Ahmed Morsy

Effect of sirolimus on malignancy and survival after kidney transplantation: systematic review and meta-analysis of individual patient data.

Objective To examine risk of malignancy and death in patients with kidney transplant who receive the immunosuppressive drug sirolimus. Design Systematic review and meta-analysis of individual patient data. Data sources Medline, Embase, and the Cochrane Central Register of Controlled Trials from inception to March 2013. Eligibility Randomized controlled trials comparing immunosuppressive regimens with and without sirolimus in recipients of kidney or combined pancreatic and renal transplant for which the author was willing to provide individual patient level data. Two reviewers independently screened titles/abstracts and full text reports of potentially eligible trials to identify studies for inclusion. All eligible trials reported data on malignancy or survival. Results The search yielded 2365 unique citations. Patient level data were available from 5876 patients from 21 randomized trials. Sirolimus was associated with a 40% reduction in the risk of malignancy (adjusted hazard ratio 0.60, 95% confidence interval 0.39 to 0.93) and a 56% reduction in the risk of non-melanoma skin cancer (0.44, 0.30 to 0.63) compared with controls. The most pronounced effect was seen in patients who converted to sirolimus from an established immunosuppressive regimen, resulting in a reduction in risk of malignancy (0.34, 0.28 to 0.41), non-melanoma skin cancer (0.32, 0.24 to 0.42), and other cancers (0.52, 0.38 to 0.69). Sirolimus was associated with an increased risk of death (1.43, 1.21 to 1.71) compared with controls. Conclusions Sirolimus was associated with a reduction in the risk of malignancy and non-melanoma skin cancer in transplant recipients. The benefit was most pronounced in patients who converted from an established immunosuppressive regimen to sirolimus. Given the risk of mortality, however, the use of this drug does not seem warranted for most patients with kidney transplant. Further research is needed to determine if different populations, such as those at high risk of cancer, might benefit from sirolimus.

Sleep Apnea Detection Using an Adaptive Fuzzy Logic Based Screening System

We report an adaptive diagnostic system for the classification of breathing events for the purpose of detecting sleep apnea syndromes. The system employs two classification engines used in series. The first engine is fuzzy logic-based and generates one of three outcomes for each breathing event: normal, abnormal, and not-sure. The second classification engine is based on a center of gravity engine which is trained using the normal and abnormal events, generated by the first engine, and is specifically designed for sorting out the not-sure events. The fuzzy logic engine can be tuned very conservatively to reduce or eliminate the chance of error at the first stage. Since the second engine is trained adaptively using normal and abnormal data of the same patient, its accuracy is generally better than relying on multi-patient training approaches. The two-step, adaptive nature of the system allows for high accuracy and lends itself well for practical implementation

A pilot study to assess the effectiveness of orthotic insoles on the reduction of plantar soft tissue strain

BACKGROUND Plantar ulcers pose a frequent serious complication in the neuropathic foot. Previous studies suggested that ulcer initiation occurs within the plantar soft tissue rather than on the plantar surface. This study investigated the effectiveness of different shaped silicone insoles on the reduction of both plantar soft tissue strain and pressure. The authors have found no previous experimental studies on the effectiveness of insole shape on reducing plantar soft tissue strain during standing. METHODS A custom molded silicone insole which allowed passage of ultrasound to the plantar surface of the foot was prototyped for this study. Soft tissue strain was computed from soft tissue thickness measured using ultrasound in five conditions: unloaded, barefoot, wearing a prefabricated silicone insole, wearing the custom molded silicone insole alone then with a metatarsal pad. Plantar pressure was measured for the same conditions. FINDINGS The custom molded insole was found to significantly reduce soft tissue strain and plantar pressure relative to both the barefoot condition and the prefabricated insole under the second and third metatarsal heads. The metatarsal pad was found to significantly reduce soft tissue strain but not significantly affect plantar pressure. INTERPRETATION A custom molded silicone insole can effectively reduce both soft tissue strain and plantar pressure and is thus preferable to a prefabricated insole. It is suggested that quantifying the reduction of soft tissue strain is an essential design requirement for orthotic insoles since plantar pressure may not be a sufficient indicator of the effectiveness of an insole in preventing ulcer initiation.

