Sepsis-3) uses the Sequential Organ Failure Assessment (SOFA) score to grade organ dysfunction in adult patients with suspected infection. However, the SOFA score is not adjusted for age and therefore not suitable for children.OBJECTIVES To adapt and validate a pediatric version of the SOFA score (pSOFA) in critically ill children and to evaluate the Sepsis-3 definitions in patients with confirmed or suspected infection. DESIGN, SETTING, AND PARTICIPANTSThis retrospective observational cohort study included all critically ill children 21 years or younger admitted to a 20-bed, multidisciplinary, tertiary pediatric intensive care unit between January 1, 2009 and August 1, 2016. Data on these children were obtained from an electronic health record database. The pSOFA score was developed by adapting the original SOFA score with age-adjusted cutoffs for the cardiovascular and renal systems and by expanding the respiratory criteria to include noninvasive surrogates of lung injury. Daily pSOFA scores were calculated from admission until day 28 of hospitalization, discharge, or death (whichever came first). Three additional pediatric organ dysfunction scores were calculated for comparison.EXPOSURES Organ dysfunction measured by the pSOFA score, and sepsis and septic shock according to the Sepsis-3 definitions. MAIN OUTCOMES AND MEASURESThe primary outcome was in-hospital mortality. The daily pSOFA scores and additional pediatric organ dysfunction scores were compared. Performance was evaluated using the area under the curve. The pSOFA score was then used to assess the Sepsis-3 definitions in the subgroup of children with confirmed or suspected infection.RESULTS In all, 6303 patients with 8711 encounters met inclusion criteria. Each encounter was treated independently. Of the 8482 survivors of hospital encounters, 4644 (54.7%) were male and the median (interquartile range [IQR]) age was 69 (17-156) months. Among the 229 nonsurvivors, 127 (55.4%) were male with a median (IQR) age of 43 (8-144) months. In-hospital mortality was 2.6%. The maximum pSOFA score had excellent discrimination for in-hospital mortality, with an area under the curve of 0.94 (95% CI, 0.92-0.95). The pSOFA score had a similar or better performance than other pediatric organ dysfunction scores. According to the Sepsis-3 definitions, 1231 patients (14.1%) were classified as having sepsis and had a mortality rate of 12.1%, and 347 (4.0%) had septic shock and a mortality rate of 32.3%. Patients with sepsis were more likely to die than patients with confirmed or suspected infection but no sepsis (odds ratio, 18; 95% CI,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28). Of the 229 patients who died during their hospitalization, 149 (65.0%) had sepsis or septic shock during their course. CONCLUSIONS AND RELEVANCEThe pSOFA score was adapted and validated with age-adjusted variables in critically ill children. Using the pSOFA score, the Sepsis-3 definitions were assessed in children with confirmed or suspected infection. This study is th...

The performance of classic regression-based and modern tree-based variable selection methods is associated with the size of the clinical dataset used. Classic regression-based variable selection methods seem to achieve better parsimony in clinical prediction problems in smaller datasets while modern tree-based methods perform better in larger datasets.

The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.

Objectives: Opioids and benzodiazepines are commonly used to provide analgesia and sedation for critically ill children with cardiac disease. These medications have been associated with adverse effects including delirium, dependence, withdrawal, bowel dysfunction, and potential neurodevelopmental abnormalities. Our objective was to implement a risk-stratified opioid and benzodiazepine weaning protocol to reduce the exposure to opioids and benzodiazepines in pediatric patients with cardiac disease. Design: A prospective pre- and postinterventional study. Patients: Critically ill patients less than or equal to 21 years old with acquired or congenital cardiac disease exposed to greater than or equal to 7 days of scheduled opioids ± scheduled benzodiazepines between January 2013 and February 2015. Setting: A 24-bed pediatric cardiac ICU and 21-bed cardiovascular acute ward of an urban stand-alone children’s hospital. Intervention: We implemented an evidence-based opioid and benzodiazepine weaning protocol using educational and quality improvement methodology. Measurements and Main Results: One-hundred nineteen critically ill children met the inclusion criteria (64 post intervention, 55 pre intervention). Demographics and risk factors did not differ between groups. Patients in the postintervention period had shorter duration of opioids (19.0 vs 30.0 d; p < 0.01) and duration of benzodiazepines (5.3 vs 22.7 d; p < 0.01). Despite the shorter duration of wean, there was a decrease in withdrawal occurrence (% Withdrawal Assessment Tool score ≥ 4, 4.9% vs 14.1%; p < 0.01). There was an 8-day reduction in hospital length of stay (34 vs 42 d; p < 0.01). There was a decrease in clonidine use (14% vs 32%; p = 0.02) and no change in dexmedetomidine exposure (59% vs 75%; p = 0.08) in the postintervention period. Conclusions: We implemented a risk-stratified opioid and benzodiazepine weaning protocol for critically ill cardiac children that resulted in reduction in opioid and benzodiazepine duration and dose exposure, a decrease in symptoms of withdrawal, and a reduction in hospital length of stay.

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Progression of acute kidney injury per the Kidney Disease Improving Global Outcomes staging criteria is independently associated with increased mortality in the PICU while its improvement is associated with a stepwise decrease in mortality.

Key Points Question Does data-driven phenotyping based on the trajectories of organ dysfunction in the acute phase of critical illness among children with multiple organ dysfunction syndrome uncover phenotypes with prognostic and therapeutic relevance? Findings In this 2-center cohort study of 20 827 pediatric intensive care encounters, a data-driven approach to phenotyping patients with multiple organ dysfunction syndrome using the trajectories of 6 organ dysfunctions uncovered 4 reproducible and distinct phenotypes with prognostic and potential therapeutic relevance. Meaning In this study, data-driven phenotyping based on the type, severity, and trajectory of 6 organ dysfunctions showed promising results in critically ill children with multiple organ dysfunction syndrome.

BackgroundThe development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.MethodsOur objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission.ResultsUtilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224 mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts.ConclusionsExperimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI.