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Record W2789999206 · doi:10.4103/ijccm.ijccm_413_17

Targeted Interventions in Critically Ill Children with Severe Dengue

2018· article· en· W2789999206 on OpenAlexaff
Niranjan Kissoon, Suchitra Ranjit, Gokul Ramanathan

Bibliographic record

VenueIndian Journal of Critical Care Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsSunny Hill Health Centre for Children
Fundersnot available
KeywordsMedicineRenal replacement therapyIntensive care unitResuscitationIntensive care medicineDengue feverPsychological interventionEmergency medicine

Abstract

fetched live from OpenAlex

IntRoductIonOutcomes of dengue infections are usually excellent; [1][2][3] however, mortality in severe dengue (SD) shock can be as high as 44%-72%, [4][5][6][7] with fluid overload (FO) and malignant edema due to capillary leak being major contributors.[1][2][3][4][5][6][7] While FO has been alluded to both in the World Health Organization (WHO) guideline and our dengue publications, [3,6,7] therapeutic interventions to manage malignant edema and prevent treatment morbidity have not been fully described.Cognizant of this shortcoming, we focused on several pathophysiology-based Intensive Care Unit (ICU) and emergency department (ED) interventions which may be useful in severe and refractory dengue shock.In this prospective observational study, we aimed to determine the effect of these proactively applied interventions on mortality, positive fluid balance (PFB), ventilator requirements, Pediatric ICU (PICU) days, and mortality as compared to a matched retrospective cohort with SD who received standard therapy (ST) as per the WHO guidelines. subjects and Methods Setting and patient selectionConsecutive patients aged 2 months to 16 years with SD admitted to a 10-bed PICU between September 2009 and November 2015 were included.All patients received ST as per the WHO guidelines.[1] However, patients admitted from October 2011 to November 2015 also received one or more targeted interventions in addition to standard therapy (ST+) in Background: The World Health Organization guidelines provide suggestions on early recognition and treatment of severe dengue (SD); however, mortality in this group can be high and is related both to disease severity and the treatment complications.Subjects and Methods: In this prospective observational study, we report our results where standard therapy (ST) was enhanced by Intensive Care Unit (ICU) supportive measures that have proven beneficial in other conditions that share similar pathophysiology of capillary leak and fluid overload.These include early albumin for crystalloid-refractory shock, proactive monitoring for symptomatic abdominal compartment syndrome (ACS), application of a high-risk intubation management protocol, and other therapies.We compared outcomes in a matched retrospective cohort who received ST.Results: We found improved outcomes using these interventions in patients with the most devastating forms of dengue (ST+ group).We could demonstrate decreased positive fluid balance on days 1-3 and less symptomatic ACS that necessitated invasive percutaneous drainage (7.7% in ST+ group vs. 30% in ST group, P = 0.025).Other benefits in ST+ group included lower intubation and positive pressure ventilation requirements (18.4% in ST+ vs. 53.3% in ST, P = 0.003), lower incidence of major hemorrhage and acute kidney injury, and reduced pediatric ICU stays and mortality (2.6% in ST+ group vs. 26% in ST group, P = 0.004).Conclusion: Children with SD with refractory shock are at extremely high mortality risk.We describe the proactive application of several targeted ICU supportive interventions in addition to ST and could show that these interventions resulted in decreased resuscitation morbidity and improved outcomes in SD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.322
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2018
Admission routes1
Has abstractyes

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