Development and validation of supervised machine learning multivariable prediction models for the diagnosis of <i>Pneumocystis jirovecii</i> pneumonia using nasopharyngeal swab PCR in adults in a low-HIV prevalence setting
The logistic regression model should be externally validated in a wider range of settings. As the predictors are simple, routinely collected patient variables, this model may represent a diagnostic advance suitable for settings where collection of lower respiratory tract specimens is difficult but PCR is available.
Authors
- David L. Paterson (ORCID: https://orcid.org/0000-0003-2079-4437)
- Rusheng Chew (ORCID: https://orcid.org/0000-0002-8992-4474)
- Marion L. Woods (ORCID: https://orcid.org/0000-0003-1094-9853)
Institutions
- The University of Queensland (AU)
- National University of Singapore (SG)
- Mahidol University (TH)
- Royal Brisbane and Women's Hospital (AU)
- Angkor Hospital for Children (KH)
- University of Oxford (GB)
- Mahidol Oxford Tropical Medicine Research Unit (TH)
- National University Health System (SG)
Publication Details
- Journal
- International Health
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1093/inthealth/ihae052
- Citations
- 2
- Primary Topic
- Pneumocystis jirovecii pneumonia detection and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.77
Funders
- Government of the United Kingdom
- Royal Australasian College of Physicians