A Cross-Sectional Study of Glomerular Hyperfiltration in Polycystic Ovary Syndrome.

Glomerular hyperfiltration (GH) has been reported to be higher in women with polycystic ovary syndrome (PCOS) and is an independent risk factor for renal function deterioration, metabolic, and cardiovascular disease. The aim of this study was to determine GH in type A PCOS subjects and to identify whether inflammatory markers, markers of CKD, renal tubule injury markers, and complement system proteins were associated. In addition, a secondary cohort study was performed to determine if the eGFR had altered over time. In this comparative cross-sectional analysis, demographic, metabolic, and proteomic data from Caucasian women aged 18-40 years from a PCOS Biobank (137 with PCOS, 97 controls) was analyzed. Slow Off-rate Modified Aptamer (SOMA)-scan plasma protein measurement was undertaken for inflammatory proteins, serum markers of chronic kidney disease (CKD), tubular renal injury markers, and complement system proteins. A total of 44.5% of the PCOS cohort had GH (eGFR ≥ 126 mL/min/1.73 m2 (n = 55)), and 12% (n = 17) eGFR ≥ 142 mL/min/1.73 m2 (super-GH(SGH)). PCOS-GH women were younger and had lower creatinine and urea versus PCOS-nonGH. C-reactive protein (CRP), white cell count (WCC), and systolic blood pressure (SBP) were higher in PCOS versus controls, but CRP correlated only with PCOS-SGH alone. Complement protein changes were seen between controls and PCOS-nonGH, and decay-accelerator factor (DAF) was decreased between PCOS-nonGH and PCOS-GSGH (p < 0.05). CRP correlated with eGFR in the PCOS-SGH group, but not with other inflammatory or complement parameters. Cystatin-c (a marker of CKD) was reduced between PCOS-nonGH and PCOS-GSGH (p < 0.05). No differences in tubular renal injury markers were found. A secondary cohort notes review of the biobank subjects 8.2-9.6 years later showed a reduction in eGFR: controls -6.4 ± 12.6 mL/min/1.73 m2 (-5.3 ± 11.5%; decrease 0.65%/year); PCOS-nonGH -11.3 ± 13.7 mL/min/1.73 m2 (-9.7 ± 12.2%; p < 0.05, decrease 1%/year); PCOS-GH (eGFR 126-140 mL/min/17.3 m2) -27.1 ± 12.8 mL/min/1.73 m2 (-19.1 ± 8.7%; p < 0.0001, decrease 2%/year); PCOS-SGH (eGFR ≥ 142 mL/min/17.3 m2) -33.7 ± 8.9 mL/min/17.3 m2 (-22.8 ± 6.0%; p < 0.0001, decrease 3.5%/year); PCOS-nonGH eGFR versus PCOS-GH and PCOS-SGH, p < 0.001; no difference PCOS-GH versus PCOS-SGH. GH was associated with PCOS and did not appear mediated through tubular renal injury; however, cystatin-c and DAF were decreased, and CRP correlated positively with PCOS-SGH, suggesting inflammation may be involved at higher GH. There were progressive eGFR decrements for PCOS-nonGH, PCOS-GH, and PCOS-SGH in the follow-up period which, in the presence of additional factors affecting renal function, may be clinically important in the development of CKD in PCOS.

Takotsubo Cardiomyopathy Associated with Acute COVID-19 Pneumonia.

Cardiogenic shock carries a high burden of morbidity and mortality because of inadequate tissue perfusion leading to end-stage multi-organ damage. The initial work-up includes a pertinent and thorough history and physical examination to identify possible cardiac and noncardiac etiologies. The following case describes a patient presenting with symptomatic acute COVID-19 (SARS-CoV-2) pneumonia with initial findings consistent with cardiogenic shock. SARS-CoV-2 pneumonia has been associated with multiple cardiac manifestations including myocarditis, heart failure, myocardial infarction, and Takotsubo cardiomyopathy. This patient was treated with conservative medical management and had complete clinical recovery and normal cardiac angiography weeks after their initial presentation. This clinical scenario highlights the significance of a broad differential and extensive work-up when faced with a patient presenting with cardiogenic shock.

