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AIM: The aim was to investigate whether COVID-19 increases new incidence of hypertension (HTN), chronic kidney disease (CKD) and diabetic ketoacidosis (DKA) in patients with type 1 diabetes (T1D) up to 40 months post-infection. MATERIALS AND METHODS: Three groups of patients from the Montefiore Health System in the Bronx (1 March 2020 to 1 July 2023) were studied: T1D patients hospitalized for COVID-19 (H-COVID-19, n = 511), T1D patients with COVID-19 but not hospitalized for COVID-19 (NH-COVID-19, n = 306) and T1D patients without a positive COVID-19 test on record (non-COVID-19, n = 1547). COVID-19 patients were those with a positive polymerase-chain-reaction test on record, and non-COVID-19 patients were either tested negative or not tested on record. Cumulative incidences and adjusted hazard ratios (aHR) with 95% confidence intervals (CI) were computed with adjustment for competing risks. RESULTS: Compared to non-COVID-19 patients, both H-COVID-19 (unadjusted 19.72% vs. 3.14%, p < 0.001; aHR = 7.55 [3.33, 17.06], p < 0.001) and NH-COVID-19 (10.26% vs. 3.14%, p = 0.004; aHR = 5.08 [2.19, 11.78], p < 0.001) patients were more likely to develop new HTN. Compared to non-COVID-19 patients, both H-COVID-19 (11.41% vs. 1.14%, p < 0.001; aHR = 9.76 [4.248, 22.25], p < 0.001) and NH-COVID-19 (7.69% vs. 1.14%, p < 0.001; aHR = 6.54 [2.91, 14.67], p < 0.001) patients were more likely to develop new CKD. Compared to non-COVID-19 patients, both H-COVID-19 (4.09% vs. 1.06%, p < 0.001; aHR = 12.24 [4.09, 36.59], p < 0.001) and NH-COVID-19 (3.06% vs. 1.06%, p = 0.035; aHR = 12.94 [4.09, 40.89], p < 0.001) patients were more likely to develop new DKA at follow-up. CONCLUSION: T1D patients with COVID-19 are at higher risk of developing new HTN, CKD and DKA compared to T1D patients without COVID-19.
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PURPOSE: To determine the incidence of newly diagnosed liver disorders (LD) up to 3.5-year post-acute COVID-19, and risk factors associated with new LD. METHODS: We analyzed 54,699 COVID-19 patients and 1,409,547 non-COVID-19 controls from March-11-2020 to Jan-03-2023. New liver disorders included abnormal liver function tests, advanced liver failure, alcohol and non-alcohol related liver disorders, and cirrhosis. Comparisons were made with ambulatory non-COVID-19 patients and patients hospitalized for other lower respiratory tract infections (LRTI). Demographics, comorbidities, laboratory data, incomes, insurance status, and unmet social needs were tabulated. The primary outcome was new LD at least two weeks following COVID-19 positive test. RESULTS: Incidence of new LD was not significantly different between COVID-19 and non-COVID-19 cohorts (incidence:1.99% vs 1.90% p>0.05, OR = 1.04[95%CI: 0.92,1.17], p = 0.53). COVID-19 patients with new LD were older, more likely to be Hispanic and had higher prevalence of diabetes, hypertension, chronic kidney disease, and obesity compared to patients without new LD. Hospitalized COVID-19 patients had no elevated risk of LD compared to hospitalized LRTI patients (2.90% vs 2.07%, p>0.05, OR = 1.29[0.98,1.69], p = 0.06). Among COVID-19 patients, those who developed LD had fewer patients with higher incomes (14.18% vs 18.35%, p<0.05) and more with lower incomes (21.72% vs 17.23%, p<0.01), more Medicare and less Medicaid insurance, and more patients with >3 unmet social needs (6.49% vs 2.98%, p<0.001) and fewer with no unmet social needs (76.19% vs 80.42%, p<0.001). CONCLUSIONS: Older age, Hispanic ethnicity, and obesity, but not COVID-19 status, posed increased risk for developing new LD. Lower socioeconomic status was associated with higher incidence of new LD.
