Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?
Abstract Background Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and five clinical/laboratory variables (PaP-without CPS). However...
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| Veröffentlicht in: | European journal of cancer (1990) 2016-09, Vol.64, p.89-95 |
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| container_title | European journal of cancer (1990) |
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| creator | Hui, David Park, Minjeong Liu, Diane Paiva, Carlos Eduardo Suh, Sang-Yeon Morita, Tatsuya Bruera, Eduardo |
| description | Abstract Background Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and five clinical/laboratory variables (PaP-without CPS). However, it is unclear if the PaP score is more accurate than PaP-CPS, and whether PaP-CPS helps to improve the accuracy of PaP score. We compared the accuracy among PaP-CPS, PaP-without CPS and PaP-total score in patients with advanced cancer. Patients and methods In this prospective study, PaP score was documented in hospitalised patients seen by palliative care. We compared the discrimination of PaP-CPS versus PaP-total and PaP-without CPS versus PaP-total using four indices: concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index and integrated discrimination improvement for 30-day survival and 100-day survival. Results A total of 216 patients were enrolled with a median survival of 109 d (95% confidence interval [CI] 71–133 d). The AUC for 30-day survival was 0.57 (95% CI 0.47–0.67) for PaP-CPS, 0.78 (95% CI 0.7–0.87) for PaP-without CPS, and 0.73 (95% CI 0.64–0.82) for PaP-total score. PaP-total was significantly more accurate than PaP-CPS according to all four indices for both 30-day and 100-day survival (P |
| doi_str_mv | 10.1016/j.ejca.2016.05.009 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4969216</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0959804916321402</els_id><sourcerecordid>1808386645</sourcerecordid><originalsourceid>FETCH-LOGICAL-c576t-ffff7addfae1f8d9b8f31d96b2b8adc6463ec617d7002be181726e458ee791753</originalsourceid><addsrcrecordid>eNp9kl2L1TAQhoMo7nH1D3ghufSmdZK2SSqyIge_YEFhFS9Dmk73pPY0NWkL--9NPeuiXpibDJN33knyDCFPGeQMmHjR59hbk_MU51DlAPU9smNK1hmoit8nO6irOlNQ1mfkUYw9AEhVwkNyxmUhOQe1I24_uNFZZ0Y6BWydnZ0fqe9oXMLqVjPQFUNcIp0PSD-bYXBmdmsKg78efZydpVfWB3xJvx2cPVAzTcGbFLhIjylPjbVLMDO-fkwedGaI-OR2Pydf3739sv-QXX56_3H_5jKzlRRz1qUlTdt2Blmn2rpRXcHaWjS8Uaa1ohQFWsFkKwF4g0wxyQWWlUKUNZNVcU4uTr7T0hyxtTjOwQx6Cu5owo32xum_T0Z30Nd-1WUtas5EMnh-axD8jwXjrI8uWhwGM6JfomYKVKGEKLde_CS1wccYsLtrw0BvjHSvN0Z6Y6Sh0olRKnr25wXvSn5DSYJXJwGmb1odBh2tw9EmPAHtrFvv_u9_8U-5_cXYDN_xBmPvlzAmAJrpyDXoq21KtiFJT-esBF78BOKMuuc</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1808386645</pqid></control><display><type>article</type><title>Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Hui, David ; Park, Minjeong ; Liu, Diane ; Paiva, Carlos Eduardo ; Suh, Sang-Yeon ; Morita, Tatsuya ; Bruera, Eduardo</creator><creatorcontrib>Hui, David ; Park, Minjeong ; Liu, Diane ; Paiva, Carlos Eduardo ; Suh, Sang-Yeon ; Morita, Tatsuya ; Bruera, Eduardo</creatorcontrib><description>Abstract Background Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and five clinical/laboratory variables (PaP-without CPS). However, it is unclear if the PaP score is more accurate than PaP-CPS, and whether PaP-CPS helps to improve the accuracy of PaP score. We compared the accuracy among PaP-CPS, PaP-without CPS and PaP-total score in patients with advanced cancer. Patients and methods In this prospective study, PaP score was documented in hospitalised patients seen by palliative care. We compared the discrimination of PaP-CPS versus PaP-total and PaP-without CPS versus PaP-total using four indices: concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index and integrated discrimination improvement for 30-day survival and 100-day survival. Results A total of 216 patients were enrolled with a median survival of 109 d (95% confidence interval [CI] 71–133 d). The AUC for 30-day survival was 0.57 (95% CI 0.47–0.67) for PaP-CPS, 0.78 (95% CI 0.7–0.87) for PaP-without CPS, and 0.73 (95% CI 0.64–0.82) for PaP-total score. PaP-total was significantly more accurate than PaP-CPS according to all four indices for both 30-day and 100-day survival (P < 0.001). PaP-without CPS was significantly more accurate than PaP-total for 30-day survival (P < 0.05). Conclusion We found that PaP score was more accurate than CPS, and the addition of CPS to the prognostic model reduced its accuracy. This study highlights the limitations of clinical gestalt and the need to use objective prognostic factors and models for survival prediction.