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Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow‐up care
Background This retrospective study harnessed an institutional cancer registry to construct a childhood cancer survivorship cohort, integrate electronic health record (EHR) and geospatial data to stratify survivors based on late‐effect risk, analyze follow‐up care patterns, and determine factors ass...
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| Published in: | Pediatric blood & cancer 2021-06, Vol.68 (6), p.e29014-n/a |
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| container_start_page | e29014 |
| container_title | Pediatric blood & cancer |
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| creator | Noyd, David H. Neely, Nigel B. Schroeder, Kristin M. Lantos, Paul M. Power, Steve Kreissman, Susan G. Oeffinger, Kevin C. |
| description | Background
This retrospective study harnessed an institutional cancer registry to construct a childhood cancer survivorship cohort, integrate electronic health record (EHR) and geospatial data to stratify survivors based on late‐effect risk, analyze follow‐up care patterns, and determine factors associated with suboptimal follow‐up care.
Procedure
The survivorship cohort included patients ≤18 years of age reported to the institutional cancer registry between January 1, 1994 and November 30, 2012. International Classification of Diseases for Oncology, third revision (ICD‐O‐3) coding and treatment exposures facilitated risk stratification of survivors. The EHR was linked to the cancer registry based on medical record number (MRN) to extract clinic visits.
Results
Five hundred and ninety pediatric hematology‐oncology (PHO) and 275 pediatric neuro‐oncology (PNO) survivors were included in the final analytic cohort. Two hundred and eight‐two survivors (32.6%) were not seen in any oncology‐related subspecialty clinic at Duke 5–7 years after initial diagnosis. Factors associated with follow‐up included age (p = .008), diagnosis (p |
| doi_str_mv | 10.1002/pbc.29014 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2503441627</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2503441627</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3534-7be918c0fcd76ad70db88541e7bf240967ddaa0167736e8ed5c26ab0d139ea2a3</originalsourceid><addsrcrecordid>eNp1kUFu1DAUhi1ERUthwQWQJTYgdVo7ieNkWUZAK1WCBayjF_u5cfHEwXZaza5H6Mk4BCfB07RdILGypff58__0E_KGs2POWHEy9eq4aBmvnpEDLiqxEozL50931u6TlzFeZbRmonlB9stSVoUoqwPy-3xMeBkgWT9Sb6iCUWGgAS9tTGFLYdQUHaoU_GgVHRBcGvJY-aCphgQ0ear8mOFZJQpUDdbpwXv9qIpzuLbXPsTBTpkcfEhH1ICyziZISIONP2l-niMYq5YgxgfqdkM0Jv8dj-5zQIwYI4VpCn4Kdjc33jl_8-f2bs5uCPiK7BlwEV8_nIfkx-dP39dnq4uvX87XpxcrVea1V7LHljeKGaVlDVoy3TeNqDjK3hQVa2upNQDjtZRljQ1qoYoaeqZ52SIUUB6S94s3R_k1Y0zdxkaFzsGIfo5dIVhZVbwuZEbf_YNe-TmMOV2meN2KQsod9WGhVPAxBjRd3nADYdtx1u1K7nLJ3X3JmX37YJz7Deon8rHVDJwswI11uP2_qfv2cb0o_wIzP7co</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2516952777</pqid></control><display><type>article</type><title>Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow‐up care</title><source>Wiley-Blackwell Read & Publish Collection</source><creator>Noyd, David H. ; Neely, Nigel B. ; Schroeder, Kristin M. ; Lantos, Paul M. ; Power, Steve ; Kreissman, Susan G. ; Oeffinger, Kevin C.</creator><creatorcontrib>Noyd, David H. ; Neely, Nigel B. ; Schroeder, Kristin M. ; Lantos, Paul M. ; Power, Steve ; Kreissman, Susan G. ; Oeffinger, Kevin C.</creatorcontrib><description>Background
This retrospective study harnessed an institutional cancer registry to construct a childhood cancer survivorship cohort, integrate electronic health record (EHR) and geospatial data to stratify survivors based on late‐effect risk, analyze follow‐up care patterns, and determine factors associated with suboptimal follow‐up care.
