Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records
Objectives To simultaneously estimate how the risk of incident dementia nonlinearly varies with the administration period and cumulative dose of benzodiazepines, the duration of disorders with an indication for benzodiazepines, and other potential confounders, with the goal of settling the controver...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-06-01
|
| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231178577 |
| _version_ | 1797744499634470912 |
|---|---|
| author | Takashi Hayakawa Takuya Nagashima Hayato Akimoto Kimino Minagawa Yasuo Takahashi Satoshi Asai |
| author_facet | Takashi Hayakawa Takuya Nagashima Hayato Akimoto Kimino Minagawa Yasuo Takahashi Satoshi Asai |
| author_sort | Takashi Hayakawa |
| collection | DOAJ |
| description | Objectives To simultaneously estimate how the risk of incident dementia nonlinearly varies with the administration period and cumulative dose of benzodiazepines, the duration of disorders with an indication for benzodiazepines, and other potential confounders, with the goal of settling the controversy over the role of benzodiazepines in the development of dementia. Methods The classical hazard model was extended using the techniques of multiple-kernel learning. Regularised maximum-likelihood estimation, including determination of hyperparameter values with 10-fold cross-validation, bootstrap goodness-of-fit test, and bootstrap estimation of confidence intervals, was applied to cohorts retrospectively extracted from electronic medical records of our university hospitals between 1 November 2004 and 31 July 2020. The analysis was mainly focused on 8160 patients aged 40 or older with new onset of insomnia, affective disorders, or anxiety disorders, who were followed up for 4.10 ± 3.47 years. Results Besides previously reported risk associations, we detected significant nonlinear risk variations over 2–4 years attributable to the duration of insomnia and anxiety disorders, and to the administration period of short-acting benzodiazepines. After nonlinear adjustment for potential confounders, we observed no significant risk associations with long-term use of benzodiazepines. Conclusions The pattern of the detected nonlinear risk variations suggested reverse causation and confounding. Their putative bias effects over 2–4 years suggested similar biases in previously reported results. These results, together with the lack of significant risk associations with long-term use of benzodiazepines, suggested the need to reconsider previous results and methods for future analysis. |
| first_indexed | 2024-03-12T15:10:33Z |
| format | Article |
| id | doaj.art-d10abf7e40164b7cbc1bc46191aa3cf0 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-03-12T15:10:33Z |
| publishDate | 2023-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-d10abf7e40164b7cbc1bc46191aa3cf02023-08-11T19:03:20ZengSAGE PublishingDigital Health2055-20762023-06-01910.1177/20552076231178577Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical recordsTakashi Hayakawa0Takuya Nagashima1Hayato Akimoto2Kimino Minagawa3Yasuo Takahashi4Satoshi Asai5 Division of Genomic Epidemiology and Clinical Trials, , Tokyo, Japan Division of Genomic Epidemiology and Clinical Trials, , Tokyo, Japan Division of Genomic Epidemiology and Clinical Trials, , Tokyo, Japan Division of Genomic Epidemiology and Clinical Trials, , Tokyo, Japan Division of Genomic Epidemiology and Clinical Trials, , Tokyo, Japan Division of Genomic Epidemiology and Clinical Trials, , Tokyo, JapanObjectives To simultaneously estimate how the risk of incident dementia nonlinearly varies with the administration period and cumulative dose of benzodiazepines, the duration of disorders with an indication for benzodiazepines, and other potential confounders, with the goal of settling the controversy over the role of benzodiazepines in the development of dementia. Methods The classical hazard model was extended using the techniques of multiple-kernel learning. Regularised maximum-likelihood estimation, including determination of hyperparameter values with 10-fold cross-validation, bootstrap goodness-of-fit test, and bootstrap estimation of confidence intervals, was applied to cohorts retrospectively extracted from electronic medical records of our university hospitals between 1 November 2004 and 31 July 2020. The analysis was mainly focused on 8160 patients aged 40 or older with new onset of insomnia, affective disorders, or anxiety disorders, who were followed up for 4.10 ± 3.47 years. Results Besides previously reported risk associations, we detected significant nonlinear risk variations over 2–4 years attributable to the duration of insomnia and anxiety disorders, and to the administration period of short-acting benzodiazepines. After nonlinear adjustment for potential confounders, we observed no significant risk associations with long-term use of benzodiazepines. Conclusions The pattern of the detected nonlinear risk variations suggested reverse causation and confounding. Their putative bias effects over 2–4 years suggested similar biases in previously reported results. These results, together with the lack of significant risk associations with long-term use of benzodiazepines, suggested the need to reconsider previous results and methods for future analysis.https://doi.org/10.1177/20552076231178577 |
| spellingShingle | Takashi Hayakawa Takuya Nagashima Hayato Akimoto Kimino Minagawa Yasuo Takahashi Satoshi Asai Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records Digital Health |
| title | Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records |
| title_full | Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records |
| title_fullStr | Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records |
| title_full_unstemmed | Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records |
| title_short | Benzodiazepine-related dementia risks and protopathic biases revealed by multiple-kernel learning with electronic medical records |
| title_sort | benzodiazepine related dementia risks and protopathic biases revealed by multiple kernel learning with electronic medical records |
| url | https://doi.org/10.1177/20552076231178577 |
| work_keys_str_mv | AT takashihayakawa benzodiazepinerelateddementiarisksandprotopathicbiasesrevealedbymultiplekernellearningwithelectronicmedicalrecords AT takuyanagashima benzodiazepinerelateddementiarisksandprotopathicbiasesrevealedbymultiplekernellearningwithelectronicmedicalrecords AT hayatoakimoto benzodiazepinerelateddementiarisksandprotopathicbiasesrevealedbymultiplekernellearningwithelectronicmedicalrecords AT kiminominagawa benzodiazepinerelateddementiarisksandprotopathicbiasesrevealedbymultiplekernellearningwithelectronicmedicalrecords AT yasuotakahashi benzodiazepinerelateddementiarisksandprotopathicbiasesrevealedbymultiplekernellearningwithelectronicmedicalrecords AT satoshiasai benzodiazepinerelateddementiarisksandprotopathicbiasesrevealedbymultiplekernellearningwithelectronicmedicalrecords |