Subject(s)
COVID-19 , Liver Diseases , Humans , COVID-19/epidemiology , Male , Female , Risk Factors , Middle Aged , Incidence , Aged , Liver Diseases/epidemiology , SARS-CoV-2/isolation & purification , Adult , New York City/epidemiology , Comorbidity , PandemicsABSTRACT
Purpose: The purpose of this study is to identify clinical and imaging characteristics associated with post-COVID pulmonary function decline. Methods: This study included 22 patients recovering from COVID-19 who underwent serial spirometry pulmonary function testing (PFT) before and after diagnosis. Patients were divided into two cohorts by difference between baseline and post-COVID follow-up PFT: Decline group (>10 % decrease in FEV1), and Stable group (≤10 % decrease or improvement in FEV1). Demographic, clinical, and laboratory data were collected, as well as PFT and chest computed tomography (CT) at the time of COVID diagnosis and follow-up. CTs were semi-quantitatively scored on a five-point severity scale for disease extent in each lobe by two radiologists. Mann-Whitney U-tests, T-tests, and Chi-Squared tests were used for comparison. P-values <0.05 were considered statistically significant. Results: The Decline group had a higher proportion of neutrophils (79.47 ± 4.83 % vs. 65.45 ± 10.22 %; p = 0.003), a higher absolute neutrophil count (5.73 ± 2.68 × 109/L vs. 3.43 ± 1.74 × 109/L; p = 0.031), and a lower proportion of lymphocytes (9.90 ± 4.20 % vs. 21.21 ± 10.97 %; p = 0.018) compared to the Stable group. The Decline group also had significantly higher involvement of ground-glass opacities (GGO) on follow-up chest CT [8.50 (4.50, 14.50) vs. 3.0 (1.50, 9.50); p = 0.032] and significantly higher extent of reticulations on chest CT at time of COVID diagnosis [6.50 (4.00, 9.00) vs. 2.00 (0.00, 6.00); p = 0.039] and follow-up [5.00 (3.00, 13.00) vs. 2.00 (0.00, 5.00); p = 0.041]. ICU admission was higher in the Decline group than in the Stable group (71.4 % vs. 13.3 %; p = 0.014). Conclusions: This study provides novel insight into factors influencing post-COVID lung function, irrespective of pre-existing pulmonary conditions. Our findings underscore the significance of neutrophil counts, reduced lymphocyte counts, pulmonary reticulation on chest CT at diagnosis, and extent of GGOs on follow-up chest CT as potential indicators of decreased post-COVID lung function. This knowledge may guide prediction and further understanding of long-term sequelae of COVID-19 infection.
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Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID's epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management; Conclusions: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Addressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition.
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This study investigated long COVID of patients in the Montefiore Health System COVID-19 (CORE) Clinics in the Bronx with an emphasis on identifying health related social needs (HRSNs). We analyzed a cohort of 643 CORE patients (6/26/2020-2/24/2023) and 52,089 non-CORE COVID-19 patients. Outcomes included symptoms, physical, emotional, and cognitive function test scores obtained at least three months post-infection. Socioeconomic variables included median incomes, insurance status, and HRSNs. The CORE cohort was older age (53.38 ± 14.50 vs. 45.91 ± 23.79 years old, p < 0.001), more female (72.47% vs. 56.86%, p < 0.001), had higher prevalence of hypertension (45.88% vs. 23.28%, p < 0.001), diabetes (22.86% vs. 13.83%, p < 0.001), COPD (7.15% vs. 2.28%, p < 0.001), asthma (25.51% vs. 12.66%, p < 0.001), lower incomes (53.81% vs. 43.67%, 1st quintile, p < 0.001), and more unmet social needs (29.81% vs. 18.49%, p < 0.001) compared to non-CORE COVID-19 survivors. CORE patients reported a wide range of severe long-COVID symptoms. CORE patients with unmet HRSNs experienced more severe symptoms, worse ESAS-r scores (tiredness, wellbeing, shortness of breath, and pain), PHQ-9 scores (12.5 (6, 17.75) vs. 7 (2, 12), p < 0.001), and GAD-7 scores (8.5 (3, 15) vs. 4 (0, 9), p < 0.001) compared to CORE patients without. Patients with unmet HRSNs experienced worse long-COVID outcomes compared to those without.
