Healthcare Epidemiology: Does Antibiotic Selection Impact Patient Outcome?

Abstract

Inadequate antibiotic therapy, generally defined as microbiologically ineffective anti-infective therapy against the causative pathogen, can influence patient outcome. However, the detrimental effects of inadequate antibiotic therapy seem to become weaker in the most severely ill patients with short life expectancies. In addition to severity of illness, other methodological issues should be carefully examined in studies assessing the excess mortality due to inadequate therapy. To adjust for confounding as much as possible in order to obtain an unbiased estimate of the magnitude of the effect of inadequate therapy is a key methodological challenge for future research. With regard to the choice of antibiotic agents, beta-lactam and aminoglycoside combination therapy does not seem to improve clinical outcome in most cases of sepsis caused by gram-negative bacteria, including Pseudomonas aeruginosa bacteremia. A potential benefit of combination therapy in the treatment of severe pneumococcal sepsis has been suggested in several observational studies, but recently published data have disputed this hypothesis. Finally, better risk scores and laboratory tools are urgently needed to improve the adequacy of empirical antibiotic therapy and patient outcomes.

Delay of Active Antimicrobial Therapy and Mortality among Patients with Bacteremia: Impact of Severe Neutropenia

“…For serious infections such as bacterial bloodstream infections, inadequate empirical antimicrobial therapy has been associated with worse clinical outcomes in some patient groups (16). Such consequences have led physicians to treat "more broadly," sometimes using combination therapy, to decrease the probability of inadequate empirical regimens.…”

“…In observational studies, significant differences that exist between treatment groups (in this study, nondelayed active antimicrobial therapy versus delayed active antimicrobial therapy) may not be adjusted sufficiently using commonly used multivariable techniques (16,31,35). In particular, multivariable models that include confounders based only on statistical significance with respect to the outcome (which is often encountered with automated variable selection methods) may inappropriately exclude important confounders that adjust for differences between treatment groups (14).…”

“…Two patients with the same propensity score have equal estimated probabilities of receiving treatment; if one receives treatment while another does not, then the treatment allocation can be considered random, conditional on the observed confounders. Such an approach has been proposed as a useful adjunct to traditional multivariable logistic regression models for observational studies of antimicrobial therapy (16,30).…”

Clinico-epidemiological profile of fever of unknown origin in an Egyptian setting: A hospital-based study (2009–2010)

“…This may be attributed to a problem with the antimicrobial group used with these patients, as the majority of the patients were cured just by switching to a more appropriate antimicrobial group. Compliance of the patients to the antimicrobial treatment raises other questions about the warranty of antimicrobial drug usage as over-the-counter medication in Egypt [26].…”

Failure of the MicroScan WalkAway System To Detect Heteroresistance to Carbapenems in a Patient with Enterobacter aerogenes Bacteremia

“…Prompt administration of effective antimicrobial therapy is crucial to improving the outcome of severe infection (4) and has led to the increased use of potent broad-spectrum agents before the results of susceptibility tests are known. The global increase in cephalosporin resistance, due to dissemination of extended-spectrum ␤-lactamases, has led to a heightened use of carbapenems as the treatment of choice when gram-negative bacteria are the primary concern.…”