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Risk factors for nosocomial infection in critically ill children: a prospective cohort study
N Singh-Naz1, B M Sprague, K M Patel
1Department of Pediatrics, George Washington University School of Medicine, Washington, DC, USA.
Insights
Identifying pediatric intensive care unit (ICU) patients at high risk for nosocomial infections is possible using a predictive model. This model helps target preventive strategies for better patient outcomes.
Area of Science:
- Pediatric critical care medicine
- Infectious disease epidemiology
- Healthcare-associated infection surveillance
Background:
- Nosocomial infections pose a significant risk to patients in pediatric intensive care units (ICUs).
- Identifying patients susceptible to these infections is crucial for implementing targeted preventive measures.
- Existing risk assessment tools may not fully capture the complexity of infection development in critically ill children.
Purpose of the Study:
- To identify key factors associated with an increased risk of nosocomial infections in pediatric ICU patients.
- To develop and validate a predictive model for identifying high-risk individuals.
- To inform the adjustment of institutional infection rates based on patient-specific risk factors.
Main Methods:
- Prospective, 1-year cohort study involving all patients admitted to a 16-bed pediatric ICU.
- Data collection on patient demographics, clinical status (including Pediatric Risk of Mortality [PRISM] score), device utilization, and treatments.
- Multivariate logistic regression analysis to identify significant risk factors for nosocomial infection development.
Main Results:
- Out of 945 admissions, 75 patients developed 96 nosocomial infections, most commonly affecting the lower respiratory tract, bloodstream, and urinary tract.
- Significant risk factors included age, weight, PRISM score, device utilization, antimicrobial therapy, H2 receptor blocker use, immune status, parenteral nutrition, and length of stay.
- A multivariate model incorporating operative status, PRISM score, device utilization, antimicrobial therapy, parenteral nutrition, and length of stay demonstrated high predictive accuracy (Area Under Curve = 0.868).
Conclusions:
- A multivariate logistic regression model effectively identifies pediatric ICU patients at high risk for nosocomial infections with high sensitivity and specificity.
- The findings support the need to adjust institutional nosocomial infection rates based on identified risk factors.
- This predictive model can guide the implementation of targeted preventive strategies for high-risk patients, potentially reducing infection incidence.
Objective:
To identify factors in pediatric intensive care unit (ICU) patients that are associated with an increased risk of nosocomial infections.
Design:
A prospective, 1-yr cohort study.
Setting:
A 16-bed pediatric ICU in a multidisciplinary, regional referral center.
Subjects:
All patients admitted to the pediatric ICU.
Interventions:
None.
Measurements And Main Results:
The primary outcome variable was the development of nosocomial infection. Out of 945 consecutive admissions, 75 patients developed 96 nosocomial infections. The most frequent infection sites were the lower respiratory tract (35%), the bloodstream (21%), and the urinary tract (21%). The most common organisms isolated were Gram-negative bacteria (53%, Gram-positive bacteria (27%), and fungi (9%). Variables significantly associated with the development of nosocomial infections included age, weight, Pediatric Risk of Mortality (PRISM) score, device utilization ratio, antimicrobial therapy, histamine-2 (H2) receptor blocker use, immune status, parenteral nutrition, and length of stay. When combined in a multivariate logistic regression model, the significant variables were operative status, PRISM score, device utilization ratio, antimicrobial therapy, parenteral nutrition, and length of stay before the onset of infection. The area under the receiver operating characteristic curve was 0.868. At a probability of 0.15, the sensitivity was 66.67%, and the specificity was 87.82%.
Conclusions:
Patients at risk for developing nosocomial infection can be identified using a multivariate logistic regression model with a high degree of sensitivity and specificity. These data indicate that institutional nosocomial rates need to be adjusted for risk factors. This model could help target patients at high risk for developing nosocomial infections for preventive strategies.