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Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records

Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient...

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Autores principales: Hjaltelin, Jessica Xin, Novitski, Sif Ingibergsdóttir, Jørgensen, Isabella Friis, Siggaard, Troels, Vulpius, Siri Amalie, Westergaard, David, Johansen, Julia Sidenius, Chen, Inna M, Juhl Jensen, Lars, Brunak, Søren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662947/
https://www.ncbi.nlm.nih.gov/pubmed/37988407
http://dx.doi.org/10.7554/eLife.84919
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author Hjaltelin, Jessica Xin
Novitski, Sif Ingibergsdóttir
Jørgensen, Isabella Friis
Siggaard, Troels
Vulpius, Siri Amalie
Westergaard, David
Johansen, Julia Sidenius
Chen, Inna M
Juhl Jensen, Lars
Brunak, Søren
author_facet Hjaltelin, Jessica Xin
Novitski, Sif Ingibergsdóttir
Jørgensen, Isabella Friis
Siggaard, Troels
Vulpius, Siri Amalie
Westergaard, David
Johansen, Julia Sidenius
Chen, Inna M
Juhl Jensen, Lars
Brunak, Søren
author_sort Hjaltelin, Jessica Xin
collection PubMed
description Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient Registry (NPR) encompassing 6.9 million patients from 1994 to 2018,, of whom 23,592 were diagnosed with pancreatic cancer. The Danish cancer registry included 18,523 of these patients. To complement and compare the registry diagnosis codes with deeper clinical data, we used a text mining approach to extract symptoms from free text clinical notes in electronic health records (3078 pancreatic cancer patients and 30,780 controls). We used both data sources to generate and compare symptom disease trajectories to uncover temporal patterns of symptoms prior to pancreatic cancer diagnosis for the same patients. We show that the text mining of the clinical notes was able to complement the registry-based symptoms by capturing more symptoms prior to pancreatic cancer diagnosis. For example, ‘Blood pressure reading without diagnosis’, ‘Abnormalities of heartbeat’, and ‘Intestinal obstruction’ were not found for the registry-based analysis. Chaining symptoms together in trajectories identified two groups of patients with lower median survival (<90 days) following the trajectories ‘Cough→Jaundice→Intestinal obstruction’ and ‘Pain→Jaundice→Abnormal results of function studies’. These results provide a comprehensive comparison of the two types of pancreatic cancer symptom trajectories, which in combination can leverage the full potential of the health data and ultimately provide a fuller picture for detection of early risk factors for pancreatic cancer.
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spelling pubmed-106629472023-11-21 Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records Hjaltelin, Jessica Xin Novitski, Sif Ingibergsdóttir Jørgensen, Isabella Friis Siggaard, Troels Vulpius, Siri Amalie Westergaard, David Johansen, Julia Sidenius Chen, Inna M Juhl Jensen, Lars Brunak, Søren eLife Cancer Biology Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient Registry (NPR) encompassing 6.9 million patients from 1994 to 2018,, of whom 23,592 were diagnosed with pancreatic cancer. The Danish cancer registry included 18,523 of these patients. To complement and compare the registry diagnosis codes with deeper clinical data, we used a text mining approach to extract symptoms from free text clinical notes in electronic health records (3078 pancreatic cancer patients and 30,780 controls). We used both data sources to generate and compare symptom disease trajectories to uncover temporal patterns of symptoms prior to pancreatic cancer diagnosis for the same patients. We show that the text mining of the clinical notes was able to complement the registry-based symptoms by capturing more symptoms prior to pancreatic cancer diagnosis. For example, ‘Blood pressure reading without diagnosis’, ‘Abnormalities of heartbeat’, and ‘Intestinal obstruction’ were not found for the registry-based analysis. Chaining symptoms together in trajectories identified two groups of patients with lower median survival (<90 days) following the trajectories ‘Cough→Jaundice→Intestinal obstruction’ and ‘Pain→Jaundice→Abnormal results of function studies’. These results provide a comprehensive comparison of the two types of pancreatic cancer symptom trajectories, which in combination can leverage the full potential of the health data and ultimately provide a fuller picture for detection of early risk factors for pancreatic cancer. eLife Sciences Publications, Ltd 2023-11-21 /pmc/articles/PMC10662947/ /pubmed/37988407 http://dx.doi.org/10.7554/eLife.84919 Text en © 2023, Hjaltelin et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Cancer Biology
Hjaltelin, Jessica Xin
Novitski, Sif Ingibergsdóttir
Jørgensen, Isabella Friis
Siggaard, Troels
Vulpius, Siri Amalie
Westergaard, David
Johansen, Julia Sidenius
Chen, Inna M
Juhl Jensen, Lars
Brunak, Søren
Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records
title Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records
title_full Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records
title_fullStr Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records
title_full_unstemmed Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records
title_short Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records
title_sort pancreatic cancer symptom trajectories from danish registry data and free text in electronic health records
topic Cancer Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662947/
https://www.ncbi.nlm.nih.gov/pubmed/37988407
http://dx.doi.org/10.7554/eLife.84919
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