How to interpret an antibiogram (antimicrobial susceptibility test) to determine the most effective antibiotic therapy for a patient with a bacterial infection?

How to Interpret an Antibiogram for Optimal Antibiotic Selection

Use your institution's antibiogram as the primary guide for empirical therapy selection, prioritizing unit-specific and specimen-specific data over hospital-wide cumulative reports, and always adjust therapy based on individual culture and susceptibility results once available. 1, 2

Understanding Antibiogram Structure and Limitations

The antibiogram displays cumulative antimicrobial susceptibility data, showing the percentage of bacterial isolates susceptible to specific antibiotics over a defined period (typically annually). 3 However, hospital-wide antibiograms may not adequately support optimal treatment decisions because susceptibility rates vary significantly across different hospital units, specimen types, and patient populations. 2

Critical variations in susceptibility rates include:

• Mean susceptibility rates of Escherichia coli to ciprofloxacin range from 64.5% to 95.1% across different hospital departments 2
• Pseudomonas aeruginosa susceptibility to imipenem ranges from 54.2% to 100% and to meropenem from 80.4% to 100% depending on the unit 2
• Intensive care unit isolates consistently show lower susceptibility rates compared to general ward isolates 2
• Follow-up isolates demonstrate significantly lower susceptibility than first isolates 2

Prioritizing Unit-Specific and Syndrome-Specific Data

Request and utilize unit-specific antibiograms rather than hospital-wide cumulative data when selecting empirical therapy. 1, 2 Unit-specific antibiograms provide more accurate predictions of susceptibility for your specific patient population. 2

Stratify antibiogram data by:

• Hospital unit (ICU vs. general ward vs. emergency department) - susceptibility rates differ substantially 2
• Specimen type (blood, urine, respiratory, wound) - anatomical site variations affect resistance patterns 2
• Isolate sequence (first isolate vs. follow-up) - subsequent isolates show increased resistance 2
• Duration of hospitalization - antimicrobial susceptibility decreases with longer hospital stays, particularly for coagulase-negative staphylococci 2

Syndromic antibiograms that incorporate resistant gram-negative phenotypes and minimum inhibitory concentration (MIC) distributions provide superior guidance for selecting new β-lactam/β-lactamase inhibitor combinations. 4

Applying Pharmacokinetic/Pharmacodynamic (PK/PD) Breakpoints

Use PK/PD breakpoints rather than solely relying on Clinical and Laboratory Standards Institute (CLSI) breakpoints when interpreting susceptibility data. 1 PK/PD breakpoints are based on the relationship between drug exposure and clinical outcomes, providing more clinically relevant predictions of treatment success. 1

For time-dependent antibiotics (β-lactams), the critical parameter is the percentage of the dosing interval that drug concentrations remain above the MIC. 1 For concentration-dependent agents (fluoroquinolones, aminoglycosides), the area under the curve (AUC) to MIC ratio determines efficacy. 1

Key PK/PD considerations:

• High-dose amoxicillin (4 g/day) achieves 95.2% predicted clinical efficacy against S. pneumoniae compared to 91.6% for standard dosing 1
• Respiratory quinolones (levofloxacin, moxifloxacin, gatifloxacin) demonstrate 90-92% predicted clinical efficacy for acute bacterial rhinosinusitis 1
• Extended-infusion carbapenems improve PK/PD target attainment for difficult-to-treat pathogens 1

Integrating Patient-Specific Risk Factors

Exclude antibiotics with high likelihood of preexisting resistance based on the patient's antibiotic exposure history. 1 Recent antibiotic use within 90 days is the strongest predictor of antimicrobial resistance. 1, 5

Risk factors requiring broader empirical coverage:

• Prior intravenous antibiotic use within 90 days increases risk for multidrug-resistant gram-negatives and MRSA 5
• Healthcare-associated infections require coverage beyond community-acquired pathogens 1
• Immunosuppression, malignancy, or chronic liver disease necessitate broader spectrum therapy 1
• Presence of indwelling devices or undrained abscesses increases resistance risk 1

Selecting Empirical Therapy Based on Antibiogram Data

Choose empirical antibiotics where local susceptibility rates exceed 80-85% for the suspected pathogen. 6 Lower susceptibility rates require combination therapy or alternative agents. 1

For carbapenem-resistant Enterobacterales (CRE):

• Ceftazidime/avibactam 2.5 g IV every 8 hours is recommended for bloodstream infections and complicated urinary tract infections 1
• Meropenem/vaborbactam 4 g IV every 8 hours or imipenem/cilastatin/relebactam 1.25 g IV every 6 hours are alternatives 1
• Polymyxin-based combinations (colistin plus tigecycline or high-dose meropenem) for salvage therapy 1

For difficult-to-treat Pseudomonas aeruginosa (DTR-PA):

• Ceftolozane/tazobactam 3 g IV every 8 hours for hospital-acquired pneumonia 1
• Ceftazidime/avibactam 2.5 g IV every 8 hours as alternative 1
• Colistin-based combination therapy when susceptibility to other agents is absent 1

Implementing Susceptibility-Based De-escalation

Transition from empirical broad-spectrum therapy to targeted narrow-spectrum therapy within 48-72 hours once culture and susceptibility results are available. 7, 5 This approach is strongly recommended by the Infectious Diseases Society of America. 7

De-escalation strategies:

• Switch from combination therapy to monotherapy if the patient is not in septic shock and the isolate is susceptible 7
• Change from carbapenem to narrower-spectrum β-lactam if susceptibilities permit 7
• Discontinue MRSA coverage if methicillin-susceptible S. aureus or gram-negative pathogen is identified 5
• Limit antimicrobial therapy to 4-7 days for most intra-abdominal infections with adequate source control 1

Avoiding Common Pitfalls

Never use aminoglycoside monotherapy for serious infections - aminoglycosides have poor tissue penetration and high treatment failure rates when used alone. 7, 5 They are only appropriate as monotherapy for uncomplicated urinary tract infections. 1

Do not continue empirical therapy beyond 7 days without reassessing for persistent infection or inadequate source control. 1 Prolonged courses without documented ongoing infection promote resistance development. 1

Avoid relying on outdated antibiogram data - resistance patterns change over time, and antibiograms should be updated at least annually with concurrent antimicrobial use data. 1, 3

Do not assume hospital-wide antibiogram data applies to all clinical scenarios - ICU patients, those with healthcare-associated infections, and patients with prolonged hospitalization require consideration of higher resistance rates. 2

Establishing Feedback Loops for Continuous Improvement

Routinely obtain post-treatment tests of cure to provide feedback on treatment effectiveness and validate antibiogram utility. 1 This data should be shared within the institution to identify increasing antimicrobial resistance patterns early. 1

Antimicrobial stewardship programs must monitor prescription patterns, measure clinical outcomes, and correlate empirical therapy choices with actual cure rates to ensure antibiogram recommendations remain effective. 1