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Evaluation of a fever-management algorithm in a pediatric cancer center in a low-resource setting
Sheena Mukkada1,2,3, Cristel Kate Smith4, Delta Aguilar4
1Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, Tennessee.
Insights
Algorithm adherence in pediatric cancer fever management in low- and middle-income countries (LMICs) showed significant deviations, impacting hospitalization length. Improving adherence is key to enhancing pediatric oncology care quality and healthcare efficiency.
Area of Science:
- Oncology
- Infectious Disease Management
- Global Health
Background:
- Fever management in pediatric cancer patients in low- and middle-income countries (LMICs) is often inconsistent, leading to adverse outcomes.
- A locally adapted clinical algorithm was developed to standardize fever management practices in Davao City, Philippines.
- The study aimed to evaluate adherence to this resource-specific algorithm.
Purpose of the Study:
- To assess adherence to a locally developed fever management algorithm for pediatric oncology patients in an LMIC.
- To identify deviations from the algorithm and their underlying reasons.
- To determine the impact of algorithm adherence on patient outcomes, specifically length of hospitalization.
Main Methods:
- Prospective cohort study involving pediatric oncology patients admitted with or developing fever.
- Data collection focused on algorithm adherence, types and reasons for deviation, and pathogen isolation.
- Statistical analyses, including univariate and multiple linear regression, were used to correlate clinical predictors with hospitalization duration.
Main Results:
- 50% of febrile episodes on day 0 and 65% of patient episodes within 7 days showed deviations from the algorithm.
- Implementation barriers at patient, provider, and institutional levels contributed to deviations.
- Failure to identify high-risk patients, missed antimicrobial doses, and pathogen isolation were linked to prolonged hospital stays.
Conclusions:
- Monitoring algorithm adherence is crucial for evaluating and improving the quality of pediatric oncology care in LMICs.
- Addressing high-frequency and high-impact deviations can potentially shorten hospitalizations and optimize healthcare resource utilization.
- Algorithm implementation requires understanding and mitigating multifaceted barriers for effective clinical practice.
Background:
In low- and middle-income countries (LMICs), inconsistent or delayed management of fever contributes to poor outcomes among pediatric patients with cancer. We hypothesized that standardizing practice with a clinical algorithm adapted to local resources would improve outcomes. Therefore, we developed a resource-specific algorithm for fever management in Davao City, Philippines. The primary objective of this study was to evaluate adherence to the algorithm.
Procedure:
This was a prospective cohort study of algorithm adherence to assess the types of deviation, reasons for deviation, and pathogens isolated. All pediatric oncology patients who were admitted with fever (defined as an axillary temperature >37.7°C on one occasion or ≥37.4°C on two occasions 1 hr apart) or who developed fever within 48 hr of admission were included. Univariate and multiple linear regression analyses were used to determine the relation between clinical predictors and length of hospitalization.
Results:
During the study, 93 patients had 141 qualifying febrile episodes. Even though the algorithm was designed locally, deviations occurred in 70 (50%) of 141 febrile episodes on day 0, reflecting implementation barriers at the patient, provider, and institutional levels. There were 259 deviations during the first 7 days of admission in 92 (65%) of 141 patient episodes. Failure to identify high-risk patients, missed antimicrobial doses, and pathogen isolation were associated with prolonged hospitalization.
Conclusions:
Monitoring algorithm adherence helps in assessing the quality of pediatric oncology care in LMICs and identifying opportunities for improvement. Measures that decrease high-frequency/high-impact algorithm deviations may shorten hospitalizations and improve healthcare use in LMICs.
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