Transportability of bacterial infection prediction models for critically ill patients.

OBJECTIVE: Bacterial infections (BIs) are common, costly, and potentially life-threatening in critically ill patients. Patients with suspected BIs may require empiric multidrug antibiotic regimens and therefore potentially be exposed to prolonged and unnecessary antibiotics. We previously developed a BI risk model to augment practices and help shorten the duration of unnecessary antibiotics to improve patient outcomes. Here, we have performed a transportability assessment of this BI risk model in 2 tertiary intensive care unit (ICU) settings and a community ICU setting. We additionally explored how simple multisite learning techniques impacted model transportability. METHODS: Patients suspected of having a community-acquired BI were identified in 3 datasets: Medical Information Mart for Intensive Care III (MIMIC), Northwestern Medicine Tertiary (NM-T) ICUs, and NM "community-based" ICUs. ICU encounters from MIMIC and NM-T datasets were split into 70/30 train and test sets. Models developed on training data were evaluated against the NM-T and MIMIC test sets, as well as NM community validation data. RESULTS: During internal validations, models achieved AUROCs of 0.78 (MIMIC) and 0.81 (NM-T) and were well calibrated. In the external community ICU validation, the NM-T model had robust transportability (AUROC 0.81) while the MIMIC model transported less favorably (AUROC 0.74), likely due to case-mix differences. Multisite learning provided no significant discrimination benefit in internal validation studies but offered more stability during transport across all evaluation datasets. DISCUSSION: These results suggest that our BI risk models maintain predictive utility when transported to external cohorts. CONCLUSION: Our findings highlight the importance of performing external model validation on myriad clinically relevant populations prior to implementation.

Dexmedetomidine leading to profound bradycardia in a pediatric liver transplant recipient.

Dexmedetomidine, an α2 -agonist, is used in the PICU for its sedative properties as it minimally affects respiratory status. However, hemodynamic instability is one of its known side effects. There is limited published experience with its use in pediatric liver transplant. We present a case of a 9-month-old infant who received a deceased donor liver transplantation for biliary atresia and received an IV dexmedetomidine infusion for sedation starting at 20 hours post-operatively. The patient received an IV bolus of 0.08 mcg/kg followed by an increase to 1 mcg/kg/hour. She was also receiving a fentanyl infusion for sedation at the time of dexmedetomidine initiation. Approximately 3 hours after initiation, she developed bradycardia as low as 30 beats-per-minute with an associated sinus pause of 7 seconds. She was given chest compressions by the bedside nurse briefly before arousing and becoming agitated. Evaluation of other etiologies for the patient's bradycardia was unrevealing. Thus, bradycardia was attributed to dexmedetomidine therapy which was discontinued without recurrence. Hemodynamic instability, specifically bradycardia, is known to occur with dexmedetomidine administration. As this medication is primarily metabolized by the liver, its use immediately after transplantation, when liver function is still recovering, may be associated with an increased risk of side effects. Understanding risk factors for bradycardia and hemodynamic instability early after liver transplantation, particularly with dexmedetomidine, is critical to allow clinicians to identify the patients for higher risk for dexmedetomidine side effects.