Arcus Experience: Bridging the Data Science Gap for Nurse Researchers.

BACKGROUND: For years, nurse researchers have been called upon to engage with "big data" in the electronic health record (EHR) by leading studies focusing on nurse-centric patient outcomes and providing clinical analysis of potential outcome indicators. However, the current gap in nurses' data science education and training pose a significant barrier. OBJECTIVES: We aimed to evaluate the viability of conducting nurse-led, big-data research projects within a custom-designed computational lab and examine the support required by a team of researchers with little to no big-data experience. METHODS: Four nurse-led research teams developed a research question reliant on existing EHR data. Each team was given its own virtual computational lab populated with raw data. A data science education team provided instruction in coding languages-primarily structured query language and R-and data science techniques to organize and analyze the data. RESULTS: Three research teams have completed studies, resulting in one manuscript currently undergoing peer-review and two manuscripts in progress. The final team is performing data analysis. Four barriers and four facilitators to big-data projects were identified. DISCUSSION: As the data-science learning curve is steep, organizations need to help bridge the gap between what is currently taught in doctoral nursing programs and what is required of clinical nurse researchers to successfully engage in big-data methods. Additionally, clinical nurse researchers require protected research time and a data science infrastructure that supports novice efforts with education, mentorship, and computational lab resources.

Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data.

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.

Implementation of an evidence-based protocol after appendectomy reduces unnecessary antibiotics.

BACKGROUND: Children with acute appendicitis have historically received intravenous antibiotics before and after appendectomy, yet recent literature supports minimizing postoperative antibiotics. In this study, we examined the impact of a standardized protocol that eliminates postoperative antibiotics for nonperforated appendicitis and discontinues antibiotics at discharge for perforated appendicitis. METHODS: A retrospective review of all pediatric patients who underwent laparoscopic appendectomy for acute appendicitis between May 2013 and March 2017 was performed. Preprotocol patients (5/1/2013-3/31/2015) were compared to postprotocol patients (5/1/2015-3/31/2017), excluding those who underwent surgery during the month of protocol introduction (4/2015). Primary outcomes were postoperative antibiotic doses for nonperforated cases and antibiotics after discharge for perforated cases. Mann-Whitney and Fisher's exact tests were performed. RESULTS: Laparoscopic appendectomy was performed in 748 children before (PRE) and in 814 children after (POST) protocol implementation. Perforation rates were similar (POST 21.5 vs. PRE 21.8%, p=0.90). For nonperforated appendicitis, postoperative antibiotics were reduced (median 0 [IQR 0-0] vs. 3 [0-5] doses, p<0.001), and more patients were discharged less than 24 h after surgery (65.7 vs. 40.9%, p<0.001). Fewer patients with perforated appendicitis underwent PICC placement (8.6 vs. 21.0%, p=0.002), and fewer patients were prescribed antibiotics on discharge (33.7 vs. 89.0%, p<0.001). There were no differences between groups for complication, readmission, or return to ED rates. CONCLUSION: For children with acute appendicitis, a standardized protocol can safely reduce unnecessary antibiotics and decrease length of stay. Furthermore, the judicious use of antibiotics does not increase SSI, readmission, or overall complication rates. LEVEL OF EVIDENCE: III.