Developing a validated methodology for identifying clozapine treatment periods in electronic health records

TY - JOUR

T1 - Developing a validated methodology for identifying clozapine treatment periods in electronic health records

AU - Segev, Aviv

AU - Govind, Risha

AU - Oloyede, Ebenezer

AU - Morrin, Hamilton

AU - Jewell, Amelia

AU - Jones, Rowena

AU - Mangiaterra, Laura

AU - Bonora, Stefano

AU - Iqbal, Ehtesham

AU - Stewart, Robert

AU - Broadbent, Matthew

AU - MacCabe, James H.

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/8/27

Y1 - 2024/8/27

N2 - Background: Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy. Aim: To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database. Methods: Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text. Results: A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days. Conclusions: The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide.

AB - Background: Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy. Aim: To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database. Methods: Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text. Results: A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days. Conclusions: The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide.

KW - Algorithm

KW - Clozapine

KW - CRIS

KW - Databases

KW - EHR

KW - Maudsley

KW - Psychosis

KW - Schizophrenia

UR - http://www.scopus.com/inward/record.url?scp=85202176391&partnerID=8YFLogxK

U2 - 10.1186/s12888-024-06022-5

DO - 10.1186/s12888-024-06022-5

M3 - Article

C2 - 39192241

AN - SCOPUS:85202176391

SN - 1471-244X

VL - 24

JO - BMC Psychiatry

JF - BMC Psychiatry

IS - 1

M1 - 584

ER -