Predicting outcome from dengue

Dr Sophie Yacoub

Imperial College LondonOxford University Clinical Research Unit, Vietnam

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The global dengue burden

• Dengue most abundant arboviral infection

• Current estimates: 390 million infections (96 million clinically

apparent) 1

1. Bhatt S, et al. Global distribution of dengue. Nature 2013

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The Virus

• Dengue caused by one of the 4 antigenically distinct dengue virus

serotypes (DENV1-4).

• Family Flaviviridae

• Single-stranded enveloped RNA virus, 30 nm in diameter.

• Transmitted from person to person by Aedes mosquitoes.

• The dengue virus genome encodes 3 structural proteins; Capsid (C)

Precursor membrane (prM) and Envelope (E), and 7 Non-structural

proteins:

C Pr/M E NS1 NS2a 2b NS3 NS4a 4b NS5

Structural Non-structural ESCMID eLibrary

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Dengue in Europe

• Reports of travellers acquiring dengue are increasing.

• Plus autochthonous cases presenting in non-endemic areas

where the mosquito vectors have become newly established

• Surveillance data shows dengue is now the most common cause

fever in travellers returning from all geographical regions except

Sub-Saharan Africa and Central America.ESCMID eLibrary

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Dengue in Vietnam

Hanoi Southern Vietnam

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WHO, 2009

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Critical

period

Capillary leakage

viraemia

0 1 2 3 4 5 6

days

Inflammatory

host response

Shock

Bleeding

Organ

impairment

Febrile phase Recovery phase

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Potential complications

Capillary leak:

-Fluid accumulation-Shock

Bleeding:

-Skin/mucosal/GI etc

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Organ involvement

Liver: Hepatitis, ALT>1000

Brain: Acute encephalitis syndrome without other manifestations

of the disease .

Heart: Acute myocarditis, conduction disturbances and myocardial

depression.

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Treatment

• No specific antivirals or adjunctive therapies have shown benefit,

management relies on symptomatic and supportive treatment.

• Meticulous fluid balance and cautious intravenous fluid

• Minimum volume of parenteral fluid should be given - adequate

organ perfusion

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Predicting outcome from dengue

• Clinical/laboratory parameters

• Viral markers

• Immunological parameters

• Novel biomarkers

• Microcirculation

• Endothelial activation markers

Simmons CP et al. N Engl J Med 2012;366:1423-1432.

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Risk Factors for severe disease

• Most commonly secondary infections with a different serotype (ADE)

• Age influences clinical phenotype;

-severe plasma leakage more likely to occur in children

-major bleeding in adults

• Older age (>60 years) or young patients (<15 years),

• Pregnant women

• Patients with co-morbidities such as diabetes and uncontrolled

hypertension.

• Patients with over and under nutritionESCMID eLibrary

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Clinical assessment of severity

DF (n = 312) DHF (n = 319) P†

Days ill, no. (%)

≤3 104 (33) 52 (16) <0.01

>3 208 (67) 267 (84)

Vomiting, no. (%) 178 (57) 234 (73) <0.01

Abdominal pain, no. (%) 156 (50) 234/318 (74) <0.01

Headache, no. (%) 140/307 (46) 88/318 (28) <0.01

Reported bleeding, no. (%) 56 (18) 36 (11) 0.02

Spontaneous petechiae, no. (%) 230 (74) 292 (92) <0.01

Liver size, no. (%)

≤1 cm 256 (82) 145 (45) <0.01

>1 cm 56 (18) 174 (55)

Haematocrit, %, median (90% range)

40 (34–49) 48 (40–54) <0.01

Platelet count ≤100,000/mm3, no. (%)

195/296 (66) 294/311 (95) <0.01

Platelet count/mm3, median (90% range)

84,500 (43,000–160,000)

66,000 (40,000–110,000)

<0.01

Phuong CX et al. Am J Trop Med Hyg. 2004

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Clinical assessment: plasma leakage

• Haemoconcentration:

(Change in haematocrit)

• Haemodynamic

monitoring

(pulse pressure)

• Albumin levels

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Laboratory biomarkers- Platelets

Platelet counts in children and adults with shock (top) and uncomplicated dengue (bottom )

Trung DT, PLoS NTD, 2012

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Clinical decision algorithm for predicting severe dengue

A. Decision algorithm for severity prediction

B. PLT = platelet count;Ct  = viral load (high Ct-value indicates low viral load)IgG = positive result indicates a secondary infectionLow = platelet nadir of 50,000/mm3 or less; high = platelet nadir greater than 50,000/mm3

Tanner L et al. Plos NTD 2008

Sensitivity of 78.2% & specificity of 80.2%ESCMID eLibrary

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Other laboratory markers: Liver enzymes

Lee LK. et al 2012 Plos Negl Trop Dis

Trung DT. et al 2010 AJTMH

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Microalbuminuria for predicting outcome

• Dengue patients generally had higher UACRs than the OFI patients • But microalbuminuria, using cutoff of 30 mg albumin/g creatinine discriminated

poorly between the two diagnostic groups. • UACRs did not prove useful in predicting either development of warning signs for

severe dengue or need for hospitalization.Hanh NT et al.Plos one 2013

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Imaging studies:

Serial ultrasounds

• Subclinical fluid accumulation had PPV of 35%

& NPV of 90% for severe dengue.

• Gallbladder wall oedema-more pronounced

in severe dengue patients and often

preceded ascites/pleural effusion.

• Performed better than serial haematocrit and

albumin measurements

Michels, M et al. Plos NTD 2013

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Viral Markers

• High viral replication associated with severe disease.

• Viraemias 10-100 times higher in DHF vs. DF.

Vaughn et al. JID 2000

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Immunological markers

Immune marker Disease phase Comment

Elevated levels

 IL-4, IL-6, IL-8, IL-10 Elevated febrile phase, peaking at defervescence

Elevated levels in DSS vs. DF, correlated with markers of disease severity. IL-10 correlated with thrombocytopenia

TNFa, sTNFR-75, sTNFR-80 Elevated in the febrile phase

Raised in severe dengue vs. mild disease. sTNFR predicted children who went on to develop shock

 IFN-γ Early febrile phase, peaking defervescence

Severe disease associated with earlier peak IFN-γ levels .

C3a, C4a, C5a,Factor D Elevated in acute phase Elevated in DHF compared with DF

SC5b-9 terminal complement complex

Elevated in acute disease Higher levels were demonstrated in DF/DHF compared with OFI, and correlated with dengue severity

Reduced levels

RANTES/CCL5 Reduced levels during the acute phase

Reduced during acute phase and correlated with thrombocytopenia

IL1-b, IL-2, EGF Reduced levels during febrile phase

Lower levels of IL-2 and EGF in DSS compared with DF

VEGF, VEGFR2 Altered levels of VEGF and VEGFR2 around day of defervescence

There are mixed reports of VEGF levels

C3, factor H Reduced levels during acute dengue

Reduced levels were found in patients with DHF compared with those with DF and healthy controls

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Data from this large cohort of patients, enrolled early with undifferentiated fever will be used to: 1) Develop a practical diagnostic algorithm and case definition for dengue and2) Identify simple clinical and laboratory parameters associated with progression

to a more severe disease course. ESCMID eLibrary

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Novel methods of predicting outcome :

Microvascular function

Rationale:

• Vascular system is the main target in dengue infections.

• Microcirculatory dysfunction plays major role in pathophysiology of multi-organ failure in severe infections.1

• Microvascular and endothelial dysfunction have prognostic value in other infections like sepsis and malaria.2

1.Vincent JL,et al Crit Care 2005. 2.De Backer D et al. Crit Care med 2013.

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Pathogenesis of capillary leak in dengue

•Disruption of endothelial glyocalyx layer by the virus or NS1 Ag adhering to the endothelial layer.

•NS1 is a glycoprotein secreted from dengue infected cells, required for viral replication.

•NS1 can selectively binds to heparan sulfate in glycocalyx.

• Altering the microvascular permeability characteristics.

Simmons CP et al. N Engl J Med 2012;366:1423-1432.

