Sheena Mukkada1,2,3, Cristel Kate Smith4, Delta Aguilar4, April Sykes5, Li Tang5, Mae Dolendo4, Miguela A Caniza1,3. 1. Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, Tennessee. 2. Division of Infectious Diseases, Department of Pediatrics, University of Tennessee Health Sciences Center, Memphis, Tennessee. 3. Department of Global Pediatric Medicine, St. Jude Children's Research Hospital, Memphis, Tennessee. 4. Department of Pediatric Oncology, Southern Philippines Medical Center, Davao City, Philippines. 5. Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee.
Abstract
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.
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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