Background and AimIntestinal dysmotility is considered a risk factor for small intestinal bacterial overgrowth (SIBO). Prokinetics improve intestinal motility and are often prescribed with proton pump inhibitors (PPIs) in patients with gastroesophageal reflux disease (GERD) and/or functional dyspepsia. The present study aimed to evaluate the prevalence of SIBO and the orocecal transit time (OCTT) in patients taking PPI compared with those taking PPI plus prokinetics.MethodsThe study is a single‐center, cross‐sectional study. Enrolled patients (with age > 12 years) were divided into two groups: patients taking PPIs for more than 3 months (Group A) and those taking PPIs with prokinetics for more than 3 months (Group B) for various indications. Lactulose breath test (LBT) for OCTT and glucose breath test (GBT) for SIBO were conducted for all patients.ResultsOf the 147 enrolled patients, SIBO was documented in 13.2% patients in Group A versus 1.8% in Group B, P = 0.018. Median OCTT in Group A was 130 (105–160) min compared with 120 (92.5–147.5) min in Group B (P = 0.010). Median OCTT among SIBO‐positive patients was 160 (140–172.5) min compared with SIBO‐negative patients, where it was 120 (103.75–150) min (P = 0.002). The duration and type of PPI used were not associated with the occurrence of SIBO in our study.ConclusionThe use of prokinetics in patients on PPI may reduce the risk of SIBO by enhancing intestinal motility and may reduce SIBO risk associated with long‐term PPI use.
Aims
Risk stratification and individual risk prediction plays a key role in making treatment decisions in patients with complex coronary artery disease (CAD).
The aim of this study was to assess whether machine learning (ML) algorithms can improve discriminative ability and identify unsuspected, but potentially important, factors in the prediction of long-term mortality following percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) in patients with complex CAD.
Methods and results
To predict long-term mortality, the ML algorisms were applied to the SYNTAXES database with 75 pre-procedural variables including demographic and clinical factors, blood sampling, imaging, and patient-reported outcomes. The discriminative ability and feature importance of the ML model was assessed in the derivation cohort of the SYNTAXES trial using a 10-fold cross validation approach.
The ML model showed an acceptable discrimination (AUC = 0.76) in cross-validation. C-reactive protein, patient-reported pre-procedural mental status, Gamma-glutamyl transferase, and HbA1c were identified as important variables predicting 10-year mortality.
Conclusions
ML algorithms disclosed unsuspected, but potentially important prognostic factors of very long-term mortality among patients with CAD. A “mega-analysis” based on large randomized or non-randomized data, so called “BIG Data”, may be warranted to confirm these findings.
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