The Effect of a Very-Low-Calorie Diet (VLCD) vs. a Moderate Energy Deficit Diet in Obese Women with Polycystic Ovary Syndrome (PCOS)-A Randomised Controlled Trial.

We performed an open-label, randomised controlled trial to compare the effects of a very-low-calorie diet (VLCD) vs. moderate energy deficit approach on body weight, body composition, free androgen index (FAI), and metabolic markers in obese women with polycystic ovary syndrome (PCOS). Forty eligible patients were randomly assigned to a VLCD (n = 21) or a conventional energy deficit approach (n = 19) over the same period. After eight weeks, both groups experienced significant weight loss; however, this was greater in the VLCD arm (-10.9% vs. -3.9%, p < 0.0001). There was also a trend towards a reduction in FAI in the VLCD group compared to the energy deficit group (-32.3% vs. -7.7%, p = 0.07). In the VLCD arm, two women (18%) had a biochemical remission of PCOS (FAI < 4); this was not the case for any of the participants in the energy deficit arm. There was a significant within-group increase in the sex-hormone-binding globulin (p = 0.002) and reductions in fasting blood glucose (p = 0.010) and waist to hip ratio (p = 0.04) in the VLCD arm, but not in the energy deficit arm. The VLCD resulted in significantly greater weight reduction and was accompanied by more pronounced improvements in hyperandrogenaemia, body composition, and several metabolic parameters in obese women with PCOS as compared to the energy deficit approach.

Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach.

Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.

Assessing the androgenic and metabolic heterogeneity in polycystic ovary syndrome using cluster analysis.

INTRODUCTION: Some but not all women with polycystic ovary syndrome (PCOS) develop the metabolic syndrome (MS). The objective of this study was to determine if a subset of women with PCOS had higher androgen levels predisposing them to MS and whether routinely measured hormonal parameters impacted the metabolic syndrome score (siMS). METHODS: We included data from a discovery (PCOS clinic data) and a replication cohort (Hull PCOS Biobank) and utilized eight routinely measured hormonal parameters in our clinics (free androgen index [FAI], sex hormone-binding globulin, dehydroepiandrosterone sulphate (DHEAS), androstenedione, luteinizing hormone [LH], follicular stimulating hormone, anti-Müllerian hormone and 17 hydroxyprogesterone [17-OHP]) to perform a K-means clustering (an unsupervised machine learning algorithm). We used NbClust Package in R to determine the best number of clusters. We estimated the siMS in each cluster and used regression analysis to evaluate the effect of hormonal parameters on SiMS. RESULTS: The study consisted of 310 women with PCOS (discovery cohort: n = 199, replication cohort: n = 111). The cluster analysis identified two clusters in both the discovery and replication cohorts. The discovery cohort identified a larger cluster (n = 137) and a smaller cluster (n = 62), with 31% of the study participants. Similarly, the replication cohort identified a larger cluster (n = 74) and a smaller cluster (n = 37) with 33% of the study participants. The smaller cluster in the discovery cohort had significantly higher levels of LH (7.26 vs. 16.1 IU/L, p < .001), FAI (5.21 vs. 9.22, p < .001), androstenedione (3.93 vs. 7.56 nmol/L, p < .001) and 17-OHP (1.59 vs. 3.12 nmol/L, p < .001). These findings were replicated in the replication cohort. The mean (±SD) siMS score was higher in the smaller cluster, 3.1 (±1.1) versus 2.8 (±0.8); however, this was not statistically significant (p = .20). In the regression analysis, higher FAI (ß = .05, p = .003) and androstenedione (ß = .03, p = .02) were independently associated with a higher risk of SiMS score, while higher DHEAS levels were associated with a lower siMS score (ß = -.07, p = .03) CONCLUSION: We identified a subset of women in our PCOS cohort with significantly higher LH, FAI, and androstenedione levels. We show that higher levels of androstenedione and FAI are associated with a higher siMS, while higher DHEAS levels were associated with lower siMS.

