Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients
Bibliographic Data
| ID | 15713636 |
|---|---|
| Authors | Josselin Carolina Corzo-Gómez (0000-0002-8681-1140, Universidad Autónoma de Chiapas), Josselin Corzo-Gómez (Escuela de Ciencias Químicas Sede Ocozocoautla, Universidad Autónoma de Chiapas, Ocozocoautla de Espinosa 29140, Mexico), Susana Guadalupe Guzmán-Aquino (Instituto Politécnico Nacional), Susana Guzmán-Aquino (Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de México 07338, Mexico), Cruz Vargas‐De‐León (0000-0001-9428-3619, Hospital Juárez de México), Mauricio Megchún-Hernández (0000-0002-0058-6983, Hospital de Especialidades), Alfredo Briones-Aranda (0000-0002-0628-2446, Universidad Autónoma de Chiapas, corresponding author) |
| Year | 2023 |
| Volume | 10 |
| Issue | 9 |
| Pages | 1508-1508 |
| Publication date | 2023-09-05 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Children (JOURNAL) |
| Journal identifiers | ISSN: 2227-9067 • E-ISSN: 2227-9067 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/children10091508 |
| PMID | 37761469 |
| OpenAlex | W4386461262 |
| Language | EN |
| References cited | 38 |
The current contribution aimed to evaluate the capacity of the naive Bayes classifier to predict the progression of dengue fever to severe infection in children based on a defined set of clinical conditions and laboratory parameters. This case-control study was conducted by reviewing patient files in two public hospitals in an endemic area in Mexico. All 99 qualifying files showed a confirmed diagnosis of dengue. The 32 cases consisted of patients who entered the intensive care unit, while the 67 control patients did not require intensive care. The naive Bayes classifier could identify factors predictive of severe dengue, evidenced by 78% sensitivity, 91% specificity, a positive predictive value of 8.7, a negative predictive value of 0.24, and a global yield of 0.69. The factors that exhibited the greatest predictive capacity in the model were seven clinical conditions (tachycardia, respiratory failure, cold hands and feet, capillary leak leading to the escape of blood plasma, dyspnea, and alterations in consciousness) and three laboratory parameters (hypoalbuminemia, hypoproteinemia, and leukocytosis). Thus, the present model showed a predictive and adaptive capacity in a small pediatric population. It also identified attributes (i.e., hypoalbuminemia and hypoproteinemia) that may strengthen the WHO criteria for predicting progression to severe dengue
Clinical prediction rule · Convalescence · Dengue fever · Hypoalbuminemia · Hypoproteinemia · Intensive care medicine · COVID-19 epidemiological studies · Dengue and Mosquito Control Research · Medicine · Mosquito-borne diseases and control · Immunology · Internal Medicine
| Highly cited | No |
|---|