Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness.
BACKGROUND
Dengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults.


RESULTS
A total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm(3) or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever.


CONCLUSIONS
This study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.
Tanner
Lukas
L
Novartis Institute for Tropical Diseases, Singapore.
Schreiber
Mark
M
Low
Jenny G H
JG
Ong
Adrian
A
Tolfvenstam
Thomas
T
Lai
Yee Ling
YL
Ng
Lee Ching
LC
Leo
Yee Sin
YS
Thi Puong
Le
L
Vasudevan
Subhash G
SG
Simmons
Cameron P
CP
Hibberd
Martin L
ML
Ooi
Eng Eong
EE
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D003663:Decision Trees; D003715:Dengue / Q000175:diagnosis* / Q000473:pathology / Q000821:virology*; D003716:Dengue Virus / Q000235:genetics; D006801:Humans; D012367:RNA, Viral / Q000235:genetics; D020133:Reverse Transcriptase Polymerase Chain Reaction; D019595:Severe Dengue / Q000175:diagnosis / Q000473:pathology / Q000821:virology; D055815:Young Adult
D012367:RNA, Viral