Invasive Management of Vertebrobasilar Artery Stenosis and Occlusion: A Meta-Analysis on Efficacy and Safety Endpoints.

Vertebrobasilar angioplasty and stenting or mechanical thrombectomy (MT) using a stent retriever or suction thrombectomy are effective interventions in managing acute ischemic stroke caused by vertebrobasilar artery occlusion (VBAO). This study aims to investigate the safety and efficacy of self-expanding stents and balloon angioplasty in managing ischemic stroke. We reviewed the literature for relevant clinical trials and included those reporting the following primary outcomes: successful recanalization, favorable clinical outcome, and stenosis degree change. We included 24 studies (858 patients). In the subgroup analysis, participants were divided into three main subgroups based on the type of intervention: mechanical thrombectomy (MT), percutaneous transluminal angioplasty and stenting (PTAS), and MT+PTAS. Regarding overall mortality, the incidence was 34.5%, 9.9%, and 28.9% in the MT, PTAS, and MT+PTAS groups, respectively. The incidence of arterial dissection was 3.6% in the MT group, 3.1% in the PTAS group, and 16.7% in the MT+PTAS group. Incidence of distal embolization, MT, PTAS, and MT+PTAS groups had 3.4%, 5.8%, and 9.5% incidence rates, respectively. Favorable clinical outcomes were reported in 42.8% of subjects in the MT+PTAS group, 64.7% in the PTAS group, and 39.2% in the MT group. The incidence of intracranial hemorrhage was 5.2%, 4.5%, and 15.3% in the MT, PTAS, MT + PTAS groups, respectively. The incidence of successful recanalization was 85.3% in the MT group, 99.4% in the PTAS group, and 92.7% in the MT+PTAS group. Our analysis concludes that PTAS is the most effective intervention for VBAO and is associated with a lower rate of mortality compared to mechanical thrombectomy alone.

Outcomes With Left Ventricular Assist Device in End-Stage Renal Disease: A Systematic Review.

Renal dysfunction is a common comorbidity in patients with advanced heart failure who may benefit from mechanical circulatory support (MCS). Unfortunately, renal function may result after left ventricular assist device (LVAD) implantation. The purpose of this study is to examine the outcomes of advanced heart failure patients with end-stage renal disease (ESRD) requiring mechanical circulatory support as a bridge to transplant (BTT) or destination therapy (DT). We searched Medline, Embase, and Cochrane in September 2021. The following keywords were used: left ventricular assist device or LVAD and end-stage renal disease or ESRD. Our study included case reports, case series, descriptive studies, and randomized control trials. Review articles, guidelines, systematic reviews, and meta-analyses were excluded. We also excluded pediatric cases. We identified 278 articles; 92 were duplicated, 186 articles entered the screening phase, and 133 articles were excluded by title and abstract. After the full-text screening, 40 articles were excluded. This systematic review included 13 articles. Among the contraindications to LVAD implantation, a general contraindication is for patients found to have stage 4 chronic kidney disease (CKD) (estimated glomerular filtration rate (eGFR): <30 mL/minute/1.73 m2), while those on dialysis are an absolute contraindication LVAD implantation. Despite the limited data and publications on LVADs in patients with ESRD, LVAD implantation as a bridge to transplantation or destination therapy may be considered in selected patients without increasing morbidity and mortality. Therefore, shared decision-making around the treatment of advanced heart failure with these patients and the care team is essential.

Factors Impacting Stent Thrombosis in Patients With Percutaneous Coronary Intervention and Coronary Stenting: A Systematic Review and Meta-Analysis.

