Development of a prediction tool for patients presenting with acute cough in primary care: a prognostic study spanning six European countries

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.

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients' electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0.7873, recall@10 of 0.9020 and hits@10 of 0.9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

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.

Prevalence, aetiologies and prognosis of the symptom cough in primary care: a systematic review and meta-analysis

Background

Cough is a relevant reason for encounter in primary care. For evidence-based decision making, general practitioners need setting-specific knowledge about prevalences, pre-test probabilities, and prognosis. Accordingly, we performed a systematic review of symptom-evaluating studies evaluating cough as reason for encounter in primary care.

Methods

We conducted a search in MEDLINE and EMBASE. Eligibility criteria and methodological quality were assessed independently by two reviewers. We extracted data on prevalence, aetiologies and prognosis, and estimated the variation across studies. If justifiable in terms of heterogeneity, we performed a meta-analysis.

Results

We identified 21 eligible studies on prevalence, 12 on aetiology, and four on prognosis. Prevalence/incidence estimates were 3.8–4.2%/12.5% (Western primary care) and 10.3–13.8%/6.3–6.5% in Africa, Asia and South America. In Western countries the underlying diagnoses for acute cough or cough of all durations were respiratory tract infections (73–91.9%), influenza (6–15.2%), asthma (3.2–15%), laryngitis/tracheitis (3.6–9%), pneumonia (4.0–4.2%), COPD (0.5–3.3%), heart failure (0.3%), and suspected malignancy (0.2–1.8%). Median time for recovery was 9 to 11 days. Complete recovery was reported by 40.2- 67% of patients after two weeks, and by 79% after four weeks. About 21.1–35% of patients re-consulted; 0–1.3% of acute cough patients were hospitalized, none died. Evidence is missing concerning subacute and chronic cough.

Conclusion

Prevalences and incidences of cough are high and show regional variation. Acute cough, mainly caused by respiratory tract infections, is usually self-limiting (supporting a “wait-and-see” strategy). We have no setting-specific evidence to support current guideline recommendations concerning subacute or chronic cough in Western primary care. Our study presents epidemiological data under non non-pandemic conditions. It will be interesting to compare these data to future research results of the post-pandemic era.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-021-01501-0.

The Effect of Amoxicillin in Adult Patients Presenting to Primary Care with Acute Cough Predicted to Have Pneumonia or a Combined Viral-Bacterial Infection

While most cases of acute cough are self-limiting, antibiotics are prescribed to over 50%. This proportion is inappropriately high given that benefit from treatment with amoxicillin could only be demonstrated in adults with pneumonia (based on chest radiograph) or combined viral-bacterial infection (based on modern microbiological methodology). As routine use of chest radiographs and microbiological testing is costly, clinical prediction rules could be used to identify these patient subsets. In this secondary analysis of data from a multicentre randomised controlled trial in adults presenting to primary care with acute cough, we used prediction rules for pneumonia or combined infection and assessed the effect of amoxicillin in patients predicted to have pneumonia or combined infection on symptom duration, symptom severity and illness deterioration. In total, 2056 patients that fulfilled all inclusion criteria were randomised, 1035 to amoxicillin, 1021 to placebo. Neither patients with a predicted pneumonia nor patients with a predicted combined infection were significantly more likely to benefit from amoxicillin. While the studied clinical prediction rules may help primary care clinicians to reduce antibiotic prescribing for low-risk patients, they did not identify adult acute cough patients that would benefit from amoxicillin treatment.

Relationship between microbiology of throat swab and clinical course among primary care patients with acute cough: a prospective cohort study

BACKGROUND

Acute lower respiratory tract infections (ALRTIs) account for most antibiotics prescribed in primary care despite lack of efficacy, partly due to clinician uncertainty about aetiology and patient concerns about illness course. Nucleic acid amplification tests could assist antibiotic targeting.

METHODS

In this prospective cohort study, 645 patients presenting to primary care with acute cough and suspected ALRTI, provided throat swabs at baseline. These were tested for respiratory pathogens by real-time polymerase chain reaction and classified as having a respiratory virus, bacteria, both or neither. Three hundred fifty-four participants scored the symptoms severity daily for 1 week in a diary (0 = absent to 4 = severe problem).

RESULTS

Organisms were identified in 346/645 (53.6%) participants. There were differences in the prevalence of seven symptoms between the organism groups at baseline. Those with a virus alone, and those with both virus and bacteria, had higher average severity scores of all symptoms combined during the week of follow-up than those in whom no organisms were detected [adjusted mean differences 0.204 (95% confidence interval 0.010 to 0.398) and 0.348 (0.098 to 0.598), respectively]. There were no differences in the duration of symptoms rated as moderate or severe between organism groups.

CONCLUSIONS

Differences in presenting symptoms and symptoms severity can be identified between patients with viruses and bacteria identified on throat swabs. The magnitude of these differences is unlikely to influence management. Most patients had mild symptoms at 7 days regardless of aetiology, which could inform patients about likely symptom duration.

Algorithm to predict poor outcomes from cough

Review of: Bruyndonckx R et al. Development of a prediction tool for patients presenting with acute cough in primary care: a prognostic study spanning six European countries. Br J Gen Pract 2018; 68: e342-50.