Independent Pre-Transplant Recipient Cancer Risk Factors after Kidney Transplantation and the Utility of G-Chart Analysis for Clinical Process Control
The aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G-chart analysis for clinical process control. This may contribute to the improvement of cancer surveillance processes in individual transplant centers. 1655...
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| Veröffentlicht in: | PloS one 2016-07, Vol.11 (7), p.e0158732-e0158732 |
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| creator | Schrem, Harald Schneider, Valentin Kurok, Marlene Goldis, Alon Dreier, Maren Kaltenborn, Alexander Gwinner, Wilfried Barthold, Marc Liebeneiner, Jan Winny, Markus Klempnauer, Jürgen Kleine, Moritz |
| description | The aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G-chart analysis for clinical process control. This may contribute to the improvement of cancer surveillance processes in individual transplant centers.
1655 patients after kidney transplantation at our institution with a total of 9,425 person-years of follow-up were compared retrospectively to the general German population using site-specific standardized-incidence-ratios (SIRs) of observed malignancies. Risk-adjusted multivariable Cox regression was used to identify independent pre-transplant cancer risk factors. G-chart analysis was applied to determine relevant differences in the frequency of cancer occurrences.
Cancer incidence rates were almost three times higher as compared to the matched general population (SIR = 2.75; 95%-CI: 2.33-3.21). Significantly increased SIRs were observed for renal cell carcinoma (SIR = 22.46), post-transplant lymphoproliferative disorder (SIR = 8.36), prostate cancer (SIR = 2.22), bladder cancer (SIR = 3.24), thyroid cancer (SIR = 10.13) and melanoma (SIR = 3.08). Independent pre-transplant risk factors for cancer-free survival were age 62.6 years (p = 0.001, HR: 1.29), polycystic kidney disease other than autosomal dominant polycystic kidney disease (ADPKD) (p = 0.001, HR: 0.68), high body mass index in kg/m2 (p |
| doi_str_mv | 10.1371/journal.pone.0158732 |
| format | Article |
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1655 patients after kidney transplantation at our institution with a total of 9,425 person-years of follow-up were compared retrospectively to the general German population using site-specific standardized-incidence-ratios (SIRs) of observed malignancies. Risk-adjusted multivariable Cox regression was used to identify independent pre-transplant cancer risk factors. G-chart analysis was applied to determine relevant differences in the frequency of cancer occurrences.
Cancer incidence rates were almost three times higher as compared to the matched general population (SIR = 2.75; 95%-CI: 2.33-3.21). Significantly increased SIRs were observed for renal cell carcinoma (SIR = 22.46), post-transplant lymphoproliferative disorder (SIR = 8.36), prostate cancer (SIR = 2.22), bladder cancer (SIR = 3.24), thyroid cancer (SIR = 10.13) and melanoma (SIR = 3.08). Independent pre-transplant risk factors for cancer-free survival were age <52.3 years (p = 0.007, Hazard ratio (HR): 0.82), age >62.6 years (p = 0.001, HR: 1.29), polycystic kidney disease other than autosomal dominant polycystic kidney disease (ADPKD) (p = 0.001, HR: 0.68), high body mass index in kg/m2 (p<0.001, HR: 1.04), ADPKD (p = 0.008, HR: 1.26) and diabetic nephropathy (p = 0.004, HR = 1.51). G-chart analysis identified relevant changes in the detection rates of cancer during aftercare with no significant relation to identified risk factors for cancer-free survival (p<0.05).