Stent thrombosis (ST) is a frequently reported complication in cardiac patients with percutaneous coronary intervention (PCI) that adversely impacts their prognostic outcomes. Medical literature reveals several baseline characteristics of PCI patients that may predict their predisposition to ST and its potential complications. Our systematic review and meta-analysis aimed to determine the diagnostic significance of these baseline parameters in terms of determining the risk of ST among adult patients with PCI.  We statistically evaluated 18 baseline characteristics of more than 15,500 PCI patients to delineate their stent thrombosis attribution. We included a number of articles focusing on baseline parameters in-stent thrombosis-related PCI scenarios. We explored the articles of interest based on inclusion/exclusion parameters across PubMed, JSTOR, Cochrane library, Google Scholar, and Embase. Medical subject headings (MeSH) words included "stent thrombosis," "percutaneous coronary intervention," and "coronary stenting." We extracted the research articles published between 2005 and 2021 on April 20, 2021. The included studies also focused on procedures and clinical factors concerning their association with PCI-related ST. Our findings ruled out the progression of abnormal left ventricular ejection fraction (LVEF)-related stent thrombosis in PCI patients (odds ratio {OR}: 9.68, 95% CI: 1.88-49.90, p=0.007). We found an insignificant clinical correlation between stent thrombosis and PCI in the setting of acute coronary syndrome (ACS). Our study outcomes further revealed the absence of stent thrombosis in PCI patients with antiplatelet prescription (OR: 32.42, 95% CI: 21.28-49.39). The findings affirmed the absence of ST in PCI patients receiving aspirin therapy (OR: 32.77, 95% CI: 18.73-57.34; OR: 4.59, 95% CI: 1.97-10.73). The majority of the included studies negated the clinical correlation of stent thrombosis with diabetes mellitus in the setting of PCI (OR: 0.49, 95% CI: 0.06-3.78). Our study did not reveal statistically significant results based on stent thrombosis in PCI patients with drug-eluting stents (OR: 2.91, 95% CI: 0.35-24.49). The findings also did not reveal the impact of cardiac biomarker elevation on stent thrombosis in PCI patients (OR: 8.42, 95% CI: 2.54-27.98, p=0.0005). Eight studies revealed a statistically insignificant correlation between myocardial infarction and stent thrombosis in PCI scenarios (OR: 2.69, 95% CI: 0.89-8.11, p=0.08). The clinical correlation between PCI and stent thrombosis/major bleeding in the setting of hypertension also proved statistically insignificant at 0.67 (OR: 1.31, 95% CI: 0.38-4.51, p=0.97). The study findings did not correlate mean body mass index and multivessel coronary artery disease with ST in PCI scenarios (OR: 1.98, 95% CI: 0.02-239.58, p=0.78; OR: 1.09, 95% CI: 0.58-2.04, p=0.80). Only two studies revealed statistically significant results confirming stent thrombosis in PCI patients with a prior history of PCI (OR: 0.49, 95% CI: 0.23-1.06; OR: 0.33, 95% CI: 0.02-5.59; p=0.03). Our findings question the clinical significance of baseline characteristics in terms of predicting stent thrombosis in PCI patients. The results support the requirement of future studies to investigate complex interactions between procedural, medicinal, genetic, and patient-related factors contributing to the development of stent thrombosis in PCI patients.

Comparative Assessment of the Long-Term Efficacy of Home-Based Versus Center-Based Cardiac Rehabilitation.

Cardiac rehabilitation programs support the health, wellness, and recovery of patients with cardiovascular conditions. This systematic review attempts to expand these findings while analyzing the latest randomized controlled trials (RCTs) focusing on the long-term advantages of home/center-based cardiac rehabilitation interventions. This study also comparatively analyzes the benefits of opting for home-based cardiac rehabilitation instead of center-based measures to improve the long-term clinical outcomes of cardiac patients. We extracted and analyzed 10 studies (based on 1,549 cardiac patients) concerning the therapeutic efficacy of center/home-based cardiac rehabilitation interventions. The included studies complied with the year range of 2000-2021. The risk of bias assessment was undertaken using the Cochrane Risk-of-Bias tool to evaluate random sequence generation, allocation concealment, blinding of subjects, outcome data completeness, and selective reporting patterns concerning the included RCTs. The findings of our systematic review confirmed the capacity of a home-based cardiac rehabilitation program to effectively improve left ventricular ejection fraction, health-related quality of life, physical fitness, recovery rate, self-efficacy, sedentary lifestyle, physical activity, satisfaction level, functional capacity, social support, and hemodynamic parameters of patients with cardiovascular diseases. Home-based cardiac rehabilitation had the potential to minimize the levels of triglycerides, anxiety, depression, waist circumference, and body mass index/weight of cardiac patients. The results of our systematic review affirmed the long-term therapeutic efficacy of a home-based cardiac rehabilitation program compared to a center-based cardiac rehabilitation program for adult cardiac patients.

Unilateral adrenal infarction in pregnancy with associated acute hypoadrenalism and subsequent spontaneous biochemical and radiological resolution.

Adrenal infarction is a rare cause of abdominal pain during pregnancy, and if missed, it can result in devastating clinical consequences for the mother and the child. The authors report a case of a young female who presented with severe abdominal pain and nausea. The biochemistry showed raised inflammatory markers and significant lactic acidosis. As the cause of the symptoms was not clear and the patient continued to deteriorate, a contrast-enhanced CT abdomen and pelvis was done which was suggestive of an acute left adrenal infarction. Subsequently, the patient was confirmed to have biochemical hypoadrenalism and required replacement doses of hydrocortisone until recovery of the adrenal glucocorticoid reserve and anticoagulation for the duration of pregnancy. We discuss the workup including diagnostic imaging, follow-up, and considerations for future pregnancies in this case.

Detection of microscopic glaucoma through fundus images using deep transfer learning approach.

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.

Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network.

Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient's life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is based on the fully convolutional neural network (FCNN) and transfer learning techniques. The proposed framework has five stages which are preprocessing, skull stripping, CNN-based tumor segmentation, postprocessing, and transfer learning-based brain tumor binary classification. In preprocessing, the MR images are filtered to eliminate the noise and are improve the contrast. For segmentation of brain tumor images, the proposed CNN architecture is used, and for postprocessing, the global threshold technique is utilized to eliminate small nontumor regions that enhanced segmentation results. In classification, GoogleNet model is employed on three publicly available datasets. The experimental results depict that the proposed method is achieved average accuracies of 96.50%, 97.50%, and 98% for segmentation and 96.49%, 97.31%, and 98.79% for classification of brain tumor on BRATS2018, BRATS2019, and BRATS2020 datasets, respectively. The outcomes demonstrate that the proposed framework is effective and efficient that attained high performance on BRATS2020 dataset than the other two datasets. According to the experimentation results, the proposed framework outperforms other recent studies in the literature. In addition, this research will uphold doctors and clinicians for automatic diagnosis of brain tumor disease.

Automatic detection of papilledema through fundus retinal images using deep learning.

Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.

Sickle cell disease-associated arrhythmias and in-hospital outcomes: Insights from the National Inpatient Sample.

