Determinants of Success After Metatarsal Head Resection for the Treatment of Neuropathic Diabetic Foot Ulcers.
- 作者列表："Kalantar Motamedi A","Kalantar Motamedi MA
:Metatarsal head resection (MHR) is an effective option for the treatment of nonhealing neuropathic diabetic foot ulcers. The present study aimed to identify factors that predict treatment success for neuropathic diabetic foot ulcers undergoing metatarsal head resection. In this prospective interventional case series, 30 consecutive diabetic patients with documented nonischemic neuropathic plantar diabetic foot ulcers beneath the metatarsal head who underwent MHR were included. The study endpoint was demographic indicators of early and late postoperative outcomes. Patients were followed up for 1 to 66 months (mean 37.6 months). Except for 1 patient, all subjects' wounds (96.6%) healed after metatarsal head resection within an average of 35 days. One of the operated patients (3.4%) suffered short-term complications; long-term complications occurred in 23.3% of the patients. One patient (3.4%) experienced ulcer recurrence, 3 patients (10%) developed wound infection, and transfer lesions occurred in 3 other patients (10%) during the follow-up period. Using 3 estimators including ordinary least squares (OLS), White's heteroscedastic standard errors, and bootstrapping procedure, we could not find any statistically significant demographic feature related to ulcer healing. Using regression modeling, we could not find any evidence for a role of age, sex, weight, height, BMI, duration of ulcer until MHR, and duration of diabetes mellitus (years since diabetes diagnosis) affecting the outcome of MHR. Hence, demographic features, duration of ulcer until MHR, and years with diabetes did not affect the outcome of MHR. In conclusion, the authors believe that MHR will have a high rate of success for neuropathic wound healing in this specific subset of patients regardless of demographic features, as long as there is no ischemia to impair healing by secondary intention.
: 跖骨头切除术 (MHR) 是治疗不愈合的神经性糖尿病足溃疡的有效选择。本研究旨在确定预测接受跖骨头切除术的神经性糖尿病足溃疡治疗成功的因素。在这个前瞻性介入病例系列中，纳入了 30 例连续糖尿病患者，这些患者在跖骨头下有记录的非缺血性神经性足底糖尿病足溃疡，他们接受了 MHR。研究终点是术后早期和晚期结局的人口统计学指标。随访 1 ~ 66 个月，平均 37.6 个月。除 1 例患者外，所有受试者的伤口 (96.6%) 均在平均 35 天内跖骨头切除后愈合。1 例手术患者 (3.4%) 发生短期并发症; 23.3% 的患者发生长期并发症。1 例 (3.4%) 患者发生溃疡复发，3 例 (10%) 患者发生伤口感染，其他 3 例 (10%) 患者在随访期间发生转移病灶。使用包括普通最小二乘法 (OLS) 、 White 的异方差标准误差和自举程序在内的 3 个估计量，我们找不到任何与溃疡愈合相关的统计学显著的人口统计学特征。使用回归模型，我们找不到年龄、性别、体重、身高、 BMI 、溃疡持续时间的任何证据，直到 MHR,和糖尿病的持续时间 (自糖尿病诊断以来的几年) 影响 MHR 的结果。因此，人口统计学特征、溃疡至 MHR 的持续时间和糖尿病患病年限不影响 MHR 的结局。总之，作者认为，无论人口统计学特征如何，MHR 在这一特定患者子集中的神经性伤口愈合成功率都很高,只要没有缺血损害愈合的次要意图。
METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.