Splenectomy outcomes in immune cytopenias: Treatment outcomes and determinants of response.

BACKGROUND: Splenectomy is commonly used to treat refractory immune-mediated cytopenia, but there are no established factors that are associated with response to the procedure. OBJECTIVES: A cohort study was conducted to evaluate the hematologic and surgical outcomes of splenectomy in adult patients with immune cytopenias and identify preoperative factors associated with response. METHODS: Data from the Cleveland Clinic Foundation for 1824 patients aged over 18 who underwent splenectomy from 2002 to 2020 were analyzed. RESULTS: The study found that the most common indications for splenectomy were immune thrombocytopenic purpura (ITP) and autoimmune hemolytic anemia, with a median age of 55 years and median time from diagnosis to splenectomy of 11 months. Hematologic response rates were 74% overall, with relapse in 12% of cases. Postsplenectomy discordant diagnoses were present in 13% of patients, associated with higher relapse rates. Surgery-related complications occurred in 12% of cases, whereas only 3% of patients died from disease complications. On univariate analysis, preoperative factors associated with splenectomy treatment failure were ≥3 lines of pharmacologic treatment, whereas isolated thrombocytopenia, primary ITP, and age ≤40 years had a strong association with response. The multivariable regression confirmed that treatment failure with multiple lines of medical therapy was associated with the failure to respond to splenectomy. CONCLUSION: Overall, the study demonstrates that splenectomy is an effective treatment option for immune-mediated cytopenias with a low complication rate.

Novel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia.

BACKGROUND: TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. METHODS: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely. RESULTS: Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases. CONCLUSION: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

An appraisal of the performance of AI tools for chronic stroke lesion segmentation.

Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a significant surge of interest in the development of supervised artificial intelligence (AI) methods for that purpose, including deep learning, with a performance comparable to trained experts. Such AI-based methods, however, require copious amounts of data. Thanks to the availability of large datasets, the development of AI-based methods for lesion segmentation has immensely accelerated in the last decade. One of these datasets is the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset which includes T1-weighted images from hundreds of chronic stroke survivors with their manually traced lesions. This systematic review offers an appraisal of the impact of the ATLAS dataset in promoting the development of AI-based segmentation of stroke lesions. An examination of all published studies, that used the ATLAS dataset to both train and test their methods, highlighted an overall moderate performance (median Dice index = 59.40%) and a huge variability across studies in terms of data preprocessing, data augmentation, AI architecture, and the mode of operation (two-dimensional versus three-dimensional methods). Perhaps most importantly, almost all AI tools were borrowed from existing AI architectures in computer vision, as 90% of all selected studies relied on conventional convolutional neural network-based architectures. Overall, current research has not led to the development of robust AI architectures than can handle spatially heterogenous lesion patterns. This review also highlights the difficulty of gauging the performance of AI tools in the presence of uncertainties in the definition of the ground truth.

Targeting HIF-2 Alpha in Renal Cell Carcinoma.

OPINION STATEMENT: Current treatment options for patients with metastatic renal cell carcinoma (mRCC) are limited to immunotherapy with checkpoint inhibitors and targeted therapies that inhibit the vascular endothelial growth factor receptors (VEFG-R) and the mammalian target of rapamycin (mTOR). Despite significantly improved outcomes over the last few decades, most patients with mRCC will ultimately develop resistance to these therapies, thus highlighting the critical need for novel treatment options. As part of the VHL-HIF-VEGF axis that rests at the foundation of RCC pathogenesis, hypoxia-inducible factor 2α (HIF-2α) has been identified as a rationale target for mRCC treatment. Indeed, one such agent (belzutifan) is already approved for VHL-associated RCC and other VHL-associated neoplasms. Early trials of belzutifan indicate encouraging efficacy and good tolerability in sporadic mRCC as well. The potential inclusion of belzutifan and other HIF-2α inhibitors into the mRCC treatment armamentarium either as a single agent or as combination therapy would be a welcome addition for patients with mRCC.

Molecular patterns identify distinct subclasses of myeloid neoplasia.

Genomic mutations drive the pathogenesis of myelodysplastic syndromes and acute myeloid leukemia. While morphological and clinical features have dominated the classical criteria for diagnosis and classification, incorporation of molecular data can illuminate functional pathobiology. Here we show that unsupervised machine learning can identify functional objective molecular clusters, irrespective of anamnestic clinico-morphological features, despite the complexity of the molecular alterations in myeloid neoplasia. Our approach reflects disease evolution, informed classification, prognostication, and molecular interactions. We apply machine learning methods on 3588 patients with myelodysplastic syndromes and secondary acute myeloid leukemia to identify 14 molecularly distinct clusters. Remarkably, our model shows clinical implications in terms of overall survival and response to treatment even after adjusting to the molecular international prognostic scoring system (IPSS-M). In addition, the model is validated on an external cohort of 412 patients. Our subclassification model is available via a web-based open-access resource ( https://drmz.shinyapps.io/mds_latent ).

Novel Scheme for Defining the Clinical Implications of TP53 Mutations in Myeloid Neoplasia.

Background: TP53 mutations ( TP53 MT ) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model to properly resolve the allelic configuration of TP53 MT and assess prognosis more precisely. Results: Overall, TP53 MT were found in 1010 patients. Following the traditional criteria, 36% of cases were classified as single hits while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53 MT . Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. These results were recapitulated by single-cell DNA studies, which unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation.

Switch Maintenance Therapy for Metastatic Urothelial Carcinoma.

INTRODUCTION: While switch maintenance therapy is being increasingly investigated in solid tumors, it is a standard in only a few. We conducted a systematic review on switch maintenance therapy for metastatic urothelial carcinoma. EVIDENCE ACQUISITION: In this systematic review, we conducted a literature search in PubMed and Cochrane databases up to 2021, based on PRISMA statement guidelines. One hundred and fifty eight articles were identified and after a three-step selection process and six articles, using different agents were included in evidence synthesis. The primary end points were effect on overall survival, progression free survival, safety and tolerability. EVIDENCE SYNTHESIS: In the pre-immunotherapy era, targeted therapies like sunitinib, lapatinib and vinflunine were studied as switch maintenance therapy in metastatic urothelial carcinoma but did not show any overall survival benefit. Use of anti-PD-1/PD-L1 agents have shown promise as switch maintenance therapy; pembrolizumab showed improvement in progression free survival in a phase 2 trial and avelumab showed improvement in overall survival and progression free survival in the phase 3 JAVELIN Bladder 100 trial. CONCLUSION: Immunotherapy with anti-PD-1/PD-L1 agents has emerged as an effective switch maintenance strategy in patients with metastatic urothelial carcinoma. Intensification of the immunotherapy backbone in this setting can potentially further enhance outcomes. Emerging evidence shows a potential role of Poly (ADP-ribose) polymerase (PARP) inhibitors in this setting as well. Results from ongoing and planned studies will help us understand which switch maintenance approaches would be most effective for improving outcomes in metastatic urothelial carcinoma.

Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy.

BACKGROUND: In anti-vascular endothelial growth factor (anti-VEGF) therapy, an accurate estimation of multi-class retinal fluid (MRF) is required for the activity prescription and intravitreal dose. This study proposes an end-to-end deep learning-based retinal fluids segmentation network (RFS-Net) to segment and recognize three MRF lesion manifestations, namely, intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), from multi-vendor optical coherence tomography (OCT) imagery. The proposed image analysis tool will optimize anti-VEGF therapy and contribute to reducing the inter- and intra-observer variability. METHOD: The proposed RFS-Net architecture integrates the atrous spatial pyramid pooling (ASPP), residual, and inception modules in the encoder path to learn better features and conserve more global information for precise segmentation and characterization of MRF lesions. The RFS-Net model is trained and validated using OCT scans from multiple vendors (Topcon, Cirrus, Spectralis), collected from three publicly available datasets. The first dataset consisted of OCT volumes obtained from 112 subjects (a total of 11,334 B-scans) is used for both training and evaluation purposes. Moreover, the remaining two datasets are only used for evaluation purposes to check the trained RFS-Net's generalizability on unseen OCT scans. The two evaluation datasets contain a total of 1572 OCT B-scans from 1255 subjects. The performance of the proposed RFS-Net model is assessed through various evaluation metrics. RESULTS: The proposed RFS-Net model achieved the mean F1 scores of 0.762, 0.796, and 0.805 for segmenting IRF, SRF, and PED. Moreover, with the automated segmentation of the three retinal manifestations, the RFS-Net brings a considerable gain in efficiency compared to the tedious and demanding manual segmentation procedure of the MRF. CONCLUSIONS: Our proposed RFS-Net is a potential diagnostic tool for the automatic segmentation of MRF (IRF, SRF, and PED) lesions. It is expected to strengthen the inter-observer agreement, and standardization of dosimetry is envisaged as a result.

