Validation of the recording of idiopathic pulmonary fibrosis in routinely collected electronic healthcare records in England
Background Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this app...
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| Published in | BMC pulmonary medicine Vol. 23; no. 1; pp. 256 - 12 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
11.07.2023
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2466 1471-2466 |
| DOI | 10.1186/s12890-023-02550-0 |
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| Abstract | Background
Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care.
Method
Using the UK’s Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time.
Result
A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3–65.3) for a “broad” codeset to 74.9% (95%CI:72.8–76.9) for a “narrow” codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4–81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes.
Conclusion
High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. |
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| AbstractList | Background Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care. Method Using the UK's Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time. Result A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3-65.3) for a "broad" codeset to 74.9% (95%CI:72.8-76.9) for a "narrow" codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4-81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes. Conclusion High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. Keywords: Interstitial lung disease, Idiopathic pulmonary fibrosis, Pulmonary fibrosis, Validation, CPRD, HES, Diagnostic codes Abstract Background Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care. Method Using the UK’s Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time. Result A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3–65.3) for a “broad” codeset to 74.9% (95%CI:72.8–76.9) for a “narrow” codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4–81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes. Conclusion High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care. Using the UK's Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time. A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3-65.3) for a "broad" codeset to 74.9% (95%CI:72.8-76.9) for a "narrow" codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4-81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes. High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. BackgroundRoutinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care.MethodUsing the UK’s Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time.ResultA total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3–65.3) for a “broad” codeset to 74.9% (95%CI:72.8–76.9) for a “narrow” codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4–81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes.ConclusionHigh diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. Background Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care. Method Using the UK’s Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time. Result A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3–65.3) for a “broad” codeset to 74.9% (95%CI:72.8–76.9) for a “narrow” codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4–81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes. Conclusion High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care. Using the UK's Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time. A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3-65.3) for a "broad" codeset to 74.9% (95%CI:72.8-76.9) for a "narrow" codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4-81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes. High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care.BACKGROUNDRoutinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple lists of clinical codes can reliably be used for case finding in primary care, however, studies exploring the robustness of this approach are lacking for diseases such as idiopathic pulmonary fibrosis (IPF) which are largely managed in secondary care.Using the UK's Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time.METHODUsing the UK's Clinical Practice Research Datalink (CPRD) Aurum dataset, which comprises patient-level primary care records linked to national hospital admissions and cause-of-death data, we compared the positive predictive value (PPV) of