How well can electronic health records from primary care identify Alzheimer’s disease cases? Article Swipe
Anna Ponjoan
,
Josep Garre‐Olmo
,
Jordi Blanch
,
Ester Fages
,
Lia Alves‐Cabratosa
,
Ruth Martí‐Lluch
,
Marc Comas‐Cufí
,
Dídac Parramon
,
Maria García-Gil
,
Rubén Trigueros
·
YOU?
·
· 2019
· Open Access
·
YOU?
·
· 2019
· Open Access
·
Anna Ponjoan,1–3 Josep Garre-Olmo,3 Jordi Blanch,1 Ester Fages,1,4 Lia Alves-Cabratosa,1 Ruth Martí-Lluch,1–3 Marc Comas-Cufí,1 Dídac Parramon,1,4 María García-Gil,1 Rafel Ramos1,51Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain; 2Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain; 3Girona Biomedical Research Institute (IDIBGI), Girona, Catalonia, Spain; 4Primary Care Services, Catalan Health Institute (ICS), Girona, Catalonia, Spain; 5Department of Medical Sciences, School of Medicine, Campus Salut, University of Girona, Girona, Catalonia, SpainBackground: Electronic health records (EHR) from primary care are emerging in Alzheimer’s disease (AD) research, but their accuracy is a concern. We aimed to validate AD diagnoses from primary care using additional information provided by general practitioners (GPs), and a register of dementias.Patients and methods: This retrospective observational study obtained data from the System for the Development of Research in Primary Care (SIDIAP). Three algorithms combined International Statistical Classification of Diseases (ICD-10) and Anatomical Therapeutic Chemical codes to identify AD cases in SIDIAP. GPs evaluated dementia diagnoses by means of an online survey. We linked data from the Register of Dementias of Girona and from SIDIAP. We estimated the positive predictive value (PPV) and sensitivity and provided results stratified by age, sex and severity.Results: Using survey data from the GPs, PPV of AD diagnosis was 89.8% (95% CI: 84.7–94.9). Using the dataset linkage, PPV was 74.8 (95% CI: 73.1–76.4) for algorithm A1 (AD diagnoses), and 72.3 (95% CI: 70.7–73.9) for algorithm A3 (diagnosed or treated patients without previous conditions); sensitivity was 71.4 (95% CI: 69.6–73.0) and 83.3 (95% CI: 81.8–84.6) for algorithms A1 (AD diagnoses) and A3, respectively. Stratified results did not differ by age, but PPV and sensitivity estimates decreased amongst men and severe patients, respectively.Conclusions: PPV estimates differed depending on the gold standard. The development of algorithms integrating diagnoses and treatment of dementia improved the AD case ascertainment. PPV and sensitivity estimates were high and indicated that AD codes recorded in a large primary care database were sufficiently accurate for research purposes.Keywords: dementia, family physician, survey, algorithm, data accuracy, real-world data, validation, electronic medical records
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- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/3139180925614a88bad789a4099a70d8
- OA Status
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- OpenAlex ID
- https://openalex.org/W4399061466
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399061466Canonical identifier for this work in OpenAlex
- Title
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How well can electronic health records from primary care identify Alzheimer’s disease cases?Work title
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articleOpenAlex work type
- Language
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enPrimary language
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2019Year of publication
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2019-07-01Full publication date if available
- Authors
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Anna Ponjoan, Josep Garre‐Olmo, Jordi Blanch, Ester Fages, Lia Alves‐Cabratosa, Ruth Martí‐Lluch, Marc Comas‐Cufí, Dídac Parramon, Maria García-Gil, Rubén TriguerosList of authors in order
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https://doaj.org/article/3139180925614a88bad789a4099a70d8Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doaj.org/article/3139180925614a88bad789a4099a70d8Direct OA link when available
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Primary care, Health records, Disease, Medicine, Family medicine, Health care, Political science, Pathology, LawTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.4Primary | 48 |
| abstract_inverted_index.Blanch,1 | 5 |
| abstract_inverted_index.Chemical | 147 |
| abstract_inverted_index.Diseases | 142 |
| abstract_inverted_index.Register | 170 |
| abstract_inverted_index.Research | 19, 28, 42, 130 |
| abstract_inverted_index.accuracy | 89 |
| abstract_inverted_index.accurate | 319 |
| abstract_inverted_index.combined | 137 |
| abstract_inverted_index.concern. | 92 |
