The Role of Pan-Immune-Inflammation Value in Determining the Severity of Coronary Artery Disease in NSTEMI Patients.

BACKGROUND: Even though medication and interventional therapy have improved the death rate for non-ST elevation myocardial infarction (NSTEMI) patients, these patients still have a substantial residual risk of cardiovascular events. Early identification of high-risk individuals is critical for improving prognosis, especially in this patient group. The focus of recent research has switched to finding new related indicators that can help distinguish high-risk patients. For this purpose, we examined the relationship between the pan-immune-inflammation value (PIV) and the severity of coronary artery disease (CAD) defined by the SYNTAX score (SxS) in NSTEMI patients. METHODS: Based on the SxS, CAD patients were split into three groups. To evaluate the risk variables of CAD, multivariate logistic analysis was employed. RESULTS: The PIV (odds ratio: 1.003; 95% CI: 1.001-1.005; p = 0.005) was found to be an independent predictor of a high SxS in the multivariate logistic regression analysis. Additionally, there was a positive association between the PIV and SxS (r: 0.68; p < 0.001). The PIV predicted the severe coronary lesion in the receiver-operating characteristic curve analysis with a sensitivity of 91% and specificity of 81.1%, using an appropriate cutoff value of 568.2. CONCLUSIONS: In patients with non-STEMI, the PIV, a cheap and easily measured laboratory variable, was substantially correlated with a high SxS and the severity of CAD.

Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma.

BACKGROUND: In this study, we aimed to compare the success rates of computed tomography image-based artificial intelligence models and magnetic resonance imaging in the diagnosis of preoperative cholesteatoma. METHODS: The files of 75 patients who underwent tympanomastoid surgery with the diagnosis of chronic otitis media between January 2010 and January 2021 in our clinic were reviewed retrospectively. The patients were classified into the chronic otitis group without cholesteatoma (n=34) and the chronic otitis group with cholesteatoma (n=41) according to the presence of cholesteatoma at surgery. A dataset was created from the preoperative computed tomography images of the patients. In this dataset, the success rates of artificial intelligence in the diagnosis of cholesteatoma were determined by using the most frequently used artificial intelligence models in the literature. In addition, preoperative MRI were evaluated and the success rates were compared. RESULTS: Among the artificial intelligence architectures used in the paper, the lowest result was obtained in MobileNetV2 with an accuracy of 83.30%, while the highest result was obtained in DenseNet201 with an accuracy of 90.99%. In our paper, the specificity of preoperative magnetic resonance imaging in the diagnosis of cholesteatoma was 88.23% and the sensitivity was 87.80%. CONCLUSION: In this study, we showed that artificial intelligence can be used with similar reliability to magnetic resonance imaging in the diagnosis of cholesteatoma. This is the first study that, to our knowledge, compares magnetic resonance imaging with artificial intelligence models for the purpose of identifying preoperative cholesteatomas.

The current state and future of mobile security in the light of the recent mobile security threat reports.

Smartphones have become small computers that meet many of our needs, from e-mail and banking transactions to communication and social media use. In line with these attractive functions, the use of smartphones has greatly increased over the years. One of the most important features of these mobile devices is that they offer users many mobile applications that they can install. However, hacker attacks and the spread of malware have also increased. Today, current mobile malware detection and defense technologies are still inadequate. Mobile security is not only directly related to the operating system and used device but also related with communication over the internet, data encryption, data summarization, and users' privacy awareness. The main aim and contribution of this study are to collect the current state of mobile security and highlight the future of mobile security in light of the recent mobile security threat reports. The studies in the field of malware, attack types, and security vulnerabilities concerning the usage of smartphones were analyzed. The malware detection techniques were analyzed into two categories: signature-based and machine learning (behavior detection)-based techniques. Additionally, the current threats and prevention methods were described. Finally, a future direction is highlighted in the light of the current mobile security reports.

A review of 10 × 10 and 20 × 20 grid-type wind turbine placement problems solving by metaheuristics.

Wind energy is the most important renewable energy source produced by wind turbines. The optimal placement of wind turbines is a challenging binary optimization problem. Maximizing production capacity and minimizing the number of turbines (accordingly minimizing the installation cost) are the main objectives. Metaheuristic algorithms can solve binary optimization problems with optimal or near-optimal solutions. In the literature, the wind turbine placement problem (WTPP) is solved by metaheuristic algorithms from 1994 to 2020. In this work, a literature review of solving 10 × 10 and 20 × 20 grid-type WTPPs with metaheuristic algorithms is conducted. Forty-six different papers were deeply discussed and presented all the experimental results to shed light on future studies for presenting more robust metaheuristics for solving WTPPs. The key results of this review are precaution against false comparisons; presenting the current experimental results; determining the comparison parameters; and demonstrating the benchmark problem clearly. Future directions were clearly spotlighted for practitioners and researchers and new research topics for potential studies are also presented.

Thrombospondin 1 and Nuclear Factor Kappa B Signaling Pathways in Non-alcoholic Fatty Liver Disease.

AIM: We aimed to evaluate the circulating thrombospondin-1 (TSP-1) and nuclear factor kappa B (NF-κB) in nonalcoholic fatty liver disease (NAFLD) in order to integrate these signaling pathways in the inflammatory and fibrogenic processes of this liver disorder. METHODS: Ninety-five NAFLD patients were recruited in the study. The study also included 83 age-sex matched healthy controls. RESULTS: The number of patients with metabolic syndrome (MetS) criteria was 57 (60%). TSP-1 level was found to be statistically significantly lower in the NAFLD group compared to the control group (p=0.037). However, NF-κB level was found to be significantly higher in the NAFLD group compared to the control group (p=0.004). There was a significant negative correlation between plasma TSP-1 levels with glucose (r=-0.235, p=0.022), alanine aminotransferase (r=-0.261, p=0.011) and aspartate transaminase (r=-0.328, p=0.001) levels. In addition, a significant negative correlation was found between plasma TSP-1 and NF-κB levels (r=-0.729, p<0.001). CONCLUSIONS: Our results suggest a close relationship between increased NF-κB and reduced TSP-1 in NAFLD. TSP-1 and NF-κB signaling pathways might have a role in the inflammatory and fibrogenic processes. Furthermore, they may be used as a noninvasive marker and could assist as a therapeutic target for NAFLD.

Prediction of Pentacam image after corneal cross-linking by linear interpolation technique and U-NET based 2D regression model.

Keratoconus is a common corneal disease that causes vision loss. In order to prevent the progression of the disease, the corneal cross-linking (CXL) treatment is applied. The follow-up of keratoconus after treatment is essential to predict the course of the disease and possible changes in the treatment. In this paper, a deep learning-based 2D regression method is proposed to predict the postoperative Pentacam map images of CXL-treated patients. New images are obtained by the linear interpolation augmentation method from the Pentacam images obtained before and after the CXL treatment. Augmented images and preoperative Pentacam images are given as input to U-Net-based 2D regression architecture. The output of the regression layer, the last layer of the U-Net architecture, provides a predicted Pentacam image of the later stage of the disease. The similarity of the predicted image in the final layer output to the Pentacam image in the postoperative period is evaluated by image similarity algorithms. As a result of the evaluation, the mean SSIM (The structural similarity index measure), PSNR (peak signal-to-noise ratio), and RMSE (root mean square error) similarity values are calculated as 0.8266, 65.85, and 0.134, respectively. These results show that our method successfully predicts the postoperative images of patients treated with CXL.

Is it useful to use computerized tomography image-based artificial intelligence modelling in the differential diagnosis of chronic otitis media with and without cholesteatoma?