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Asthma , COVID-19 , Humans , Female , Young Adult , Adult , Middle Aged , Aged , Post-Acute COVID-19 Syndrome , COVID-19/epidemiology , Chronic Disease , Disease ProgressionABSTRACT
BACKGROUND: Acute neurological manifestation is a common complication of acute Coronavirus Disease 2019 (COVID-19) disease. This retrospective cohort study investigated the 3-year outcomes of patients with and without significant neurological manifestations during initial COVID-19 hospitalization. METHODS AND FINDINGS: Patients hospitalized for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection between 03/01/2020 and 4/16/2020 in the Montefiore Health System in the Bronx, an epicenter of the early pandemic, were included. Follow-up data was captured up to 01/23/2023 (3 years post-COVID-19). This cohort consisted of 414 patients with COVID-19 with significant neurological manifestations and 1,199 propensity-matched patients (for age and COVID-19 severity score) with COVID-19 without neurological manifestations. Neurological involvement during the acute phase included acute stroke, new or recrudescent seizures, anatomic brain lesions, presence of altered mentation with evidence for impaired cognition or arousal, and neuro-COVID-19 complex (headache, anosmia, ageusia, chemesthesis, vertigo, presyncope, paresthesias, cranial nerve abnormalities, ataxia, dysautonomia, and skeletal muscle injury with normal orientation and arousal signs). There were no significant group differences in female sex composition (44.93% versus 48.21%, p = 0.249), ICU and IMV status, white, not Hispanic (6.52% versus 7.84%, p = 0.380), and Hispanic (33.57% versus 38.20%, p = 0.093), except black non-Hispanic (42.51% versus 36.03%, p = 0.019). Primary outcomes were mortality, stroke, heart attack, major adverse cardiovascular events (MACE), reinfection, and hospital readmission post-discharge. Secondary outcomes were neuroimaging findings (hemorrhage, active and prior stroke, mass effect, microhemorrhages, white matter changes, microvascular disease (MVD), and volume loss). More patients in the neurological cohort were discharged to acute rehabilitation (10.39% versus 3.34%, p < 0.001) or skilled nursing facilities (35.75% versus 25.35%, p < 0.001) and fewer to home (50.24% versus 66.64%, p < 0.001) than matched controls. Incidence of readmission for any reason (65.70% versus 60.72%, p = 0.036), stroke (6.28% versus 2.34%, p < 0.001), and MACE (20.53% versus 16.51%, p = 0.032) was higher in the neurological cohort post-discharge. Per Kaplan-Meier univariate survival curve analysis, such patients in the neurological cohort were more likely to die post-discharge compared to controls (hazard ratio: 2.346, (95% confidence interval (CI) [1.586, 3.470]; p < 0.001)). Across both cohorts, the major causes of death post-discharge were heart disease (13.79% neurological, 15.38% control), sepsis (8.63%, 17.58%), influenza and pneumonia (13.79%, 9.89%), COVID-19 (10.34%, 7.69%), and acute respiratory distress syndrome (ARDS) (10.34%, 6.59%). Factors associated with mortality after leaving the hospital involved the neurological cohort (odds ratio (OR): 1.802 (95% CI [1.237, 2.608]; p = 0.002)), discharge disposition (OR: 1.508 (95% CI [1.276, 1.775]; p < 0.001)), congestive heart failure (OR: 2.281 (95% CI [1.429, 3.593]; p < 0.001)), higher COVID-19 severity score (OR: 1.177 (95% CI [1.062, 1.304]; p = 0.002)), and older age (OR: 1.027 (95% CI [1.010, 1.044]; p = 0.002)). There were no group differences in radiological findings, except that the neurological cohort showed significantly more age-adjusted brain volume loss (p = 0.045) than controls. The study's patient cohort was limited to patients infected with COVID-19 during the first wave of the pandemic, when hospitals were overburdened, vaccines were not yet available, and treatments were limited. Patient profiles might differ when interrogating subsequent waves. CONCLUSIONS: Patients with COVID-19 with neurological manifestations had worse long-term outcomes compared to matched controls. These findings raise awareness and the need for closer monitoring and timely interventions for patients with COVID-19 with neurological manifestations, as their disease course involving initial neurological manifestations is associated with enhanced morbidity and mortality.
