Napolitano, Natalie; An, Yuan; Traynor, Danielle; Kelly, Nancy-Ann; Emeriaud, Guillaume; Nishisaki, Akira Author Information

Glau, Christie; Conlon, Thomas; Mehta, Sanjiv; Himebauch, Adam; Kelly, Nancy-Ann; Traynor, Danielle; Keim, Garrett; Weber, Mark; Rosenblatt, Samuel; Loscalzo, Steve; Baker, David; Nishisaki, Akira Author Information

Background: Survivors of pediatric sepsis often develop new morbidities and deterioration in quality of life after sepsis, leading to a need for improved follow-up for children who survive sepsis. Objective: To implement a follow-up system for pediatric sepsis survivors in a pediatric health system. Methods: We performed a retrospective case series of patients treated for sepsis from October 2018 through October 2019 in a pediatric intensive care unit in a quaternary children's hospital, and describe implementation of a follow-up system for sepsis survivors. Program planning started in 2017 with multidisciplinary meetings including physical, occupational, and speech therapists, teachers, neuropsychologists, and coordinators from other survivorship programs (neonatology, stroke, and oncology). In 2018, a workshop was held to consult with local and national experts. The Pediatric Sepsis Survivorship Program launched in October 2018 led by a nurse coordinator who met with families to educate about sepsis and offer post-discharge follow-up. Patients with high pre-existing medical complexity or established subspecialty care were referred for follow-up through existing care coordination or subspecialty services plus guidance to monitor for post-sepsis morbidity. For patients with low-moderate medical complexity, the nurse coordinator administered a telephone-based health-assessment 2-3 months after discharge to screen for new physical or psychosocial morbidity. Patients flagged with concerns were referred to their primary physician and/or to expedited neuropsychological evaluation to utilize existing medical services. Results: Of 80 sepsis patients, 10 died, 20 were referred to care coordination by the program, and 13 had subspecialty follow-up. Five patients were followed in different health systems, four were adults not appropriate for existing follow-up programs, four remained hospitalized, and four were missed due to short stay or unavailable caregivers. The remaining 20 patients were scheduled for follow-up with the Pediatric Sepsis Program. Nine patients completed the telephone assessment. Four patients were receiving new physical or occupational therapy, and one patient was referred for neuropsychology evaluation due to new difficulties with attention, behavior, and completion of school tasks. Conclusions: Implementation of an efficient, low-cost pediatric sepsis survivorship program was successful by utilizing existing systems of care, when available, and filling a follow-up gap in screening for select patients.

Objectives: A method to identify pediatric sepsis episodes that is not affected by changing diagnosis and claims-based coding practices does not exist. We derived and validated a surveillance algorithm to identify pediatric sepsis using routine clinical data and applied the algorithm to study longitudinal trends in sepsis epidemiology. Design: Retrospective observational study. Setting: Single academic children's hospital. Patients: All emergency and hospital encounters from January 2011 to January 2019, excluding neonatal ICU and cardiac center. Exposure: Sepsis episodes identified by a surveillance algorithm using clinical data to identify infection and concurrent organ dysfunction. Interventions None. Measurements and Main Results: A surveillance algorithm was derived and validated in separate cohorts with suspected sepsis after clinician-adjudication of final sepsis diagnosis. We then applied the surveillance algorithm to determine longitudinal trends in incidence and mortality of pediatric sepsis over 8 years. Among 93,987 hospital encounters and 1,065 episodes of suspected sepsis in the derivation period, the surveillance algorithm yielded sensitivity 78% (95% CI, 72-84%), specificity 76% (95% CI, 74-79%), positive predictive value 41% (95% CI, 36-46%), and negative predictive value 94% (95% CI, 92-96%). In the validation period, the surveillance algorithm yielded sensitivity 84% (95% CI, 77-92%), specificity of 65% (95% CI, 59-70%), positive predictive value 43% (95% CI, 35-50%), and negative predictive value 93% (95% CI, 90-97%). Notably, most "false-positives" were deemed clinically relevant sepsis cases after manual review. The hospital-wide incidence of sepsis was 0.69% (95% CI, 0.67-0.71%), and the inpatient incidence was 2.8% (95% CI, 2.7-2.9%). Risk-adjusted sepsis incidence, without bias from changing diagnosis or coding practices, increased over time (adjusted incidence rate ratio per year 1.07; 95% CI, 1.06-1.08; p < 0.001). Mortality was 6.7% and did not change over time (adjusted odds ratio per year 0.98; 95% CI, 0.93-1.03; p = 0.38). Conclusions: An algorithm using routine clinical data provided an objective, efficient, and reliable method for pediatric sepsis surveillance. An increased sepsis incidence and stable mortality, free from influence of changes in diagnosis or billing practices, were evident.