eight diagnostic algorithms. Algorithms were developed based on the literature and IPF diagnostic guidelines using combinations of clinical codes in primary and secondary care (SNOMED-CT or ICD-10) with/without additional information. The positive predictive value (PPV) was estimated for each algorithm using the death record as the gold standard. Utilization of the reviewed codes across the study period was observed to evaluate any change in coding practices over time.A total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3-65.3) for a "broad" codeset to 74.9% (95%CI:72.8-76.9) for a "narrow" codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4-81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes.RESULTA total of 17,559 individuals had a least one record indicative of IPF in one or more of our three linked datasets between 2008 and 2018. The PPV of case-finding algorithms based on clinical codes alone ranged from 64.4% (95%CI:63.3-65.3) for a "broad" codeset to 74.9% (95%CI:72.8-76.9) for a "narrow" codeset comprising highly-specific codes. Adding confirmatory evidence, such as a CT scan, increased the PPV of our narrow code-based algorithm to 79.2% (95%CI:76.4-81.8) but reduced the sensitivity to under 10%. Adding evidence of hospitalisation to the standalone code-based algorithms also improved PPV, (PPV = 78.4 vs. 64.4%; sensitivity = 53.5% vs. 38.1%). IPF coding practices changed over time, with the increased use of specific IPF codes.High diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation.CONCLUSIONHigh diagnostic validity was achieved by using a restricted set of IPF codes. While adding confirmatory evidence increased diagnostic accuracy, the benefits of this approach need to be weighed against the inevitable loss of sample size and convenience. We would recommend use of an algorithm based on a broader IPF code set coupled with evidence of hospitalisation. |
| ArticleNumber | 256 |
| Audience | Academic |
| Author | Morgan, Ann Quint, Jennifer K. Gupta, Rikisha Shah George, Peter M. |
| Author_xml | – sequence: 1 givenname: Ann surname: Morgan fullname: Morgan, Ann organization: School of Public Health, Imperial College London, National Heart and Lung Institute, Imperial College London – sequence: 2 givenname: Rikisha Shah surname: Gupta fullname: Gupta, Rikisha Shah organization: School of Public Health, Imperial College London, National Heart and Lung Institute, Imperial College London – sequence: 3 givenname: Peter M. surname: George fullname: George, Peter M. organization: National Heart and Lung Institute, Imperial College London, Interstitial Lung Disease Unit, Royal Brompton and Harefield NHS Foundation Trust, NIHR Imperial Biomedical Research Centre – sequence: 4 givenname: Jennifer K. surname: Quint fullname: Quint, Jennifer K. email: j.quint@imperial.ac.uk organization: School of Public Health, Imperial College London, National Heart and Lung Institute, Imperial College London, NIHR Imperial Biomedical Research Centre |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37434192$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1183_13993003_02080_2023 crossref_primary_10_1183_23120541_00823_2024 crossref_primary_10_1136_thorax_2023_220887 crossref_primary_10_1136_thorax_2024_221865 crossref_primary_10_1186_s41479_024_00155_7 crossref_primary_10_2147_CLEP_S437937 |
| Cites_doi | 10.1111/j.1365-2125.2009.03537.x 10.1007/s12325-018-0693-1 10.1164/rccm.200805-725OC 10.1002/pds.2338 10.1164/rccm.2009-040GL 10.1136/thx.46.8.589 10.1164/ajrccm.161.1.9906062 10.1164/rccm.201006-0894CI 10.1093/ije/dyx015 10.3399/bjgp10X483562 10.1136/bmjopen-2014-005540 10.1186/s12931-021-01791-z 10.1007/s10654-018-0442-4 10.1164/rccm.201311-1951OC 10.1164/rccm.201807-1255ST 10.1093/ije/dyz034 10.1183/23120541.00170-2018 10.1007/s41030-023-00216-0 10.3399/bjgpopen20X101050 10.1136/bmjresp-2022-001291 |
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| Keywords | Validation CPRD Pulmonary fibrosis Idiopathic pulmonary fibrosis HES Interstitial lung disease Diagnostic codes |
| Language | English |
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| PublicationTitle | BMC pulmonary medicine |