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COVID-19 , Stroke , Humans , Female , COVID-19/complications , COVID-19/epidemiology , COVID-19/therapy , SARS-CoV-2 , Retrospective Studies , Follow-Up Studies , Aftercare , Patient Discharge , Seizures , Stroke/epidemiologyABSTRACT
BACKGROUND: Although certain subsets patients with multiple sclerosis (MS), an immune-mediated disorder, are at higher risk of worse acute COVID-19 outcomes compared to the general population, it is not clear whether SARS-CoV-2 infection impacts long-term outcomes compared with MS patients without COVID-19 infection. OBJECTIVES: This study investigated MS disease activity and mortality 3.5 years post SARS-CoV-2 infection and compared with MS patients without COVID-19. METHODS: This retrospective study evaluated 1,633 patients with MS in the Montefiore Health System in the Bronx from January 2016 to July 2023. This health system serves a large minority population and was an epicenter for the early pandemic and subsequent surges of infection. Positive SARS-CoV-2 infection was determined by a positive polymerase-chain-reaction test. Primary outcomes were all-cause mortality, and optic neuritis post SARS-CoV-2 infection. Secondary outcomes included change in disease-modifying therapy (DMT), treatment with high-dose methylprednisolone, cerebellar deficits, relapse, and all-cause hospitalization post-infection. RESULTS: MS patients with COVID-19 had similar demographics but higher prevalence of pre-existing major comorbidities (hypertension, type-2 diabetes, chronic obstructive pulmonary disease, congestive heart failure, chronic kidney disease, and coronary artery disease), optic neuritis, and history of high dose steroid treatment for relapses compared to MS patients without COVID-19. MS patients with COVID-19 had greater risk of mortality (adjusted HR=4.34[1.67, 11.30], p < 0.005), greater risk of post infection optic neuritis (adjusted HR=2.97[1.58, 5.58], p < 0.005), higher incidence of methylprednisolone treatment for post infection acute relapse (12.65% vs. 2.54 %, p < 0.001), and more hospitalization (78.92% vs. 66.81 %, p < 0.01), compared to MS patients without COVID-19. CONCLUSIONS: MS patients who survived COVID-19 infection experienced worse long-term outcomes, as measured by treatment for relapse, hospitalization and mortality. Identifying risk factors for worse long-term outcomes may draw clinical attention to the need for careful follow-up of at-risk individuals post-SARS-CoV-2 infection.
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COVID-19 , Multiple Sclerosis , Humans , COVID-19/mortality , COVID-19/complications , COVID-19/epidemiology , Male , Female , Middle Aged , Multiple Sclerosis/mortality , Multiple Sclerosis/epidemiology , Multiple Sclerosis/drug therapy , Retrospective Studies , Adult , Comorbidity , Optic Neuritis/epidemiology , Urban Population/statistics & numerical data , Hospitalization/statistics & numerical data , New York City/epidemiologyABSTRACT
While ground-glass opacity, consolidation, and fibrosis in the lungs are some of the hallmarks of acute SAR-CoV-2 infection, it remains unclear whether these pulmonary radiological findings would resolve after acute symptoms have subsided. We conducted a systematic review and meta-analysis to evaluate chest computed tomography (CT) abnormalities stratified by COVID-19 disease severity and multiple timepoints post-infection. PubMed/MEDLINE was searched for relevant articles until 23 May 2023. Studies with COVID-19-recovered patients and follow-up chest CT at least 12 months post-infection were included. CT findings were evaluated at short-term (1-6 months) and long-term (12-24 months) follow-ups and by disease severity (severe and non-severe). A generalized linear mixed-effects model with random effects was used to estimate event rates for CT findings. A total of 2517 studies were identified, of which 43 met the inclusion (N = 8858 patients). Fibrotic-like changes had the highest event rate at short-term (0.44 [0.3-0.59]) and long-term (0.38 [0.23-0.56]) follow-ups. A meta-regression showed that over time the event rates decreased for any abnormality (ß = -0.137, p = 0.002), ground-glass opacities (ß = -0.169, p < 0.001), increased for honeycombing (ß = 0.075, p = 0.03), and did not change for fibrotic-like changes, bronchiectasis, reticulation, and interlobular septal thickening (p > 0.05 for all). The severe subgroup had significantly higher rates of any abnormalities (p < 0.001), bronchiectasis (p = 0.02), fibrotic-like changes (p = 0.03), and reticulation (p < 0.001) at long-term follow-ups when compared to the non-severe subgroup. In conclusion, significant CT abnormalities remained up to 2 years post-COVID-19, especially in patients with severe disease. Long-lasting pulmonary abnormalities post-SARS-CoV-2 infection signal a future public health concern, necessitating extended monitoring, rehabilitation, survivor support, vaccination, and ongoing research for targeted therapies.