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Assessing microvascular function clinically

1. Videomicroscopy (Side stream dark field imaging)

Assessment of vessel density, flow, perfusion & glycocalyx depth.

2. Endothelium-dependent vasodilation. (Tests that stimulate increase in endothelium-derived NO)

3. Biochemical markers of endothelial activation:

Angiopoietin 2/ ICAM-1/VCAM-1/ & L- Arginine/ADMA

4. Glyocalyx shedding:

Syndecan/EndocanESCMID eLibrary

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Microcirculatory assessment:

Normal example Dengue late febrile phase

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Proportion of perfused small vessels (top panel) and mean flow index (bottom panel) by day of illness in dengue patients with and without plasma leakage

Plasma leak grades0: No evidence of leak

1: moderate (15-20% ΔHCT and/or fluid accumulation)

2: Severe leakage (ΔHCT>20% and/or pleural effusion with Resp compromise, or shock.

P<0.01

P<0.01

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Endothelial glycocalyx assessment

Perfused boundary region in dengue patients with different plasma leak

severities

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Plasma syndecan levels in dengue patients with and without plasma leakage by illness phase

P<0.01ESCMID eLibrary

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Receiver operating characteristics for detecting plasma leakage illness days 4-6 using the Glycocalyx health score

AUC=0.70 (95% CI 0.52-0.88)

Glycocalyx health score –Combination of PBR and flow parameters

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Vascular cell adhesion molecule-1 in dengue patients with and without plasma leakage over illness phases.

P<0.01ESCMID eLibrary

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Angiopoietin 2/1 ratio in dengue patients with and without plasma leakage over illness phases.

P<0.01ESCMID eLibrary

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Clinical endothelial function tests

• EndoPAT- Measures digital pulse volume amplitude changes

following an arterial occlusion test.

• Stimulates production & release of endothelial-derived NO, causing

vasodilation.

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EndoPAT results

Example of PAT signal:

Occluded Arm

Control Arm

RHI=2.63

RHI=1.51

Dengue

Severe dengue

Reactive Hyperaemic Index(RHI) is the post occlusion- pre occlusion ratio

Normal RHI =>1.67

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Endothelial function in first 3 days of illness by bleeding and plasma leakage

P=0.006P=0.78

n=48 n=22 n=62 n=12ESCMID eLibrary

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Novel ways of assessing intravascular volume

• The compensatory reserve Index (CRI) is a new physiological parameter

that tracks changes in central volume.

• Derived from feature analysis of pulse arterial waveform, using finger

pulse oximetry.

• Detects subtle volume changes during compensatory phase of shock.

• Arterial waveform data processed by an algorithm to calculate the CRI,

giving a value between 0 and 1.ESCMID eLibrary

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Point of Decompensation

Time

CR = Compensatory Reserve

DR = Decompensatory Reserve

1.0

0.8

0.6

0.4

0.2

0

Co

mp

en

sato

ry R

ese

rve

Ind

ex

Compensatory Reserve Index (CRI)

Progressive Volume Loss

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Summary

• Many factors including differences in virus burden, virulence, host immune response, likely contribute to the final disease outcome

• Predicting outcome in dengue remains challenging and the search for more robust methods continues

• Although WHO warning signs are key for early recognition of progression to severe disease, the current evidence for any particular clinical or laboratory marker is weak.

• Potential algorithms incorporating clinical, viral, and/or endothelial biomarkers – may prove useful if validated in a larger cohort. ESCMID eLibrary

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Acknowledgements

OUCRU

Dengue group

Bridget Wills

Cameron Simmons

Heiman Wertheim

Lam Phung Khanh

Marcel Wolbers

HTD, HCMC

Nguyen Van Vinh Chau

Huynh Trung Trieu

Dong Thi Hoai Tam

Phan Tu Quí

Huynh Ngoc Thien Vuong

Hong Hanh Nguyen Ho

NHTD, Hanoi

Nguyen Van Kinh

Le Thi Lien

Tran Thi Toan

Menzies School of Health

Research, Darwin

Tsin Wen Yeo

Imperial College London

Gavin Screaton

Juthathip Mongkolsapaya

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