Cost of Medical Treatment for Primary Open-Angle Glaucoma in Relation to Patients’ Income and Its Impact on the Prognosis of the Disease, in Bouaké University Hospital

Introduction: Medical treatment for POAG is continuous and lifelong treatment. The aim of this study was to evaluate the relationship between the cost of this treatment and patients’ income and the impact of this relationship on treatment compliance. Materials and Methods: Prospective cross-sectional study with a descriptive aim covering sociodemographic data, average incomes, and direct and indirect costs of treatment of 57 patients followed for POAG during the period from January 1, 2012, to December 31, 2016 (5 years). Results: The patients were aged 25 to 77 years (mean = 54.4 years) with a male predominance (sex ratio = 1.5). Retirees were the most represented (26.32%), followed by workers in the informal sector (14.04%) and housewives (12.28%). Patients who had an annual income less than or equal to 900,000 CFA francs (€1370.83) per year represented 56.14% and those who did not have health coverage represented 57.89%. The treatment was monotherapy (64.91%), dual therapy (31.58%) or triple therapy (3.05%) and the average ratio of “annual cost of treatment to annual income” was 0.56 with for maximum 2.23 and 0.02 as minimum. Patients who considered the cost of treatment unbearable for their income represented 78.95%. Conclusion: Prevention of blindness due to glaucoma requires early detection but also the establishment of health coverage mechanisms to improve compliance with medical treatment. In addition, consideration should be given to the development of glaucoma surgery in our country, the indication of which could be the first intention in certain patients, considering for those patients, the geographical and financial accessibility of medical treatment. .

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

Exploration and Research on the Integrated Development of“Internet Plus Medical Treatment”

Objective To analyze the development of“internet plus medical treatment”and to explore advantages.Methods The literature of“internet plus medical treatment”was systematically combed and analyzed.Results and Conclusion After exploring the status quo of“internet plus hospitals”,smart pharmacy and web-assisted health management in China,we find that there are some problems in the medical service at present,such as the imperfect laws and regulations,the hidden dangers of information security and the obstacles of medical insurance payment.Therefore,we propose that the development of web-assisted medical service should be led by the government and relevant policies must be improved.Then,self-regulation should be strengthened,and industry standards should be enhanced.Three suggestions are made to improve medical insurance payment and benefit both hospitals and patients,which can provide reference for promoting the development of“internet plus medical treatment”in China.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Efficient Certificateless Authenticated Key Agreement for Blockchain-Enabled Internet of Medical Things

Internet of Medical Things(IoMT)plays an essential role in collecting and managing personal medical data.In recent years,blockchain technology has put power in traditional IoMT systems for data sharing between different medical institutions and improved the utilization of medical data.However,some problems in the information transfer process between wireless medical devices and mobile medical apps,such as information leakage and privacy disclosure.This paper first designs a cross-device key agreement model for blockchain-enabled IoMT.This model can establish a key agreement mechanism for secure medical data sharing.Meanwhile,a certificateless authenticated key agreement(KA)protocol has been proposed to strengthen the information transfer security in the cross-device key agreement model.The proposed KA protocol only requires one exchange of messages between the two parties,which can improve the protocol execution efficiency.Then,any unauthorized tampering of the transmitted signed message sent by the sender can be detected by the receiver,so this can guarantee the success of the establishment of a session key between the strange entities.The blockchain ledger can ensure that the medical data cannot be tampered with,and the certificateless mechanism can weaken the key escrow problem.Moreover,the security proof and performance analysis are given,which show that the proposed model and KA protocol are more secure and efficient than other schemes in similar literature.

Machine Learning-Enabled Communication Approach for the Internet of Medical Things

The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

Determinants of internet addiction among medical students in Casablanca: a cross-sectional study

Objective:This study aimed to estimate the prevalence and determinants of Internet addiction among medical students at the Faculty of Medicine and Pharmacy of Casablanca,Morocco.Methods:This was a cross-sectional study conducted among students at the Faculty of Medicine and Pharmacy in Casablanca between October and March 2020.An online questionnaire was administered to students to collect data and internet addiction was assessed by the Young questionnaire.A score threshold≥50 was adopted to define addiction.Univariate and multivariate logistic regression analyses were used to identify factors associated with internet addiction.Results:Out of a total of 4093 FMPC students enrolled in the 2020-2021 academic year,506 agreed to participate in this study,including 303 females and 203 males.The mean addiction score assessed on the Young scale was(49.08±16.11).The prevalence of Internet addiction was 44.5%(225/506,95% CI:40% to 49%).Multiple regression analysis showed that being older than 20 years(OR=0.17,95% CI:0.40 to 0.64),being female(OR=1.70,95% CI:1.04 to 2.78),being in the dissertation year(6th year)(OR=5.17,95% CI:2.23 to 11.44),having a history of psychiatric consultation(OR=2.64,95% CI:1.34 to 5.21),having divorced parents(OR=2.64,95% CI:1.05 to 5.87),use of sleeping medication(OR=2.9,95% CI:1.05 to 3.70),sleep disorders(OR=2.06,95% CI:1.25 to 3.79),sleep deprivation(OR=2.26,95% CI:1.39 to 3.65),excessive daytime sleepiness(OR=5.39,95% CI:2.19 to 13.24),anxiety disorders(OR=1.47,95% CI:1.18 to 2.30),duration of internet connection(>4 h)(OR=11.43,95% CI:4.85 to 27.66),and having frequent conflicts with parents(OR=2.37,95% CI:1.49 to 3.79)and friends(OR=0.26,95% CI:0.11 to 0.65)were independently associated with internet addiction.Conclusion:The prevalence of Internet addiction among medical students in Casablanca remains high.Targeted action on the determinants would be of great value in prevention.

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things(IoMT)enabled e-healthcare has the potential to greately improve conventional healthcare services significantly.However,security and privacy become major issues of IoMT because of the restricted processing abilities,storage,and energy constraints of the sensors.Therefore,it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors.In order to ensure security on sensitive medical data,effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers.In this view,this study designs an effective metaheuristic optimization based encryption with user authentication(EMOE-UA)technique for IoMT environment.This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity.Moreover,the EMOE-UA technique employs optimal multikey homomorphic encryption(OMKHE)technique to encrypt the IoMT data.Furthermore,the improved social spider optimization algorithm(ISSOA)was employed for the optimal multikey generation of the MKHE technique.The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects.The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

In this paper,the Internet ofMedical Things(IoMT)is identified as a promising solution,which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service(QoS)in the healthcare sector.However,problems with the present architectural models such as those related to energy consumption,service latency,execution cost,and resource usage,remain a major concern for adopting IoMT applications.To address these problems,this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming(MILP),with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment,while maintaining certain quality standards(e.g.,energy consumption,service latency,network utilization).A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements.In comparison to the other existing models,the performance analysis of the proposed approach shows a maximum saving of 38%in energy consumption and a 73%reduction in service latency.The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs.the extra power consumed by edge or fog resources.

On the design of an AI-driven secure communication scheme for internet of medical things environment

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the Internet.It is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data.In other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare outcomes.However,this system is prone to securityand privacy-related attacks on healthcare data.Therefore,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential.To mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT.The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT.Furthermore,we provide the security analysis of the proposed scheme to show its security against different possible attacks.Moreover,a comparative study of the proposed scheme with other similar schemes is presented.Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality attributes.Finally,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

Relationships of loneliness and mobile phone dependence with Internet addiction in Japanese medical students

We investigated factors contributing to Internet addiction in 105 Japanese medical students. The subjects were administered by a self-reporting questionnaire designed to evaluate demographic factors, Internet addiction, loneliness, health-related lifestyle factors, depressive state, patterns of behavior, and mobile phone dependence. Results of multivariate logistic regression analysis indicated that loneliness and mobile phone dependence were positively related to degree of addiction. Our findings suggest that Internet addiction is associated with loneliness and mobile phone dependence in Japanese students.

Applications of nanotechnology in medical field: a brief review

Nanotechnology has extensive application as nanomedicine in the medical field.Some nanoparticles have possible applications in novel diagnostic instruments,imagery and methodologies,targeted medicinal products,pharmaceutical products,biomedical implants,and tissue engineering.Today treatments of high toxicity can be administered with improved safety using nanotechnology,such as chemotherapeutic cancer drugs.Further,wearable gadgets can detect crucial changes in vital signs,cancer cell conditions,and infections that are genuinely happening in the body.We anticipate these technologies to provide doctors with considerably much better direct access to critical data on the reasons for changes in the signs of life or illness because of the technological presence at the source of the problem.Biomedicine can be utilised for therapies with predictive analytics and artificial intelligence.For carrying out this study,relevant papers on Nanotechnology in the medical field from Scopus,Google scholar,ResearchGate,and other research platforms are identified and studied.The study discusses different types of Nanoparticles used in the medical field.This paper discusses nanotechnology applications in the medical field.The class,features,and characteristics of Nanotechnology for medicine are also briefed.Scientists,governments,civil society organisations,and the general public will need to collaborate across sectors to assess the significance of nanotechnology and guide its advancement in various fields.The current research includes several possible Nanotechnology uses in the medical field.As a result,the study provides a brief and well-organised report on nanotechnology that should be valuable to researchers,engineers,and scientists for future research projects.

Efficient Group Blind Signature for Medical Data Anonymous Authentication in Blockchain-Enabled IoMT

Blockchain technology promotes the development of the Internet of medical things(IoMT)from the centralized form to distributed trust mode as blockchain-based Internet of medical things(BIoMT).Although blockchain improves the cross-institution data sharing ability,there still exist the problems of authentication difficulty and privacy leakage.This paper first describes the architecture of the BIoMT system and designs an anonymous authentication model for medical data sharing.This BIoMT system is divided into four layers:perceptual,network,platform,and application.The model integrates an anonymous authentication scheme to guarantee secure data sharing in the network ledger.Utilizing the untampered blockchain ledger can protect the privacy of medical data and system users.Then,an anonymous authentication scheme called the group blind signature(GBS)scheme is designed.This scheme can provide anonymity for the signer as that one member can represent the group to sign without exposing his identity.The blind property also can protect the message from being signed as it is anonymous to the signer.More-over,this GBS scheme is created with the lattice assumption,which makes it more secure against quantum attacks.In addition,the security proof shows that this GBS scheme can achieve the security properties of dynamical-almost-full anonymity,blindness,traceability,and non-frameability.The comparison analysis and performance evaluation of key size show that this GBS scheme is more efficient than similar schemes in other literature.