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BACKGROUND: Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population. METHODS: Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation. RESULTS: pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05). CONCLUSION: Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.
Subject(s)
Breast Neoplasms , Humans , Female , Breast Neoplasms/epidemiology , Breast Neoplasms/genetics , Breast Neoplasms/therapy , Ethnicity , Machine Learning , Neoadjuvant Therapy , Neural Networks, ComputerABSTRACT
BACKGROUND: Prognosis of future risk of dementia from neuroimaging and cognitive data is important for optimizing clinical management for patients at early stage of Alzheimer's disease (AD). However, existing studies lack an efficient way to integrate longitudinal information from both modalities to improve prognosis performance. OBJECTIVE: In this study, we aim to develop and evaluate an explainable deep learning-based framework to predict mild cognitive impairment (MCI) to AD conversion within four years using longitudinal whole-brain 3D MRI and neurocognitive tests. METHODS: We proposed a two-stage framework that first uses a 3D convolutional neural network to extract single-timepoint MRI-based AD-related latent features, followed by multi-modal longitudinal feature concatenation and a 1D convolutional neural network to predict the risk of future dementia onset in four years. RESULTS: The proposed deep learning framework showed promising to predict MCI to AD conversion within 4 years using longitudinal whole-brain 3D MRI and cognitive data without extracting regional brain volumes or cortical thickness, reaching a balanced accuracy of 0.834, significantly improved from models trained from single timepoint or single modality. The post hoc model explainability revealed heatmap indicating regions that are important for predicting future risk of AD. CONCLUSIONS: The proposed framework sets the stage for future studies for using multi-modal longitudinal data to achieve optimal prediction for prognosis of AD onset, leading to better management of the diseases, thereby improving the quality of life.
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Alzheimer Disease , Cognitive Dysfunction , Humans , Alzheimer Disease/diagnostic imaging , Quality of Life , Magnetic Resonance Imaging/methods , Neural Networks, Computer , Neuroimaging/methods , Cognitive Dysfunction/diagnostic imagingABSTRACT
Introduction: Neurocognitive symptoms and dysfunction of various severities have become increasingly recognized as potential consequences of SARS-CoV-2 infection. Although there are numerous observational and subjective survey-reporting studies of neurological symptoms, by contrast, those studies describing imaging abnormalities are fewer in number. Methods: This study conducted a metanalysis of 32 studies to determine the incidence of the common neurological abnormalities using magnetic resonance imaging (MRI) in patients with COVID-19. Results: We also present the common clinical findings associated with MRI abnormalities. We report the incidence of any MRI abnormality to be 55% in COVID-19 patients with perfusion abnormalities (53%) and SWI abnormalities (44%) being the most commonly reported injuries. Cognitive impairment, ICU admission and/or mechanical ventilation status, older age, and hospitalization or longer length of hospital stay were the most common clinical findings associated with brain injury in COVID-19 patients. Discussion: Overall, the presentation of brain injury in this study was diverse with no substantial pattern of injury emerging, yet most injuries appear to be of vascular origin. Moreover, analysis of the association between MRI abnormalities and clinical findings suggests that there are likely many mechanisms, both direct and indirect, by which brain injury occurs in COVID-19 patients.