Endocrine Toxicity and Outcomes in Patients With Metastatic Malignancies Treated With Immune Checkpoint Inhibitors.

CONTEXT: Immune checkpoint inhibitors (ICIs) have gained a revolutionary role in management of many advanced malignancies. However, immune-related endocrine events (irEEs), have been associated with their use. irEEs have nonspecific clinical presentations and variable timelines, making their early diagnosis challenging. OBJECTIVE: To identify risk factors, timelines, and prognosis associated with irEEs development. DESIGN AND SETTING: Retrospective observational study within the Cleveland Clinic center. PATIENTS: Metastatic cancer adult patients who received ICIs were included. METHODS: 570 charts were reviewed to obtain information on demographics, ICIs used, endocrine toxicities, cancer response to treatment with ICI, and overall survival. MAIN OUTCOME MEASURES: Incidence of irEEs, time to irEEs development and overall survival of patients who develop irEEs. RESULTS: The final cohort included 551 patients. The median time for the diagnosis of irEEs was 9 weeks. Melanoma was associated with the highest risk for irEEs (31.3%). Ipilimumab appeared to have the highest percentage of irEEs (29.4%), including the highest risk of pituitary insufficiency (11.7%), the most severe (Grade 4 in 60%) and irreversible (100%) forms of irEEs. Forty-five percent of patients with irEEs had adequate cancer response to ICI compared to 28.3% of patients without irEEs (P = 0.002). Patients with irEEs had significantly better survival compared to patients without irEEs (P < 0.001). CONCLUSIONS: In the adult population with metastatic cancer receiving treatment with ICI, irEEs development may predict tumor response to immunotherapy and a favorable prognosis. Ipilimumab use, combination ICI therapy, and melanoma are associated with a higher incidence of irEEs.

Validation of the IMPEDE VTE score for prediction of venous thromboembolism in multiple myeloma: a retrospective cohort study.

The IMPEDE VTE score has recently emerged as a novel risk prediction tool for venous thromboembolism (VTE) in multiple myeloma (MM). We retrospectively reviewed 839 patients with newly diagnosed MM between 2010 and 2015 at Cleveland Clinic and included 575 patients in final analysis to validate this score. The c-statistic of the IMPEDE VTE score to predict VTE within 6 months of treatment start was 0·68 (95% CI: 0·61-0·75). The 6-month cumulative incidence of VTE was 5·0% (95% CI: 2·1-7·9) in the low risk group, compared to 12·6% (95% CI: 8·9-16·4%) and 24·1% (95% CI: 12·2-36·1) in the intermediate and high risk groups (P < 0·001 for both). In addition, a higher proportion of patients in the VTE cohort had ECOG performance status of ≥2 as compared to the no VTE cohort (33% vs. 16%, P = 0·001). Other MM characteristics such as stage, immunoglobulin subtype, and cytogenetics were not predictors of VTE. In summary, we have validated the IMPEDE VTE score in our patient cohort and our findings suggest that it can be utilized as a VTE risk stratification tool in prospective studies looking into investigating VTE prophylaxis strategies in MM patients.

Causes of diabetic ketoacidosis among adults with type 1 diabetes mellitus: insulin pump users and non-users.

