Background and objectives Although several standardized definitions for AKI have been developed, no consensus exists regarding which to use in children. This study applied the Pediatric RIFLE (pRIFLE), AKI Network (AKIN), and Kidney Disease Improving Global Outcomes (KDIGO) criteria to an anonymized cohort of hospitalizations extracted from the electronic medical record to compare AKI incidence and outcomes in intensive care unit (ICU) and non-ICU pediatric populations. Design, setting, participants, & measurements Observational, electronic medical record–enabled study of 14,795 hospitalizations at the Lucile Packard Children’s Hospital between 2006 and 2010. AKI and AKI severity stage were defined by the pRIFLE, AKIN, and KDIGO definitions according to creatinine change criteria; urine output criteria were not used. The incidences of AKI and each AKI stage were calculated for each classification system. All-cause, in-hospital mortality and total hospital length of stay (LOS) were compared at each subsequent AKI stage by Fisher exact and Kolmogorov–Smirnov tests, respectively. Results AKI incidences across the cohort according to pRIFLE, AKIN, and KDIGO were 51.1%, 37.3%, and 40.3%. Mortality was higher among patients with AKI across all definitions (pRIFLE, 2.3%; AKIN, 2.7%; KDIGO, 2.5%; P<0.001 versus no AKI [0.8%–1.0%]). Within the ICU, pRIFLE, AKIN, and KDIGO demonstrated progressively higher mortality at each AKI severity stage; AKI was not associated with mortality outside the ICU by any definition. Both in and outside the ICU, AKI was associated with significantly higher LOS at each AKI severity stage across all three definitions (P<0.001). Definitions resulted in differences in diagnosis and staging of AKI; staging agreement ranged from 76.7% to 92.5%. Conclusions Application of the three definitions led to differences in AKI incidence and staging. AKI was associated with greater mortality and LOS in the ICU and greater LOS outside the ICU. All three definitions demonstrated excellent interstage discrimination. While each definition offers advantages, these results underscore the need to adopt a single, universal AKI definition.

Objective: To describe the implementation of technological support important for optimizing clinical management of the COVID-19 pandemic. Materials and Methods: Our health system has confirmed prior and current cases of COVID-19. An Incident Command Center was established early in the crisis and helped identify electronic health record (EHR)-based tools to support clinical care. Results: We outline the design and implementation of EHR-based rapid screening processes, laboratory testing, clinical decision support, reporting tools, and patient-facing technology related to COVID-19. Discussion: The EHR is a useful tool to enable rapid deployment of standardized processes. UC San Diego Health built multiple COVID-19-specific tools to support outbreak management, including scripted triaging, electronic check-in, standard ordering and documentation, secure messaging, real-time data analytics, and telemedicine capabilities. Challenges included the need to frequently adjust build to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders while maintaining high-quality, prepandemic medical care. Conclusion: The EHR is an essential tool in supporting the clinical needs of a health system managing the COVID-19 pandemic.

Data from phase 3 clinical trials of messenger RNA (mRNA) vaccines through November 2020 showed 94.1% efficacy for the prevention of symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection at 14 days after the second dose of the mRNA-1273 vaccine (Moderna)¹ and 95% efficacy at 7 days after the second dose of the BNT162b2 vaccine (Pfizer).² Since the results of these trials were published, a nationwide surge in coronavirus disease 2019 (Covid-19) has been noted, SARS-CoV-2 variants with increased infectivity have emerged, the Food and Drug Administration has granted emergency use authorization for these two mRNA vaccines, and vaccination has been initiated across the United States.

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%–3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%–8.0%), and a 4% (95% CI, 1.1%–7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance.

The impact of statins, angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) on coronavirus disease 2019 (COVID-19) severity and recovery is important given their high prevalence of use among individuals at risk for severe COVID-19. We studied the association between use of statin/angiotensin-converting enzyme inhibitors/ARB in the month before hospital admission, with risk of severe outcome, and with time to severe outcome or disease recovery, among patients hospitalized for COVID-19. We performed a retrospective single-center study of all patients hospitalized at University of California San Diego Health between February 10, 2020 and June 17, 2020 (n = 170 hospitalized for COVID-19, n = 5,281 COVID-negative controls). Logistic regression and competing risks analyses were used to investigate progression to severe disease (death or intensive care unit admission), and time to discharge without severe disease. Severe disease occurred in 53% of COVID-positive inpatients. Median time from hospitalization to severe disease was 2 days; median time to recovery was 7 days. Statin use prior to admission was associated with reduced risk of severe COVID-19 (adjusted OR 0.29, 95%CI 0.11 to 0.71, p < 0.01) and faster time to recovery among those without severe disease (adjusted HR for recovery 2.69, 95%CI 1.36 to 5.33, p < 0.01). The association between statin use and severe disease was smaller in the COVID-negative cohort (p for interaction = 0.07). There was potential evidence of faster time to recovery with ARB use (adjusted HR 1.92, 95%CI 0.81 to 4.56). In conclusion, statin use during the 30 days prior to admission for COVID-19 was associated with a lower risk of developing severe COVID-19, and a faster time to recovery among patients without severe disease.

Pediatricians facing critical clinical decisions often lack data on which to draw. The authors recently put their institution's electronic medical record to unusual use to inform a decision about anticoagulation in a patient with systemic lupus erythematosus.