First name
Akira
Last name
Nishisaki

Title

Comparison of Methods for Identification of Pediatric Severe Sepsis and Septic Shock in the Virtual Pediatric Systems Database.

Year of Publication

2018

Date Published

2018 Oct 31

ISSN Number

1530-0293

Abstract

<p><strong>OBJECTIVES: </strong>To compare the performance of three methods of identifying children with severe sepsis and septic shock from the Virtual Pediatric Systems database to prospective screening using consensus criteria.</p>

<p><strong>DESIGN: </strong>Observational cohort study.</p>

<p><strong>SETTING: </strong>Single-center PICU.</p>

<p><strong>PATIENTS: </strong>Children admitted to the PICU in the period between March 1, 2012, and March 31, 2014.</p>

<p><strong>INTERVENTIONS: </strong>None.</p>

<p><strong>MEASUREMENTS AND MAIN RESULTS: </strong>During the study period, all PICU patients were prospectively screened daily for sepsis, and those meeting consensus criteria for severe sepsis or septic shock on manual chart review were entered into the sepsis registry. Of 7,459 patients admitted to the PICU during the study period, 401 met consensus criteria for severe sepsis or septic shock (reference standard cohort). Within Virtual Pediatric Systems, patients identified using "Martin" (n = 970; κ = 0.43; positive predictive value = 34%; F1 = 0.48) and "Angus" International Classification of Diseases, 9th Edition, Clinical Modification codes (n = 1387; κ = 0.28; positive predictive value = 22%; F1 = 0.34) showed limited agreement with the reference standard cohort. By comparison, explicit International Classification of Diseases, 9th Edition, Clinical Modification codes for severe sepsis (995.92) and septic shock (785.52) identified a smaller, more accurate cohort of children (n = 515; κ = 0.61; positive predictive value = 57%; F1 = 0.64). PICU mortality was 8% in the reference standard cohort and the cohort identified by explicit codes; age, illness severity scores, and resource utilization did not differ between groups. Analysis of discrepancies between the reference standard and Virtual Pediatric Systems explicit codes revealed that prospective screening missed 66 patients with severe sepsis or septic shock. After including these patients in the reference standard cohort as an exploratory analysis, agreement between the cohort of patients identified by Virtual Pediatric Systems explicit codes and the reference standard cohort improved (κ = 0.73; positive predictive value = 70%; F1 = 0.75).</p>

<p><strong>CONCLUSIONS: </strong>Children with severe sepsis and septic shock are best identified in the Virtual Pediatric Systems database using explicit diagnosis codes for severe sepsis and septic shock. The accuracy of these codes and level of clinical detail available in the Virtual Pediatric Systems database allow for sophisticated epidemiologic studies of pediatric severe sepsis and septic shock in this large, multicenter database.</p>

DOI

10.1097/CCM.0000000000003541

Alternate Title

Crit. Care Med.

PMID

30394917

Title

Focused Training for the Handover of Critical Patient Information During Simulated Pediatric Emergencies.

Year of Publication

2018

Number of Pages

227-31

Date Published

2018 Apr

ISSN Number

2154-1663

Abstract

<p><strong>OBJECTIVES: </strong>Miscommunication has been implicated as a leading cause of medical errors, and standardized handover programs have been associated with improved patient outcomes. However, the role of structured handovers in pediatric emergencies remains unclear. We sought to determine if training with an airway, breathing, circulation, situation, background, assessment, recommendation handover tool could improve the transmission of essential patient information during multidisciplinary simulations of critically ill children.</p>

<p><strong>METHODS: </strong>We conducted a prospective, randomized, intervention study with first-year pediatric residents at a quaternary academic children's hospital. Baseline and second handovers were recorded for residents in the intervention group (12) and residents in the control group (= 8) during multidisciplinary simulations throughout the academic year. The intervention group received handover education after baseline handover observation and a cognitive aid before second handover observation. Audio-recorded handovers were scored by using a Delphi-developed assessment tool by a blinded rater.</p>

<p><strong>RESULTS: </strong>There was no difference in baseline handover scores between groups (= .69), but second handover scores were significantly higher in the intervention group (median 12.5 [interquartile range 12-13] versus median 7.5 [interquartile range 6-8] in the control group;&lt; .01). Trained residents were more likely to include a reason for the call (&lt; .01), focused history (= .02), and summative assessment (= .03). Neither timing of the second observation in the academic year nor duration between first and second observation were associated with the second handover scores (both&gt; .5).</p>

<p><strong>CONCLUSIONS: </strong>Structured handover training and provision of a cognitive aid may improve the inclusion of essential patient information in the handover of simulated critically ill children.</p>

DOI

10.1542/hpeds.2017-0173

Alternate Title

Hosp Pediatr

PMID

29514852

Title

A pragmatic checklist to identify pediatric ICU patients at risk for cardiac arrest or code bell activation.

Year of Publication

2016

Number of Pages

33-7

Date Published

2016 Feb

ISSN Number

1873-1570

Abstract

<p><strong>BACKGROUND: </strong>In-hospital cardiac arrest is a rare event associated with significant morbidity and mortality. The ability to identify the ICU patients at risk for cardiac arrest could allow the clinical team to prepare staff and equipment in anticipation.</p>

<p><strong>METHODS: </strong>This pilot study was completed at a large tertiary care pediatric intensive care unit to determine the feasibility of a simple checklist of clinical variables to predict deterioration. The daily checklist assessed patient risk for critical deterioration defined as cardiac arrest or code bell activation within 24h of the checklist screen. The Phase I checklist was developed by expert consensus and evaluated to determine standard diagnostic test performance. A modified Phase II checklist was developed to prospectively test the feasibility and bedside provider "number needed to train".</p>

<p><strong>RESULTS: </strong>For identifying patients requiring code bell activation, both checklists demonstrated a sensitivity of 100% with specificity of 76.0% during Phase I and 97.7% during Phase II. The positive likelihood ratio improved from 4.2 to 43.7. For identifying patients that had a cardiac arrest within 24h, the Phase I and II checklists demonstrated a sensitivity of 100% with specificity again improving from 75.7% to 97.6%. There was an improved positive likelihood ratio from 4.1 in Phase I to 41.9 in Phase II, with improvement of "number needed to train" from 149 to 7.4 providers.</p>

<p><strong>CONCLUSIONS: </strong>A novel high-risk clinical indicators checklist is feasible and provides timely and accurate identification of the ICU patients at risk for cardiac arrest or code bell activation.</p>

DOI

10.1016/j.resuscitation.2015.11.017

Alternate Title

Resuscitation

PMID

26703460

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