Dataset Information

The detection of anti-dengue virus IgM in urine in participants enrolled in an acute febrile illness study in Puerto Rico.

ABSTRACT: BACKGROUND:Dengue is an important arboviral disease with about 100 million dengue cases per year, of which, ~5% result in severe disease. Clinical differentiation of dengue from other acute febrile illnesses (AFI) is difficult, and diagnostic blood tests are costly. We evaluated the utility of anti-DENV IgM in urine to identify dengue cases among AFI patients enrolled in a clinical study. METHODS:Between May 2012-March 2013, 1538 study participants with fever for ≤7 days were enrolled, a medical history was obtained, and serum and urine specimens were collected. Serum was tested for DENV RNA and anti-DENV IgM. Urine was tested for anti-DENV IgM, and its sensitivity and specificity to detect sera laboratory-positive dengue cases were calculated. We evaluated if urine anti-DENV IgM positivity early (≤5 days post-illness onset [DPO]) and late (6-14 DPO) in the clinical course was associated with dengue severity. RESULTS:Urine anti-DENV IgM sensitivity and specificity were 47.4% and 98.5%, respectively, when compared with serum anti-DENV IgM ELISA results, and 29.7% and 91.1% when compared with serum rRT-PCR results. There was no correlation between urine anti-DENV IgM positivity and patient sex or pre-existing chronic disease. Early in the clinical course, a significantly higher proportion of those who developed dengue with warning signs had anti-DENV IgM in their urine when compared to those without warning signs (20.4% vs. 4.3%). There was no difference in the proportion with urine anti-DENV IgM positivity between severity groups late in the clinical course. CONCLUSION:While detection of urine anti-DENV IgM lacked adequate diagnostic sensitivity, it is a highly specific marker for laboratory-positive dengue, and its presence early in the clinical course may distinguish those with more severe disease. Further assessment of urine anti-DENV IgM by DPO is warranted to determine its utility as an early diagnostic (and possibly prognostic) marker for dengue.

SUBMITTER: Caraballo E

PROVIDER: S-EPMC6988914 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Identifying etiologies of acute febrile illnesses (AFI) is challenging due to non-specific presentation and limited availability of diagnostics. Prospective AFI studies provide a methodology to describe the syndrome by age and etiology, findings that can be used to develop case definitions and multiplexed diagnostics to optimize management. We conducted a 3-year prospective AFI study in Puerto Rico. Patients with fever ≤7 days were offered enrollment, and clinical data and specimens were collected at enrollment and upon discharge or follow-up. Blood and oro-nasopharyngeal specimens were tested by RT-PCR and immunodiagnostic methods for infection with dengue viruses (DENV) 1-4, chikungunya virus (CHIKV), influenza A and B viruses (FLU A/B), 12 other respiratory viruses (ORV), enterovirus, Leptospira spp., and Burkholderia pseudomallei. Clinical presentation and laboratory findings of participants infected with DENV were compared to those infected with CHIKV, FLU A/B, and ORV. Clinical predictors of laboratory-positive dengue compared to all other AFI etiologies were determined by age and day post-illness onset (DPO) at presentation. Of 8,996 participants enrolled from May 7, 2012 through May 6, 2015, more than half (54.8%, 4,930) had a pathogen detected. Pathogens most frequently detected were CHIKV (1,635, 18.2%), FLU A/B (1,074, 11.9%), DENV 1-4 (970, 10.8%), and ORV (904, 10.3%). Participants with DENV infection presented later and a higher proportion were hospitalized than those with other diagnoses (46.7% versus 27.3% with ORV, 18.8% with FLU A/B, and 11.2% with CHIKV). Predictors of dengue in participants presenting <3 DPO included leukopenia, thrombocytopenia, headache, eye pain, nausea, and dizziness, while negative predictors were irritability and rhinorrhea. Predictors of dengue in participants presenting 3-5 DPO were leukopenia, thrombocytopenia, facial/neck erythema, nausea, eye pain, signs of poor circulation, and diarrhea; presence of rhinorrhea, cough, and red conjunctiva predicted non-dengue AFI. By enrolling febrile patients at clinical presentation, we identified unbiased predictors of laboratory-positive dengue as compared to other common causes of AFI. These findings can be used to assist in early identification of dengue patients, as well as direct anticipatory guidance and timely initiation of correct clinical management.

