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Record W15074459 · doi:10.1093/pch/10.3.169

Does Lactobacillus GG prevent antibiotic-associated diarrhea in children?

2005· article· en· W15074459 on OpenAlexaff
Bradley C. Johnston, Kristie Cramer, Sunita Vohra

Bibliographic record

VenuePaediatrics & Child Health · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsStollery Children's HospitalUniversity of Alberta
Fundersnot available
KeywordsAntibiotic-associated diarrheaDiarrheaProbioticAntibioticsClindamycinSaccharomyces boulardiiPopulationMedicineAmpicillinMicrobiologyClostridium difficileBiologyInternal medicineBacteria

Abstract

fetched live from OpenAlex

Probiotics refer to ‘friendly’ nonpathogenic microorganisms that are thought to benefit the host by improving the properties of indigenous microflora (1). The rationale behind probiotic administration is based on the reinoculation and normalization of unbalanced indigenous microflora using specific probiotic strains. These microorganisms have been shown to improve microbial balance in the intestinal tract and display both antibacterial and immune regulatory effects in humans (2,3). Antibiotics have the potential to disturb the colonization resistance of gastrointestinal flora, resulting in a range of clinical symptoms, of which diarrhea is the most frequent. Specifically, antibiotics that act on anaerobes are most associated with diarrhea. Aminopenicillins, cephalosporins and clindamycin have the highest risk of diarrhea side effects (4). Although the overgrowth of many enteropathogens has been demonstrated in antibiotic-associated diarrhea (AAD), Clostridium difficile overgrowth has become known as the most pathogenic bacterial agent associated with AAD (5). Reports in the general population indicate that AAD occurs in approximately 5% to 39% of patients during the time period between the initiation of antibiotic therapy and up to two months after the end of treatment (4,5). The incidence of diarrhea in children receiving broad-spectrum antibiotics ranges from 20% to 40% (6). Two recent meta-analyses (7,8) on probiotics provided evidence suggesting that probiotics prevent AAD in the general population. Cremonini et al (8) analyzed seven randomized controlled trials (RCTs) of two probiotic strains, Lactobacillus GG (LGG) and Saccharomyces boulardii (n=881; RR=0.40, 95% CI 0.28 to 0.57); whereas D’Souza et al (7) analyzed nine RCTs of seven different probiotic strains (n=1380; OR=0.37, 95% CI 0.26 to 0.53). Both meta-analyses involved a diverse population (ie, mixed socioeconomic backgrounds, inpatients and outpatients). The results strongly favoured probiotic coadministration with antibiotics for the prevention of diarrheal side effects. A review of 143 human studies (eg, case reports, clinical trials, pharmacokinetic trials and prospective epidemiological surveillance studies) demonstrated the overall safety of probiotic consumption over a wide range of probiotic doses (eg, one million to 450 billion colony-forming units [CFUs] per day), strains and intervention intervals (9). While safety does not appear to be a concern in healthy individuals, serious infections (eg, bacteremia, endocarditis, septicemia, pneumonia and deep abdominal abscesses) resulting from probiotic use have been reported in neonates, severely debilitated individuals and immunocompromised individuals (9,10). However, it is unclear whether exogenous or endogenous lactobacilli were the cause of the few Lactobacillus bacteremia case reports in the literature (11). On the basis of the overall safety and efficacy data on LGG, LGG dairy products were placed on the market in Finland in 1990 (11). In a four-year prospective epidemiological surveillance study of approximately 2.5 million people in southern Finland, 3317 blood culture isolates associated with bacteremia were detected. None of the bacteria strains in these patients were identical to the LGG strain in the dairy products (11). With the widespread consumption of LGG in southern Finland, epidemiological surveillance data suggest that their pathogenic potential is very low (11). However, recently, evidence representing the first paediatric case reports (a six-week old male and six-year old female, both with severe underlying diseases) of probiotic and, in particular, LGG-attributed bacteremia (10 billion CFU/day) has emerged (12). The evidence from these two well-documented paediatric case reports is in keeping with similar reports in adults; probiotic therapy may occasionally be associated with adverse reactions in those with severe underlying disease or in immunocompromised individuals (12–14). In a systematic search of MEDLINE, we identified and reviewed the two RCTs (15,16) that evaluated LGG for the prevention of AAD in children. In both trials, the dose was 10 to 20 billion CFU/day of LGG. In particular, Vanderhoof et al (15) administered 10 billion CFU/day to children weighing less than 12 kg, and 20 billion CFU/day to children weighing 12 kg or more (age range of six months to 10 years) (15). Arvola et al (16) administered 20 billion CFU/day to children two weeks to 12 years of age, irrespective of their weight. In the Vanderhoof et al (15) study (n=202), 8% of LGG-treated versus 26% of placebo-treated children had diarrhea; whereas in the Arvola et al (16) study (n=167), the incidence of diarrhea was 5% in the LGG group and 16% in the placebo group. The treatment effect of LGG for the incidence of diarrhea was −11% (95% CI, −21% to 0%) (16). In the Vanderhoof et al (15) study, the mean duration of diarrhea was 4.7 days in the LGG group and 5.9 days in the placebo group (P<0.02). Arvola et al (16) did not find a significant difference in mean duration of diarrhea between the LGG and placebo groups (mean four days, range two to eight days). The methodological quality of these two trials, using the validated zero to five Jadad scale, was four for Vanderhoof et al (15) and three for Arvola et al (16) (a score of less than three indicates a poor quality study and a score of five indicates highest quality [17]). Based on these two studies, the number needed to treat to prevent one diarrhea event was six and the results substantially favoured LGG coadministration with antibiotics (number needed to treat six; 95% CI 5 to 11). If further data confirm the utility of probiotics, the potential cost-benefit to the consumer would be $120.00 (6×$20.00=$120.00, 95% CI $100.00 to $220.00); meaning that it would cost $120.00 to treat six patients prescribed antibiotics to prevent one case of diarrhea. Considering the inconsistent efficacy data from these trials, further RCTs that assess the clinical relevance of LGG for paediatric AAD are needed. Moreover, although both trials did not report an adverse event, neither trial defined a priori what was considered to be an adverse event/reaction. Considering the limited safety data that can be obtained from small clinical trials on doses of LGG that far exceed those monitored for in epidemiological surveillance studies, larger longitudinal trials and/or postmarket surveillance studies that monitor for LGG adverse events are required. NOT SURE WHAT THE EVIDENCE IS FOR TREATING A PARTICULAR PATIENT PROBLEM? – submit your question to “Evidence for Clinicians” in Paediatrics & Child Health Example of a question: “In patients presenting to the emergency department with mild to moderate croup, are glucocorticoids more effective than placebo in causing clinical improvement?” To promote evidence-based child health among paediatricians, health care workers and trainees, the Cochrane Child Health field is contributing a section titled “Evidence for Clinicians” to Paediatrics & Child Health. You may pose clinical questions that you frequently encounter in the course of practice where you believe there is some controversy. Evidence-based answers for these questions are systematically searched, appraised and summarized along with a description of some of the strengths and weaknesses of the studies. In addition, a clinical expert on the topic will be invited to provide clinical commentary. Specify the type of patient (Population), Intervention, treatment options you are aware of (Comparison) and a clinical result (Outcome) (PICO). You can submit questions online at under the “Evidence for Clinicians” link.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.225
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2005
Admission routes1
Has abstractyes

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