Foot complications are a well known factor which contribute to the morbidity of diabetes and increases the chance of amputation. A total of 126 consecutive diabetic patients were evaluated by diabetic foot screening. Forty-one patients showed an impaired protective sense when tested with Semmes-Weinstein monofilament 5.07 (10 g), and 92% of them showed peripheral polyneuropathy in nerve conduction study (NCS). The mean vibration score of the Rydel-Seiffer graduated tuning fork in patients with peripheral polyneuropathy in nerve conduction (NCV) study was 5.38+/-2.0, which was significantly different from that of patients without polyneuropathy in NCS. Among the deformities identified on examination, callus, corn, and hallux valgus were the greatest. While checking the ankle/ brachial index (ABI), we also evaluated the integrity of vasculature in the lower extremities. After extensive evaluation, we classified the patients into eight groups (category 0,1,2,3,4A,4B,5,6). The result of this study suggested that the Semmes-Weinstein monofilament test, Rydel-Seiffer graduated tuning fork test, and checking the ankle/brachial index were simple techniques for evaluating pathologic change in the diabetic foot by office screening, and that this screening based on treatment-oriented classification helps to reduce pedal complications in a diabetic population
Diabetic Angiopathies/diagnosis ; Diabetic Angiopathies/complications ; Diabetic Foot/physiopathology ; Diabetic Foot/diagnosis* ; Diabetic Foot/classification ; Diabetic Neuropathies/diagnosis ; Diabetic Neuropathies/complications ; Female ; Foot/physiopathology ; Human ; Male ; Mass Screening ; Middle Age ; Podiatry/methods ; Sensory Thresholds

A 59-year-old man was admitted with numbness, pain, and a tingling sensation in both lower legs. He was initially diagnosed with diabetic peripheral neuropathy based on a symptom questionnaire and a quantitative sensory test. Despite symptomatic treatment of diabetic neuropathy, he complained of worsening sensory symptoms and additional motor weakness in both lower extremities. As the motor weakness of both extremities became more aggravated over time, brain and spine imaging tests and a nerve conduction test were performed. The nerve conduction study revealed motor and sensory axonal neuropathy. In his cerebrospinal analysis, albumino-cytologic dissociation, which is compatible to the Gillian-Barre syndrome, was found. Cerebrospinal fluid analysis showed albumino-cytologic dissociation. He was treated with intravenous immunoglobulin and his neurologic deficits were gradually improved.
Diabetic Neuropathies/*complications/*diagnosis ; Electromyography ; Guillain-Barre Syndrome/*complications/*diagnosis ; Humans ; Male ; Middle Aged ; Neural Conduction ; Neurologic Examination

BACKGROUNDVarious methods can be applied to build predictive models for the clinical data with binary outcome variable. This research aims to explore the process of constructing common predictive models, Logistic regression (LR), decision tree (DT) and multilayer perceptron (MLP), as well as focus on specific details when applying the methods mentioned above: what preconditions should be satisfied, how to set parameters of the model, how to screen variables and build accuracy models quickly and efficiently, and how to assess the generalization ability (that is, prediction performance) reliably by Monte Carlo method in the case of small sample size.

METHODSAll the 274 patients (include 137 type 2 diabetes mellitus with diabetic peripheral neuropathy and 137 type 2 diabetes mellitus without diabetic peripheral neuropathy) from the Metabolic Disease Hospital in Tianjin participated in the study. There were 30 variables such as sex, age, glycosylated hemoglobin, etc. On account of small sample size, the classification and regression tree (CART) with the chi-squared automatic interaction detector tree (CHAID) were combined by means of the 100 times 5-7 fold stratified cross-validation to build DT. The MLP was constructed by Schwarz Bayes Criterion to choose the number of hidden layers and hidden layer units, alone with levenberg-marquardt (L-M) optimization algorithm, weight decay and preliminary training method. Subsequently, LR was applied by the best subset method with the Akaike Information Criterion (AIC) to make the best used of information and avoid overfitting. Eventually, a 10 to 100 times 3-10 fold stratified cross-validation method was used to compare the generalization ability of DT, MLP and LR in view of the areas under the receiver operating characteristic (ROC) curves (AUC).

