| 1. |
- Pallon, Jon, et al.
(författare)
-
The use and usefulness of point-of-care tests in patients with pharyngotonsillitis - an observational study in primary health care
- 2024
-
Ingår i: BMC Primary Care. - : BioMed Central (BMC). - 2731-4553. ; 25:1
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Rapid antigen detection tests (RADT) for Group A streptococci (GAS) and point-of-care tests for C-reactive protein (CRP) are commonly used in patients with pharyngotonsillitis in Sweden and Denmark although CRP testing is not supported by guidelines. We aimed to describe (1) the proportion of patients tested with RADT and/or CRP, (2) the relation between test results and antibiotic prescribing, and (3) the association between CRP level and microbial aetiology.METHODS: We used a post-hoc-analysis of data collected in primary health care in a prospective aetiological study of 220 patients 15-45 years old diagnosed with pharyngotonsillitis. The outcomes of RADTs and CRP tests were related to antibiotic prescribing and microbial aetiology.RESULTS: A RADT was used in 94% of the patients. A CRP test was used in 50% of the patients but more commonly in those with a negative RADT (59%) than in those with a positive RADT (38%) (p = 0.005). Most (74%) CRP tests were used in patients with a negative RADT. Antibiotic prescribing differed greatly between patients with a positive RADT (96%) and patients with a negative RADT (17%) (p < 0.001). In patients with a negative RADT, there was a positive association between CRP value and antibiotic prescribing (OR 1.05; 95% CI 1.02-1.07; p < 0.001). Patients with CRP values ≤ 30 mg/l were seldomly prescribed antibiotics. Patients with GAS in culture had the highest median CRP (46 mg/l), which was higher than in patients without GAS (8 mg/l; p < 0.001). However, the positive predictive value for GAS never exceeded 0.60 (95% CI 0.31-0.83) at the investigated CRP levels.CONCLUSIONS: The widespread use of tests is a major deviation from national guidelines. Most CRP tests were used in patients with a negative RADT, suggesting a belief in the added value of a CRP test, and the CRP result seemed to influence antibiotic prescribing. However, as an aetiological test, CRP is not useful for predicting GAS.
|
|
| 2. |
- Papachristou, Panagiotis, et al.
(författare)
-
Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial
- 2024
-
Ingår i: BRITISH JOURNAL OF DERMATOLOGY. - : OXFORD UNIV PRESS. - 0007-0963 .- 1365-2133.
-
Tidskriftsartikel (refereegranskat)abstract
- Background Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics.Objectives To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion.Methods This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data.Results In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively.Conclusions The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma. We investigated the diagnostic performance of an AI-based decision support in the form of a mobile app to detect melanoma when used by primary care physicians. The app proved to have high levels of diagnostic accuracy in distinguishing melanomas from other skin lesions. We conclude that it appears to be a potentially valuable diagnostic aid for the primary care physician in the assessment of skin lesions of concern.
|
|
