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Predicting new case...
Predicting new cases of hypertension in Swedish primary care with a machine learning tool
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- Norrman, Anders (författare)
- Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.;Reg Stockholm, Acad Primary Hlth Care Ctr, Stockholm, Sweden.
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- Hasselstrom, Jan (författare)
- Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.;Reg Stockholm, Acad Primary Hlth Care Ctr, Stockholm, Sweden.
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- Ljunggren, Gunnar (författare)
- Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.;Reg Stockholm, Acad Primary Hlth Care Ctr, Stockholm, Sweden.
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- Wachtler, Caroline (författare)
- Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.;Reg Stockholm, Acad Primary Hlth Care Ctr, Stockholm, Sweden.
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- Eriksson, Julia (författare)
- Karolinska Inst, Inst Environm Med, Div Biostat, Stockholm, Sweden.
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- Kahan, Thomas (författare)
- Danderyd Hosp, Karolinska Inst, Dept Clin Sci, Div Cardiovasc Med, Stockholm, Sweden.
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- Wandell, Per (författare)
- Karolinska Institutet,Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.
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- Gudjonsdottir, Hrafnhildur (författare)
- Reg Stockholm, Ctr Epidemiol & Community Med, Stockholm, Sweden.;Karolinska Inst, Dept Global Publ Hlth, Stockholm, Sweden.
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- Lindblom, Sebastian (författare)
- Karolinska Institutet,Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.;Karolinska Univ Hosp, Women Hlth & Allied Hlth Profess Theme, Stockholm, Sweden.
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- Ruge, Toralph (författare)
- Lund Univ, Dept Clin Sci Malmö, Malmö, Sweden.;Skane Univ Hosp, Dept Internal Med, Malmö, Sweden.
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- Rosenblad, Andreas, fil. dr, docent, 1973- (författare)
- Uppsala universitet,Klinisk diabetologi och metabolism,Statistiska institutionen,Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.;Reg Stockholm, Reg Canc Ctr Stockholm Gotland, Stockholm, Sweden.
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- Brynedal, Boel (författare)
- Reg Stockholm, Ctr Epidemiol & Community Med, Stockholm, Sweden.;Karolinska Inst, Dept Global Publ Hlth, Stockholm, Sweden.
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- Carlsson, Axel C. (författare)
- Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden.;Reg Stockholm, Acad Primary Hlth Care Ctr, Stockholm, Sweden.
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Karolinska Inst, Care Sci & Soc, Div Family Med & Primary Care, Dept Neurobiol, Huddinge, Sweden;Reg Stockholm, Acad Primary Hlth Care Ctr, Stockholm, Sweden. Karolinska Inst, Inst Environm Med, Div Biostat, Stockholm, Sweden. (creator_code:org_t)
- Elsevier, 2024
- 2024
- Engelska.
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Ingår i: Preventive Medicine Reports. - : Elsevier. - 2211-3355. ; 44
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Background: Many individuals with hypertension remain undiagnosed. We aimed to develop a predictive model for hypertension using diagnostic codes from prevailing electronic medical records in Swedish primary care.Methods: This sex- and age-matched case-control (1:5) study included patients aged 30 -65 years living in the Stockholm Region, Sweden, with a newly recorded diagnosis of hypertension during 2010 -19 (cases) and individuals without a recorded hypertension diagnosis during 2010 -19 (controls), in total 507,618 individuals. Patients with diagnoses of cardiovascular diseases or diabetes were excluded. A stochastic gradient boosting machine learning model was constructed using the 1,309 most registered ICD-10 codes from primary care for three years prior the hypertension diagnosis.Results: The model showed an area under the curve (95 % confidence interval) of 0.748 (0.742 -0.753) for females and 0.745 (0.740 -0.751) for males for predicting diagnosis of hypertension within three years. The sensitivity was 63 % and 68 %, and the specificity 76 % and 73 %, for females and males, respectively. The 25 diagnoses that contributed the most to the model for females and males all exhibited a normalized relative influence >1 %. The codes contributing most to the model, all with an odds ratio of marginal effects >1 for both sexes, were dyslipidaemia, obesity, and encountering health services in other circumstances.Conclusions: This machine learning model, using prevailing recorded diagnoses within primary health care, may contribute to the identification of patients at risk of unrecognized hypertension. The added value of this predictive model beyond information of blood pressure warrants further study.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- Artificial intelligence
- Hypertension
- Family practice
- Gradient boosting
- Prediction
- Opportunistic screening
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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- Av författaren/redakt...
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Norrman, Anders
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Hasselstrom, Jan
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Ljunggren, Gunna ...
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Wachtler, Caroli ...
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Eriksson, Julia
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Kahan, Thomas
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visa fler...
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Wandell, Per
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Gudjonsdottir, H ...
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Lindblom, Sebast ...
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Ruge, Toralph
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Rosenblad, Andre ...
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Brynedal, Boel
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Carlsson, Axel C ...
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Kardiologi
- Artiklar i publikationen
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Preventive Medic ...
- Av lärosätet
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Uppsala universitet
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Karolinska Institutet