Prediction of individuals at high risk of chronic kidney disease during treatment with lithium for bipolar disorder.


Journal

BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723

Informations de publication

Date de publication:
28 04 2021
Historique:
received: 05 01 2021
accepted: 17 03 2021
entrez: 28 4 2021
pubmed: 29 4 2021
medline: 16 10 2021
Statut: epublish

Résumé

Lithium is the most effective treatment in bipolar disorder. Its use is limited by concerns about risk of chronic kidney disease (CKD). We aimed to develop a model to predict risk of CKD following lithium treatment initiation, by identifying individuals with a high-risk trajectory of kidney function. We used United Kingdom Clinical Practice Research Datalink (CPRD) electronic health records (EHRs) from 2000 to 2018. CPRD Aurum for prediction model development and CPRD Gold for external validation. We used elastic net regularised regression to generate a prediction model from potential features. We performed discrimination and calibration assessments in an external validation data set. We included all patients aged ≥ 16 with bipolar disorder prescribed lithium. To be included patients had to have ≥ 1 year of follow-up before lithium initiation, ≥ 3 estimated glomerular filtration rate (eGFR) measures after lithium initiation (to be able to determine a trajectory) and a normal (≥ 60 mL/min/1.73 m The high risk of deteriorating eGFR group included 191 (11.87%) of the Aurum cohort and 137 (14.67%) of the Gold cohort. Of these, 168 (87.96%) and 117 (85.40%) respectively developed CKD 3a or more severe during follow-up. The model, developed in Aurum, had a ROC area of 0.879 (95%CI 0.853-0.904) in the Gold external validation data set. At the empirical optimal cut-point defined in the development dataset, the model had a sensitivity of 0.91 (95%CI 0.84-0.97) and a specificity of 0.74 (95% CI 0.67-0.82). However, a 3-variable elastic net model (including only age, sex and baseline eGFR) performed similarly well (ROC area 0.888; 95%CI 0.864-0.912), as did the KFRE (ROC area 0.870; 95%CI 0.841-0.898). Individuals at high risk of a poor eGFR trajectory can be identified before initiation of lithium treatment by a simple equation including age, sex and baseline eGFR. Risk was increased in individuals who were younger at commencement of lithium, female and had a lower baseline eGFR. We did not identify strong predicters of eGFR decline specific to lithium-treated patients. Notably, lithium duration and toxicity were not associated with high-risk trajectory.

Sections du résumé

BACKGROUND
Lithium is the most effective treatment in bipolar disorder. Its use is limited by concerns about risk of chronic kidney disease (CKD). We aimed to develop a model to predict risk of CKD following lithium treatment initiation, by identifying individuals with a high-risk trajectory of kidney function.
METHODS
We used United Kingdom Clinical Practice Research Datalink (CPRD) electronic health records (EHRs) from 2000 to 2018. CPRD Aurum for prediction model development and CPRD Gold for external validation. We used elastic net regularised regression to generate a prediction model from potential features. We performed discrimination and calibration assessments in an external validation data set. We included all patients aged ≥ 16 with bipolar disorder prescribed lithium. To be included patients had to have ≥ 1 year of follow-up before lithium initiation, ≥ 3 estimated glomerular filtration rate (eGFR) measures after lithium initiation (to be able to determine a trajectory) and a normal (≥ 60 mL/min/1.73 m
RESULTS
The high risk of deteriorating eGFR group included 191 (11.87%) of the Aurum cohort and 137 (14.67%) of the Gold cohort. Of these, 168 (87.96%) and 117 (85.40%) respectively developed CKD 3a or more severe during follow-up. The model, developed in Aurum, had a ROC area of 0.879 (95%CI 0.853-0.904) in the Gold external validation data set. At the empirical optimal cut-point defined in the development dataset, the model had a sensitivity of 0.91 (95%CI 0.84-0.97) and a specificity of 0.74 (95% CI 0.67-0.82). However, a 3-variable elastic net model (including only age, sex and baseline eGFR) performed similarly well (ROC area 0.888; 95%CI 0.864-0.912), as did the KFRE (ROC area 0.870; 95%CI 0.841-0.898).
CONCLUSIONS
Individuals at high risk of a poor eGFR trajectory can be identified before initiation of lithium treatment by a simple equation including age, sex and baseline eGFR. Risk was increased in individuals who were younger at commencement of lithium, female and had a lower baseline eGFR. We did not identify strong predicters of eGFR decline specific to lithium-treated patients. Notably, lithium duration and toxicity were not associated with high-risk trajectory.

Identifiants

pubmed: 33906644
doi: 10.1186/s12916-021-01964-z
pii: 10.1186/s12916-021-01964-z
pmc: PMC8080385
doi:

Substances chimiques

Lithium 9FN79X2M3F

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

99

Subventions

Organisme : Wellcome Trust
ID : 211085/Z/18/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 101143/Z/13/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N013867/1
Pays : United Kingdom

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Auteurs

Joseph F Hayes (JF)

Division of Psychiatry, UCL, London, UK. joseph.hayes@ucl.ac.uk.
Camden and Islington NHS Foundation Trust, London, UK. joseph.hayes@ucl.ac.uk.

David P J Osborn (DPJ)

Division of Psychiatry, UCL, London, UK.
Camden and Islington NHS Foundation Trust, London, UK.

Emma Francis (E)

Division of Psychiatry, UCL, London, UK.

Gareth Ambler (G)

Department of Statistical Science, UCL, London, UK.

Laurie A Tomlinson (LA)

Department of non-Communicable Disease Epidemiology, LSHTM, London, UK.

Magnus Boman (M)

Division of Software and Computer Systems, School of Electrical Engineering and Computer Science KTH, Stockholm, Sweden.
Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Solna, Sweden.

Ian C K Wong (ICK)

Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong.
Research Department of Practice and Policy, School of Pharmacy, UCL, London, UK.

John R Geddes (JR)

Department of Psychiatry, University of Oxford, Oxford, UK.

Christina Dalman (C)

Department of Global Public Health, Karolinska Institute, Stockholm, Sweden.

Glyn Lewis (G)

Division of Psychiatry, UCL, London, UK.
Camden and Islington NHS Foundation Trust, London, UK.

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