Botulinum Toxin-A for Painful Diabetic Neuropathy: A Meta-Analysis.

OBJECTIVES: Painful diabetic neuropathy (PDN) is a debilitating complication of diabetes that greatly affects the quality of life of those afflicted. There are many treatment options for neuropathic pain. Recent studies show a promising analgesic effect using botulinum toxin-A (BTX-A) for neuropathic pain. METHODS: This article is a meta-analysis of two studies using BTX-A in the treatment of neuropathic pain. Electronic searches of MEDLINE/PubMed, EMBASE, and Cochrane Libraries using the terms "botulinum neurotoxin" and "neuropathic pain" were conducted. Only class I and class II therapeutic trials, as classified by the American Academy of Neurology were included. The primary outcome measured was the difference in visual analogue scale (VAS) from pre-intervention and post-intervention after 1 month. Data were analyzed for biases and heterogeneity following Cochrane and PRISMA guidelines. RESULTS: Two studies on PDN were analyzed in the meta-analysis showing improvement of 1.96 VAS points (95% CI, -3.09 to -0.84; Z score = 3.43, P < 0.001) following treatment with BTX-A. This corresponds to clinically significant improvement of "minimum change in pain." The adverse effects of infection at injection site was not statistically significant (P = 0.49). BTX-A may be effective for PDN. CONCLUSION: Tests for significance, low overall risk of bias, and almost no statistical heterogeneity suggests that there is a correlation between BTX-A and improvement of pain scores in PDN. Further large-scale controlled trials are needed.

Estimating kidney depth among Filipino adults using Tonnesen, Taylor, and Inoue algorithms

Nuclear medicine departments use the camera-based method for determining glomerular filtration rate (GFR) with 99mTc-DPTA. Camera based techniques provide a fast and convenient way of determining GFR with excellent reproducubility however its accuracy remains in question. The accuracy of camera-based renal scintigraphy depends on an attenuation correlation from estimating for renal depth and an attenuation correction from estimating for renal depth and an attenuation coefficient. Algorithms were formulated by Tonnesen, Taylor, and Inoue to calculate the estimated renal depth through multiple stepwise linear regression analysis. The goal of this study was to analyze the accuracy of these algorithms in Filipino patients. Renal depth was determined from CT scans of 41 consecutive patients. We calculated the mean absolute error of the estimated kidney depths and compared them with each other to determine the accuracy of each algorithm. The Tonnesen algorithm had the highest mean absolute error of 1.25 cm plus minus 0.88 cm in the left kidney estimates while the Taylor algorithm had the lowest mean absolute error of 0.81 cm plus minus 0.58 cm in the left kidney estimate and 1.04 cm plus minus 0.82 cm in the right kidney estimate (p-value = 0.01 left kidney estimates and p-value = 0.02 right kidney estimates). The Tonnesen algorithm was the least accurate in predicting kidney depth. There was no significant difference between the Taylor and Inoue algorithms, which were more accurate in predicting kidney depth

Diagnostic accuracy of breast specific gamma imaging in the detection of breast cancer in high risk women: A meta-analysis

Mammography remains the mainstay of screening for primary breast cancer, however, some limitations persists in women with dense breast, in its inability to differentiate a benign from a malignant lesion, and in its heavy reliance on the radiologists' skill. Breast specific gamma imaging, which evaluates the functional images rather than anatomic images seen in mammography, addresses these limitations and can be a potential adjunct in screening for breast cancer. This meta-analysis aims to determine the diagnostic accuracy of breast specific gamma imaging for diagnosing breast cancer in women who are high risk. We searched PUBMED (1999-2009) and Google Scholar (1999-2009) for diagnostic accuracy studies that compared breast specific gamma imaging with histopathologic diagnosis of breast cancer in high risk women. Citation searches and screening of references of included studies were conducted. Two authors searched citations that correlated with the criteria using a data collection form. The methodological quality was then assessed by three authors using the QUADAS method. HSROC meta-analytical tool was used to estimate summary ROC curves. Four studies with 360 participants and 411 lesions were included. Breast specific gamma imaging has a high sensitivity (84%-97%) and specificity (60%-86%). SROC shows the included studies have high overall accuracy. Breast specific gamma imaging has high diagnostic accuracy in detecting breast cancer in women at high risk.