Risk-adapted cancer surveillance combined with prospective G-chart analysis likely improves cancer surveillance schemes by adapting processes to identified risk factors and by using G-chart alarm signals to trigger Kaizen events and audits for root-cause analysis of relevant detection rate changes. Further, comparative G-chart analysis would enable benchmarking of cancer surveillance processes between centers.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0158732</identifier><identifier>PMID: 27398803</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adult ; Age ; Aged ; Armed forces ; Bladder ; Bladder cancer ; Body mass ; Body mass index ; Body size ; Bone surgery ; Cancer ; Case-Control Studies ; Change detection ; Child ; Comparative analysis ; Complications and side effects ; Control charts ; Diabetes ; Diabetes mellitus ; Diabetic nephropathy ; Disease-Free Survival ; Epidemiological Monitoring ; Epidemiology ; Ethics ; Failure ; Female ; Health aspects ; Health risks ; Humans ; Incidence ; Infections ; Kaizen ; Kidney cancer ; Kidney diseases ; Kidney Transplantation ; Kidney transplants ; Kidneys ; Lymphocytes ; Male ; Medical diagnosis ; Medical schools ; Medicine and Health Sciences ; Melanoma ; Middle Aged ; Neoplasms - epidemiology ; Neoplasms - therapy ; Nephropathy ; Patient outcomes ; Patients ; Pediatrics ; Polycystic kidney ; Process control ; Process controls ; Prostate cancer ; Quality management ; Regression analysis ; Renal cell carcinoma ; Retrospective Studies ; Risk analysis ; Risk Assessment ; Risk Factors ; Skin cancer ; Surveillance ; Survival ; Survival analysis ; Thyroid ; Thyroid cancer ; Transplantation ; Transplants & implants ; Young Adult</subject><ispartof>PloS one, 2016-07, Vol.11 (7), p.e0158732-e0158732</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Schrem et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Schrem et al 2016 Schrem et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-64db0631613c247f708065ec6e97ac4c0e1265de0032393cff92f65fb926f183</citedby><cites>FETCH-LOGICAL-c725t-64db0631613c247f708065ec6e97ac4c0e1265de0032393cff92f65fb926f183</cites><orcidid>0000-0002-5527-7555</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4939933/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4939933/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27398803$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Sung, Shian-Ying</contributor><creatorcontrib>Schrem, Harald</creatorcontrib><creatorcontrib>Schneider, Valentin</creatorcontrib><creatorcontrib>Kurok, Marlene</creatorcontrib><creatorcontrib>Goldis, Alon</creatorcontrib><creatorcontrib>Dreier, Maren</creatorcontrib><creatorcontrib>Kaltenborn, Alexander</creatorcontrib><creatorcontrib>Gwinner, Wilfried</creatorcontrib><creatorcontrib>Barthold, Marc</creatorcontrib><creatorcontrib>Liebeneiner, Jan</creatorcontrib><creatorcontrib>Winny, Markus</creatorcontrib><creatorcontrib>Klempnauer, Jürgen</creatorcontrib><creatorcontrib>Kleine, Moritz</creatorcontrib><title>Independent Pre-Transplant Recipient Cancer Risk Factors after Kidney Transplantation and the Utility of G-Chart Analysis for Clinical Process Control</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G-chart analysis for clinical process control. This may contribute to the improvement of cancer surveillance processes in individual transplant centers.
1655 patients after kidney transplantation at our institution with a total of 9,425 person-years of follow-up were compared retrospectively to the general German population using site-specific standardized-incidence-ratios (SIRs) of observed malignancies. Risk-adjusted multivariable Cox regression was used to identify independent pre-transplant cancer risk factors. G-chart analysis was applied to determine relevant differences in the frequency of cancer occurrences.
Cancer incidence rates were almost three times higher as compared to the matched general population (SIR = 2.75; 95%-CI: 2.33-3.21). Significantly increased SIRs were observed for renal cell carcinoma (SIR = 22.46), post-transplant lymphoproliferative disorder (SIR = 8.36), prostate cancer (SIR = 2.22), bladder cancer (SIR = 3.24), thyroid cancer (SIR = 10.13) and melanoma (SIR = 3.08). Independent pre-transplant risk factors for cancer-free survival were age <52.3 years (p = 0.007, Hazard ratio (HR): 0.82), age >62.6 years (p = 0.001, HR: 1.29), polycystic kidney disease other than autosomal dominant polycystic kidney disease (ADPKD) (p = 0.001, HR: 0.68), high body mass index in kg/m2 (p<0.001, HR: 1.04), ADPKD (p = 0.008, HR: 1.26) and diabetic nephropathy (p = 0.004, HR = 1.51). G-chart analysis identified relevant changes in the detection rates of cancer during aftercare with no significant relation to identified risk factors for cancer-free survival (p<0.05).