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| References | TE King Jr (2550_CR3) 2014; 189 A Herbert (2550_CR15) 2017; 46 2550_CR5 R Hubbard (2550_CR11) 2000; 161 B Ley (2550_CR21) 2011; 15 ID Johnston (2550_CR19) 1991; 46 E Herrett (2550_CR10) 2010; 69 RB Hubbard (2550_CR8) 2008; 178 S Padmanabhan (2550_CR14) 2019; 34 H Alsomali (2550_CR6) 2023 J Kaunisto (2550_CR2) 2019; 5 NF Khan (2550_CR17) 2010; 60 TM Maher (2550_CR4) 2021; 22 JSP Tulloch (2550_CR18) 2020; 4 G Raghu (2550_CR9) 2018; 198 N Jones (2550_CR22) 2012; 21 2550_CR12 G Raghu (2550_CR1) 2011; 183 A Wolf (2550_CR13) 2019; 48 H Strongman (2550_CR7) 2018; 35 V Navaratnam (2550_CR16) 2011; 66 JK Quint (2550_CR20) 2014; 4 |
| References_xml | – volume: 69 start-page: 4 issue: 1 year: 2010 ident: 2550_CR10 publication-title: Br J Clin Pharmacol doi: 10.1111/j.1365-2125.2009.03537.x – volume: 35 start-page: 724 issue: 5 year: 2018 ident: 2550_CR7 publication-title: Adv Ther doi: 10.1007/s12325-018-0693-1 – volume: 178 start-page: 1257 issue: 12 year: 2008 ident: 2550_CR8 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.200805-725OC – volume: 21 start-page: 256 issue: Suppl 1 year: 2012 ident: 2550_CR22 publication-title: Pharmacoepidemiol Drug Saf doi: 10.1002/pds.2338 – volume: 183 start-page: 788 issue: 6 year: 2011 ident: 2550_CR1 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.2009-040GL – volume: 46 start-page: 589 issue: 8 year: 1991 ident: 2550_CR19 publication-title: Thorax doi: 10.1136/thx.46.8.589 – ident: 2550_CR12 – volume: 161 start-page: 5 issue: 1 year: 2000 ident: 2550_CR11 publication-title: Am J Respir Crit Care Med doi: 10.1164/ajrccm.161.1.9906062 – volume: 15 start-page: 431 issue: 4 year: 2011 ident: 2550_CR21 publication-title: Am J Respir Crit Care Med. doi: 10.1164/rccm.201006-0894CI – volume: 46 start-page: 1093 issue: 4 year: 2017 ident: 2550_CR15 publication-title: Int J Epidemiol doi: 10.1093/ije/dyx015 – volume: 60 start-page: e128 issue: 572 year: 2010 ident: 2550_CR17 publication-title: Br J Gen Pract doi: 10.3399/bjgp10X483562 – volume: 4 start-page: e005540 issue: 7 year: 2014 ident: 2550_CR20 publication-title: BMJ Open doi: 10.1136/bmjopen-2014-005540 – volume: 22 start-page: 197 issue: 1 year: 2021 ident: 2550_CR4 publication-title: Respir Res doi: 10.1186/s12931-021-01791-z – volume: 34 start-page: 91 issue: 1 year: 2019 ident: 2550_CR14 publication-title: Eur J Epidemiol doi: 10.1007/s10654-018-0442-4 – volume: 189 start-page: 825 issue: 7 year: 2014 ident: 2550_CR3 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201311-1951OC – volume: 198 start-page: e44 issue: 5 year: 2018 ident: 2550_CR9 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201807-1255ST – volume: 48 start-page: 1740 issue: 6 year: 2019 ident: 2550_CR13 publication-title: Int J Epidemiol doi: 10.1093/ije/dyz034 – volume: 66 start-page: 462 issue: 6 year: 2011 ident: 2550_CR16 publication-title: The rising incidence of idiopathic pulmonary fibrosis in the U K Thorax – volume: 5 start-page: 00170 issue: 3 year: 2019 ident: 2550_CR2 publication-title: ERJ Open Res doi: 10.1183/23120541.00170-2018 – year: 2023 ident: 2550_CR6 publication-title: Pulm Ther doi: 10.1007/s41030-023-00216-0 – volume: 4 start-page: bjgpopen20X1010 issue: 3 year: 2020 ident: 2550_CR18 publication-title: BJGP Open doi: 10.3399/bjgpopen20X101050 – ident: 2550_CR5 doi: 10.1136/bmjresp-2022-001291 |
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| Snippet | Background
Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most... Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions, simple... Background Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most... BackgroundRoutinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most conditions,... Abstract Background Routinely-collected healthcare data provide a valuable resource for epidemiological research. Validation studies have shown that for most... |
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| SubjectTerms | Algorithms Analysis Care and treatment Computed tomography CPRD Critical Care Medicine Datasets Diagnosis Electronic health records Electronic records Epidemiology Fibrosis Health aspects Health care HES Hospitalization Hospitals Idiopathic pulmonary fibrosis Intensive Internal Medicine Interstitial lung disease Lung diseases Medical advice systems Medical coding Medical prognosis Medical records Medicine Medicine & Public Health Mortality Patients Pneumology/Respiratory System Population Primary care Pulmonary fibrosis Pulmonology Registration Sensitivity analysis Validation Validation studies |
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| Title | Validation of the recording of idiopathic pulmonary fibrosis in routinely collected electronic healthcare records in England |
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