Machine Learning for Antibiotic Stewardship in the Treatment of Stapholycoccus Bacterial Infections

Abstract

Antibiotic resistance is one of the leading issues in modern healthcare due to the inability to treat common infections with available antibiotics. Many of the mechanisms of resistance have been caused by the inappropriate prescription of antibiotics to treat illnesses such as the cold or flu or the over-prescription of broad-spectrum antibiotics. Epitomizing this problem is the Staphylococcus bacteria where certain strains have become resistant to penicillin-related drugs and Vancomycin, one of the treatments for MRSA. To address this, we developed machine learning models to predict antibiotic activity and susceptibility using a patient's entire available electronic health record. We selected patients who were suspected of having a staph infection from the Medical Information Mart for Intensive Care III (MIMIC-III) data set and utilized their microbiological culture results to identify the number of patients that were prescribed an inappropriate antibiotic and then propose suitable alternatives. In our test set, we identified that empiric prescriptions had an efficiency rate of 40 percent (the rate at which an antibiotic that would provide activity was prescribed), and the other 60 percent of cases were not susceptible to the prescribed antibiotic or the antibiotic that they were given was not tested for susceptibility against their infection. Our best models identified antibiotic susceptibility with AUROCs up to 0.9 and raw specificity up to 0.7. The models were also able to propose suitable alternatives in all but 10 cases. Overall these results demonstrate the need for implementing clinical decision support systems advising clinicians during the prescription process, and our further work will address this issue.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The MIMIC-III dataset was openly available and was initiated before the start of our study. The data is available at https://physionet.org/content/mimiciii/1.4/

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

留言 (0)

沒有登入
gif