Risk-adapted cancer surveillance combined with prospective G-chart analysis likely improves cancer surveillance schemes by adapting processes to identified risk factors and by using G-chart alarm signals to trigger Kaizen events and audits for root-cause analysis of relevant detection rate changes. Further, comparative G-chart analysis would enable benchmarking of cancer surveillance processes between centers.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Age</subject><subject>Aged</subject><subject>Armed forces</subject><subject>Bladder</subject><subject>Bladder cancer</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Bone surgery</subject><subject>Cancer</subject><subject>Case-Control Studies</subject><subject>Change detection</subject><subject>Child</subject><subject>Comparative analysis</subject><subject>Complications and side effects</subject><subject>Control charts</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetic nephropathy</subject><subject>Disease-Free Survival</subject><subject>Epidemiological Monitoring</subject><subject>Epidemiology</subject><subject>Ethics</subject><subject>Failure</subject><subject>Female</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Humans</subject><subject>Incidence</subject><subject>Infections</subject><subject>Kaizen</subject><subject>Kidney cancer</subject><subject>Kidney diseases</subject><subject>Kidney Transplantation</subject><subject>Kidney transplants</subject><subject>Kidneys</subject><subject>Lymphocytes</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical schools</subject><subject>Medicine and Health Sciences</subject><subject>Melanoma</subject><subject>Middle Aged</subject><subject>Neoplasms - epidemiology</subject><subject>Neoplasms - therapy</subject><subject>Nephropathy</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Pediatrics</subject><subject>Polycystic kidney</subject><subject>Process control</subject><subject>Process controls</subject><subject>Prostate cancer</subject><subject>Quality management</subject><subject>Regression analysis</subject><subject>Renal cell carcinoma</subject><subject>Retrospective Studies</subject><subject>Risk analysis</subject><subject>Risk Assessment</subject><subject>Risk Factors</subject><subject>Skin cancer</subject><subject>Surveillance</subject><subject>Survival</subject><subject>Survival analysis</subject><subject>Thyroid</subject><subject>Thyroid cancer</subject><subject>Transplantation</subject><subject>Transplants & implants</subject><subject>Young 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Pre-Transplant Recipient Cancer Risk Factors after Kidney Transplantation and the Utility of G-Chart Analysis for Clinical Process Control</title><author>Schrem, Harald ; Schneider, Valentin ; Kurok, Marlene ; Goldis, Alon ; Dreier, Maren ; Kaltenborn, Alexander ; Gwinner, Wilfried ; Barthold, Marc ; Liebeneiner, Jan ; Winny, Markus ; Klempnauer, Jürgen ; Kleine, Moritz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c725t-64db0631613c247f708065ec6e97ac4c0e1265de0032393cff92f65fb926f183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Age</topic><topic>Aged</topic><topic>Armed forces</topic><topic>Bladder</topic><topic>Bladder cancer</topic><topic>Body mass</topic><topic>Body mass index</topic><topic>Body size</topic><topic>Bone surgery</topic><topic>Cancer</topic><topic>Case-Control Studies</topic><topic>Change 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Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schrem, Harald</au><au>Schneider, Valentin</au><au>Kurok, Marlene</au><au>Goldis, Alon</au><au>Dreier, Maren</au><au>Kaltenborn, Alexander</au><au>Gwinner, Wilfried</au><au>Barthold, Marc</au><au>Liebeneiner, Jan</au><au>Winny, Markus</au><au>Klempnauer, Jürgen</au><au>Kleine, Moritz</au><au>Sung, Shian-Ying</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Independent Pre-Transplant Recipient Cancer Risk Factors after Kidney Transplantation and the Utility of G-Chart Analysis for Clinical Process Control</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-07-11</date><risdate>2016</risdate><volume>11</volume><issue>7</issue><spage>e0158732</spage><epage>e0158732</epage><pages>e0158732-e0158732</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The aim of this study is to identify independent pre-transplant cancer risk factors after kidney transplantation and to assess the utility of G-chart analysis for clinical process control. This may contribute to the improvement of cancer surveillance processes in individual transplant centers.
