Examining the Evidence for Escalating Antimicrobial Regimens in Febrile Oncology and Hematopoietic Stem Cell Transplant Patients Admitted to the PICU: An Observational Study.

OBJECTIVES: To examine whether escalating antimicrobial treatment in pediatric oncology and hematopoietic cell transplantation (HSCT) patients admitted to the PICU is supported by culture data or affects patient outcomes. DESIGN: Retrospective cross-sectional study. SETTING: Quaternary care PICU. PATIENTS: Patients younger than 18 years old who were admitted to the PICU at Boston Children's Hospital from 2012 to 2017 with a diagnosis of cancer or who had received HSCT and who had suspected sepsis at the time of PICU admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 791 PICU admissions for 544 patients that met inclusion criteria, 71 (9%) had escalation of antimicrobial therapy. Median Pediatric Logistic Organ Dysfunction (PELOD) score was higher in the escalation group (4 vs 3; p = 0.01). There were 14 admissions (20%) with a positive culture in the escalation group and 110 (15%) in the no escalation group ( p = 0.31). In the escalation group, there were only 2 (3%) cultures with organisms resistant to the initial antimicrobial regimen, compared with 28 (4%) cultures with resistant organisms in the no escalation group ( p = 1). Mortality in the escalation group was higher (17%) compared with the nonescalation group (5%; p < 0.001). The escalation group had more acute kidney injury (AKI) (25%) during treatment compared with the no escalation group (15%; p = 0.04), although this difference was not statistically significant when controlling for age, neutropenia, and PELOD-2 score (odds ratio, 1.75; 95% CI, 0.95-3.08; p = 0.06). CONCLUSIONS: Few patients who had escalation of antimicrobials proved on culture data to have an organism resistant to the initial antimicrobials, and more patients developed AKI during escalated treatment. While the escalation group likely represents a sicker population, whether some of these patients would be safer without escalation of antimicrobial therapy warrants further study.

A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation.

OBJECTIVES: To determine whether machine learning algorithms can better predict PICU mortality than the Pediatric Logistic Organ Dysfunction-2 score. DESIGN: Retrospective study. SETTING: Quaternary care medical-surgical PICU. PATIENTS: All patients admitted to the PICU from 2013 to 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We investigated the performance of various machine learning algorithms using the same variables used to calculate the Pediatric Logistic Organ Dysfunction-2 score to predict PICU mortality. We used 10,194 patient records from 2013 to 2017 for training and 4,043 patient records from 2018 to 2019 as a holdout validation cohort. Mortality rate was 3.0% in the training cohort and 3.4% in the validation cohort. The best performing algorithm was a random forest model (area under the receiver operating characteristic curve, 0.867 [95% CI, 0.863-0.895]; area under the precision-recall curve, 0.327 [95% CI, 0.246-0.414]; F1, 0.396 [95% CI, 0.321-0.468]) and significantly outperformed the Pediatric Logistic Organ Dysfunction-2 score (area under the receiver operating characteristic curve, 0.761 [95% CI, 0.713-0.810]; area under the precision-recall curve (0.239 [95% CI, 0.165-0.316]; F1, 0.284 [95% CI, 0.209-0.360]), although this difference was reduced after retraining the Pediatric Logistic Organ Dysfunction-2 logistic regression model at the study institution. The random forest model also showed better calibration than the Pediatric Logistic Organ Dysfunction-2 score, and calibration of the random forest model remained superior to the retrained Pediatric Logistic Organ Dysfunction-2 model. CONCLUSIONS: A machine learning model achieved better performance than a logistic regression-based score for predicting ICU mortality. Better estimation of mortality risk can improve our ability to adjust for severity of illness in future studies, although external validation is required before this method can be widely deployed.