Prior culture-guided prediction of antibiotic susceptibility in recurrent respiratory tract infections: a retrospective cohort analysis
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Recurrent respiratory tract infections (RRTIs) are a serious problem in older adults, necessitating appropriate early empiric antibiotic therapy. However, rising antimicrobial resistance complicates empiric treatment selection. Although prior culture results can offer guidance for empiric decisions, little data exist to quantify their predictive value in RRTIs. Therefore, we constructed a respiratory antibiogram and assessed Bayesian metrics of prior gram-negative isolates for predicting subsequent resistance or susceptibility in this population. Method: A retrospective cohort included hospitalized RRTI patients (defined as RTIs occurring ≥2 times in 6 months or ≥3 times in 12 months). Patient-specific antibiograms were constructed. A Bayesian prevalence-calibrated estimation was used to assess the predictive validity of prior respiratory cultures for subsequent antimicrobial susceptibility in paired isolates. Sensitivity (Sen), specificity (Spec), Bayesian positive predictive value (PPV), negative predictive value (NPV), and resistance prevalence were calculated for 24 antibiotics, with bootstrap 95% confidence intervals. Analyses were restricted to bacterial pathogens. Result: exhibited >35% resistance to 16 antibiotics. Prior cultures had good predictive value (>70%) for detecting future resistance to 22 antibiotics, with ciprofloxacin, levofloxacin, cefuroxime, cefotaxime, ampicillin, ampicillin/sulbactam, and ceftriaxone showing excellent predictive values (≥85%). Additionally, prior cultures had excellent predictive value (≥85%) for detecting future susceptibility to imipenem, amikacin, tobramycin, meropenem, minocycline, and tigecycline. Conclusion: Considerable antibiotic resistance was detected among isolates in patients with RRTIs. Using a prior culture as a guide can enhance the probability of selecting an effective empirical agent.
Language: English
Subfield: Applied Microbiology and Biotechnology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7118544162