≥4 on the PCST had increased odds of death (odds ratio 24.68 [95% CI 10.66–57.13] and 12.18 [95% CI 5.66–26.21], respectively). We also developed a clinical risk model for one-year mortality that included sex, dialysis vintage, coronary artery disease, malignancy, normalized protein nitrogen appearance, white blood cell count, and serum albumin and sodium levels. Integrating the SQ, PCST, and clinical risk model exhibited good discrimination with an area under the receiver operating characteristic curve of 0.95. Kaplan-Meier analysis showed worse survival in high-risk patients predicted by the integrated model (log-rank P < 0.001).
Screening with the use of the integrated measurement can identify high-risk PD patients. This approach may facilitate palliative care interventions for at-risk subpopulations.</description><subject>Cardiovascular disease</subject><subject>chronic kidney failure</subject><subject>Cohort analysis</subject><subject>Coronary artery disease</subject><subject>Dialysis</subject><subject>Discrimination</subject><subject>First year</subject><subject>High risk</subject><subject>Leukocytes</subject><subject>Measurement</subject><subject>Medical screening</subject><subject>Mortality</subject><subject>Nurses</subject><subject>observational study</subject><subject>Palliative care</subject><subject>Peritoneal dialysis</subject><subject>Prediction models</subject><subject>Primary care</subject><subject>Serum</subject><subject>Sodium</subject><subject>Vulnerability</subject><issn>0885-3924</issn><issn>1873-6513</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>7QJ</sourceid><recordid>eNqNkc1u1DAUhSMEotPCKyAjNiyawbETx1miAO1IRS1QhFhZjnMz4-DYU9uplMfhTfFoCkKskK7khb9z7s_JspcFXhe4YG_G9biX2oZlmqRdE0zwGtNU1aNsVfCa5qwq6ONshTmvctqQ8iQ7DWHEGFeU0afZCSWk5rjhq-znxkbYehm13aK4A_Rl9nuvA6BPM4SonT1HN9IYnYh7QK30CVEewB4Et86ZcyRtj1qjrVbSoM86_EAfXQ8moOjQpgcb9bCgG_A6OgsJeaelWYIOyTjq9B3QNx136FJvd-jaQv4dpE8WPkqj4_IsezJIE-D5w3uWff3w_ra9zK-uLzbt26tclRWLeVMrGBqiyoFg3KmKSlz0De56xoZesbR7XxMmB8UY6wrJ6wYXmHUcSCkpkIGeZa-Pvnvv7g67i0kHBcZIC24OglDOOeF1xRL66h90dLO3aTpBypKXtKkYT1RzpJR3IXgYRDrsJP0iCiwOOYpR_JWjOOQoME1VJe2Lhw5zN0H_R_k7uAS0RyDdGe41eBFUuqWCXntQUfRO_0ebXzYfttc</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Lai, Chun-Fu</creator><creator>Cheng, Ching-I</creator><creator>Chang, Chin-Hao</creator><creator>Chen, Yi-Ting</creator><creator>Hwang, Hsiau-Chien</creator><creator>Lin, Shuei-Liong</creator><creator>Huang, Jenq-Wen</creator><creator>Huang, Sheng-Jean</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QJ</scope><scope>ASE</scope><scope>FPQ</scope><scope>K6X</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope></search><sort><creationdate>20200901</creationdate><title>Integrating the Surprise Question, Palliative Care Screening Tool, and Clinical Risk Models to Identify Peritoneal Dialysis Patients With High One-Year Mortality</title><author>Lai, Chun-Fu ; Cheng, Ching-I ; Chang, Chin-Hao ; Chen, Yi-Ting ; Hwang, Hsiau-Chien ; Lin, Shuei-Liong ; Huang, Jenq-Wen ; Huang, Sheng-Jean</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c456t-97cef92c4f200bc53a01d90bd66fdc6000d726afc666b1a8790106b8e24a3e2f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Cardiovascular disease</topic><topic>chronic kidney failure</topic><topic>Cohort analysis</topic><topic>Coronary artery disease</topic><topic>Dialysis</topic><topic>Discrimination</topic><topic>First year</topic><topic>High risk</topic><topic>Leukocytes</topic><topic>Measurement</topic><topic>Medical screening</topic><topic>Mortality</topic><topic>Nurses</topic><topic>observational study</topic><topic>Palliative care</topic><topic>Peritoneal dialysis</topic><topic>Prediction models</topic><topic>Primary