Predicting adverse outcomes in adults with a community-acquired lower respiratory tract infection: a protocol for the development and validation of two prediction models for (i) all-cause hospitalisation and mortality and (ii) cardiovascular outcomes.
Merijn H RijkTamara N PlatteelGeert-Jan GeersingMonika HollanderBert L G P DalmolenPaul LittleFrans H RuttenMaarten van SmedenRoderick P VenekampPublished in: Diagnostic and prognostic research (2023)
Implementation of currently available prediction models for primary care LRTI patients is hampered by limited assessment of model performance. While considering the role of CVD in LRTI prognosis, we aim to develop and externally validate two models that predict clinically relevant outcomes to aid GPs in clinical decision-making. Challenges that we anticipate include the possibility of low event rates and common problems related to the use of EHR data, such as candidate predictor measurement and missingness, how best to retrieve information from free text fields, and potential misclassification of outcome events.
Keyphrases
- primary care
- respiratory tract
- mental health
- end stage renal disease
- decision making
- healthcare
- electronic health record
- newly diagnosed
- ejection fraction
- prognostic factors
- peritoneal dialysis
- cardiovascular events
- risk factors
- metabolic syndrome
- patient reported outcomes
- machine learning
- coronary artery disease
- quality improvement
- general practice
- patient reported
- smoking cessation
- artificial intelligence
- social media
- drug induced
- human health