Diagnosis and management of severe sepsis in the paediatric patient

Abstract Sepsis is a systemic inflammatory response to suspected or proven infection. Given its importance in terms of morbidity and mortality, a number of initiatives by several professional societies in recent years have led to the development of guidelines for the recognition and timely management of sepsis. The principal elements of the most recent guidelines are summarized in this practice point. These elements include recognition of changes in clinical condition and vital signs, such as fever, tachycardia, and changes in peripheral perfusion, which should raise concern for sepsis; initial stabilization of airway, breathing, and circulation; timely administration of empiric antimicrobial therapy; use of fluid boluses and vasoactive medications; and specific considerations in patients with underlying medical conditions, such as the use of corticosteroids for possible adrenal insufficiency due to hypothalamic-adrenal suppression. Two changes from previous guidelines are the concern for fluid overload, implying the need for clinical re-assessment after administration of each fluid bolus, and the removal of dopamine as the initial vasoactive agent for use in hypotensive paediatric patients, with recommendations for the use of epinephrine or norepinephrine as dictated by the clinical context. This practice point focuses primarily on sepsis management in older infants, children, and youth.

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LO34: System and patient level determinants of EMS offload delay

CJEM ◽

10.1017/cem.2017.96 ◽

2017 ◽

Vol 19 (S1) ◽

pp. S39

Author(s):

G. Innes ◽

D. Stewart ◽

D. Wang ◽

E. Lang

Keyword(s):

Mental Health ◽

Odds Ratio ◽

Night Shift ◽

Vital Signs ◽

Care Needs ◽

Fluid Bolus ◽

Living Situation ◽

Patient Level ◽

Age Dependent ◽

Offload Delay

Introduction: Arriving EMS patients often experience offload delay due to a lack of available care spaces. Arrival in an overcrowded ED is the primary cause of offload delay, but patient characteristics may also play a role. Our objective was to describe system and patient level determinants of offload delay. Methods: From July 2013 to June 2016, administrative data was collated from the four Calgary Zone adult EDs. All CTAS level 2 and 3 patients arriving by ambulance were eligible for study. To define patient complexity and illness severity, we captured patient demographic data, living situation (homecare/facility vs. independent), vital signs, complaint category (medical, cardiovascular, mental health/neuro, GI, trauma/MS, other), biochemical parameters (serum Na, K, creatinine, hemoglobin, WBC), patient care needs (IV fluid bolus, IV antibiotics, CT scan, admission) and mortality at 7 and 30 days. Results: 162,002 EMS patients were studied. Of these, 67,785 went to a care space within 15 minutes (minimal offload delay), 53,185 between 15 and 59 minutes (moderate offload delay), and 41,032 at ≥60 minutes (severe offload delay). Vital signs, biochemical and hematologic parameters did not differ between groups. ED site was a strong predictor of offload delay (odds ratio {OR}=1.0, 2.03, 2.14, 3.5 for the 4 EDs), as was arrival on weekday (OR=1.38) or night shift (OR=0.71). After adjusting for site, day and time of arrival, multivariate logistic regression models showed the following associations with offload delays of more than 15 minutes: male sex (OR=0.94), age (OR=1.01 per year of age), dependent living situation (OR=1.15), CTAS 3 acuity (OR=1.27), number of prior ED visits within a year (OR=1.06 per visit), and complaint category: general medical (1.0), cardiovascular (0.90), mental health/neuro (0.90), GI (0.85), trauma/MS (0.61). Odds ratio estimates were precise—all with p<0.001. Offload delay was associated with prolonged time to MD, increased EDLOS and higher LWBS/AMA rates. Delayed patients had similar rates of IV antibiotic use, but lower rates of IV fluid bolus, CT use, admission, and 7-day mortality. Conclusion: The strongest predictor of offload delay is arrival to a crowded ED, but patient factors including female sex, older age, dependent living status and repeat hospital use increase risk. Patients subjected to offload delay also appear to have lesser immediate care needs and lower short-term mortality.

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Ultrafiltration in Acute Heart Failure

Cardiac Failure Review ◽

10.15420/cfr.2018.29.2 ◽

2019 ◽

Vol 5 (1) ◽

pp. 9-18 ◽

Cited By ~ 2

Author(s):

Maria Rosa Costanzo

Keyword(s):

Heart Failure ◽

Renal Function ◽

Serum Creatinine ◽

Acute Heart Failure ◽

Fluid Overload ◽

Vital Signs ◽

Venous Access ◽

Safety And Efficacy ◽

Fluid Removal ◽

Poor Outcomes

Congestion is the predominant cause of more than 1 million annual heart failure hospitalisations and recurrent fluid overload predicts poor outcomes. Unresolved congestion trumps serum creatinine increases in predicting adverse heart failure outcomes. No pharmacological approach for acute heart failure has reduced these deleterious consequences. Simplified ultrafiltration devices permit fluid removal in lower acuity hospital settings, but results regarding safety and efficacy have been variable. However, adjustment of ultrafiltration rates to patients’ vital signs and renal function has been associated with more effective decongestion and fewer heart failure events. Many aspects of ultrafiltration, including patient selection, fluid removal rates, venous access, prevention of therapy- related complications and costs, require further investigation.

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Identifying Sepsis Subphenotypes via Time-Aware Multi-Modal Auto-Encoder

10.1101/2020.07.26.20162214 ◽

2020 ◽

Author(s):

Changchang Yin ◽

Ruoqi Liu ◽

Dongdong Zhang ◽

Ping Zhang

Keyword(s):

Missing Values ◽

Imputation Accuracy ◽

Vital Signs ◽

Clinical Syndrome ◽

Sepsis Management ◽

Health Records ◽

Real World Datasets ◽

Dynamic Time ◽

Dynamic Time Wrapping ◽

Time Aware

Sepsis is a heterogeneous clinical syndrome that is the leading cause of mortality in hospital intensive care units (ICUs). Identification of sepsis subphenotypes may allow for more precise treatments and lead to more targeted clinical interventions. Recently, sepsis subtyping on electronic health records (EHRs) has attracted interest from healthcare researchers. However, most sepsis subtyping studies ignore the temporality of EHR data and suffer from missing values. In this paper, we propose a new sepsis subtyping framework to address the two issues. Our subtyping framework consists of a novel Time-Aware Multi-modal auto-Encoder (TAME) model which introduces time-aware attention mechanism and incorporates multi-modal inputs (e.g., demographics, diagnoses, medications, lab tests and vital signs) to impute missing values, a dynamic time wrapping (DTW) method to measure patients' temporal similarity based on the imputed EHR data, and a weighted k-means algorithm to cluster patients. Comprehensive experiments on real-world datasets show TAME outperforms the baselines on imputation accuracy. After analyzing TAME-imputed EHR data, we identify four novel subphenotypes of sepsis patients, paving the way for improved personalization of sepsis management.

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