1655 patients after kidney transplantation at our institution with a total of 9,425 person-years of follow-up were compared retrospectively to the general German population using site-specific standardized-incidence-ratios (SIRs) of observed malignancies. Risk-adjusted multivariable Cox regression was used to identify independent pre-transplant cancer risk factors. G-chart analysis was applied to determine relevant differences in the frequency of cancer occurrences.
Cancer incidence rates were almost three times higher as compared to the matched general population (SIR = 2.75; 95%-CI: 2.33-3.21). Significantly increased SIRs were observed for renal cell carcinoma (SIR = 22.46), post-transplant lymphoproliferative disorder (SIR = 8.36), prostate cancer (SIR = 2.22), bladder cancer (SIR = 3.24), thyroid cancer (SIR = 10.13) and melanoma (SIR = 3.08). Independent pre-transplant risk factors for cancer-free survival were age <52.3 years (p = 0.007, Hazard ratio (HR): 0.82), age >62.6 years (p = 0.001, HR: 1.29), polycystic kidney disease other than autosomal dominant polycystic kidney disease (ADPKD) (p = 0.001, HR: 0.68), high body mass index in kg/m2 (p<0.001, HR: 1.04), ADPKD (p = 0.008, HR: 1.26) and diabetic nephropathy (p = 0.004, HR = 1.51). G-chart analysis identified relevant changes in the detection rates of cancer during aftercare with no significant relation to identified risk factors for cancer-free survival (p<0.05).
Risk-adapted cancer surveillance combined with prospective G-chart analysis likely improves cancer surveillance schemes by adapting processes to identified risk factors and by using G-chart alarm signals to trigger Kaizen events and audits for root-cause analysis of relevant detection rate changes. Further, comparative G-chart analysis would enable benchmarking of cancer surveillance processes between centers.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27398803</pmid><doi>10.1371/journal.pone.0158732</doi><orcidid>https://orcid.org/0000-0002-5527-7555</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2016-07, Vol.11 (7), p.e0158732-e0158732 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1810859700 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adolescent Adult Age Aged Armed forces Bladder Bladder cancer Body mass Body mass index Body size Bone surgery Cancer Case-Control Studies Change detection Child Comparative analysis Complications and side effects Control charts Diabetes Diabetes mellitus Diabetic nephropathy Disease-Free Survival Epidemiological Monitoring Epidemiology Ethics Failure Female Health aspects Health risks Humans Incidence Infections Kaizen Kidney cancer Kidney diseases Kidney Transplantation Kidney transplants Kidneys Lymphocytes Male Medical diagnosis Medical schools Medicine and Health Sciences Melanoma Middle Aged Neoplasms - epidemiology Neoplasms - therapy Nephropathy Patient outcomes Patients Pediatrics Polycystic kidney Process control Process controls Prostate cancer Quality management Regression analysis Renal cell carcinoma Retrospective Studies Risk analysis Risk Assessment Risk Factors Skin cancer Surveillance Survival Survival analysis Thyroid Thyroid cancer Transplantation Transplants & implants Young Adult |
| title | Independent Pre-Transplant Recipient Cancer Risk Factors after Kidney Transplantation and the Utility of G-Chart Analysis for Clinical Process Control |
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