RESULTSThe AUC of DT, MLP and LR were 0.8863, 0.8536 and 0.8802, respectively. As the larger the AUC of a specific prediction model is, the higher diagnostic ability presents, MLP performed optimally, and then followed by LR and DT in terms of 10-100 times 2-10 fold stratified cross-validation in our study. Neural network model is a preferred option for the data. However, the best subset of multiple LR would be a better choice in view of efficiency and accuracy.

CONCLUSIONWhen dealing with data from small size sample, multiple independent variables and a dichotomous outcome variable, more strategies and statistical techniques (such as AIC criteria, L-M optimization algorithm, the best subset, etc.) should be considered to build a forecast model and some available methods (such as cross-validation, AUC, etc.) could be used for evaluation.

Case-Control Studies ; Decision Trees ; Diabetes Mellitus, Type 2 ; complications ; Diabetic Neuropathies ; diagnosis ; etiology ; Humans ; Logistic Models

OBJECTIVE: To evaluate the application value of Toronto clinical scoring system (TCSS) and its grading of neuropathy for diabetic peripheral neuropathy (DPN), and to explore the relationship between TCSS grading of neuropathy and the grading of diabetic nephropathy and diabetic retinopathy. METHODS: A total of 209 patients of Type 2 diabtes (T2DM) underwent TCSS. Taking electrophysiological examination as a gold standard for diagnosing DPN, We compared the results of TCSS score > or = 6 with electrophysiological examination, and tried to select the optimal cut-off points of TCSS. RESULTS: The corresponding accuracy, sensitivity, and specificity of TCSS score > or = 6 were 76.6%, 77.2%, and 75.6%, respectively.The Youden index and Kappa were 0.53 and 0.52, which implied TCSS score > or = 6 had a moderate consistency with electrophysiological examination. There was a linear positive correlation between TCSS grading of neuropathy and the grading of diabetic nephropathy and diabetic retinopathy (P<0.05). The optimal cut-off point was 5 or 6 among these patients. CONCLUSION TCSS is reliable in diagnosing DPN and its grading of neuropathy has clinical value.
Adult ; Aged ; Diabetes Mellitus, Type 2 ; complications ; Diabetic Neuropathies ; diagnosis ; physiopathology ; Electrophysiology ; Female ; Humans ; Male ; Middle Aged ; Neurologic Examination ; methods

Diabetes mellitus (DM) can accompany many musculoskeletal (MSK) diseases. It is difficult to distinguish the DM-related MSK diseases based on clinical symptoms alone. Sonography is frequently used as a first imaging study for these MSK symptoms and is helpful to differentiate the various DM-related MSK diseases. This pictorial essay focuses on sonographic findings of various MSK diseases that can occur in diabetic patients.
Adult ; Cellulitis/ultrasonography ; Diabetes Mellitus, Type 2/*complications ; Diabetic Neuropathies/ultrasonography ; Female ; Humans ; Male ; Musculoskeletal Diseases/complications/*diagnosis/ultrasonography ; Pyomyositis/microbiology/ultrasonography ; Tenosynovitis/microbiology/ultrasonography ; Vascular Diseases/ultrasonography

Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes and is diagnosed as the presence of symptoms and/or signs of peripheral nerve dysfunction in people with diabetes. The prevalence of DPN was reported at 33.5% of type 2 diabetes patients by the Korean diabetes neuropathy study group. Early diagnosis is recommended to prevent diabetic foot ulcers, amputation, or disability. A questionnaire asking about symptoms and neurologic examination of feet is commonly used as a screening tool. However, complete diagnostic tests for DPN are not well established because of incomplete understanding of the pathogenetic mechanisms leading to the nerve injury, the various clinical manifestations, and the unclear natural history. Therefore, DPN has not been paid sufficient attention by clinicians. The roles of glycemic control and management of cardiovascular risk factors in the prevention and treatment of neuropathic complications are well known. Pathogenetically oriented or symptomatic agents are other options, though such treatments do not always produce a satisfactory outcome. Therefore, DPN remains a challenge for physicians to screen, diagnose, and treat. There have been recent advances in understanding the mechanisms underlying DPN and in the development of new diagnostic modalities and treatments. In this review, diagnosis and management of DPN will be discussed.
Amputation ; Diabetes Complications ; Diabetic Foot ; Diabetic Neuropathies ; Diagnosis* ; Diagnostic Tests, Routine ; Early Diagnosis ; Foot ; Humans ; Mass Screening ; Natural History ; Neurologic Examination ; Peripheral Nerves ; Peripheral Nervous System Diseases* ; Prevalence ; Risk Factors ; Ulcer

The present study was done to validate the two-site Semmes-Weinstein (SW) monofilament test in identifying patients at risk of lower-extremity complications in clinical setting. The SW monofilament test and nerve conduction study were conducted on type 2 diabetic patients (n=37) at Pusan National University Hospital in Korea. As the duration of diabetes mellitus was longer, neuropathy identified by nerve conduction study and complications of diabetes were more severe (p<0.01). The number of sites unable to perceive SW monofilament (p<0.001) was larger in patients with lower-extremity neuropathy symptoms than those without symptoms. Sensitivity and specificity at two sites (the third and fifth metatarsal head sites) were 93% and 100%, respectively. In conclusion, the two-site SW monofilament test was a sensitive, specific, simple, and inexpensive screening tool for identifying diabetic peripheral neuropathy in clinical setting.
Aged ; Comparative Study ; Diabetes Mellitus, Type II/complications* ; Diabetic Neuropathies/diagnosis* ; Female ; Human ; Male ; Middle Aged ; Neural Conduction ; Neurologic Examination/instrumentation* ; Neurologic Examination/methods ; Pressure ; Sensation Disorders/diagnosis* ; Sensation Disorders/etiology ; Sensitivity and Specificity ; Touch

OBJECTIVE: To evaluate the diagnostic value of vibration perception threshold (VPT) in diabetic peripheral neuropathy (DPN) by the receiver operating characteristic curve (ROC) and to establish its cut-off threshold. METHODS: All patients had the VPT examination and nerve conduction velocity (NCV) examination. NCV examination showed that 283 patients with Type 2 diabetes were divided into a DPN group (n=151) and an NDPN group (n=132). The VPT diagnosis was evaluated by Youden index, sensitivity, specificity and the area under ROC curve. The best cut-off threshold was defined by the Youden index. RESULTS: 1) The NCV was significantly slower, while the VPT was higher in the DPN group than those in the NDPN group (both P values <0.05). 2) The VPT and NCV of both sides of the limb had no difference in all patients. 3) With NCV as the golden diagnosis criterion, the area under ROC of VPT was 0.707, the best cut-off threshold was 10.54 V, the sensitivity was 0.596, the specificity was 0.848, and the Youden index was 0.445. 4) The diagnosis ratio of NCV combined with VPT was 60.4%, significantly higher than that of NCV alone (P<0.05). CONCLUSION Compared with NCV examination, VPT has good diagnostic value for DPN. The best cut-off value is 10.54 V.
Adult ; Diabetes Mellitus, Type 2 ; complications ; Diabetic Neuropathies ; diagnosis ; Female ; Humans ; Male ; Middle Aged ; Neural Conduction ; physiology ; Perception ; Peripheral Nervous System Diseases ; diagnosis ; ROC Curve ; Sensory Thresholds ; physiology ; Vibration