Procedure
The survivorship cohort included patients ≤18 years of age reported to the institutional cancer registry between January 1, 1994 and November 30, 2012. International Classification of Diseases for Oncology, third revision (ICD‐O‐3) coding and treatment exposures facilitated risk stratification of survivors. The EHR was linked to the cancer registry based on medical record number (MRN) to extract clinic visits.
Results
Five hundred and ninety pediatric hematology‐oncology (PHO) and 275 pediatric neuro‐oncology (PNO) survivors were included in the final analytic cohort. Two hundred and eight‐two survivors (32.6%) were not seen in any oncology‐related subspecialty clinic at Duke 5–7 years after initial diagnosis. Factors associated with follow‐up included age (p = .008), diagnosis (p < .001), race/ethnicity (p = .010), late‐effect risk strata (p = .001), distance to treatment center (p < .0001), and area deprivation index (ADI) (p = .011). Multivariable logistic modeling attenuated the association for high‐risk (OR 1.72; 95% CI 0.805, 3.66) and intermediate‐risk (OR 1.23, 95% CI 0.644, 2.36) survivors compared to survivors at low risk of late effects among the PHO cohort. PNO survivors at high risk for late effects were more likely to follow up (adjusted OR 3.66; 95% CI 1.76, 7.61).
Conclusions
Nearly a third of survivors received suboptimal follow‐up care. This study provides a reproducible model to integrate cancer registry and EHR data to construct risk‐stratified survivorship cohorts to assess follow‐up care.</description><identifier>ISSN: 1545-5009</identifier><identifier>EISSN: 1545-5017</identifier><identifier>DOI: 10.1002/pbc.29014</identifier><identifier>PMID: 33742534</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Aftercare - methods ; Aftercare - statistics & numerical data ; biomedical informatics ; Cancer ; Cancer Survivors - statistics & numerical data ; Child ; Child, Preschool ; Childhood ; childhood cancer survivorship ; Children ; Databases, Factual - statistics & numerical data ; Diagnosis ; Electronic Health Records ; Electronic medical records ; Female ; Hematology ; Humans ; Logistic Models ; Male ; Neoplasms - classification ; Neoplasms - therapy ; Neural coding ; Oncology ; Pediatrics ; Registries ; Retrospective Studies ; Risk ; Survival ; Survivorship</subject><ispartof>Pediatric blood & cancer, 2021-06, Vol.68 (6), p.e29014-n/a</ispartof><rights>2021 Wiley Periodicals LLC</rights><rights>2021 Wiley Periodicals LLC.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3534-7be918c0fcd76ad70db88541e7bf240967ddaa0167736e8ed5c26ab0d139ea2a3</citedby><cites>FETCH-LOGICAL-c3534-7be918c0fcd76ad70db88541e7bf240967ddaa0167736e8ed5c26ab0d139ea2a3</cites><orcidid>0000-0002-6433-6174 ; 0000-0002-3465-5000</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33742534$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Noyd, David H.</creatorcontrib><creatorcontrib>Neely, Nigel B.</creatorcontrib><creatorcontrib>Schroeder, Kristin M.</creatorcontrib><creatorcontrib>Lantos, Paul M.</creatorcontrib><creatorcontrib>Power, Steve</creatorcontrib><creatorcontrib>Kreissman, Susan G.</creatorcontrib><creatorcontrib>Oeffinger, Kevin C.</creatorcontrib><title>Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow‐up care</title><title>Pediatric blood & cancer</title><addtitle>Pediatr Blood Cancer</addtitle><description>Background
This retrospective study harnessed an institutional cancer registry to construct a childhood cancer survivorship cohort, integrate electronic health record (EHR) and geospatial data to stratify survivors based on late‐effect risk, analyze follow‐up care patterns, and determine factors associated with suboptimal follow‐up care.
Procedure
The survivorship cohort included patients ≤18 years of age reported to the institutional cancer registry between January 1, 1994 and November 30, 2012. International Classification of Diseases for Oncology, third revision (ICD‐O‐3) coding and treatment exposures facilitated risk stratification of survivors. The EHR was linked to the cancer registry based on medical record number (MRN) to extract clinic visits.