</description><identifier>ISSN: 0959-8049</identifier><identifier>EISSN: 1879-0852</identifier><identifier>DOI: 10.1016/j.ejca.2016.05.009</identifier><identifier>PMID: 27372208</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adult ; Aged ; Clinical prediction rule ; Decision Support Techniques ; Female ; Forecasting ; Hematology, Oncology and Palliative Medicine ; Humans ; Karnofsky Performance Status ; Male ; Middle Aged ; Models, Statistical ; Neoplasms ; Neoplasms - diagnosis ; Neoplasms - mortality ; Palliative Care - methods ; Prognosis ; Prospective Studies ; ROC Curve ; Statistical data analysis ; Survival ; Survival Analysis ; Young Adult</subject><ispartof>European journal of cancer (1990), 2016-09, Vol.64, p.89-95</ispartof><rights>Elsevier Ltd</rights><rights>2016 Elsevier Ltd</rights><rights>Copyright © 2016 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c576t-ffff7addfae1f8d9b8f31d96b2b8adc6463ec617d7002be181726e458ee791753</citedby><cites>FETCH-LOGICAL-c576t-ffff7addfae1f8d9b8f31d96b2b8adc6463ec617d7002be181726e458ee791753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0959804916321402$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27372208$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hui, David</creatorcontrib><creatorcontrib>Park, Minjeong</creatorcontrib><creatorcontrib>Liu, Diane</creatorcontrib><creatorcontrib>Paiva, Carlos Eduardo</creatorcontrib><creatorcontrib>Suh, Sang-Yeon</creatorcontrib><creatorcontrib>Morita, Tatsuya</creatorcontrib><creatorcontrib>Bruera, Eduardo</creatorcontrib><title>Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?</title><title>European journal of cancer (1990)</title><addtitle>Eur J Cancer</addtitle><description>Abstract Background Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and five clinical/laboratory variables (PaP-without CPS). However, it is unclear if the PaP score is more accurate than PaP-CPS, and whether PaP-CPS helps to improve the accuracy of PaP score. We compared the accuracy among PaP-CPS, PaP-without CPS and PaP-total score in patients with advanced cancer. Patients and methods In this prospective study, PaP score was documented in hospitalised patients seen by palliative care. We compared the discrimination of PaP-CPS versus PaP-total and PaP-without CPS versus PaP-total using four indices: concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index and integrated discrimination improvement for 30-day survival and 100-day survival. Results A total of 216 patients were enrolled with a median survival of 109 d (95% confidence interval [CI] 71–133 d). The AUC for 30-day survival was 0.57 (95% CI 0.47–0.67) for PaP-CPS, 0.78 (95% CI 0.7–0.87) for PaP-without CPS, and 0.73 (95% CI 0.64–0.82) for PaP-total score. PaP-total was significantly more accurate than PaP-CPS according to all four indices for both 30-day and 100-day survival (P < 0.001). PaP-without CPS was significantly more accurate than PaP-total for 30-day survival (P < 0.05). Conclusion We found that PaP score was more accurate than CPS, and the addition of CPS to the prognostic model reduced its accuracy. This study highlights the limitations of clinical gestalt and the need to use objective prognostic factors and models for survival prediction.</description><subject>Adult</subject><subject>Aged</subject><subject>Clinical prediction rule</subject><subject>Decision Support Techniques</subject><subject>Female</subject><subject>Forecasting</subject><subject>Hematology, Oncology and Palliative Medicine</subject><subject>Humans</subject><subject>Karnofsky Performance Status</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Neoplasms</subject><subject>Neoplasms - diagnosis</subject><subject>Neoplasms - mortality</subject><subject>Palliative Care - methods</subject><subject>Prognosis</subject><subject>Prospective Studies</subject><subject>ROC Curve</subject><subject>Statistical data analysis</subject><subject>Survival</subject><subject>Survival Analysis</subject><subject>Young Adult</subject><issn>0959-8049</issn><issn>1879-0852</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kl2L1TAQhoMo7nH1D3ghufSmdZK2SSqyIge_YEFhFS9Dmk73pPY0NWkL--9NPeuiXpibDJN33knyDCFPGeQMmHjR59hbk_MU51DlAPU9smNK1hmoit8nO6irOlNQ1mfkUYw9AEhVwkNyxmUhOQe1I24_uNFZZ0Y6BWydnZ0fqe9oXMLqVjPQFUNcIp0PSD-bYXBmdmsKg78efZydpVfWB3xJvx2cPVAzTcGbFLhIjylPjbVLMDO-fkwedGaI-OR2Pydf3739sv-QXX56_3H_5jKzlRRz1qUlTdt2Blmn2rpRXcHaWjS8Uaa1ohQFWsFkKwF4g0wxyQWWlUKUNZNVcU4uTr7T0hyxtTjOwQx6Cu5owo32xum_T0Z30Nd-1WUtas5EMnh-axD8jwXjrI8uWhwGM6JfomYKVKGEKLde_CS1wccYsLtrw0BvjHSvN0Z6Y6Sh0olRKnr25wXvSn5DSYJXJwGmb1odBh2tw9EmPAHtrFvv_u9_8U-5_cXYDN_xBmPvlzAmAJrpyDXoq21KtiFJT-esBF78BOKMuuc</recordid><startdate>20160901</startdate><enddate>20160901</enddate><creator>Hui, David</creator><creator>Park, Minjeong</creator><creator>Liu, Diane</creator><creator>Paiva, Carlos Eduardo</creator><creator>Suh, Sang-Yeon</creator><creator>Morita, Tatsuya</creator><creator>Bruera, Eduardo</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160901</creationdate><title>Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?