Long-term medical treatment of patients with severe burns at exposed sites

BACKGROUND Body parts such as the face and hands are highly exposed during daily life and burns may accumulate in these areas.In addition,residual wounds,scar hyperplasia and contracture often exist in the late stage of a deep burn in these areas,which may affect patients’appearance,movements,and mental health.However,inadequate attention has been paid to this issue which can result in problems,such as difficulty in healing,possibility of carcinoma,chronic pain and a heavy mental burden.AIM To investigate the long-term medical treatment of patients with severe burns at exposed sites following a mass burn casualty event.METHODS A retrospective analysis of 13 patients with severe burns at exposed sites was performed to determine their respective long-term medical treatment.A combined wound dressing scheme consisting of traditional Chinese and Western medicine was introduced to repair residual wounds.Active and passive functional exercises with massage,Chinese herbal baths and compression fixation were proposed to ameliorate the condition of the hands.A combination of physical,chemical and photoelectrical measures was adopted for anti-scar treatment.A psychological intervention and recovery guide was provided which corresponded to the patients’psychological status.RESULTS Compared to patients who did not simultaneously receive the same treatment,patients who underwent systematic treatment recovered with a lower woundinfection rate(P<0.05),a shorter healing time(13.6±3.2 d)compared with(19.1±3.5 d)and more bearable pain during wound dressing at three days,one week and two weeks after a Chinese herbal bath(P<0.05).Satisfactory results were achieved with regard to restored function of patients’joints and blood supply to nerve endings,closure of the eyelids and the size of mouth opening tended to be normal,and only 7.1%of patients were diagnosed with severe scar hyperplasia and contracture deformity compared with 30.7%in the control group.In addition,the color,thickness,vascular distribution and softness score of the scars improved(P<0.01),and the effects of the psychological intervention was remarkable as shown by the Self-Rating Anxiety Scale and Self-Rating Depression Scale.CONCLUSION A better prognosis can be achieved in patients in the late stage of a burn with active residual wound repair,limb functional exercise,anti-scar and psychological rehabilitation.

Energy Aware Clustering with Medical Data Classification Model in IoT Environment

With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.

Advances in medical treatment for pancreatic neuroendocrine neoplasms

Pancreatic neuroendocrine neoplasms(PanNENs)are rare neoplasms with strong heterogeneity that have experienced an increasing incidence rate in recent years.For patients with locally advanced or distant metastatic PanNENs,systemic treatment options vary due to the different differentiations,grades and stages.The available options for systemic therapy include somatostatin analogs,molecularly targeted agents,cytotoxic chemotherapeutic agents,immune checkpoint inhibitors,and peptide receptor radionuclide therapy.In addition,the development of novel molecularly targeted agents is currently in progress.The sequence of selection between different chemotherapy regimens has been of great interest,and resistance to chemotherapeutic agents is the major limitation in their clinical application.Novel agents and high-level clinical evidence continue to emerge in the field of antiangiogenic agents.Peptide receptor radionuclide therapy is increasingly employed for the treatment of advanced neuroendocrine tumors,and greater therapeutic efficacy may be achieved by emerging radiolabeled peptides.Since immune checkpoint inhibitor monotherapies for PanNENs appear to have limited antitumor activity,dual immune checkpoint inhibitor therapies or combinations of antiangiogenic therapies and immune checkpoint inhibitors have been applied in the clinic to improve clinical efficacy.Combining the use of a variety of agents with different mechanisms of action provides new possibilities for clinical treatments.In the future,the study of systemic therapies will continue to focus on the screening of the optimal benefit population and the selection of the best treatment sequence strategy with the aim of truly achieving individualized precise treatment of PanNENs.

Review of Medical Treatment of Stable Ischemic Heart Disease

Medical treatment is the initial treatment strategy and is the cornerstone of management in patients with stable ischemic heart disease (IHD). Many patients are not suitable for percutaneous or surgical revascularization because of unfavourable anatomy, or the presence of co-morbidities. In addition, many patients have recurrence of angina following revascularization due to restenosis or incomplete revascularization. Furthermore, randomized clinical trials comparing optimal medical treatment to revascularization have not clearly shown that myocardial revascularization is superior to optimal medical treatment. Traditional drugs for angina treatment include b-blockers, calcium channel blockers and nitrates. Newer drugs are available with different mechanisms of action and with equal efficacy that do not cause significant hemodynamic deterioration. The availability of these newer drugs expands the therapeutic potential of medical treatment to even a wider population with stable IHD. Revascularization in patients with stable ischemic heart disease has never been shown to reduce hard endpoints (death or myocardial infarction) in randomized clinical trials.