Open AccessA data driven clinical algorithm for differential diagnosis of pertussis and other respiratory infections in infants
Author(s) -
Alberto Eugenio Tozzi,
Francesco Gesualdo,
Caterina Rizzo,
Emanuela Carloni,
Luisa Russo,
Ilaria Campagna,
Alberto Villani,
Antonino Reale,
Carlo Concato,
Giulia Linardos,
Elisabetta Pandolfi
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0236041
Subject(s) - medicine , whooping cough , bordetella pertussis , leukocytosis , pediatrics , lymphocytosis , vomiting , algorithm , immunology , vaccination , computer science , genetics , biology , bacteria
Background Clinical criteria for pertussis diagnosis and clinical case definitions for surveillance are based on a cough lasting two or more weeks. As several pertussis cases seek care earlier, a clinical tool independent of cough duration may support earlier recognition. We developed a data-driven algorithm aimed at predicting a laboratory confirmed pertussis. Methods We enrolled children <12 months of age presenting with apnoea, paroxistic cough, whooping, or post-tussive vomiting, irrespective of the duration of cough. Patients underwent a RT-PCR test for pertussis and other viruses. Through a logistic regression model, we identified symptoms associated with laboratory confirmed pertussis. We then developed a predictive decision tree through Quinlan's C4.5 algorithm to predict laboratory confirmed pertussis. Results We enrolled 543 children, of which 160 had a positive RT-PCR for pertussis. A suspicion of pertussis by a physician (aOR 5.44) or a blood count showing leukocytosis and lymphocytosis (aOR 4.48) were highly predictive of lab confirmed pertussis. An algorithm including a suspicion of pertussis by a physician, whooping, cyanosis and absence of fever was accurate (79.9%) and specific (94.0%) and had high positive and negative predictive values (PPV 76.3% NPV 80.7%). Conclusions An algorithm based on clinical symptoms, not including the duration of cough, is accurate and has high predictive values for lab confirmed pertussis. Such a tool may be useful in low resource settings where lab confirmation is unavailable, to guide differential diagnosis and clinical decisions. Algorithms may also be useful to improve surveillance for pertussis and anticipating classification of cases.