| abstract_inverted_index.database | 316 |
| abstract_inverted_index.dementia | 157, 293 |
| abstract_inverted_index.differed | 278 |
| abstract_inverted_index.emerging | 81 |
| abstract_inverted_index.identify | 150 |
| abstract_inverted_index.improved | 294 |
| abstract_inverted_index.linkage, | 214 |
| abstract_inverted_index.methods: | 116 |
| abstract_inverted_index.obtained | 121 |
| abstract_inverted_index.patients | 237 |
| abstract_inverted_index.positive | 181 |
| abstract_inverted_index.previous | 239 |
| abstract_inverted_index.provided | 105, 188 |
| abstract_inverted_index.recorded | 310 |
| abstract_inverted_index.register | 112 |
| abstract_inverted_index.research | 321 |
| abstract_inverted_index.validate | 96 |
| abstract_inverted_index.(IDIBGI), | 44 |
| abstract_inverted_index.(SIDIAP). | 134 |
| abstract_inverted_index.Dementias | 172 |
| abstract_inverted_index.Institute | 24, 43, 53 |
| abstract_inverted_index.Medicine, | 64 |
| abstract_inverted_index.Sciences, | 61 |
| abstract_inverted_index.Services, | 50 |
| abstract_inverted_index.accuracy, | 329 |
| abstract_inverted_index.algorithm | 222, 232 |
| abstract_inverted_index.decreased | 269 |
| abstract_inverted_index.dementia, | 323 |
| abstract_inverted_index.depending | 279 |
| abstract_inverted_index.diagnoses | 98, 158, 289 |
| abstract_inverted_index.diagnosis | 205 |
| abstract_inverted_index.estimated | 179 |
| abstract_inverted_index.estimates | 268, 277, 302 |
| abstract_inverted_index.evaluated | 156 |
| abstract_inverted_index.indicated | 306 |
| abstract_inverted_index.patients, | 274 |
| abstract_inverted_index.research, | 86 |
| abstract_inverted_index.standard. | 283 |
| abstract_inverted_index.treatment | 291 |
| abstract_inverted_index.(diagnosed | 234 |
| abstract_inverted_index.Anatomical | 145 |
| abstract_inverted_index.Barcelona, | 30, 36 |
| abstract_inverted_index.Biomedical | 41 |
| abstract_inverted_index.Catalonia, | 31, 38, 46, 56, 71 |
| abstract_inverted_index.Electronic | 73 |
| abstract_inverted_index.University | 67 |
| abstract_inverted_index.additional | 103 |
| abstract_inverted_index.algorithm, | 327 |
| abstract_inverted_index.algorithms | 136, 287 |
| abstract_inverted_index.diagnoses) | 254 |
| abstract_inverted_index.electronic | 333 |
| abstract_inverted_index.physician, | 325 |
| abstract_inverted_index.predictive | 182 |
| abstract_inverted_index.real-world | 330 |
| abstract_inverted_index.stratified | 190 |
| abstract_inverted_index.5Department | 58 |
| abstract_inverted_index.Bellaterra, | 37 |
| abstract_inverted_index.Development | 128 |
| abstract_inverted_index.Statistical | 139 |
| abstract_inverted_index.Therapeutic | 146 |
| abstract_inverted_index.development | 285 |
| abstract_inverted_index.diagnoses), | 225 |
| abstract_inverted_index.information | 104 |
| abstract_inverted_index.integrating | 288 |
| abstract_inverted_index.sensitivity | 186, 241, 267, 301 |
| abstract_inverted_index.validation, | 332 |
| abstract_inverted_index.(IDIAPJGol), | 29 |
| abstract_inverted_index.2Universitat | 33 |
| abstract_inverted_index.Dídac | 13 |
| abstract_inverted_index.Garre-Olmo,3 | 3 |
| abstract_inverted_index.conditions); | 240 |
| abstract_inverted_index.sufficiently | 318 |
| abstract_inverted_index.(ISV-Girona), | 21 |
| abstract_inverted_index.International | 138 |
| abstract_inverted_index.observational | 119 |
| abstract_inverted_index.practitioners | 108 |
| abstract_inverted_index.retrospective | 118 |
| abstract_inverted_index.Classification | 140 |
| abstract_inverted_index.ascertainment. | 298 |
| abstract_inverted_index.Autònoma | 34 |
| abstract_inverted_index.70.7–73.9) | 230 |
| abstract_inverted_index.73.1–76.4) | 220 |
| abstract_inverted_index.81.8–84.6) | 250 |
| abstract_inverted_index.SpainBackground: | 72 |
| abstract_inverted_index.84.7–94.9). | 210 |
| abstract_inverted_index.Alves-Cabratosa,1 | 8 |
| abstract_inverted_index.Alzheimer’s | 83 |
| abstract_inverted_index.Ponjoan,1–3 | 1 |
| abstract_inverted_index.Ramos1,51Vascular | 17 |
| abstract_inverted_index.severity.Results: | 195 |
| abstract_inverted_index.Fages,1,4 Lia | 7 |
| abstract_inverted_index.algorithms A1 | 252 |
| abstract_inverted_index.dementias.Patients | 114 |
| abstract_inverted_index.purposes.Keywords: | 322 |
| abstract_inverted_index.Comas-Cufí,1 | 12 |
| abstract_inverted_index.García-Gil,1 | 15 |
| abstract_inverted_index.69.6–73.0) and | 246 |
| abstract_inverted_index.respectively.Conclusions: | 275 |
| abstract_inverted_index.Martí-Lluch,1–3 | 10 |
| abstract_inverted_index.respectively. Stratified | 257 |
| abstract_inverted_index.Parramon,1,4 María | 14 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.24423457 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |