Imran Kurt

Analgesic Effects of Gabapentin after Spinal Surgery

BackgroundA combination of opioid and nonopioid analgesic drugs may improve the quality of postoperative analgesia as well as reduce opioid requirements and their associated side effects. Studies have shown synergism between gabapentin and morphine in animal and human experiments and in the treatment of incisional pain. Therefore, the authors investigated, in a randomized, placebo-controlled, double-blind study, the effects of gabapentin on acute postoperative pain and morphine consumption in patients undergoing spinal surgery. MethodsAfter standard premedication, 25 patients in the control group received oral placebo, and 25 patients in the gabapentin group received 1,200 mg of gabapentin, 1 h before surgery in a randomized fashion. Anesthesia was induced with propofol and cisatracurium and was maintained with sevoflurane and remifentanil. The total intraoperative remifentanil consumption by each patient was noted. All patients postoperatively received patient-controlled analgesia with morphine (1 mg/ml) with an incremental dose of 2 mg, a lockout interval of 10 min, and a 4-h limit of 40 mg. The incremental dose was increased to 3 mg, and the 4-h limit to 50 mg, if analgesia was inadequate after 1 h. Patients were questioned for the first 1 h in the PACU and were later evaluated in the ward at 1, 2, 4, 6, 12, and 24 h. Pain scores, heart rate, oxygen saturation measured by pulse oximetry, mean blood pressure, respiratory rate, sedation, morphine use, and total dose of morphine were recorded. ResultsOverall, pain scores at 1, 2, and 4 h were significantly lower in the gabapentin group when compared with the placebo group. Total morphine consumption in the gabapentin group was 16.3 ± 8.9 mg (mean ± SD) versus 42.8 ± 10.9 mg in the placebo patients. The incidence of vomiting and urinary retention was significantly (P < 0.05) higher in the placebo group, but there was no difference in incidence of other adverse effects between the groups. ConclusionsPreoperative oral gabapentin decreased pain scores in the early postoperative period and postoperative morphine consumption in spinal surgery patients while decreasing some morphine-associated side effects.

Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease

In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAD and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron (MLP), radial basis function (RBF), and self-organizing feature maps (SOFM). Predictor variables were age, sex, family history of CAD, smoking status, diabetes mellitus, systemic hypertension, hypercholesterolemia, and body mass index (BMI). Performances of classification techniques were compared using ROC curve, Hierarchical Cluster Analysis (HCA), and Multidimensional Scaling (MDS). Areas under the ROC curves are 0.783, 0.753, 0.745, 0.721, and 0.675, respectively for MLP, LR, CART, RBF, and SOFM. MLP was found the best technique to predict presence of CAD in this data set, given its good classificatory performance. MLP, CART, LR, and RBF performed better than SOFM in predicting CAD in according to HCA and MDS.

Comparing classification techniques for predicting essential hypertension

Hypertension is a leading cause of heart disease and stroke. In this study, performance of classification techniques is compared in order to predict the risk of essential hypertension disease. A retrospective analysis was performed in 694 subjects (452 patients and 242 controls). We compared performances of three decision trees, four statistical algorithms, and two neural networks. Predictor variables were age, sex, family history of hypertension, smoking habits, lipoprotein (a), triglyceride, uric acid, total cholesterol, and body mass index (BMI). Classification techniques were grouped using hierarchical cluster analysis (HCA). The data points appeared to cluster in three groups. The first cluster included MLP and RBF. Furthermore CART which was more similar than other techniques linked this cluster. The second cluster included FDA/MARS (degree=1), LR and QUEST, but FDA/MARS (degree=1) and LR was more similar than QUEST. The third cluster included FDA/MARS (degree=2), CHAID and FDA, but FDA/MARS (degree=2) and CHAID was more similar than FDA. MLP and RBF which are one each of neural networks procedures, performed better than other techniques in predicting hypertension. QUEST had a lesser performance than other techniques.

Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients

Current evidence supports a clear association between clinical and pathologic factors and recurrence-free survival (RFS) in breast cancer patients. The Cox regression model is the most common tool for investigating simultaneously the influence of several factors on the survival time of patients. But it gives no estimate of the degree of separation of the different subgroups. We propose to analyze different decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) and use them additionally to the well-known Kaplan-Meier estimates to investigate the predictive power of these methods. Five hundred patients were included to the study. Two hundred and seventy-nine of them had complete data for prognostic factors and median follow-up is about 40.5 months. First, decision tree methods were analyzed for prognostic factors. Then, according to multidimensional scaling method C4.5 (error rate 0.2258 for training set and 0.3259 for cross-validation) performed slightly better than other methods in predicting risk factors for recurrence. Tumor size, age of menarche, hormonal therapy, histological grade and axillary nodal status are found that an important risk factors for the recurrence. Eight terminal nodes were found and stratified by Kaplan-Meier survival curves. Larger tumor size (>=4.4cm) and receiving no hormonal therapy in a small subgroup of patients were associated with worse prognosis. The five-year RFS is 71.3% in the whole patient population. The sensitivity, specificity and predictive rates calculated by C4.5 method were found 43.8%, 91% and 77.4%, respectively. In this study, C4.5 showed a better degree of separation. As a result, we recommend to use decision tree methods together with Kaplan-Meier analysis to determine risk factors and effect of this factors on survival.

Comparison of four different time series methods to forecast hepatitis A virus infection

Abstract Hepatitis A virus (HAV) infection is not a problem of only developing countries, but also of developed countries. In this study, we compared time series prediction capabilities of three artificial neural networks (ANN) algorithms (multi-layer perceptron (MLP), radial basis function (RBF), and time delay neural networks (TDNN)), and auto-regressive integrated moving average (ARIMA) model to HAV forecasting. To assess the effectiveness of these methods, we used in forecasting 13 years of time series (January 1992–June 2004) monthly records for HAV data, in Turkey. Results show that MLP is more accurate and performs better than RBF, TDNN and ARIMA model.

Predictive value of thyroid hormones on the first day in adult respiratory distress syndrome patients admitted to ICU : Comparison with SOFA and APACHE II scores.

BACKGROUND Thyroid hormone dysfunction could affect outcome and increase mortality in critical illness. Therefore, in a prospective, observational study we analyzed and compared the prognostic accuracy of free tri-iodothyronine (fT3), free thyroxine (fT4), thyroid-stimulating hormone (TSH), along with the APACHE II and SOFA scoring systems in predicting intensive care unit (ICU) mortality in critically ill patients. PATIENTS AND METHODS Physiology scores were calculated for the first 24 hours after ICU admission in 206 patients with acute respiratory distress syndrome. APACHE II and SOFA scores were employed to determine the initial severity of illness. Thyroid hormones were measured within the first 24 hours. Logistic regression models were created for APACHE II scores, SOFA scores, and thyroid hormone levels. The models predicted high- and low-risk subgroups. Models that showed a good fit were stratified by Kaplan-Meier survival curves. RESULTS There were 98 (47.6%) survivors and 108 (52.4%) non-survivors. The survivors had a lower APACHE II score (11.50 vs 15.82, P<0.0005), a lower SOFA score (6.06 vs 9.42, P<0.0005), a younger age (57 vs 70 years, P=0.008), a shorter ICU stay (13 vs 16 days, P=0.012), and a higher fT3 level (2.18 vs 1.72 pg/mL, P=0.002) than non-survivors. ICU survival was most closely predicted by a model that included age and fT3 and a model that included APACHE II and APACHE II*sex. CONCLUSION In critically ill patients, serum fT3 concentrations markedly decreased after ICU admission among non-survivors. According to our findings, fT3 levels might have additive discriminatory power to age, SOFA and APACHE II scores in predicting short-term mortality in ARDS patients admitted to ICU.

Comparison of dimension reduction methods using patient satisfaction data

Abstract In this study, we compared classical principal components analysis (PCA), generalized principal components analysis (GPCA), linear principal components analysis using neural networks (PCA-NN), and non-linear principal components analysis using neural networks (NLPCA-NN). Data were extracted from the patient satisfaction query with regard to the satisfaction of patients from hospital staff, which was applied in 2005 at the outpatient clinics of Trakya University Medical Faculty. We found that percentages of explained variance of principal components from PCA-NN and NLPCA-NN were highest for doctor, nurse, radiology technician, laboratory technician, and other staff using a patient satisfaction data set. Results show that methods using NN which have higher percentages of explained variances than classical methods could be used for dimension reduction.