Costs associated with informal health care pathway for patients with suspected Lyme borreliosis.

OBJECTIVES: To compare the direct and indirect medical costs for patients with suspected Lyme borreliosis according to whether or not they had used an informal care pathway. PATIENTS AND METHODS: We retraced the care pathways of participating patients by a prospective questionnaire survey and a retrospective analysis of care records. Direct and indirect costs were estimated using a micro-costing method from different perspectives. We compared the costs of patients who had consulted a "Lyme Doctor" (informal care pathway) with those who had only used the formal care pathway. Non-parametric tests were appraised the significance of the differences between the two groups of patients. RESULTS: Out of 103 eligible patients, 49 (including 12 having used an informal health care pathway) agreed to be investigated. Five expenditure items entirely borne by patients were significantly higher for patients following an informal care pathway: productivity loss (3041 ± 6580 vs 194 ± 1177 euros, p = 0.01), alternative therapies (3484 ± 7308 vs 369 ± 956 euros), biological tests sent abroad (571 ± 1415 vs 17 ± 92 euros, p < 0.01), self-medication (918 ± 1998 vs 133 ± 689, p = 0.02) and transport (3 094 ± 3456 vs 1 123 ± 1903p = 0.01). CONCLUSIONS: From the patient's standpoint, the informal care pathway involving consultation with a Lyme Doctor is far more expensive than the formal care pathway. More specifically, the patient has to bear the costs of alternative treatments and repeated, non-recommended examinations.

Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images.

BACKGROUND AND OBJECTIVE: Lyme disease which is one of the most common infectious vector-borne diseases manifests itself in most cases with erythema migrans (EM) skin lesions. Recent studies show that convolutional neural networks (CNNs) perform well to identify skin lesions from images. Lightweight CNN based pre-scanner applications for resource-constrained mobile devices can help users with early diagnosis of Lyme disease and prevent the transition to a severe late form thanks to appropriate antibiotic therapy. Also, resource-intensive CNN based robust computer applications can assist non-expert practitioners with an accurate diagnosis. The main objective of this study is to extensively analyze the effectiveness of CNNs for diagnosing Lyme disease from images and to find out the best CNN architectures considering resource constraints. METHODS: First, we created an EM dataset with the help of expert dermatologists from Clermont-Ferrand University Hospital Center of France. Second, we benchmarked this dataset for twenty-three CNN architectures customized from VGG, ResNet, DenseNet, MobileNet, Xception, NASNet, and EfficientNet architectures in terms of predictive performance, computational complexity, and statistical significance. Third, to improve the performance of the CNNs, we used custom transfer learning from ImageNet pre-trained models as well as pre-trained the CNNs with the skin lesion dataset HAM10000. Fourth, for model explainability, we utilized Gradient-weighted Class Activation Mapping to visualize the regions of input that are significant to the CNNs for making predictions. Fifth, we provided guidelines for model selection based on predictive performance and computational complexity. RESULTS: Customized ResNet50 architecture gave the best classification accuracy of 84.42% ±1.36, AUC of 0.9189±0.0115, precision of 83.1%±2.49, sensitivity of 87.93%±1.47, and specificity of 80.65%±3.59. A lightweight model customized from EfficientNetB0 also performed well with an accuracy of 83.13%±1.2, AUC of 0.9094±0.0129, precision of 82.83%±1.75, sensitivity of 85.21% ±3.91, and specificity of 80.89%±2.95. All the trained models are publicly available at https://dappem.limos.fr/download.html, which can be used by others for transfer learning and building pre-scanners for Lyme disease. CONCLUSION: Our study confirmed the effectiveness of even some lightweight CNNs for building Lyme disease pre-scanner mobile applications to assist people with an initial self-assessment and referring them to expert dermatologist for further diagnosis.

Comparison of microbiological results obtained from per-wound bone biopsies versus transcutaneous bone biopsies in diabetic foot osteomyelitis: a prospective cohort study.

Transcutaneous bone biopsy (TCB) is the gold standard for taking microbiological specimens in diabetic foot osteomyelitis (DFO), but this technique is not widely used in diabetic foot care centers. We aimed to evaluate the reliability of per-wound bone biopsy (PWB) cultures by comparing them with concomitant TCB cultures obtained through healthy skin. This is a prospective monocentric study including patients seen in consultation for clinical and radiological diabetic foot osteomyelitis with positive probe-bone tests between April 2015 and May 2018. Two bone biopsies were performed on each consenting patient: TCB through a cutaneous incision in healthy skin, and PWB, after careful debridement of the wound. A total of 46 paired cultures were available from 43 eligible patients. Overall, 16 (42%) of the PWB and TCB pairs had identical culture results, but the TCB cultures were sterile in 8 (17%) cases. For 38 paired cultures with positive TCB, the correlation between PWB results and TCB results was 58.4%. PWB revealed all microorganisms found in the transcutaneous specimen in 26/38 samples (68.5%). In patients with DFO, the culture results of specimens taken by per-wound biopsies did not correlate well with those obtained by TCB. PWB should be reserved for cases where the transcutaneous biopsy is sterile or not feasible.