RESUMEN
OBJECTIVES: We previously derived the updated Pediatric Sepsis Biomarker Risk for Acute Kidney Injury (PERSEVERE-II AKI) prediction model, which had robust diagnostic test characteristics for severe AKI on day 3 (D3 severe AKI) of septic shock. We now sought to validate this model in an independent cohort of children to the one in which the model was developed. DESIGN: A secondary analysis of a multicenter, prospective, observational study carried out from January 2019 to December 2022. SETTING: Ten PICUs in the United States. PATIENTS: Children with septic shock 1 week to 18 years old admitted to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Seventy-nine of 363 patients (22%) had D3 severe AKI, defined as Kidney Disease Improving Global Outcomes stage 2 or higher. Patients were assigned a probability of D3 severe AKI using the PERSEVERE-II AKI model. The model predicted D3 severe AKI with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.85-0.93), sensitivity of 77% (95% CI, 66-86%), specificity of 88% (95% CI, 84-92%), positive predictive value of 65% (95% CI, 54-74%), and negative predictive value of 93% (95% CI, 89-96%). These data represent an increase in post-test probability of D3 severe AKI with a positive test from 22% to 65%, and a prevalence threshold of 28%. On multivariable regression, the PERSEVERE-II AKI prediction model demonstrated greater adjusted odds ratio (aOR) for D3 severe AKI (aOR, 11.2; 95% CI, 4.9-25.3) and lesser aOR for failure of D3 renal recovery from early AKI (aOR, 0.31; 95% CI, 0.13-0.69). CONCLUSIONS: The PERSEVERE-II AKI model demonstrates consistently robust performance for prediction of new or persistent D3 severe AKI in children with septic shock. A major limitation is that actual D3 severe AKI prevalence is below the prevalence threshold for the test, and thus future work should focus on evaluating use in enriched populations.
RESUMEN
BACKGROUND AND OBJECTIVES: Alarms at hospitals are frequent and can lead to alarm fatigue posing patient safety risks. We aimed to describe alarm burden over a 1-year period and explored variations in alarm rates stratified by unit type, alarm source, and cause. METHODS: A retrospective study of inpatient alarm and patient census data at 1 children's hospital from January 1, 2019, to December 31, 2019, including 8 inpatient units: 6 medical/surgical unit (MSU), 1 PICU, and 1 NICU. Rates of alarms per patient day (appd) were calculated overall and by unit type, alarm source, and cause. Poisson regression was used for comparisons. RESULTS: There were 7 934 997 alarms over 84 077 patient days (94.4 appd). Significant differences in alarm rates existed across inpatient unit types (MSU 81.3 appd, PICU 90.7, NICU 117.5). Pulse oximetry (POx) probes were the alarm source with highest rate, followed by cardiorespiratory leads (54.4 appd versus 31). PICU had lowest rate of POx alarms (33.3 appd, MSU 37.6, NICU 92.6), whereas NICU had lowest rate of cardiorespiratory lead alarms (16.2 appd, MSU 40.1, PICU 31.4). Alarms stratified by cause displayed variation across unit types where "low oxygen saturation" had the highest overall rate, followed by "technical" alarms (43.4 appp versus 16.3). ICUs had higher rates of low oxygenation saturation alarms, but lower rates of technical alarms than MSUs. CONCLUSIONS: Clinical alarms are frequent and vary across unit types, sources, and causes. Unit-level alarm rates and frequent alarm sources (eg, POx) should be considered when implementing alarm reduction strategies.