OBJECTIVE: Cholesteatoma is an aggressive form of chronic otitis media (COM). For this reason, it is important to distinguish between COM with and without cholesteatoma. In this study, the role of artificial intelligence modelling in differentiating COM with and without cholesteatoma on computed tomography images was evaluated. METHODS: The files of 200 patients who underwent mastoidectomy and/or tympanoplasty for COM in our clinic between January 2016 and January 2021 were retrospectively reviewed. According to the presence of cholesteatoma, the patients were divided into two groups as chronic otitis with cholesteatoma (n = 100) and chronic otitis without cholesteatoma (n = 100). The control group (n = 100) consisted of patients who did not have any previous ear disease and did not have any active complaints about the ear. Temporal bone computed tomography (CT) images of all patients were analyzed. The distinction between cholesteatoma and COM was evaluated by using 80% of the CT images obtained for the training of artificial intelligence modelling and the remaining 20% for testing purposes. RESULTS: The accuracy rate obtained in the hybrid model we used in our study was 95.4%. The proposed model correctly predicted 2952 out of 3093 CT images, while it predicted 141 incorrectly. It correctly predicted 936 (93.78%) of 998 images in the COM group with cholesteatoma, 835 (92.77%) of 900 images in the COM group without cholesteatoma, and 1181 (98.82%) of 1195 images in the normal group. CONCLUSION: In our study, it has been shown that the differentiation of COM with and without cholesteatoma with artificial intelligence modelling can be made with highly accurate diagnosis rates by using CT images. With the deep learning modelling we proposed, the highest correct diagnosis rate in the literature was obtained. According to the results of our study, we think that with the use of artificial intelligence in practice, the diagnosis of cholesteatoma can be made earlier, it will help in the selection of the most appropriate treatment approach, and the complications can be reduced.

Prognostic nutritional index as indicator of immune nutritional status of patients with COVID-19.

Purpose: This study aimed to investigate the effect of the nutritional status, as assessed by the prognostic nutritional index (PNI) on the disease prognosis of patients with COVID-19. Methods: This retrospective study included 282 patients with COVID-19. The PNI score of all patients, 147 of whom were male, with a mean age of 56.4±15.3 years, was calculated. According to the PNI score, the patients with normal and mild malnutrition constituted group-1 (n=159) and the patients with moderate-to-severe and serious malnutrition constituted group-2 (n=123). Results: The PNI score was correlated with age (r=-0.146, p=0.014); oxygen saturation (r=0.190, p=0.001); heart rate (r=-0.117, p=0.05); hospitalization duration (r=-0.266, p<0.001); white blood cells (r=0.156, p=0.009); hemoglobin (r=0.307, p<0.001); C-reactive protein (CRP) (r=-0.346, p<0.001); creatinine (r=-0.184, p=0.002); D-dimer (r=-0.304, p<0.001); ferritin (r=-0.283, p<0.001); procalcitonin (r=-0.287, p<0.001); the confusion, urea, respiratory rate, blood pressure, and age ≥65 years score (r=-0.217, p<0.001); and the quick sequential organ failure assessment score (r=-0.261, p<0.001) in patients with COVID-19. Mortality was significantly higher in Group 2 (p<0.001). Survival was significantly higher if PNI score was >41.2 (p<0.001, sensitivity: 78.7% and specificity: 84.2%). In multivariate regression analysis, among various other parameters, only PNI score and oxygen saturation had a significant effect on the disease course (p=0.02 and p=0.045, respectively). Conclusion: PNI, calculated from the serum albumin concentration and total lymphocyte count, is a simple and objective indicator that assesses the immune nutritional status of patients with COVID-19. The presence of malnutrition has a high predictive value in predicting the severity of COVID-19. Our data suggest that the PNI might be useful for risk stratification of patients with COVID-19 in clinical practice.

The effect of deep feature concatenation in the classification problem: An approach on COVID-19 disease detection.

In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet-B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: "COVID-19 Image Dataset," "COVID-19 Pneumonia Normal Chest X-ray (PA) Dataset," and "COVID-19 Radiography Database" for COVID-19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1-score values, as well. Contribution of paper is as follows: COVID-19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer-aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods. METHODS: Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients. RESULTS: In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%. CONCLUSIONS: It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images.

Endothelial Dysfunction and Atherosclerosis in Patients With Autosomal Dominant Polycystic Kidney Disease.

Introduction In this study, we aimed to determine the endothelial dysfunction (ED) and atherosclerosis in patients with autosomal dominant polycystic kidney disease (ADPKD). Materials and methods This study was conducted with 83 subjects (26 male, mean age: 46±11 years) consisted of three groups including ADPKD, hypertension (HT) and healthy control groups. The groups were evaluated in terms of serum endocan and asymmetric dimethylarginine (ADMA) levels, flow-mediated dilatation (FMD), nitroglycerin-mediated dilation (NMD) and carotid intima-media thickness (CIMT). Results Serum endocan and ADMA levels and CIMT were significantly higher while NMD was significantly lower in ADPKD group than control group. FMD and NMD were lower but serum ADMA level was higher in the ADPKD group than HT group; while serum endocan level and CIMT were not significantly different in ADPKD and HT groups. In ADPKD patients, CIMT value and serum endocan and ADMA levels were higher while NMD was lower in patients with eGFR≤60 mL/min/1.73 m2 than patients with eGFR>60 mL/min/1.73 m2. Serum ADMA level was higher and NMD was lower in hypertensive ADPKD patients than non-hypertensive ones. Serum endocan level was higher in ADPKD patients with nephrolithiasis and a negative correlation was detected between serum endocan level and 24-hour urine volume. Conclusions Endothelial dysfunction and atherosclerosis are common conditions in ADPKD patients and it was further reinforced in our study. In order to clarify the relationship between serum endocan level and 24-hour urine volume, which is a remarkable finding in our study, larger studies that including the measurement of urine endocan may be useful.

Selective Attention and Information Processing Speed in Graves' Disease: Stroop Interference Effect.

Background Cognitive functions are affected by thyroid hormones. In this study, we aimed to investigate the selective attention and information processing speed in thyrotoxic Graves' disease. Methodology This study was conducted among 40 patients with thyrotoxic Graves' disease and age and gender-matched 40 healthy controls. Stroop Color and Word test were applied to healthy controls once and to patients with Graves' disease during thyrotoxic and euthyroid periods. Stroop interference effect was calculated. Results The mean age was 34.67 ± 11 in the Graves' group and 34.72 ± 9.16 in the control group (p > 0.05). The number of errors and self-corrections in Stroop Color and Word test was higher in patients with thyrotoxic Graves' disease than both patients with euthyroid Graves' disease and healthy controls (p < 0.05). Stroop interference effect was significantly longer in patients with thyrotoxic Graves' disease than both patients with euthyroid Graves' disease and healthy controls (p < 0.05). All parameters obtained from the Stroop Color and Word test including errors, self-corrections, and Stroop interference effect were similar in patients with euthyroid Graves' disease and healthy controls. Conclusions Selective attention was impaired and information processing speed was slow in patients with thyrotoxic Graves' disease, and these findings were associated with age and educational level. After becoming euthyroid through antithyroid medication, these pathological findings returned to normal levels. Additionally, Stroop interference effect was significantly decreased when patients with Graves' disease became euthyroid.

Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR.

Early diagnosis of breast lesions and differentiation of malignant lesions from benign lesions are important for the prognosis of breast cancer. In the diagnosis of this disease ultrasound is an extremely important radiological imaging method because it enables biopsy as well as lesion characterization. Since ultrasonographic diagnosis depends on the expert, the knowledge level and experience of the user is very important. In addition, the contribution of computer aided systems is quite high, as these systems can reduce the workload of radiologists and reinforce their knowledge and experience when considered together with a dense patient population in hospital conditions. In this paper, a hybrid based CNN system is developed for diagnosing breast cancer lesions with respect to benign, malignant and normal. Alexnet, MobilenetV2, and Resnet50 models are used as the base for the Hybrid structure. The features of these models used are obtained and concatenated separately. Thus, the number of features used are increased. Later, the most valuable of these features are selected by the mRMR (Minimum Redundancy Maximum Relevance) feature selection method and classified with machine learning classifiers such as SVM, KNN. The highest rate is obtained in the SVM classifier with 95.6% in accuracy.

Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.

Determination of cerebral blood flow velocity and microembolic signals in essential thrombocytosis by transcranial doppler ultrasonography.

OBJECTIVES: The goal of treatment in essential thrombocytosis (ET) is to prevent vascular complications such as thrombosis and hemorrhage. This study aimed to evaluate the risk of cerebrovascular microembolism in ET patients due to detection of microembolic signals (MES) and measure cerebral blood flow velocity (CBFV) by Transcranial Doppler (TCD) ultrasonography. MATERIAL AND METHODS: In this prospective case-control study, forty patients with diagnosed ET and age and sex-matched forty healthy controls were examined by the TCD sonography. RESULTS: The ET group had a higher rate of MES (8/40) in the right MCA than that in the control group (none), as measured by TCD. Five patients had MES at the left MCA compared to that in no subjects in the control group. The comparison of the ET and control groups in terms of CBFV parameters showed significantly lower end-diastolic FV at the right MCA in the ET group compared to that in the control group (p < 0.05). On the other hand; both pulsatility and resistance indices in the right and left MCA and the ratios of systolic to diastolic blood flow rates in the right and left MCA were significantly higher in the ET group than that in the control group. DISCUSSION: This study revealed that MES seems to be more common in patients with ET despite treatment. We could suggest that ET patients should be monitored more closely to address the potential risk of developing a cerebrovascular disease, which can be estimated by detection MES and raised CBFV, combine antiplatelet therapies to standard treatments.

A method for fracture toughness measurement in trabecular bone using computed tomography, image correlation and finite element methods.

The fracture resistance of load-bearing trabecular bone is adversely affected by diseases such as osteoporosis. However, there are few published measurements of trabecular bone fracture toughness due to the difficulty of conducting reliable tests in small specimens of this highly porous material. A new approach is demonstrated that uses digital volume correlation of X-ray computed tomographs to measure 3D displacement fields in which the crack shape and size can be objectively identified using a phase congruency analysis. The criteria for crack propagation, i.e. fracture toughness, can then be derived by finite element simulation, with knowledge of the elastic properties.

Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture.

Brain tumor is one of the dangerous and deadly cancer types seen in adults and children. Early and accurate diagnosis of brain tumor is important for the treatment process. It is an important step for specialists to detect the brain tumor using computer aided systems. These systems allow specialists to perform tumor detection more easily. However, mistakes made with traditional methods are also prevented. In this paper, it is aimed to diagnose the brain tumor using MRI images. CNN models, one of the deep learning networks, are used for the diagnosis process. Resnet50 architecture, one of the CNN models, is used as the base. The last 5 layers of the Resnet50 model have been removed and added 8 new layers. With this model, 97.2% accuracy value is obtained. Also, results are obtained with Alexnet, Resnet50, Densenet201, InceptionV3 and Googlenet models. Of all these models, the model developed with the highest performance has classified the brain tumor images. As a result, when analyzed in other studies in the literature, it is concluded that the developed method is effective and can be used in computer-aided systems to detect brain tumor.

Can platelet indices be used as predictors of complication in subjects with appendicitis?

