IMPORTANCE: The Third International Consensus Definitions for Sepsis and Septic Shock (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 PARTICIPANTS: This 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 MEASURES: The 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, 11-28). Of the 229 patients who died during their hospitalization, 149 (65.0%) had sepsis or septic shock during their course. CONCLUSIONS AND RELEVANCE: The 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 the first assessment, to date, of the Sepsis-3 definitions in critically ill children. Use of these definitions in children is feasible and shows promising results.
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.
•Modern, machine learning-based modeling techniques are increasingly applied to clinical problems, including variable selection methods for predictive modeling using Electronic Health Record ...data.•Prior studies have shown that modern modeling techniques are “data hungry”.•The performance of classic and modern variable selection methods appears to be associated with the size of the clinical dataset and the event-per-variable rate.•In our study, we showed that classic regression-based variable selection methods perform better in smaller datasets, while modern tree-based methods do better in larger datasets.
Modern machine learning-based modeling methods are increasingly applied to clinical problems. One such application is in variable selection methods for predictive modeling. However, there is limited research comparing the performance of classic and modern for variable selection in clinical datasets.
We analyzed the performance of eight different variable selection methods: four regression-based methods (stepwise backward selection using p-value and AIC, Least Absolute Shrinkage and Selection Operator, and Elastic Net) and four tree-based methods (Variable Selection Using Random Forest, Regularized Random Forests, Boruta, and Gradient Boosted Feature Selection). We used two clinical datasets of different sizes, a multicenter adult clinical deterioration cohort and a single center pediatric acute kidney injury cohort. Method evaluation included measures of parsimony, variable importance, and discrimination.
In the large, multicenter dataset, the modern tree-based Variable Selection Using Random Forest and the Gradient Boosted Feature Selection methods achieved the best parsimony. In the smaller, single-center dataset, the classic regression-based stepwise backward selection using p-value and AIC methods achieved the best parsimony. In both datasets, variable selection tended to decrease the accuracy of the random forest models and increase the accuracy of logistic regression models.
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.
Large volumes of non-resuscitation fluids are often administered to critically ill children. We hypothesize that excess maintenance fluid is a significant contributor to non-resuscitation fluid and ...that non-resuscitation fluid administered beyond hydration requirements is associated with worse clinical outcomes in critically ill children.
We evaluated all patients admitted to two large urban pediatric intensive care units (PICU) between January 2010-August 2016 and January 2010-August 2018, respectively, who survived and remained in the hospital for at least 3 days following PICU admission. The primary outcome was in-hospital mortality. Association of excess fluid with outcomes was adjusted for confounders (age, Pediatric Risk of Mortality III score, study site, day 3 acute kidney injury, PICU era, resuscitation volume, and volume output) using multivariable regression.
We evaluated 14,483 patients; 52% received non-resuscitation fluid in excess of hydration requirements. Non-resuscitation fluid in excess of hydration requirements was associated with higher in-hospital mortality after adjustment for confounders (adjusted odds ratio 1.01 per 10 mL/kg in excess fluid, 95% confidence interval: 1.002-1.02).
Non-resuscitation fluid in excess of hydration requirements is associated with increased mortality in critically ill children. Excess maintenance fluid is a modifiable contributor to this fluid volume. Strategies to reduce excess maintenance fluids warrant further study.
Critically ill children frequently receive non-resuscitation fluid in excess of their estimated hydration requirements. Non-resuscitation fluid volume in excess of estimated hydration requirements is associated with higher morbidity and mortality in critically ill children. Critically ill children receive a large volume burden from maintenance fluid. Maintenance fluid represents a modifiable contributor of non-resuscitation fluid in excess of hydration requirements. Strategies focused on limitation of maintenance fluid warrant further study.
The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.
Our 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.
Utilizing 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.
Experimental 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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 180 million people since the onset of the pandemic. Despite similar viral load and infectivity rates between ...children and adults, children rarely develop severe illness. Differences in the host response to the virus at the primary infection site are among the mechanisms proposed to account for this disparity. Our objective was to investigate the host response to SARS-CoV-2 in the nasal mucosa in children and adults and compare it with the host response to respiratory syncytial virus (RSV) and influenza virus. We analyzed clinical outcomes and gene expression in the nasal mucosa of 36 children with SARS-CoV-2, 24 children with RSV, 9 children with influenza virus, 16 adults with SARS-CoV-2, and 7 healthy pediatric and 13 healthy adult controls. In both children and adults, infection with SARS-CoV-2 led to an IFN response in the nasal mucosa. The magnitude of the IFN response correlated with the abundance of viral reads, not the severity of illness, and was comparable between children and adults infected with SARS-CoV-2 and children with severe RSV infection. Expression of
and
did not correlate with age or presence of viral infection. SARS-CoV-2-infected adults had increased expression of genes involved in neutrophil activation and T-cell receptor signaling pathways compared with SARS-CoV-2-infected children, despite similar severity of illness and viral reads. Age-related differences in the immune response to SARS-CoV-2 may place adults at increased risk of developing severe illness.
