PMID- 38345264
OWN - NLM
STAT- MEDLINE
DCOM- 20240321
LR  - 20240411
IS  - 1532-6535 (Electronic)
IS  - 0009-9236 (Linking)
VI  - 115
IP  - 4
DP  - 2024 Apr
TI  - Predictive Modeling of Drug-Related Adverse Events with Real-World Data: A Case 
      Study of Linezolid Hematologic Outcomes.
PG  - 847-859
LID - 10.1002/cpt.3201 [doi]
AB  - Electronic health records (EHRs) provide meaningful knowledge of drug-related 
      adverse events (AEs) that are not captured in standard drug development and 
      postmarketing surveillance. Using variables obtained from EHR data in the 
      University of California San Francisco de-identified Clinical Data Warehouse, we 
      aimed to evaluate the potential of machine learning to predict two hematological 
      AEs, thrombocytopenia and anemia, in a cohort of patients treated with linezolid 
      for 3 or more days. Features for model input were extracted at linezolid 
      initiation (index), and outcomes were characterized from index to 14 days 
      post-treatment. Random forest classification (RFC) was used for AE prediction, 
      and reduced feature models were evaluated using cumulative importance (cImp) for 
      feature selection. Grade 3+ thrombocytopenia and anemia occurred in 31% of 2,171 
      and 56% of 2,170 evaluable patients, respectively. Of the total 53 features, as 
      few as 7 contributed at least 50% cImp, resulting in prediction accuracies of 70% 
      or higher and area under the receiver operating characteristic curves of 0.886 
      for grade 3+ thrombocytopenia and 0.759 for grade 3+ anemia. Sensitivity analyses 
      in strictly defined patient subgroups revealed similarly high predictive 
      performance in full and reduced feature models. A logistic regression model with 
      the same 50% cImp features showed similar predictive performance as RFC and good 
      concordance with RFC probability predictions after isotonic calibration, adding 
      interpretability. Collectively, this work demonstrates potential for machine 
      learning prediction of AE risk in real-world patients using few variables 
      regularly available in EHRs, which may aid in clinical decision making and/or 
      monitoring.
CI  - (c) 2024 The Authors. Clinical Pharmacology & Therapeutics published by Wiley 
      Periodicals LLC on behalf of American Society for Clinical Pharmacology and 
      Therapeutics.
FAU - Patel, Anu
AU  - Patel A
AUID- ORCID: 0000-0003-0628-668X
AD  - Department of Bioengineering and Therapeutic Sciences, University of California 
      San Francisco, San Francisco, California, USA.
FAU - Doernberg, Sarah B
AU  - Doernberg SB
AUID- ORCID: 0000-0003-1727-6014
AD  - Division of Infectious Diseases, Department of Medicine, University of California 
      San Francisco, San Francisco, California, USA.
FAU - Zack, Travis
AU  - Zack T
AUID- ORCID: 0000-0002-1620-6455
AD  - Bakar Computational Health Sciences Institute, University of California San 
      Francisco, San Francisco, California, USA.
AD  - Helen Diller Family Comprehensive Cancer Center, University of California San 
      Francisco, San Francisco, California, USA.
FAU - Butte, Atul J
AU  - Butte AJ
AUID- ORCID: 0000-0002-7433-2740
AD  - Department of Bioengineering and Therapeutic Sciences, University of California 
      San Francisco, San Francisco, California, USA.
AD  - Bakar Computational Health Sciences Institute, University of California San 
      Francisco, San Francisco, California, USA.
AD  - University of California Health, University of California, Office of the 
      President, Oakland, California, USA.
FAU - Radtke, Kendra K
AU  - Radtke KK
AUID- ORCID: 0000-0002-0578-6554
AD  - Department of Bioengineering and Therapeutic Sciences, University of California 
      San Francisco, San Francisco, California, USA.
AD  - Bakar Computational Health Sciences Institute, University of California San 
      Francisco, San Francisco, California, USA.
LA  - eng
GR  - GM/NIGMS NIH HHS/United States
GR  - TR/NCATS NIH HHS/United States
GR  - NH/NIH HHS/United States
GR  - GM/NIGMS NIH HHS/United States
GR  - TR/NCATS NIH HHS/United States
GR  - NH/NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20240212
PL  - United States
TA  - Clin Pharmacol Ther
JT  - Clinical pharmacology and therapeutics
JID - 0372741
RN  - ISQ9I6J12J (Linezolid)
SB  - IM
MH  - Humans
MH  - Linezolid/adverse effects
MH  - *Anemia/chemically induced/epidemiology
MH  - *Thrombocytopenia/chemically induced/diagnosis/epidemiology
MH  - Logistic Models
MH  - San Francisco
EDAT- 2024/02/12 15:44
MHDA- 2024/03/21 12:45
CRDT- 2024/02/12 08:12
PHST- 2023/09/29 00:00 [received]
PHST- 2024/01/29 00:00 [accepted]
PHST- 2024/03/21 12:45 [medline]
PHST- 2024/02/12 15:44 [pubmed]
PHST- 2024/02/12 08:12 [entrez]
AID - 10.1002/cpt.3201 [doi]
PST - ppublish
SO  - Clin Pharmacol Ther. 2024 Apr;115(4):847-859. doi: 10.1002/cpt.3201. Epub 2024 
      Feb 12.