Understanding and avoiding late presentation for HIV diagnosis - study protocol of a trial using mixed methods (FindHIV).
Frederik ValbertEva WolfStefan PreisSven SchellbergKnud ScheweNikola HanhoffBirgit MückChristine KöglPaul LauscherJürgen WasemSilke NeusserAnja NeumannPublished in: AIDS care (2021)
Many patients infected with HIV are diagnosed at an advanced stage of illness. These late presenters are individuals with a CD4 cell count of less than 350 cells/µL and/or an AIDS defining disease at initial HIV diagnosis. Purpose of FindHIV is to develop and distribute a questionnaire/scoring system aimed at a reduction in late presentation. FindHIV uses a mixed methods approach. In a first step, primary data of patients were collected. Inclusion criteria were: age ≥ 18 years, cognitive ability and language skills to participate in the study, initial HIV diagnosis within the past 6 months, and patient informed consent. Descriptive methods and regression models are used to identify: (1) patient characteristics associated with late presentation and (2) contacts to the healthcare system with indicator diseases that did not lead to HIV testing. Secondly, a questionnaire/scoring system is created by an expert panel. Afterwards the questionnaire/scoring system is to be disseminated. The greatest challenge was in reaching an adequate sample size. Another risk may be a recall bias. Nevertheless, FindHIV is devised as an in-depth study of the phenomenon of late presentation with potential to significantly improve HIV detection.
Keyphrases
- hiv testing
- men who have sex with men
- antiretroviral therapy
- hiv positive
- hiv infected
- study protocol
- human immunodeficiency virus
- hiv aids
- case report
- hepatitis c virus
- end stage renal disease
- ejection fraction
- newly diagnosed
- randomized controlled trial
- clinical trial
- prognostic factors
- induced apoptosis
- chronic kidney disease
- clinical practice
- deep learning
- signaling pathway
- single cell
- patient reported
- optical coherence tomography
- south africa
- risk assessment
- artificial intelligence
- electronic health record
- cell therapy
- machine learning
- patient reported outcomes
- sensitive detection
- human health
- endoplasmic reticulum stress