INTRODUCTION: Insulin pumps are increasingly being used as a method of insulin delivery in patients with type 1 diabetes mellitus (T1DM). Diabetic ketoacidosis (DKA) is a serious complication of T1DM. This study aims to identify the causes of DKA in patients with T1DM on continuous subcutaneous insulin infusion (CSII) and to compare these with patients with T1DM on multiple daily insulin injections (MDIIs). RESEARCH DESIGN AND METHODS: This is a prospective observational study between January and June 2019 at the Cleveland Clinic Fairview Hospital. Demographic, clinical, and biochemical data were obtained from chart review. A questionnaire to explore additional clinical data relating to DKA was administered, with additional items for patients on the insulin pump. RESULTS: Seventy-four patients were admitted with a diagnosis of DKA between the period of January and June 2019. Of these, 45 met the inclusion criteria and 43 consented. These were divided into two groups: group 1 included patients on MDII and group 2 included CSII. Overall, the most common precipitating factor for developing DKA was insulin non-adherence, seen in 51.2% of the cases. The most common cause of DKA in group 2 was pump/tubing related to 55% of the cases. CONCLUSION: Despite non-adherence being common in both CSII and MDII, a combination of social factors, education and insulin pump malfunction, such as pump/tubing problems, might be playing a pivotal role in DKA etiology in young adults with T1DM, especially in CSII users. Continued education on pump use may reduce the rate of DKA in pump users.

Clinical Characteristics and Outcomes of Primary Breast Lymphoma: The Cleveland Clinic Experience.

Introduction Primary breast lymphoma (PBL) is a rare malignancy that accounts for less than 0.5% of all breast malignancies. Materials and Methods We retrospectively analyzed 36 PBL patients to report the clinical characteristics and outcomes of patients with indolent and aggressive histologic subtypes. Results Thirteen (36%) patients had aggressive and 23 (64%) had indolent PBL. Marginal zone lymphoma was the most common histologic subtype (33%). Stage IE, IIE, and IV disease were seen in 27 (75%), six (17%), and three (8%) patients, respectively. Patients with aggressive PBL more often presented with a breast lump and/or B symptoms (unexplained weight loss, fever, night sweats) (78% vs. 31%, p = 0.005). Commonly used treatment modalities for aggressive vs. indolent PBL were chemotherapy alone (23% vs. 26%, p = 0.8), chemoradiotherapy (46% vs. 9%, p = 0.009), radiotherapy alone (15% vs. 22%, p = 0.6), and observation (0% vs. 26%, p = 0.07), respectively. The five-year overall survival (OS) and progression-free survival (PFS) of PBL patients were 82% (95% CI: 67 - 100) and 63% (95% CI: 45 - 89), respectively. The five-year OS of patients with aggressive vs. indolent PBL were 92% (95% CI: 77 - 100) vs. 80% (95% CI: 63 - 100), respectively (p = 0.6). The five-year OS of patients who received > 1, 1, and 0 treatment modalities were 92% (95% CI: 77 - 100), 86% (95% CI: 63 - 100), and 53% (95% CI: 21 - 100), respectively. Conclusion In our cohort, the higher utilization of chemoradiotherapy in aggressive PBL was able to overcome the worse prognosis of these patients. At least one treatment modality should be considered in patients with indolent PBL, given that observation alone was associated with a poor prognosis.

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.

Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.

A comprehensive study capturing vision loss burden in Pakistan (1990-2025): Findings from the Global Burden of Disease (GBD) 2017 study.

This study aims to provide estimates, trends and projections of vision loss burden in Pakistan from 1990 to 2025. Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2017) was used to observe the vision loss burden in terms of prevalence and Years Lived with Disability (YLDs). As of 2017, out of 207.7 million people in Pakistan, an estimated 1.12 million (95% Uncertainty Interval [UI] 1.07-1.19) were blind (Visual Acuity [VA] <3/60), 1.09 million [0.93-1.24] people had severe vision loss (3/60≤VA<6/60) and 6.79 million [6.00-7.74] people had moderate vision loss (6/60≤VA<6/18). Presbyopia was found to be the most common ocular condition that affected an estimated 12.64 million [11.94-13.41] people (crude prevalence 6.08% [5.75-6.45]; 61% female). In terms of age-standardized YLDs rate, Pakistan is ranked fourth among other South Asian countries and twenty-first among other 42 low-middle income countries (classified by World Bank), with 552.98 YLDs [392.98-752.95] per 100,000. Compared with 1990, all-age YLDs count of blindness and vision impairment increased by 55% in 2017, which is the tenth highest increase among major health loss causes (such as dietary iron deficiency, headache disorders, low back pain etc.) in Pakistan. Moreover, our statistics show an increase in vision loss burden by 2025 for which Pakistan needs to make more efforts to encounter the growing burden of eye diseases.