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- W2912398418 abstract "Introduction:Helicobacter pylori is the causative agent of one of the most common infectious diseases and is the main cause of gastritis and gastric cancer. The empirical eradication strategies for H. pylori have proven to be ineffective due to a variety of environmental factors and patient attributes. Our hypothesis is that the treatment outcome in a patient can be optimized by accounting for various factors that influence bacterial resistance to antibiotics. We aimed to create a globally applicable, evidenced-based algorithm to design customized therapies for effective eradication of antibiotic resistant strains of H. pylori based on analyzed patient data. Implementation of this algorithm will help to alleviate the economic burden of H. pylori-related diseases, and reduce the incidence of peptic ulcer and gastric cancer. Methods: We carried out an extensive survey of literature and compiled a list of fifteen variables that are involved in the emergence of antibiotic resistance of H. pylori (Table 1). We then collected data from over 2000 patients with H. pylori infection with respect to each of these factors. Patients treated for H. pylori infection in past 5 years were included. Patients (i) lost to follow up, (ii) with incomplete course of treatment or inadequate documentation were excluded.Table: Table. Factors that contribute to H. pylori antibiotic resistanceResults: Our data showed a clear pattern of H. pylori prevalence and antibiotic resistance in our study population. We observed that 80% of patients were over the age of 40, and 62% of patients were women. The prevalence of infection was higher in the African American (30%) and Hispanic populations (36%) compared to the Caucasian population (20%). Patients with a median income of less than $54,000 and smokers had a 19% and 19.5% decreased chance of eradication, respectively. Previous omeprazole use increased the chance of eradication two-fold. Previous use of nitrofurantoin and cefepime significantly decreased the chance of eradication by 48% and 70%, respectively. We have already created a partial algorithm (Figure 1) using predictive markers that emerged through our analysis.Figure: Algorithm for predicting antibiotic resistance of H. pylori.Conclusion: Our final proposed algorithm will be comprehensive as it is based on a number of environmental factors, variations in patient attributes and data from a large number of patients.Itwill be useful to formulate customized treatments with predicted outcomes, which will minimize failures. Accuracy of algorithm will be further tested using patient stool samples." @default.
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- W2912398418 date "2017-10-01" @default.
- W2912398418 modified "2023-09-23" @default.
- W2912398418 title "Evidence-Based Strategies to Improve Treatment Outcomes for Helicobacter pylori Infection" @default.
- W2912398418 doi "https://doi.org/10.14309/00000434-201710001-01230" @default.
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