Results
Five hundred and ninety pediatric hematology‐oncology (PHO) and 275 pediatric neuro‐oncology (PNO) survivors were included in the final analytic cohort. Two hundred and eight‐two survivors (32.6%) were not seen in any oncology‐related subspecialty clinic at Duke 5–7 years after initial diagnosis. Factors associated with follow‐up included age (p = .008), diagnosis (p < .001), race/ethnicity (p = .010), late‐effect risk strata (p = .001), distance to treatment center (p < .0001), and area deprivation index (ADI) (p = .011). Multivariable logistic modeling attenuated the association for high‐risk (OR 1.72; 95% CI 0.805, 3.66) and intermediate‐risk (OR 1.23, 95% CI 0.644, 2.36) survivors compared to survivors at low risk of late effects among the PHO cohort. PNO survivors at high risk for late effects were more likely to follow up (adjusted OR 3.66; 95% CI 1.76, 7.61).
Conclusions
Nearly a third of survivors received suboptimal follow‐up care. This study provides a reproducible model to integrate cancer registry and EHR data to construct risk‐stratified survivorship cohorts to assess follow‐up care.</description><subject>Aftercare - methods</subject><subject>Aftercare - statistics & numerical data</subject><subject>biomedical informatics</subject><subject>Cancer</subject><subject>Cancer Survivors - statistics & numerical data</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Childhood</subject><subject>childhood cancer survivorship</subject><subject>Children</subject><subject>Databases, Factual - statistics & numerical data</subject><subject>Diagnosis</subject><subject>Electronic Health Records</subject><subject>Electronic medical records</subject><subject>Female</subject><subject>Hematology</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Neoplasms - classification</subject><subject>Neoplasms - therapy</subject><subject>Neural coding</subject><subject>Oncology</subject><subject>Pediatrics</subject><subject>Registries</subject><subject>Retrospective Studies</subject><subject>Risk</subject><subject>Survival</subject><subject>Survivorship</subject><issn>1545-5009</issn><issn>1545-5017</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kUFu1DAUhi1ERUthwQWQJTYgdVo7ieNkWUZAK1WCBayjF_u5cfHEwXZaza5H6Mk4BCfB07RdILGypff58__0E_KGs2POWHEy9eq4aBmvnpEDLiqxEozL50931u6TlzFeZbRmonlB9stSVoUoqwPy-3xMeBkgWT9Sb6iCUWGgAS9tTGFLYdQUHaoU_GgVHRBcGvJY-aCphgQ0ear8mOFZJQpUDdbpwXv9qIpzuLbXPsTBTpkcfEhH1ICyziZISIONP2l-niMYq5YgxgfqdkM0Jv8dj-5zQIwYI4VpCn4Kdjc33jl_8-f2bs5uCPiK7BlwEV8_nIfkx-dP39dnq4uvX87XpxcrVea1V7LHljeKGaVlDVoy3TeNqDjK3hQVa2upNQDjtZRljQ1qoYoaeqZ52SIUUB6S94s3R_k1Y0zdxkaFzsGIfo5dIVhZVbwuZEbf_YNe-TmMOV2meN2KQsod9WGhVPAxBjRd3nADYdtx1u1K7nLJ3X3JmX37YJz7Deon8rHVDJwswI11uP2_qfv2cb0o_wIzP7co</recordid><startdate>202106</startdate><enddate>202106</enddate><creator>Noyd, David H.</creator><creator>Neely, Nigel B.</creator><creator>Schroeder, Kristin M.</creator><creator>Lantos, Paul M.</creator><creator>Power, Steve</creator><creator>Kreissman, Susan G.</creator><creator>Oeffinger, Kevin C.</creator><general>Wiley Subscription Services, Inc</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>7T5</scope><scope>7TK</scope><scope>7TO</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6433-6174</orcidid><orcidid>https://orcid.org/0000-0002-3465-5000</orcidid></search><sort><creationdate>202106</creationdate><title>Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow‐up care</title><author>Noyd, David H. ; Neely, Nigel B. ; Schroeder, Kristin M. ; Lantos, Paul M. ; Power, Steve ; Kreissman, Susan G. ; Oeffinger, Kevin C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3534-7be918c0fcd76ad70db88541e7bf240967ddaa0167736e8ed5c26ab0d139ea2a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aftercare - methods</topic><topic>Aftercare - statistics & numerical data</topic><topic>biomedical informatics</topic><topic>Cancer</topic><topic>Cancer Survivors - statistics & numerical data</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Childhood</topic><topic>childhood cancer survivorship</topic><topic>Children</topic><topic>Databases, Factual - statistics & numerical data</topic><topic>Diagnosis</topic><topic>Electronic Health Records</topic><topic>Electronic medical records</topic><topic>Female</topic><topic>Hematology</topic><topic>Humans</topic><topic>Logistic Models</topic><topic>Male</topic><topic>Neoplasms - classification</topic><topic>Neoplasms - therapy</topic><topic>Neural coding</topic><topic>Oncology</topic><topic>Pediatrics</topic><topic>Registries</topic><topic>Retrospective Studies</topic><topic>Risk</topic><topic>Survival</topic><topic>Survivorship</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Noyd, David H.