BACKGROUND: The frequency and temporal trend in the prevalence of arrhythmias and associated in-hospital outcomes in patients with sickle cell disease (SCD) have never been quantified. METHODS: Our study cohort of SCD patients and sub-types of arrhythmias were derived from the 2010-2014 National Inpatient Sample using relevant diagnostic codes. The frequency and trends of arrhythmia and odds of inpatient mortality were measured. RESULTS: A total of 891 450 hospitalized SCD patients were identified, of which, 55 616 (6.2%) patients experienced arrhythmias. The SCD cohort with arrhythmia demonstrated higher all-cause mortality (2.7% vs 0.4%; adjusted OR 2.53, 95% CI 2.15-2.97, P < .001), prolonged hospital stays (6.9 vs 5.0 days) and higher hospital charges ($53 871 vs $30 905) relative to those without arrhythmias (P < .001).The frequency of supraventricular arrhythmia (AFib, SVT, and AF) and ventricular arrhythmia (VFib and VT) were 1893 and 362 per 100 000 SCD-related admissions, respectively. Unspecified arrhythmias (4126) were seen most frequently followed by AFib (1622) per 100 000 SCD-related admissions. From 2010 to 2014, the frequency of any arrhythmias and atrial fibrillation in hospitalized SCD patients relatively increased by 29.6% and 38.5%, respectively. There was nearly a twofold (2.4% in 2010 to 5.0% in 2014) increase in the frequency of arrhythmia among patients aged <18 years. The frequency of arrhythmias in hospitalized male and female SCD patients relatively increased by 28.8% and 31.4%, respectively (P trend < .001). CONCLUSIONS: The frequency of arrhythmias among SCD patients is on the rise with worse hospitalization outcomes, including higher in-hospital mortality and higher resource utilization as compared to those without arrhythmias.

Data on fundus images for vessels segmentation, detection of hypertensive retinopathy, diabetic retinopathy and papilledema.

This paper presents a dataset that contains 100 high quality fundus images which are acquired from Armed Forces Institute of Ophthalmology (AFIO), Rawalpindi Pakistan. The dataset has been marked by four expert ophthalmologists to aid clinicians and researchers in screening hypertensive retinopathy, diabetic retinopathy and papilledema cases. Moreover, it contains highly detailed annotations for retinal blood vascular patterns, arteries and veins to calculate arteriovenous ratio (AVR), optic nerve head (ONH) region and other retinal anomalies such as hard exudates and cotton wool spots etc. The dataset is extremely useful for the researchers who are working in the ophthalmic image analysis.

Clinical Characteristics, Mortality and Associated risk factors in COVID-19 patients reported in ten major hospitals of Khyber Pakhtunkhwa, Pakistan.

BACKGROUND: COVID-19 is an ongoing public health issue across the world. Several risk factors associated with mortality in COVID-19 have been reported. The present study aims to describe clinical and epidemiological characteristics and predictors of mortality in hospitalized patients from Khyber Pakhtunkhwa, a province in Pakistan with highest COVID-19 associated case fatality rate. METHODS: This multicentre, retrospective study was conducted in hospitalized COVID-19 patients who died or discharged alive until 1st May 2020. Data about sociodemographic characteristics, clinical and laboratory findings, treatment and outcome were obtained from hospital records and compared between survivors and non-survivors. Statistical tests were applied to determine the risk factors associated with mortality in hospitalized patients. RESULTS: Of the total 179 patients from the 10 designated hospitals, 127 (70.9%) were discharged alive while 52 (29.1%) died in the hospital. Overall, 109 (60.9%) patients had an underlying comorbidity with hypertension being the commonest. Multivariate logistics regression analysis showed significantly higher odds of in-hospital death from COVID-19 in patients with multiple morbidities (OR 3.2, 95% CI 1.1, 9.1, p-value=0.03), length of hospital stay (OR 0.8, 95% CI 0.7, 0.9, p-value <0.001), those presenting with dyspnoea (OR 4.0, 95% CI 1.1, 14.0, p-value=0.03) and oxygen saturation below 90 (OR 9.6, 95% CI: 3.1, 29.2, p-value <0.001). CONCLUSION: Comorbidity, oxygen saturation and dyspnoea on arrival and length of stay in hospital (late admission) are associated with COVID-19 mortality. The demographic, clinical and lab characteristics could potentially help clinician and policy makers before potential second wave in the country.

Frequency of Post-operative Surgical Site Infections in a Tertiary Care Hospital in Abbottabad, Pakistan.

