Application of a Diabetic Foot Smart APP in the measurement of diabetic foot ulcers.

AIMS: In the early stage, we developed an intelligent measurement APP for diabetic foot ulcers, named Diabetic Foot Smart APP. This study aimed to validate the APP in the measurement of ulcer area for diabetic foot ulcer (DFU). METHODS: We selected 150 DFU images to measure the ulcer areas using three assessment tools: the Smart APP software package, the ruler method, and the gold standard Image J software, and compared the measurement results and measurement time of the three tools. The intra-rater and inter-rater reliability were described by Pearson correlation coefficient, intra-group correlation coefficient, and coefficient of variation. RESULTS: The Image J software showed a median ulcer area of 4.02 cm2, with a mean measurement time of 66.37 ± 7.95 s. The ruler method showed a median ulcer area of 5.14 cm2, with a mean measurement time of 171.47 ± 46.43 s. The APP software showed a median ulcer area of 3.70 cm2, with a mean measurement time of 38.25 ± 6.81 s. There were significant differences between the ruler method and the golden standard Image J software (Z = -4.123, p < 0.05), but no significant difference between the APP software and the Image J software (Z = 1.103, p > 0.05). The APP software also showed good inter-rater reliability and intra-rater reliability, with both reaching 0.99. CONCLUSION: The Diabetic Foot Smart APP is a fast and reliable measurement tool with high measurement accuracy that can be easily used in clinical practice for the measurement of ulcer areas of DFU. TRIAL REGISTRATION: Chinese clinical trial registration number: ChiCTR2100047210.

Screening behaviors for diabetic foot risk and their influencing factors among general practitioners: a cross-sectional study in Changsha, China.

BACKGROUND: Diabetic foot is a serious complication of diabetes with a high disability and mortality rate, which can be prevented by early screening. General practitioners play an essential role in diabetic foot risk screening, yet the screening behaviors of general practitioners have rarely been studied in primary care settings. This study aimed to investigate foot risk screening behaviors and analyze their influencing factors among general practitioners. METHODS: A cross-sectional study was conducted among 844 general practitioners from 78 community health centers in Changsha, China. A self-designed and validated questionnaire was used to assess the general practitioner's cognition, attitude, and behaviors on performing diabetic foot risk screening. Multivariate linear regression was conducted to investigate the influencing factors of risk screening behaviors. RESULTS: The average score of diabetic foot risk screening behaviors among the general practitioners was 61.53 ± 14.69, and 271 (32.1%) always or frequently performed foot risk screening for diabetic patients. Higher training frequency (ß = 3.197, p < 0.001), higher screening cognition (ß = 2.947, p < 0.001), and more positive screening attitude (ß = 4.564, p < 0.001) were associated with more diabetic foot risk screening behaviors, while limited time and energy (ß=-5.184, p < 0.001) and lack of screening tools (ß=-6.226, p < 0.001) were associated with fewer diabetic foot screening behaviors. CONCLUSION: The score of risk screening behaviors for the diabetic foot of general practitioners in Changsha was at a medium level. General practitioners' diabetic foot risk screening behaviors may be improved through strengthening training on relevant guidelines and evidence-based screening techniques, improving cognition and attitude towards foot risk screening among general practitioners, provision of more general practitioners or nurse practitioners, and user-friendly screening tools.

Health Economic Indicators in Patients With Diabetic Foot: Outpatient Multidisciplinary Management.

The purpose of this study is to investigate the influence of an outpatient multidisciplinary diagnosis and treatment model on the health economic indices of diabetic foot patients. We included 142 diabetic foot patients who received treatment in 2 target hospitals from January to April 2018 in this prospective cohort study. According to their exposure factors, the patients were divided into a MDT group and a control group, with 71 patients in each group. The patients' baseline data were collected. The follow-up period was 12 months; all patients were followed up to April 30, 2019. Health economic indicators were collected when the patients were discharged from the hospital. The prognosis of each group was followed every month. If a wound healed, a major amputation occurred, or the patient died within 12 months, the follow-up was stopped. A total of 129 patients were followed, and their baseline data were comparable. During the follow-up period, the healing rate of the 2 groups was significantly different (P = .034). The healing rate of Wagner grade 4 patients was significantly better than Wagner grade 2 and grade 3 patients (P = .001). Health economic indicators demonstrated significant differences in bed waiting time (P = .038), transfer time (P = .001), surgery waiting time (P = .003), length of hospital stay (P = .047), and hospitalization expenses (P = .011). In conclusion, an outpatient multidisciplinary diagnosis and treatment model for diabetic foot can support cost-effective patient management.

Construction and verification of an intelligent measurement model for diabetic foot ulcer. / ä¸€ç§ç³–å°¿ç— è¶³æºƒç–¡æ™ºèƒ½æµ‹é‡æ¨¡åž‹çš„æž„å»ºä¸ŽéªŒè¯.

OBJECTIVES: The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification. METHODS: The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model. RESULTS: This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm. CONCLUSIONS: The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.

Application of the Ipswich Touch Test for diabetic peripheral neuropathy screening: a systematic review and meta-analysis.

