IMPORTANCE Venous thromboembolism (VTE) is a major cause of preventable morbidity and mortality after cancer surgery. Venous thromboembolism events that are significant enough to require hospital readmission are potentially life threatening, yet data regarding the frequency of these events beyond the 30-day postoperative period remain limited.OBJECTIVE To determine the rates, outcomes, and predictive factors of readmissions owing to VTE up to 180 days after complex cancer operations, using a national data set. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort study of the 2016 Nationwide Readmissions Database was performed to study adult patients readmitted with a primary VTE diagnosis. Data obtained from 197 510 visits for 126 104 patients were analyzed. This was a multicenter, population-based, nationally representative study of patients who underwent a complex cancer operation (defined as cystectomy, colectomy, esophagectomy, gastrectomy, liver/biliary resection, lung/bronchus resection, pancreatectomy, proctectomy, prostatectomy, or hysterectomy) from January 1 through September 30, 2016, for a corresponding cancer diagnosis. EXPOSURES Readmission with a primary diagnosis of VTE.MAIN OUTCOMES AND MEASURES Proportion of 30-, 90-, and 180-day VTE readmissions after complex cancer surgery, factors associated with readmissions, and outcomes observed during readmission visit, including mortality, length of stay, hospital cost, and readmission to index vs nonindex hospital. RESULTSFor the 126 104 patients included in the study, 30-, 90-, and 180-day VTE-associated readmission rates were 0.6% (767 patients), 1.1% (1331 patients), and 1.7% (1449 of 83 337 patients), respectively. A majority of patients were men (58.7%), and the mean age was 65 years (SD, 11.5 years). For the 1331 patients readmitted for VTE within 90 days, 456 initial readmissions (34.3%) were to a different hospital than the index surgery hospital, median length of stay was 5 days (IQR, 3-7 days), median cost was $8102 (IQR, $5311-$10 982), and 122 patients died (9.2%). Independent factors associated with readmission included type of operation, scores for severity and risk of mortality, age of 75 to 84 years (odds ratio [OR], 1.30; 95% CI, 1.02-1.78), female sex (OR, 1.23; 95% CI,, nonelective index admission (OR, 1.31; 95% CI, 1.03-1.68), higher number of comorbidities (OR, 1.30; 95% CI, 1.06-1.60), and experiencing a major postoperative complication during the index admission (OR, 2.08; 95% CI, 1.85-2.33). CONCLUSIONS AND RELEVANCEIn this cohort study, VTE-related readmissions after complex cancer surgery continued to increase well beyond 30 days after surgery. Quality improvement efforts to decrease the burden of VTE in postoperative patients should measure and account for these late VTE-related readmissions.

Introduction: Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-ts-all treatment modalities. Radiomics uses machine-learning to identify salient features of the tumor on brain imaging and promises patient speci c management in glioblastoma patients.Methods: We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, strati cation, prognostication as well as treatment planning and monitoring of glioblastoma.Results: Classi ers based on combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.Conclusion: Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine. Glioblastoma:Glioblastoma has an incidence of 3.22 per 100,000 and a median overall survival (OS) of 14.6 months following standard treatment, which includes a combination of surgical resection, radiation therapy and chemotherapy. [1] This "one-size-ts-all" model for the treatment of glioblastoma is now being questioned following research on various pathways implied in intratumoral heterogeneity, arising as a result of genetic and epigenetic makeup, levels of protein expression, metabolic or bioenergetic behavior, microenvironment biochemistry and structural composition.[2] Consequently, features differ on histopathology and imaging across patients as well as spatially throughout a single tumor. [3,4,5] Personalized treatment protocols targeting individual patient's tumor characteristics are thus being increasingly advocated for improved success rates in glioblastoma management. [4,6,7] Radiomics And Radiogenomics:Radiomics is an emerging application of neuroimaging where advanced computational methods are used to quantitatively extract characteristics from clinical images that are too complex for a human eye to appreciate.[8,9] These imaging characteristics, called "features" re ect tumor characteristics and inner organization as well as the tumor microenvironment. [9]Radiomics is a multi-step process including the acquisition and preprocessing of images, segmentation, feature extraction and selection, and advanced statistics using machine learning (ML) algorithms (Figure 1). The pipeline of radiomics is highly

IntroductionCOVID-19 has manifested a striking disarray in healthcare access and provision, particularly amongst patients presenting with life-threatening ischemic heart disease (IHD). The paucity of data from low-middle income countries has limited our understanding of the consequential burden in the developing world. We aim to compare volumes, presentations, management strategies, and outcomes of IHD amongst patients presenting in the same calendar months before and during the COVID-19 pandemic. MethodsWe conducted a retrospective cross-sectional analysis at the Aga Khan University Hospital, one of the premier tertiary care centres in Pakistan. Data were collected on all adult patients (>18 years) who were admitted with IHD (acute coronary syndrome (ACS) and stable angina) from March 1 to June 30, 2019 (pre-COVID) and March 1 to June 30, 2020 (during-COVID), respectively. Group differences for continuous variables were evaluated using student t-test or Mann-Whitney U test. The chi-squared test or Fisher test was used for categorical variables. Values of p less than 0.05 were considered statistically significant. Pvalue trend calculation and graphical visualization were done using STATA (StataCorp, College Station, TX). ResultsData were assimilated on 1019 patients, with 706 (69.3%) and 313 (30.7%) patients presenting in each respective group (pre-COVID and during-COVID). Current smoking status (p=0.019), admission source (p<0.001), month of admission (p<0.001), proportions ACS (p<0.001), non-ST-elevation-myocardialinfarction (NSTEMI; p<0.001), unstable angina (p=0.025) and medical management (p=0.002) showed significant differences between the two groups, with a sharp decline in the during-COVID group. Monthly trend analysis of ACS patients showed the most significant differences in admissions (p=0.001), geographic region (intra-district vs intracity vs outside city) (p<0.001), time of admission (p=0.038), NSTEMI (p=0.002) and medical management (p=0.001). ConclusionThese data showcase stark declines in ACS admissions, diagnostic procedures (angiography) and revascularization interventions (angioplasty and coronary artery bypass graft surgery, CABG) in a developing country where resources and research are already inadequate. This study paves the way for further investigations downstream on the short-and long-term consequences of untreated IHD and reluctance in health-seeking behaviour.

Introduction: Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine-learning to identify salient features of the tumor on brain imaging and promises patient specific management in glioblastoma patients. Methods: We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.Results: Classifiers based on combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice. Conclusion: Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.

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