Background Any infection occurring in a wound after a surgical procedure is called a post-operative surgical wound infection. Surgical wound infection is a type of nosocomial infection that is acquired in hospitals when a person is admitted for reasons other than the infection. Surgical site infections (SSIs) are the second most common nosocomial (hospital-acquired) infections after urinary tract infections. SSIs contribute to a significant rate of morbidity and mortality in patients and has become a major public health problem worldwide. The increase in antibiotic resistance associated with SSIs has also become a therapeutic challenge for physicians worldwide. Methods This cross-sectional study was carried out among the patients admitted in various surgical wards of the Ayub Teaching Hospital. A total of 95 patients were enrolled for the study using a non-probability convenient sampling technique. Data were collected using a structured questionnaire by students carrying out the research for a time period of one month. SSIs were assessed by: 1. Pus or purulent discharge from the wound along with pain, 2. Any two cardinal signs of inflammation, and 3. Diagnosis of SSI by the surgeon. Results The mean age of the patients was 35.73 ± 19.73 years. SSI rate was found to be 33.68% with 32 patients developing SSIs out of 95 patients. The rate of SSIs was greater in older patients with four (44.4%) out of nine patients above 60 years developing SSIs. Patients belonging to urban areas had a higher incidence rate (52.77%) of SSIs as compared to rural areas (32.20%). The rate of SSIs in patients operated with an elective surgical plan was greater (37.93%) as compared to patients operated with an emergency surgical plan (27.77%). Patients who were obese were more prone to SSIs with seven (36.8%) patients developing SSIs out of 19. Surgeries performed by trainee medical officers had a greater rate of SSIs with 24 (35.3%) patients developing SSIs out of 68 patients. Three (66.66%) out of four patients with diabetes and 18 (40.9%) out of 44 patients with anemia developed SSIs. Conclusion This study concluded that the incidence rate of SSIs was high in patients admitted in Ayub Teaching Hospital. Major risk factors identified were co-morbidities, old age, obesity, duration of surgery, major surgeries, and anemia. Steps should be taken to decrease SSIs in these high-risk groups.

Automated techniques for blood vessels segmentation through fundus retinal images: A review.

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.

Decision support system for detection of hypertensive retinopathy using arteriovenous ratio.

Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR).The presented system includes three modules i.e. main component extraction, artery/vein (A/V) classification and finally AVR calculation and grading of HR. Proposed system uses vascular map and a set of hybrid features for A/V classification. The evaluation of proposed system is carried out using three datasets. The proposed system shows average accuracies of 95.14% for images of INSPIRE-AVR database, 96.82% for images of VICAVR database and 98.76% for local dataset AVRDB. These results support that the proposed system is trustworthy for clinical use in detection and grading of HR disease. Main contribution of proposed system is that it utilizes complete blood vessel map for A/V classification. These arteries and veins are then used to calculate AVR and grade HR cases based on AVR values. Another contribution of this article is that it presents a new dataset AVRDB for A/V classification and HR detection.

Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy.

BACKGROUND AND OBJECTIVES: Hypertensive Retinopathy (HR) is a retinal disease which happened due to consistent high blood pressure (hypertension). In this paper, an automated system is presented that detects the HR at various stages using arteriovenous ratio and papilledema signs through fundus retinal images. METHODS: The proposed system consists of two modules i.e. vascular analysis for calculation of arteriovenous ratio and optic nerve head (ONH) region analysis for papilledema.  First module uses a set of hybrid features in Artery or Vein (A/V) classification using support vector machine (SVM) along with its radial basis function (RBF) kernel for arteriovenous ratio. In second module, proposed system performs analysis of ONH region for possible signs of papilledema. This stage utilizes different features along with SVM and RBF for classification of papilledema. RESULTS: The first module of proposed method shows average accuracies of 95.10%, 95.64% and 98.09%for images of INSPIRE-AVR, VICAVR, and local dataset respectively. The second module of proposed method achieves average accuracies of 95.93% and 97.50% on STARE and local dataset respectively. CONCLUSIONS: The system finally utilizes results from both modules to grade HR with good results. The presented system is a novel step towards automated detection and grading of HR disease and can be used as clinical decision support system.