</creatorcontrib><creatorcontrib>Neely, Nigel B.</creatorcontrib><creatorcontrib>Schroeder, Kristin M.</creatorcontrib><creatorcontrib>Lantos, Paul M.</creatorcontrib><creatorcontrib>Power, Steve</creatorcontrib><creatorcontrib>Kreissman, Susan G.</creatorcontrib><creatorcontrib>Oeffinger, Kevin C.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Pediatric blood & cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Noyd, David H.</au><au>Neely, Nigel B.</au><au>Schroeder, Kristin M.</au><au>Lantos, Paul M.</au><au>Power, Steve</au><au>Kreissman, Susan G.</au><au>Oeffinger, Kevin C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow‐up care</atitle><jtitle>Pediatric blood & cancer</jtitle><addtitle>Pediatr Blood Cancer</addtitle><date>2021-06</date><risdate>2021</risdate><volume>68</volume><issue>6</issue><spage>e29014</spage><epage>n/a</epage><pages>e29014-n/a</pages><issn>1545-5009</issn><eissn>1545-5017</eissn><abstract>Background
This retrospective study harnessed an institutional cancer registry to construct a childhood cancer survivorship cohort, integrate electronic health record (EHR) and geospatial data to stratify survivors based on late‐effect risk, analyze follow‐up care patterns, and determine factors associated with suboptimal follow‐up care.
Procedure
The survivorship cohort included patients ≤18 years of age reported to the institutional cancer registry between January 1, 1994 and November 30, 2012. International Classification of Diseases for Oncology, third revision (ICD‐O‐3) coding and treatment exposures facilitated risk stratification of survivors. The EHR was linked to the cancer registry based on medical record number (MRN) to extract clinic visits.
Results
Five hundred and ninety pediatric hematology‐oncology (PHO) and 275 pediatric neuro‐oncology (PNO) survivors were included in the final analytic cohort. Two hundred and eight‐two survivors (32.6%) were not seen in any oncology‐related subspecialty clinic at Duke 5–7 years after initial diagnosis. Factors associated with follow‐up included age (p = .008), diagnosis (p < .001), race/ethnicity (p = .010), late‐effect risk strata (p = .001), distance to treatment center (p < .0001), and area deprivation index (ADI) (p = .011). Multivariable logistic modeling attenuated the association for high‐risk (OR 1.72; 95% CI 0.805, 3.66) and intermediate‐risk (OR 1.23, 95% CI 0.644, 2.36) survivors compared to survivors at low risk of late effects among the PHO cohort. PNO survivors at high risk for late effects were more likely to follow up (adjusted OR 3.66; 95% CI 1.76, 7.61).
Conclusions
Nearly a third of survivors received suboptimal follow‐up care. This study provides a reproducible model to integrate cancer registry and EHR data to construct risk‐stratified survivorship cohorts to assess follow‐up care.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>33742534</pmid><doi>10.1002/pbc.29014</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-6433-6174</orcidid><orcidid>https://orcid.org/0000-0002-3465-5000</orcidid></addata></record> |
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| subjects | Aftercare - methods Aftercare - statistics & numerical data biomedical informatics Cancer Cancer Survivors - statistics & numerical data Child Child, Preschool Childhood childhood cancer survivorship Children Databases, Factual - statistics & numerical data Diagnosis Electronic Health Records Electronic medical records Female Hematology Humans Logistic Models Male Neoplasms - classification Neoplasms - therapy Neural coding Oncology Pediatrics Registries Retrospective Studies Risk Survival Survivorship |
| title | Integration of cancer registry and electronic health record data to construct a childhood cancer survivorship cohort, facilitate risk stratification for late effects, and assess appropriate follow‐up care |
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