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Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease involving immune-mediated damage. Iron deposition in deep gray matter (DGM) structures like the thalamus and basal ganglia have been suggested to play a role in MS pathogenesis. Magnetic Resonance Imaging (MRI) imaging methods like T2 and T2* imaging, susceptibility-weighted imaging, and quantitative susceptibility mapping can track iron deposition storage in the brain primarily from ferritin and hemosiderin (paramagnetic iron storage proteins) with varying levels of tissue contrast and sensitivity. In this systematic review, we evaluated the role of DGM iron deposition as detected by MRI techniques in relation to MS-related neuroinflammation and its potential as a novel therapeutic target. We searched through PubMed, Embase, and Web of Science databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, against predetermined inclusion and exclusion criteria. We included 89 articles (n = 6630 patients), and then grouped them into different categories: i) methodological techniques to measure DGM iron, ii) cross-sectional and group comparison of DGM iron content, iii) longitudinal comparisons of DGM iron, iv) associations between DGM iron and other imaging and neurobiological markers, v) associations with disability, and vi) associations with cognitive impairment. The review revealed that iron deposition in DGM is independent yet concurrent with demyelination, and that these iron deposits contribute to MS-related cognitive impairment and disability. Variability in iron distributions appears to rely on a positive feedback loop between inflammation, and release of iron by oligodendrocytes. DGM iron seems to be a promising prognostic biomarker for MS pathophysiology.
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Multiple Sclerosis , Neurodegenerative Diseases , Humans , Gray Matter/diagnostic imaging , Neurodegenerative Diseases/pathology , Cross-Sectional Studies , Multiple Sclerosis/diagnostic imaging , Multiple Sclerosis/pathology , Magnetic Resonance Imaging/methods , Brain/pathology , Inflammation/pathology , Iron/metabolismABSTRACT
INTRODUCTION: This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS). METHODS: This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks. RESULTS: Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction. DISCUSSION: Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
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OBJECTIVE: To investigate the clinical outcomes of patients with rheumatoid arthritis (RA) with COVID-19. METHODS: This retrospective study consisted of 361 patients with RA+ and 45,954 patients with RA- (March 2020 to August 2022) who tested positive for SARS-CoV-2 by polymerase-chain-reaction in the Montefiore Health System, which serves a large low-income, minority-predominant population in the Bronx and was an epicenter of the initial pandemic and subsequent surges. Primary outcomes were hospitalization, critical illness, and all-cause mortality associated with SARS-CoV-2 infection. Comparisons were made with and without adjustment for covariates, as well as with 1083 matched controls of patients with RA- and COVID-19. RESULTS: Patients with RA+ and COVID-19 were older (62.2 ± 23.5 vs. 45.5 ± 26.3; P < 0.001), were more likely females (83.1% vs. 55.8%; P < 0.001), were Black (35.5% vs. 30.3%; P < 0.05), and had higher rates of comorbidities (P < 0.05), hospitalization (52.4% vs. 32.5%; P < 0.005), critical illness (10.5% vs. 6.9%; P < 0.05), and mortality (11.1% vs. 6.2%; P < 0.01) compared with patients with RA- and COVID-19. Patients with RA+ with COVID-19 had higher odds of critical illness (adjusted odds ratio [aOR] = 1.46, 95% confidence interval [CI]: 1.09-1.93; P = 0.008) but no differences in hospitalization (aOR = 1.18 [95% CI: 0.93-1.49]; P = 0.16) and mortality (aOR = 1.34 [95% CI: 0.92-1.89]; P = 0.10) after adjusting for covariates. Odds ratio analysis identified age, hospitalization status, female sex, chronic kidney disease, chronic obstructive pulmonary disease, and Black race to be significant risk factors for COVID-19-related mortality. Pre-COVID-19 steroid and biologic therapy to treat RA were not significantly associated with worse outcomes (P > 0.05). Outcomes were not different between patients with RA+ and propensity-matched RA- controls (P > 0.05). CONCLUSION: Our findings suggest that risk factors for adverse COVID-19 outcomes were not attributed to RA per se but rather age and preexisting medical conditions of patients with RA.
