Prognostic factors and prediction models for hospitalisation and all-cause mortality in adults presenting to primary care with a lower respiratory tract infection: a systematic review.

OBJECTIVE: To identify and synthesise relevant existing prognostic factors (PF) and prediction models (PM) for hospitalisation and all-cause mortality within 90 days in primary care patients with acute lower respiratory tract infections (LRTI). DESIGN: Systematic review. METHODS: Systematic searches of MEDLINE, Embase and the Cochrane Library were performed. All PF and PM studies on the risk of hospitalisation or all-cause mortality within 90 days in adult primary care LRTI patients were included. The risk of bias was assessed using the Quality in Prognostic Studies tool and Prediction Model Risk Of Bias Assessment Tool tools for PF and PM studies, respectively. The results of included PF and PM studies were descriptively summarised. RESULTS: Of 2799 unique records identified, 16 were included: 9 PF studies, 6 PM studies and 1 combination of both. The risk of bias was judged high for all studies, mainly due to limitations in the analysis domain. Based on reported multivariable associations in PF studies, increasing age, sex, current smoking, diabetes, a history of stroke, cancer or heart failure, previous hospitalisation, influenza vaccination (negative association), current use of systemic corticosteroids, recent antibiotic use, respiratory rate ≥25/min and diagnosis of pneumonia were identified as most promising candidate predictors. One newly developed PM was externally validated (c statistic 0.74, 95% CI 0.71 to 0.78) whereas the previously hospital-derived CRB-65 was externally validated in primary care in five studies (c statistic ranging from 0.72 (95% CI 0.63 to 0.81) to 0.79 (95% CI 0.65 to 0.92)). None of the PM studies reported measures of model calibration. CONCLUSIONS: Implementation of existing models for individualised risk prediction of 90-day hospitalisation or mortality in primary care LRTI patients in everyday practice is hampered by incomplete assessment of model performance. The identified candidate predictors provide useful information for clinicians and warrant consideration when developing or updating PMs using state-of-the-art development and validation techniques. PROSPERO REGISTRATION NUMBER: CRD42022341233.

Incomplete and possibly selective recording of signs, symptoms, and measurements in free text fields of primary care electronic health records of adults with lower respiratory tract infections.

OBJECTIVES: To assess the completeness of recording of relevant signs, symptoms, and measurements in Dutch free text fields of primary care electronic health records (EHR) of adults with lower respiratory tract infections (LRTI). STUDY DESIGN AND SETTING: Retrospective cohort study embedded in a prediction modeling project using routine health care data of the Julius General Practitioners' Network of adult patients with LRTI. Free text fields of 1,000 primary care consultations of LRTI episodes between 2016 and 2019 were manually annotated to retrieve data on the recording of sixteen relevant signs, symptoms, and measurements. RESULTS: For 12/16 (75%) of the relevant signs, symptoms, and measurements, more than 50% of the values was not recorded. The patterns of recorded values indicated selective recording of positive or abnormal values. Recording rates varied across consultation type (physical consultation vs. home visit), diagnosis (acute bronchitis vs. pneumonia), antibiotic prescription issued (yes vs. no), and between practices. CONCLUSION: In EHR of primary care LRTI patients, recording of signs, symptoms, and measurements in free text fields is incomplete and possibly selective. When using free text data in EHR-based research, careful consideration of its recording patterns and appropriate missing data handling techniques is therefore required.

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.

BACKGROUND: Community-acquired lower respiratory tract infections (LRTI) are common in primary care and patients at particular risk of adverse outcomes, e.g., hospitalisation and mortality, are challenging to identify. LRTIs are also linked to an increased incidence of cardiovascular diseases (CVD) following the initial infection, whereas concurrent CVD might negatively impact overall prognosis in LRTI patients. Accurate risk prediction of adverse outcomes in LRTI patients, while considering the interplay with CVD, can aid general practitioners (GP) in the clinical decision-making process, and may allow for early detection of deterioration. This paper therefore presents the design of the development and external validation of two models for predicting individual risk of all-cause hospitalisation or mortality (model 1) and short-term incidence of CVD (model 2) in adults presenting to primary care with LRTI. METHODS: Both models will be developed using linked routine electronic health records (EHR) data from Dutch primary and secondary care, and the mortality registry. Adults aged ≥ 40 years with a GP-diagnosis of LRTI between 2016 and 2019 are eligible for inclusion. Relevant patient demographics, medical history, medication use, presenting signs and symptoms, and vital and laboratory measurements will be considered as candidate predictors. Outcomes of interest include 30-day all-cause hospitalisation or mortality (model 1) and 90-day CVD (model 2). Multivariable elastic net regression techniques will be used for model development. During the modelling process, the incremental predictive value of CVD for hospitalisation or all-cause mortality (model 1) will also be assessed. The models will be validated through internal-external cross-validation and external validation in an equivalent cohort of primary care LRTI patients. DISCUSSION: 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.

Incidence and management of acute otitis media in adults: a primary care-based cohort study.

BACKGROUND: Although primarily considered a childhood disease, acute otitis media (AOM) also occurs in adults. Data on the burden of this condition in adults are, however, scarce. OBJECTIVE: To explore the primary care incidence and current management of AOM in adults. METHODS: All patients aged 15 and older included in the routine health care database of the Julius General Practitioners' Network were followed from 2015 to 2018 (contributing to a total of 1 261 575 person-years). We extracted data on AOM episodes, AOM-related consultations, comorbidities, and antibiotic and analgesic prescriptions. RESULTS: Five thousand three hundred and fifty-eight patients experienced one or more AOM episodes (total number of AOM episodes: 6667; mean 1.2 per patient). The overall AOM incidence was 5.3/1000 person-years and was fairly stable over the study period. Incidence was particularly high in atopic patients (7.3/1000 person-years) and declined with age (from 7.1 in patients 15-39 years of age to 2.7/1000 person-years in those aged 64 years and older). Oral antibiotics, predominantly amoxicillin, were prescribed in 46%, and topical antibiotics in 21% of all episodes. CONCLUSION: Over the past years, the incidence of AOM in adults in primary care has been stable. Oral antibiotic prescription rates resemble those in children with AOM, whereas a remarkably high topical antibiotic prescription rate was observed. Future prognostic research should inform on the need and feasibility of prospective studies into the best management strategy in this condition.