Dataset Information

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia.

ABSTRACT: Diagnosing dengue in endemic areas remains problematic because of the low specificity of the symptoms and lack of accurate diagnostic tests. This study aimed to develop and prospectively validate, under routine care, dengue diagnostic clinical algorithms. The study was carried out in two phases. First, diagnostic algorithms were developed using a database of 1,130 dengue and 918 non-dengue patients, expert opinion, and literature review. Algorithms with > 70% sensitivity were prospectively validated in a single-group quasi-experimental trial with an adaptive Bayesian design. In the first phase, the algorithms that were developed with the continuous Bayes formula and included leukocytes and platelet counts, in addition to selected signs and symptoms, showed the highest sensitivities (> 80%). In the second phase, the algorithms were applied on admission to 1,039 consecutive febrile subjects in three endemic areas in Colombia of whom 25 were laboratory-confirmed dengue, 307 non-dengue, 514 probable dengue, and 193 undetermined. Including parameters of the hemogram consistently improved specificity without affecting sensitivity. In the final analysis, considering only confirmed dengue and non-dengue cases, an algorithm with a sensitivity and specificity of 65.4% (95% credibility interval 50-83) and 40.1% (34.7-45.7) was identified. All tested algorithms had likelihood ratios close to 1, and hence, they are not useful to confirm or rule out dengue in endemic areas. The findings support the use of hemograms to aid dengue diagnosis and highlight the challenges of clinical diagnosis of dengue.

SUBMITTER: Caicedo-Borrero DM

PROVIDER: S-EPMC7253082 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia.

Caicedo-Borrero Diana María DM Tovar José Rafael JR Méndez Andrés A Parra Beatriz B Bonelo Anilza A Celis Jairo J Villegas Liliana L Collazos Constanza C Osorio Lyda L

The American journal of tropical medicine and hygiene 20200601 6

Diagnosing dengue in endemic areas remains problematic because of the low specificity of the symptoms and lack of accurate diagnostic tests. This study aimed to develop and prospectively validate, under routine care, dengue diagnostic clinical algorithms. The study was carried out in two phases. First, diagnostic algorithms were developed using a database of 1,130 dengue and 918 non-dengue patients, expert opinion, and literature review. Algorithms with > 70% sensitivity were prospectively valid ...[more]

PMID: 32342839

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Project description:BackgroundSerologic testing algorithms for recent HIV seroconversion (STARHS) provide important information for HIV surveillance. We have previously demonstrated that a patient's antibody reaction pattern in a confirmatory line immunoassay (INNO-LIA™ HIV I/II Score) provides information on the duration of infection, which is unaffected by clinical, immunological and viral variables. In this report we have set out to determine the diagnostic performance of Inno-Lia algorithms for identifying incident infections in patients with known duration of infection and evaluated the algorithms in annual cohorts of HIV notifications.MethodsDiagnostic sensitivity was determined in 527 treatment-naive patients infected for up to 12 months. Specificity was determined in 740 patients infected for longer than 12 months. Plasma was tested by Inno-Lia and classified as either incident (< = 12 m) or older infection by 26 different algorithms. Incident infection rates (IIR) were calculated based on diagnostic sensitivity and specificity of each algorithm and the rule that the total of incident results is the sum of true-incident and false-incident results, which can be calculated by means of the pre-determined sensitivity and specificity.ResultsThe 10 best algorithms had a mean raw sensitivity of 59.4% and a mean specificity of 95.1%. Adjustment for overrepresentation of patients in the first quarter year of infection further reduced the sensitivity. In the preferred model, the mean adjusted sensitivity was 37.4%. Application of the 10 best algorithms to four annual cohorts of HIV-1 notifications totalling 2'595 patients yielded a mean IIR of 0.35 in 2005/6 (baseline) and of 0.45, 0.42 and 0.35 in 2008, 2009 and 2010, respectively. The increase between baseline and 2008 and the ensuing decreases were highly significant. Other adjustment models yielded different absolute IIR, although the relative changes between the cohorts were identical for all models.ConclusionsThe method can be used for comparing IIR in annual cohorts of HIV notifications. The use of several different algorithms in combination, each with its own sensitivity and specificity to detect incident infection, is advisable as this reduces the impact of individual imperfections stemming primarily from relatively low sensitivities and sampling bias.

Dataset Information

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia.

Publications

Development and Performance of Dengue Diagnostic Clinical Algorithms in Colombia.

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