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Optimizing Antibiotic Treatment with a Causal Probabilistic Network

This study addresses the challenges in treating bacterial infections efficiently, exploring a decision support system to assess infection severity, pathogen susceptibility, and treatment costs. A modularized design allows for tailored analysis irrespective of the region or hospital. Enhancements aim to improve accuracy and decrease human input error for better patient outcomes. Calibrating the system for urinary tract infections shows promising results, but further clinical trials are needed for validation.

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Optimizing Antibiotic Treatment with a Causal Probabilistic Network

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  1. A Causal Probabilitic Network for Optimal Treatment of Bachterial Infections Alicia Ruvinsky Scott Langevin

  2. Problem: Bacterial Infections • 30 percent mortality rate from severe bacterial infection • 1/3 given inappropriate treatment • 20% prescribed superfluous drugs • Anti-biotic drugs account for 20-50 percent of hospitals drug expenses • Bacterial resistance to anti-biotic treatment aggravated by miss-diagnoses

  3. Research Objectives Build a Decision Support System to provide: • Likelihood of a bacterial infection • Measure of its severity • Most likely site of infection • Most likely pathogen • Susceptibility of pathogen to drugs • Gain in life expectancy through treatment • Cost of drug treatment (price, side-effect, ecological impact, future resistance) • Ranking of anti-biotic drugs (Cost-Benefit)

  4. Problems with initial BN Approach • Model is not portable (specific to region/hospital) • Dichotomous data (local vs universal) • Human input error (20% of cases) Obviating Enhancements • Fix by normalizing system (localizing model) • Fix by objective input requirements (symptoms, test results, etc)

  5. General Modularized Design Nodes in BN: • Pathogen - Represent the potential pathogens of infections at the given site • M_Distrib – Major patient-groups exhibiting a particular pathogen within the given site • Minor – minor distribution factors; factors that change the likelihood of one or more pathogens without affecting the overall risk for infection. • Infection – the different patterns in which infections manifest • Local-respo – local responses caused by an infection • Local-sign – manifestation of local responses. • Sys-respo – systemic response caused by infection and common to all sites of infections • Sign – manifestations of sys-respo • Spec-cultu – ability of pathogen to grow in local specimen • Blood-cultu – ability of pathogen to grow in the blood • Lab-site – ability of pathogen to grow at local site • Antibiotic_tr – antibiotic treatment prescribed for an infection • Coverage – the percentage of pathogens of a given infection susceptible to an antibiotic drug • Resistance – in-vitro susceptibility of pathogen to treatment • Cost – accounts for cost of using antibiotic: cost of purchase, side effects, and ecological impact • Gain – net gain in life expectancy gotten by prescribing an antibiotic drug • Underlying – disorders of the patient

  6. General Scheme for Site of Infection Network

  7. Urinary Tract Infections Network

  8. Calibrating the System

  9. Results • Addresses all important decision-points in first days of patient care • Expect the system to perform better than clinician • No test data showing it improves clinical practice and patient outcome - need a clinical trial • Convenience of calibrating system for new locations?

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