Background Fluoroquinolones, while clinically indispensable, carry underappreciated cardiovascular risks, particularly QT prolongation and life-threatening arrhythmias. Emerging evidence suggests geographic and genetic variations in susceptibility, yet Middle Eastern populations remain underrepresented in global pharmacovigilance datasets.

Objective This study investigates the prescribing trends and awareness of fluoroquinolone-related adverse effects among healthcare providers in the UAE using a multimodal combination of artificial intelligence (AI) integrating pharmacovigilance data, environmental exposure mapping, predictive ECG analytics and natural language (NLP) of electronic health records (EHRs)

Methods We conducted a retrospective cohort study (2018–2023) combining structured ADR reports from UAE MOHAP, WHO-VigiAccess, FAERS, and EMA with unstructured clinical narratives. A hybrid NLP pipeline (BioBERT-based NER, sentiment analysis, and relationship extraction) identified unreported risk patterns. Machine learning (Random Forest, SVM, BioBERT-NLP) stratified high-risk cases, validated against MIMIC-IV ECG waveforms. Geospatial modeling correlated wastewater fluoroquinolone levels with regional arrhythmia incidence.

Results Among 1,522 adjudicated ADRs, moxifloxacin demonstrated the strongest cardiotoxicity signal (OR=1.45, 95% CI 1.2–1.8, *p*<0.001), with AI-ECG models detecting subclinical torsades de pointes at 96% sensitivity (AUC 0.97). NLP revealed significant ECG monitoring disparities in Northern Emirates (under documentation rate: 43%). Environmental analyses identified a dose-dependent relationship between moxifloxacin water contamination and arrhythmia hospitalizations (+22% in high-exposure regions, *p*=0.01). Molecular dynamics simulations implicated C7 substituent modification as a viable strategy to reduce hERG channel binding.

Conclusion We integrated multi-omics analysis with pharmacovigilance mining to stratify cardiotoxic risk among fluoroquinolone users in the UAE bridging pharmacovigilance, environmental epidemiology, and structural pharmacology. Our framework enables precision monitoring through AI-ECG integration, policy interventions targeting high-risk prescribing, and drug redesigning to mitigate hERG liability.

The authors have declared no competing interest.

No funding

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:

1. UAE Ministry of Health and Prevention (MOHAP) Open Data Portal. https://www.mohap.gov.ae/en/open-data (Includes anonymized ADR reports, prescription volume data, and environmental health indicators.) 2. UAE Ministry of Climate Change and Environment (MOCCAE) - National Water Quality Reports. https://www.moccae.gov.ae/en/open-data.aspx (Provides publicly available environmental surveillance data including fluoroquinolone contamination levels.) 3. MIMIC-IV ECG Demo Dataset - PhysioNet. https://physionet.org/content/mimic-iv-ecg-demo/0.1/ (Used for algorithm validation of ECG-based cardiotoxicity signals.) 4. FDA Drug-Induced Cardiotoxicity Rank (DICTrank) Dataset. https://www.fda.gov/science-research/bioinformatics-tools/drug-induced-cardiotoxicity-rank-dictrank-dataset No patient-level EHR or identifiable clinical data from NMC or Tawam Hospital were used or accessed.

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