Sleep Apnea Detection and Classification Using Fuzzy Logic: Clinical Evaluation

We have previously reported a system suitable for detection and classification of sleep apnea syndromes. This paper reports the results of the clinical evaluation of the proposed system. In the current implementation, the system uses breathing signals: nasal flow, thorax movement, and abdomen movement. The detection part of the system uses only the nasal flow signal to detect apnea employing two engines used in series. It then feeds segments labeled as abnormal to the classification part of the system, which uses the center of gravity of each segment to determine the type of abnormality: obstructive, central or hypopnea. In comparison to other systems, this implementation can be shown to be simpler and more accurate. When the low implementation cost is taken into consideration, the proposed system has a substantial potential for being used as a screening device

A new gradient-based algorithm for edge detection in ultrasonic carotid artery images

In human clinical studies, digital B-Mode ultrasound images of carotid and femoral artery walls are used to measure Intima-Media Thickness (IMT). IMT represents the arterial intima-media complex and is a validated surrogate parameter for atherosclerosis and cardiovascular disease risk. Conventionally, IMT is obtained by tracing the ultrasound interfaces of the arterial far walls manually. The manual tracing, however, may be replaced by an automated approach in order to decrease image analysis variability and improve consistency and efficiency of the imaging laboratory. In this paper, we present and test a novel automated edge detection method which employs a multi-step gradient based algorithm. The new method principally uses intensity, intensity gradient, and interface continuity of pixels to determine the ultrasound interfaces. In our investigations, we used the far wall of the common carotid artery to test the proposed algorithm. As our results show, the novel algorithm greatly eliminates subjectivity associated with conventional manual tracing and semi-automated gradient methods that employ seed point selection. The new method can therefore have a great potential in atherosclerosis studies and clinical trials.

FPGA-based Sleep Apnea Screening Device for Home Monitoring

We present the hardware design of an FPGA-based portable device for home screening of sleep apnea syndromes. The device is simple to use, inexpensive, and uses only three signals, namely the nasal air flow and the thorax and abdomen effort signals. The device hardware stores data of overnight sleep on a Secure Digital card. At the clinic, the sleep specialist reads in the stored data and uses an algorithm for the detection and classification of sleep apnea. The device is fairly low-cost and may help spread the ability to diagnose more cases of sleep apnea. Most sleep apnea cases currently go un-diagnosed because of cost and practicality limitations of overnight polysomnography at sleep labs

Adaptive hybrid system for automatic sleep staging

We present a new adaptive system for automated sleep staging. The proposed system relies on each subjects own data for self-training. Conventional automatic sleep staging algorithms are either rule based, which typically fail to accurately model the complex nature of sleep signals, or numerical methods that use multi-patient training schemes, which suffer from inaccuracies caused by inherent inter-patient variability. The proposed system employs two stages. The first stage is a rule based reasoning engine that can be tuned conservatively to decrease or eliminate false positives, generating just enough samples to train the second stage, which is comprised of a neural network classifier. Results show that this hybrid approach provides an adaptive training scheme that performs more accurately compared to one of the popular commercially available systems.

Ethics in Biomedical Engineering

This session focuses on a number of aspects of the subject of Ethics in Biomedical Engineering. The session starts by providing a case study of a company that manufactures artificial heart valves where the valves were failing at an unexpected rate. The case study focuses on Biomedical Engineers working at the company and how their education and training did not prepare them to deal properly with such situation. The second part of the session highlights the need to learn about various ethics rules and policies regulating research involving human or animal subjects.

Epileptiform events detection using a two-stage approach based on multiscale edge detection and artificial neural networks

An automated detection of epileptiform events can streamline the process of reviewing lengthy EEG records and provide a more quantitative evaluation. This paper introduces a novel two-stage approach of merging human expert knowledge and artificial neural network modeling capabilities. In the first stage, singularities marked through wavelet transform maxima are grouped into epileptiform candidates. These candidates are ranked according to expert mimetic measures that judge candidate morphology, spatial confinement and temporal reproducibility. The second engine is based on ANN. It is trained with the raw EEG data of the topmost candidates to capture high confidence events characteristics. During the evaluation phase, the ANN tests how these characteristics generalize to other candidates. A total of 600 minutes of EEG recordings using all channels were utilized for this study. Data was acquired from 5 patients having different epileptic syndromes. The results showed an average sensitivity of 92% and average selectivity of 96%.

National registries in developing countries: Understanding construction challenges and implementation steps

National registries are databases intended to collect data useful for better planning and regulation of healthcare delivery at a national level. National registries can help government officials, health practitioners, and clinical researchers answer a variety of critical questions. These questions can be related to the efficacy of new drugs, spread patterns of a particular disease, or risk factors of certain sub-groups. Construction of health registries in developing countries has been considered a challenging process as evidenced by the lack of such necessary registries in a number of countries and the failure of registry projects in some others. This paper attempts to provide a methodical approach for understanding the main challenges that face national registry projects in developing countries. The main issues are discussed and, whenever is appropriate, suggestions for handling these issues and are provided together with practical examples to support the presented arguments.