Project description:Chikungunya, a mosquito-borne viral, acute febrile illness (AFI) is associated with polyarthralgia and polyarthritis. Differentiation from other AFI is difficult due to the non-specific presentation and limited availability of diagnostics. This 3-year study identified independent clinical predictors by day post-illness onset (DPO) at presentation and age-group that distinguish chikungunya cases from two groups: other AFI and dengue. Specimens collected from participants with fever ≤7 days were tested for chikungunya, dengue viruses 1-4, and 20 other pathogens. Of 8,996 participants, 18.2% had chikungunya, and 10.8% had dengue. Chikungunya cases were more likely than other groups to be older, report a chronic condition, and present <3 DPO. Regardless of timing of presentation, significant positive predictors for chikungunya versus other AFI were: joint pain, muscle, bone or back pain, skin rash, and red conjunctiva; with dengue as the comparator, red swollen joints (arthritis), joint pain, skin rash, any bleeding, and irritability were predictors. Chikungunya cases were less likely than AFI and dengue to present with thrombocytopenia, signs of poor circulation, diarrhea, headache, and cough. Among participants presenting <3 DPO, predictors for chikungunya versus other AFI included: joint pain, skin rash, and muscle, bone or back pain, and absence of thrombocytopenia, poor circulation and respiratory or gastrointestinal symptoms; when the comparator was dengue, joint pain and arthritis, and absence of thrombocytopenia, leukopenia, and nausea were early predictors. Among all groups presenting 3-5 DPO, pruritic skin became a predictor for chikungunya, joint, muscle, bone or back pain were no longer predictive, while arthritis became predictive in all age-groups. Absence of thrombocytopenia was a significant predictor regardless of DPO or comparison group. This study identified robust clinical indicators such as joint pain, skin rash and absence of thrombocytopenia that can allow early identification of and accurate differentiation between patients with chikungunya and other common causes of AFI.

Project description:BackgroundThe incidence and severity of dengue in Latin America has increased substantially in recent decades and data from Puerto Rico suggests an increase in severe cases. Successful clinical management of severe dengue requires early recognition and supportive care.MethodsFatal cases were identified among suspected dengue cases reported to two disease surveillance systems and from death certificates. To be included, fatal cases had to have specimen submitted for dengue diagnostic testing including nucleic acid amplification for dengue virus (DENV) in serum or tissue, immunohistochemical testing of tissue, and immunoassay detection of anti-DENV IgM from serum. Medical records from laboratory-positive dengue fatal case-patients were reviewed to identify possible determinants for death.ResultsAmong 10,576 reported dengue cases, 40 suspect fatal cases were identified, of which 11 were laboratory-positive, 14 were laboratory-negative, and 15 laboratory-indeterminate. The median age of laboratory-positive case-patients was 26 years (range 5 months to 78 years), including five children aged < 15 years; 7 sought medical care at least once prior to hospital admission, 9 were admitted to hospital and 2 died upon arrival. The nine hospitalized case-patients stayed a mean of 15 hours (range: 3-48 hours) in the emergency department (ED) before inpatient admission. Five of the nine case-patients received intravenous methylprednisolone and four received non-isotonic saline while in shock. Eight case-patients died in the hospital; five had their terminal event on the inpatient ward and six died during a weekend. Dengue was listed on the death certificate in only 5 instances.ConclusionsDuring a dengue epidemic in an endemic area, none of the 11 laboratory-positive case-patients who died were managed according to current WHO Guidelines. Management issues identified in this case-series included failure to recognize warning signs for severe dengue and shock, prolonged ED stays, and infrequent patient monitoring.

Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Dataset Information

The detection of anti-dengue virus IgM in urine in participants enrolled in an acute febrile illness study in Puerto Rico.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets