A data driven clinical algorithm for differential diagnosis of pertussis and other respiratory infections in infants
Alberto Eugenio Tozzi1, Francesco Gesualdo1, Caterina Rizzo1
1Predictive and Preventive Medicine Research Unit, Multifactorial and Systemic Diseases Research Area, Bambino Gesù Children's Hospital, Rome, Italy.
Insights
A new algorithm accurately predicts pertussis in infants using clinical symptoms, not cough duration. This tool aids early diagnosis and improves pertussis surveillance, especially in resource-limited settings.
Area of Science:
- Pediatrics
- Infectious Diseases
- Medical Diagnostics
Background:
- Current pertussis diagnosis relies on cough duration, delaying recognition in early-presenting cases.
- A need exists for clinical tools that support earlier pertussis recognition, independent of cough length.
Purpose of the Study:
- To develop a data-driven algorithm for predicting laboratory-confirmed pertussis in infants.
- To create a clinical decision tool for earlier pertussis identification.
Main Methods:
- Children under 12 months with specific symptoms were enrolled, regardless of cough duration.
- Reverse transcription-polymerase chain reaction (RT-PCR) was used for pertussis testing.
- A logistic regression model identified predictive symptoms, and a decision tree algorithm was developed.
Main Results:
- Physician suspicion of pertussis and blood findings (leukocytosis, lymphocytosis) were highly predictive.
- An algorithm using physician suspicion, whooping, cyanosis, and absence of fever showed 79.9% accuracy and 94.0% specificity.
Conclusions:
- A cough-duration-independent algorithm accurately predicts pertussis.
- This tool can guide differential diagnosis and clinical decisions, particularly in resource-limited areas.
- The algorithm may enhance pertussis surveillance and case classification.
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.
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