care</topic><topic>Serum</topic><topic>Sodium</topic><topic>Vulnerability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lai, Chun-Fu</creatorcontrib><creatorcontrib>Cheng, Ching-I</creatorcontrib><creatorcontrib>Chang, Chin-Hao</creatorcontrib><creatorcontrib>Chen, Yi-Ting</creatorcontrib><creatorcontrib>Hwang, Hsiau-Chien</creatorcontrib><creatorcontrib>Lin, Shuei-Liong</creatorcontrib><creatorcontrib>Huang, Jenq-Wen</creatorcontrib><creatorcontrib>Huang, Sheng-Jean</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>British Nursing Index</collection><collection>British Nursing Index (BNI) (1985 to Present)</collection><collection>British Nursing Index</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of pain and symptom management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lai, Chun-Fu</au><au>Cheng, Ching-I</au><au>Chang, Chin-Hao</au><au>Chen, Yi-Ting</au><au>Hwang, Hsiau-Chien</au><au>Lin, Shuei-Liong</au><au>Huang, Jenq-Wen</au><au>Huang, Sheng-Jean</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integrating the Surprise Question, Palliative Care Screening Tool, and Clinical Risk Models to Identify Peritoneal Dialysis Patients With High One-Year Mortality</atitle><jtitle>Journal of pain and symptom management</jtitle><addtitle>J Pain Symptom Manage</addtitle><date>2020-09-01</date><risdate>2020</risdate><volume>60</volume><issue>3</issue><spage>613</spage><epage>621.e6</epage><pages>613-621.e6</pages><issn>0885-3924</issn><eissn>1873-6513</eissn><abstract>Universal screening to identify vulnerable patients who may receive limited benefits from life-sustaining treatments can facilitate palliative care in dialysis populations.
We aimed to develop prediction models for one-year mortality in peritoneal dialysis (PD) patients.
This prospective cohort study included 401 adult Taiwanese prevalent PD patients (average age 56.2 ± 14 years). In addition to obtaining clinical characteristics and laboratory data, the primary care nurses evaluated the surprise question (SQ) and palliative care screening tool (PCST) for each patient in March 2015. Multivariate logistic regression models were conducted to predict the primary outcome of one-year all-cause mortality.
There were 34 (8.5%) patients who died during the first year of follow-up. Patients allocated to the not surprised group according to the SQ and those who received a score of ≥4 on the PCST had increased odds of death (odds ratio 24.68 [95% CI 10.66–57.13] and 12.18 [95% CI 5.66–26.21], respectively). We also developed a clinical risk model for one-year mortality that included sex, dialysis vintage, coronary artery disease, malignancy, normalized protein nitrogen appearance, white blood cell count, and serum albumin and sodium levels. Integrating the SQ, PCST, and clinical risk model exhibited good discrimination with an area under the receiver operating characteristic curve of 0.95. Kaplan-Meier analysis showed worse survival in high-risk patients predicted by the integrated model (log-rank P < 0.001).
Screening with the use of the integrated measurement can identify high-risk PD patients. This approach may facilitate palliative care interventions for at-risk subpopulations.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32278098</pmid><doi>10.1016/j.jpainsymman.2020.03.035</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | Journal of pain and symptom management, 2020-09, Vol.60 (3), p.613-621.e6 |
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| source | Applied Social Sciences Index & Abstracts (ASSIA); Elsevier |
| subjects | Cardiovascular disease chronic kidney failure Cohort analysis Coronary artery disease Dialysis Discrimination First year High risk Leukocytes Measurement Medical screening Mortality Nurses observational study Palliative care Peritoneal dialysis Prediction models Primary care Serum Sodium Vulnerability |
| title | Integrating the Surprise Question, Palliative Care Screening Tool, and Clinical Risk Models to Identify Peritoneal Dialysis Patients With High One-Year Mortality |
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