Comorbidities Predict <i>Clostridium difficile</i> Infection
Notice bibliographique
Résumé
Source: El-Matary W, Nugent Z, Yu BN, et al. Trends and predictors of Clostridium difficile infection among children: a Canadian population-based study. J Pediatr. 2019; 206: 20– 25; doi: 10.1016/j.jpeds.2018.10.041Investigators from multiple institutions conducted a case-control study to determine rates and predictors of Clostridium difficile infection (CDI) in children. Children were eligible if they were 2–17 years old and lived in Manitoba, Canada from 2005–2015. CDI cases were defined as positive laboratory test results for C difficile toxin in the context of watery, loose stools. Cases were identified using a Manitoba surveillance database that included all cases of CDI since 2005. Controls were children without CDI and were matched with cases on age, gender, postal code, and duration of residence in Manitoba. Demographics, health care utilization, and clinical characteristics were obtained from Manitoba population and provider claims databases.CDIs were classified as (a) hospital-associated (HA), defined as a toxin-positive specimen collected >48 hours after admission; (b) community-onset, hospital-associated (COHA), defined as a toxin-positive specimen collected while in the community or within 48 hours after admission in children who had been discharged from a hospital <4 weeks prior; (c) community-associated (CA), defined similarly with the exception that the individual had never been hospitalized or was discharged from a hospital >12 weeks before CDI onset; or (d) indeterminate. An incident case of CDI was defined as a positive specimen >8 weeks after a previous positive result or after no previous positive result. Recurrent CDI was defined as a positive specimen result 2 to <8 weeks from the last positive result.Investigators compared health care utilization and clinical characteristics of cases and controls. The overall rate of CDI over the study period was also calculated in person-years using the follow-up time of cases over the 10-year study period. Regression models were used to determine predictors for recurrent CDI after controlling for potential confounders.There were 193 incident CDI cases occurring in 162 children that were included in the analysis, along with 615 controls. CDIs were most often CA (51%); 19% were HA; and 15% were COHA. Recurrent CDIs accounted for 10.4% of CDI episodes. The overall CDI rate was 7.8 per 100,000 person-years.Compared to controls, cases had a significantly greater number of outpatient visits and hospitalizations in the year before CDI diagnosis. Cases also had significantly more prevalent comorbid conditions compared to controls, including chronic neurodegenerative disease, liver disease, kidney disease, diabetes, malignancy, Hirschsprung disease, and inflammatory bowel disease. In regression models, predictors of recurrent CDIs included malignancy, diabetes, chronic liver disease, chronic kidney disease, neurodegenerative disease, and exposure to antibiotics in the 3 months before CDI diagnosis.Investigators conclude that several comorbidities make children more susceptible to CDI and recurrent CDI.Dr Brady has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device.The investigators in the current study sought to determine the rates and predictors of CDI among children 2–17 years old using a population-based electronic database. Children <2 years old were excluded because interpretation of a C difficile–positive stool in those in this age range is difficult as approximately 10% are colonized with C difficile.1 The number of CDI cases was small and restricted to Manitoba, which may limit generalizability of the study results. Case fatality was significantly higher in children with CDI (10%) compared to controls (no deaths). However, the study design did not allow for determination of the specific cause of death and whether it was a direct effect of the CDI or worsening of an underlying comorbid condition.The authors report a significant difference in CDI incidence between age groups, with declining rates between 2 and 12 years and increasing infection rates between 13 and 17 years. Wendt et al also noted a decline in CDI incidence comparing the 2–3-year-old with the 4–9-year-old group. (See AAP Grand Rounds. 2014;32[2]:20.2) This may, in part, be explained by the lower overall prescribing of antibiotics as children progress from toddlers to preteens.3It is not clear why the incidence of CDI increased among those between 13 and 17 years of age in the current study. However, investigators4 from the Netherlands reported that for bronchitis episodes, adolescents were more likely to be prescribed antibiotics than children 0–4 and 5–11 years of age, respectively. Bronchitis is usually caused by viruses, so this is an example of unnecessary antibiotic prescribing among adolescents that may increase their risk for CDI.Children with comorbid conditions are at increased risk for acquisition and recurrence of CDI. Avoiding unnecessary antibiotics is the best measure to prevent CDI.The results of the current study underscore the increased frequency and predominance of CA-CDI versus HA-CDI.5 The oft prescribed cephalosporins and fluoroquinolones pose the highest risk, although virtually every antimicrobial has been associated with CDI.5
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».