Impact of a hospital-wide sepsis pathway on improved quality of care and clinical outcomes in surgical patients at a comprehensive cancer centre

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.

Prevalence and predictors of poor outcome in children with febrile neutropaenia presenting to the emergency department

OBJECTIVE

Children with acquired neutropaenia due to cancer chemotherapy are at high risk of severe infection. The present study aims to describe the prevalence and predictors of poor outcomes in children with febrile neutropaenia (FN).

METHODS

This is a multicentre, prospective observational study in tertiary Australian EDs. Cancer patients with FN were included. Fever was defined as a single temperature ≥38°C, and neutropaenia was defined as an absolute neutrophil count <1000/mm³ . The primary outcome was the ICU admission for organ support therapy (inotropic support, mechanical ventilation, renal replacement therapy, extracorporeal life support). Secondary outcomes were: ICU admission, ICU length of stay (LOS) ≥3 days, proven or probable bacterial infection, hospital LOS ≥7 days and 28-day mortality. Initial vital signs, biomarkers (including lactate) and clinical sepsis scores, including Systemic Inflammatory Response Syndrome, quick Sequential Organ Failure Assessment and quick Paediatric Logistic Organ Dysfunction-2 were evaluated as predictors of poor outcomes.

RESULTS

Between December 2016 and January 2018, 2124 episodes of fever in children with cancer were screened, 547 episodes in 334 children met inclusion criteria. Four episodes resulted in ICU admission for organ support therapy, nine episodes required ICU admission, ICU LOS was ≥3 days in four, hospital LOS was ≥7 days in 153 and two patients died within 28 days. Vital signs, blood tests and clinical sepsis scores, including Systemic Inflammatory Response Syndrome, quick Sequential Organ Failure Assessment and quick Paediatric Logistic Organ Dysfunction-2, performed poorly as predictors of these outcomes (area under the receiver operating characteristic curve <0.6).

CONCLUSIONS

Very few patients with FN required ICU-level care. Vital signs, biomarkers and clinical sepsis scores for the prediction of poor outcomes are of limited utility in children with FN.