OBJECTIVE: Diabetic peripheral neuropathy (DPN) is one of the most important risk factors of diabetic foot ulcers, and early screening and treatment of DPN are crucial. The Ipswich Touch Test (IPTT) is a new method for screening for DPN and, compared with traditional methods, is more simple to operate and requires no equipment. However, the screening accuracy of IPTT in patients with DPN has not been well characterised. We aim to conduct a systematic review and meta-analysis to characterise the sensitivity and specificity of IPTT compared with traditional methods and to understand the potential screening value of IPTT. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang, Chinese Biomedical Literature Database up to 16 April 2020. METHODS: Stata V.15.1 software was used for analysis, and the screening value of IPTT in DPN was described using 10 g monofilament (10g-MF), neuropathy disability scores (NDS), Pin prick, 128 Hz tuning fork, and ankle reflex as reference standards. Sensitivity, specificity and other measures of accuracy of IPTT for screening DPN were pooled based on a quality effects model. The protocol was registered with PROSPERO (42020168420). RESULTS: Of the 441 records retrieved, 7 studies were evaluated for the screening value of IPTT. Five studies with 10g-MF as the reference standard were included in the meta-analysis, and the pooled sensitivity and specificity were 0.77 (95％CI 0.69-0.84) and 0.96(95％CI 0.93-0.98), respectively, and the area under curve was 0.897. Compared with vibration perception threshold, IPTT showed a sensitivity between 0.76 and 1, and a specificity between 0.90 and 0.97. Compared with NDS, IPTT showed a sensitivity between 0.53 and 1, and a specificity between 0.90 and 0.97. Compared with Pin prick, IPTT showed a sensitivity and specificity of 0.8 and 0.88, respectively. Compared with 128 Hz tuning fork, IPTT showed a sensitivity and specificity of 0.4 and 0.27, respectively. Compared with ankle reflex, IPTT had a sensitivity of 0.2 and a specificity of 0.97. CONCLUSIONS: IPTT shows a high degree of agreement with other commonly used screening tools for DPN screening. It can be used clinically, especially in remote areas and in primary medical institutions, and by self-monitoring patients. More high-quality studies are needed to assess and promote more effective screening practices. PROSPERO REGISTRATION NUMBER: Registration Number is CRD (42020168420).

Knowledge, attitude, and practice survey regarding coronavirus disease 2019 among residents in Hunan Province. / æ¹–å—çœå± æ°‘2019å† çŠ¶ç— æ¯’ç— çŸ¥è¯†ã€æ€åº¦ä¸Žåº”å¯¹è¡Œä¸º.

OBJECTIVES: To evaluate residents' knowledge, attitude and behavior towards coronavirus disease 2019 (COVID-19) in Hunan Province, and to explore the factors influencing behaviors. METHODS: A self-designed questionnaire was used to conduct an online survey for 4 139 Hunan residents. The contents included general population information, residents' knowledge, attitude and practice to COVID-19. RESULTS: Mean scores of knowledge, attitude, and behavior were 29.82±3.16, 6.71±1.12, and 14.93±1.45, respectively. Residents had the highest score of major symptoms of COVID-19 (3.96±0.39), but the lowest was the main transmission routes (3.47±0.89). A total of 22.68% of the residents were very or relatively afraid of the outbreak, but 95.22% of the residents had confidence in defeating COVID-19. In behavior dimension, "handling of suspicious symptoms" had the lowest score (3.58±0.75). The behavior implementation rate of "keep the surfaces of household items clean" (80.50%), "doing more exercise, reasonable diet, working and resting regularly" (84.59%), and "avoid hand contacting with eyes, mouth or nose" (89.51%) were relatively low. Pearson correlation coefficient showed that the knowledge, attitude, and practices score were correlated with each other (knowledge vs behavior: r=0.366; knowledge vs attitude: r=0.041; attitude vs behavior: r=0.100; all P<0.05). Multiple linear regression analysis showed that the knowledge, attitude and behavior on COVID-19 were mostly influenced by education background (all P<0.05), and the independent factors affecting behavior included knowledge and attitude, gender, permanent residence, education background (all P<0.05). CONCLUSIONS: Residents in Hunan Province have a good knowledge, attitude, and behavior to COVID-19. Nevertheless there are still weak links to be improved in all dimensions. It is necessary to strengthen knowledge and behavior of family protection, and care for residents' psychological health, especially persons with low education degree, male and rural residents.

[Clinical analysis for patients with diabetic foot among multiple centers in China].

OBJECTIVE: To explore the clinical characteristics, risk factors and factors affecting the severity of the disease in patients with diabetic foot at the current stage through a multi-center cross-sectional survey.
 Methods: Clinical data of 326 patients with diabetic foot (205 males and 121 females) from 13 general hospitals nationwide were collected from October to November 2017 using a unified clinical data collection table. The clinical characteristics were analyzed, and the influential factors for severe diabetic foot were analyzed by logistic regression analysis.
 Results: Among 326 patients with diabetic foot, 68.4% of the patients were more than 60 years old, and 60.1% of the patients received primary or junior high school education; 96.3% of the patients developed Type 2 diabetes; 80.1% of patients had glycated hemoglobin (HbA1c)≥7%; 60.1% of patients suffered dyslipidemia. Improper wearing of footwear (38.5%) is the main cause of diabetic foot. Diabetic neuropathy (76.7%), diabetic retinopathy (62.3%) and lower limb vascular disease (57.4%) were the most common complications. Logistic regression analysis showed that diabetic nephropathy, diabetic lower extremity vascular disease, and HbA1c levels were independent risk factors for severe diabetic foot, and receiving foot care education can be regarded as a protective factor.
 Conclusion: The diabetic foot occurs mostly in male patients, and Type 2 diabetes with older age, lower education level, poor glycemic control and dyslipidemia are the risk factors. Diabetic nephropathy, diabetic lower extremity vascular disease, HbA1c, and receiving foot care education are independent influential factors for the severity of diabetic foot.