...identification of richly phenotyped and reproducible disease subtypes through existing observational or interventional studies, development of pragmatic strategies for real-time identification of ...disease subtypes, and determination of heterogeneity of treatment effect (HTE) of therapeutic interventions have been emphasized as the path forward to achieving precision medicine in critical care 2. ...non-randomized administration of corticosteroids was associated with lower mortality among PedSep D subclass, but no difference and higher mortality was observed among patients belonging to PedSep B and C, respectively. ...the approach detailed by Qin et al. may also facilitate predictive enrichment, i.e., identification of patients subclasses with shared biological pathways that make them more susceptible to respond to a given therapy.
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•Unnecessary antibiotic regimens can harm patients without bacterial infections.•Our random forest-based prediction model can help predict bacterial infection risk.•Data-driven ...approaches can enhance antibiotic stewardship efforts.
Unnecessary antibiotic regimens in the intensive care unit (ICU) are associated with adverse patient outcomes and antimicrobial resistance. Bacterial infections (BI) are both common and deadly in ICUs, and as a result, patients with a suspected BI are routinely started on broad-spectrum antibiotics prior to having confirmatory microbiologic culture results or when an occult BI is suspected, a practice known as empiric antibiotic therapy (EAT). However, EAT guidelines lack consensus and existing methods to quantify patient-level BI risk rely largely on clinical judgement and inaccurate biomarkers or expensive diagnostic tests. As a consequence, patients with low risk of BI often are continued on EAT, exposing them to unnecessary side effects. Augmenting current intuition-based practices with data-driven predictions of BI risk could help inform clinical decisions to shorten the duration of unnecessary EAT and improve patient outcomes. We propose a novel framework to identify ICU patients with low risk of BI as candidates for earlier EAT discontinuation. For this study, patients suspected of having a community-acquired BI were identified in the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and categorized based on microbiologic culture results and EAT duration. Using structured longitudinal data collected up to 24-, 48-, and 72-hours after starting EAT, our best models identified patients at low risk of BI with AUROCs up to 0.8 and negative predictive values >93%. Overall, these results demonstrate the feasibility of forecasting BI risk in a critical care setting using patient features found in the electronic health record and call for more extensive research in this promising, yet relatively understudied, area.
Vitamin therapy in sepsis Wald, Eric L; Badke, Colleen M; Hintz, Lauren K ...
Pediatric research,
01/2022, Letnik:
91, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Vitamins are essential micronutrients with key roles in many biological pathways relevant to sepsis. Some of these relevant biological mechanisms include antioxidant and anti-inflammatory effects, ...protein and hormone synthesis, energy generation, and regulation of gene transcription. Moreover, relative vitamin deficiencies in plasma are common during sepsis and vitamin therapy has been associated with improved outcomes in some adult and pediatric studies. High-dose intravenous vitamin C has been the vitamin therapy most extensively studied in adult patients with sepsis and septic shock. This includes three randomized control trials (RCTs) as monotherapy with a total of 219 patients showing significant reduction in organ dysfunction and lower mortality when compared to placebo, and five RCTs as a combination therapy with thiamine and hydrocortisone with a total of 1134 patients showing no difference in clinical outcomes. Likewise, the evidence for the role of other vitamins in sepsis remains mixed. In this narrative review, we present the preclinical, clinical, and safety evidence of the most studied vitamins in sepsis, including vitamin C, thiamine (i.e., vitamin B
), and vitamin D. We also present the relevant evidence of the other vitamins that have been studied in sepsis and critical illness in both children and adults, including vitamins A, B
, B
, B
, and E. IMPACT: Vitamins are key effectors in many biological processes relevant to sepsis. We present the preclinical, clinical, and safety evidence of the most studied vitamins in pediatric sepsis. Designing response-adaptive platform trials may help fill in knowledge gaps regarding vitamin use for critical illness and association with clinical outcomes.
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