</title><author>Hui, David ; Park, Minjeong ; Liu, Diane ; Paiva, Carlos Eduardo ; Suh, Sang-Yeon ; Morita, Tatsuya ; Bruera, Eduardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c576t-ffff7addfae1f8d9b8f31d96b2b8adc6463ec617d7002be181726e458ee791753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Clinical prediction rule</topic><topic>Decision Support Techniques</topic><topic>Female</topic><topic>Forecasting</topic><topic>Hematology, Oncology and Palliative Medicine</topic><topic>Humans</topic><topic>Karnofsky Performance Status</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Models, Statistical</topic><topic>Neoplasms</topic><topic>Neoplasms - diagnosis</topic><topic>Neoplasms - mortality</topic><topic>Palliative Care - methods</topic><topic>Prognosis</topic><topic>Prospective Studies</topic><topic>ROC Curve</topic><topic>Statistical data analysis</topic><topic>Survival</topic><topic>Survival Analysis</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hui, David</creatorcontrib><creatorcontrib>Park, Minjeong</creatorcontrib><creatorcontrib>Liu, Diane</creatorcontrib><creatorcontrib>Paiva, Carlos Eduardo</creatorcontrib><creatorcontrib>Suh, Sang-Yeon</creatorcontrib><creatorcontrib>Morita, Tatsuya</creatorcontrib><creatorcontrib>Bruera, Eduardo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>European journal of cancer (1990)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hui, David</au><au>Park, Minjeong</au><au>Liu, Diane</au><au>Paiva, Carlos Eduardo</au><au>Suh, Sang-Yeon</au><au>Morita, Tatsuya</au><au>Bruera, Eduardo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?</atitle><jtitle>European journal of cancer (1990)</jtitle><addtitle>Eur J Cancer</addtitle><date>2016-09-01</date><risdate>2016</risdate><volume>64</volume><spage>89</spage><epage>95</epage><pages>89-95</pages><issn>0959-8049</issn><eissn>1879-0852</eissn><abstract>Abstract Background Clinician prediction of survival (CPS) has low accuracy in the advanced cancer setting, raising the need for prediction models such as the palliative prognostic (PaP) score that includes a transformed CPS (PaP-CPS) and five clinical/laboratory variables (PaP-without CPS). However, it is unclear if the PaP score is more accurate than PaP-CPS, and whether PaP-CPS helps to improve the accuracy of PaP score. We compared the accuracy among PaP-CPS, PaP-without CPS and PaP-total score in patients with advanced cancer. Patients and methods In this prospective study, PaP score was documented in hospitalised patients seen by palliative care. We compared the discrimination of PaP-CPS versus PaP-total and PaP-without CPS versus PaP-total using four indices: concordance statistics, area under the receiver-operating characteristics curve (AUC), net reclassification index and integrated discrimination improvement for 30-day survival and 100-day survival. Results A total of 216 patients were enrolled with a median survival of 109 d (95% confidence interval [CI] 71–133 d). The AUC for 30-day survival was 0.57 (95% CI 0.47–0.67) for PaP-CPS, 0.78 (95% CI 0.7–0.87) for PaP-without CPS, and 0.73 (95% CI 0.64–0.82) for PaP-total score. PaP-total was significantly more accurate than PaP-CPS according to all four indices for both 30-day and 100-day survival (P < 0.001). PaP-without CPS was significantly more accurate than PaP-total for 30-day survival (P < 0.05). Conclusion We found that PaP score was more accurate than CPS, and the addition of CPS to the prognostic model reduced its accuracy. This study highlights the limitations of clinical gestalt and the need to use objective prognostic factors and models for survival prediction.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>27372208</pmid><doi>10.1016/j.ejca.2016.05.009</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | European journal of cancer (1990), 2016-09, Vol.64, p.89-95 |
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| subjects | Adult Aged Clinical prediction rule Decision Support Techniques Female Forecasting Hematology, Oncology and Palliative Medicine Humans Karnofsky Performance Status Male Middle Aged Models, Statistical Neoplasms Neoplasms - diagnosis Neoplasms - mortality Palliative Care - methods Prognosis Prospective Studies ROC Curve Statistical data analysis Survival Survival Analysis Young Adult |
| title | Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate? |
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