BACKGROUND: We examined the changes of mean platelet volume (MPV) and platelet distribution width (PDW) in subjects with appendicitis and whether MPV and PDW could be used to predict the development of complication due to appendicitis. METHODS: The healthy control group, the cases of appendicitis with perforation, and the cases of appendicitis without perforation were compared with regard to MPV and PDW. We determined whether MPV and PDW were independent variables predictive of the development of complication in subjects with appendicitis. RESULTS: This retrospective case-control study included a total of 362 patients (249 of which were male (68.8 %) and 113 were female (31.2 %); median age, 30 [range, 18-84 years]). One hundred and ninety-two subjects (53 %) presented with appendicitis and 170 (47 %) comprised the healthy control group. Sixty-six (18.2 %) of the subjects with appendicitis developed complication. MPVs were lower in subjects of appendicitis without complication compared to the subjects of appendicitis with complication and the control group (MPV, 9.78 ± 0.99 vs. 10.20 ± 1.21 and 10.14 ± 1.03, respectively [p = 0.005]). The PDW levels were not different between the three groups. Independent variables predictive of the presence of complication included increased MPV and time from onset of symptoms to hospital presentation (odds ratio[confidence interval], p-value: 1.507[1.064-2.133], 0.021 and 18.887[5.139-69.410], 0.0001, respectively). CONCLUSIONS: Our findings suggested these, MPV values in cases of appendicitis without complication were lower than the cases with complication and healthy control and MPV is a predictor of the development of complication in subjects with appendicitis.

Variables determining the development of colistin-associated renal impairment.

AIM: The aim of this study was to identify the predictors of acute renal injury associated with colistin treatment. METHODS: The patients who received treatment with colistin for more than 3 days were included in this retrospective cohort study. Acute renal injury was defined by the RIFLE (Risk Injury Failure Loss End stage renal disease) criteria. Patients whose serum creatinine levels increased at least 1.5-fold compared with baseline value were considered as cases with renal injury. The independent variables determining the development of acute renal injury were investigated by survival analysis. RESULTS: A total of 112 cases [67 (59.8 %) were male, median age 64 (range: 18-93) years] were included in the study. Acute renal injury occurred in 66 (58.9 %) patients. Renal injury developed in first 7 days of the colistin therapy in 52 (78.8 %) cases and at day 8-23 in 14 (21.2 %) cases. On the day with highest levels of creatinine, 25 (22.3 %), 17 (15.2 %), and 33 (29.5 %) cases were in 'Risk', 'Injury', and 'Failure' group, respectively, according to RIFLE criteria. We identified three independent risk factors predicting acute colistin-induced renal injury: advanced age, low serum albumin levels, and high serum total bilirubin levels [odds ratio (confidence interval) = 1.022 (1.006-1.037), 0.643 (0.415-0.994), and 1.129 (1.014-1.257), respectively]. CONCLUSIONS: The advanced age, low serum albumin levels, and high serum total bilirubin levels are independent risk factors for colistin-induced nephrotoxicity.

A randomized controlled trial of home monitoring versus hospitalization for mild non-alcoholic acute interstitial pancreatitis: a pilot study.

BACKGROUND/OBJECTIVES: Acute pancreatitis (AP) is a disease typically requiring in-hospital treatment. We conducted a trial to assess the feasibility of early discharge from the hospital for patients with mild non-alcoholic acute pancreatitis (NAAP). METHODS: Eighty-four patients with mild NAAP were randomized to home or hospital groups after a short hospital stay (≤24 h). AP was defined by the revised Atlanta criteria. Mild AP was defined as an Imrie score≤5 and a harmless acute pancreatitis score (HAPS)≤2 in the first 24-h of presentation. A nurse visited all patients in the home group on the 2nd, 3rd and 5th days. All patients presented for follow-up in clinic on the 7th, 14th, and 30th days. The primary outcome was the time to resolution of pain. Secondary outcomes evaluated included time to resumption of an oral diet, 30 day hospital readmission rate as well as the total costs associated with either approach to care. RESULTS: There was no difference between the groups with regards to demographics, prognostic severity scores, symptoms, and biliary findings. No patients developed organ failure, pancreatic necrosis, or died in either group. Time to the resolution of pain and resumption of solid food intake were similar. Three (3.6%) patients required readmission within 30 days, 1 from home and 2 from the hospital groups. The total cost was significantly less in home group ($139 ± 73 vs. $951 ± 715,p < 0.001). CONCLUSIONS: Mild NAAP can be safely treated at home with regular visits by a nurse under the supervision of a physician. Widespread adoption of this practice may result in large cost savings.