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BACKGROUND: SARS-CoV-2 may trigger new-onset persistent hypertension. This study investigated the incidence and risk factors associated with new-onset persistent hypertension during COVID-19 hospitalization and at ≈6-month follow-up compared with influenza. METHODS: This retrospective observational study was conducted in a major academic health system in New York City. Participants included 45 398 patients with COVID-19 (March 2020 to August 2022) and 13 864 influenza patients (January 2018 to August 2022) without a history of hypertension. RESULTS: At 6-month follow-up, new-onset persistent hypertension was seen in 20.6% of hospitalized patients with COVID-19 and 10.85% of nonhospitalized patients with COVID-19. Persistent hypertension incidence among hospitalized patients did not vary across the pandemic, whereas that of hospitalized patients decreased from 20% in March 2020 to ≈10% in October 2020 (R2=0.79, P=0.003) and then plateaued thereafter. Hospitalized patients with COVID-19 were 2.23 ([95% CI, 1.48-3.54]; P<0.001) times and nonhospitalized patients with COVID-19 were 1.52 ([95% CI, 1.22-1.90]; P<0.01) times more likely to develop persistent hypertension than influenza counterparts. Persistent hypertension was more common among older adults, males, Black, patients with preexisting comorbidities (chronic obstructive pulmonary disease, coronary artery disease, chronic kidney disease), and those who were treated with pressor and corticosteroid medications. Mathematical models predicted persistent hypertension with 79% to 86% accuracy. In addition, 21.0% of hospitalized patients with COVID-19 with no prior hypertension developed hypertension during COVID-19 hospitalization. CONCLUSIONS: Incidence of new-onset persistent hypertension in patients with COVID-19 is higher than those with influenza, likely constituting a major health burden given the sheer number of patients with COVID-19. Screening at-risk patients for hypertension following COVID-19 illness may be warranted.
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COVID-19 , Hypertension , Influenza, Human , Male , Humans , Aged , COVID-19/complications , COVID-19/epidemiology , SARS-CoV-2 , Incidence , Influenza, Human/complications , Influenza, Human/epidemiology , Hypertension/epidemiologyABSTRACT
OBJECTIVES: This study investigated whether patients with sickle cell disease (SCD) had elevated risk of worse long-term clinical outcomes and healthcare utilization 2.5 years post-SARS-CoV-2 infection. METHODS: This study consisted of 178 patients with SCD who tested positive for COVID-19 between February 1, 2020 and January 30, 2022 in a major academic health system in New York City. The control cohort consisted of two-to-one matches of 356 SCD patients without a COVID-19 positive test. The last follow-up was July 18, 2022. The primary outcome was mortality. Secondary outcomes were annualized emergency department visits due to pain, pain hospital admission, length of stay due to pain, acute chest syndrome, episodic transfusion, and episodic exchange transfusion. RESULTS: There was no significant difference in mortality between SCD patients with and without COVID-19 (p > .05). There were no significant differences in secondary outcomes between pre- and postpandemic (p > .05). There were also no significant differences in these outcomes between SCD patients with and without COVID-19 (p > .05). SCD care utilization was not significantly associated with COVID-19 hospitalization status (p > .05). CONCLUSIONS: SCD patients with SARS-CoV-2 infection incurred no additional risk of worse long-term outcomes compared to matched controls of SCD patients not infected by SARS-CoV-2.
Subject(s)
Anemia, Sickle Cell , COVID-19 , Humans , Follow-Up Studies , COVID-19/epidemiology , COVID-19/complications , SARS-CoV-2 , Anemia, Sickle Cell/complications , Anemia, Sickle Cell/diagnosis , Anemia, Sickle Cell/epidemiology , Patient Acceptance of Health Care , PainABSTRACT
Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.
Subject(s)
Breast Neoplasms , Humans , Female , Magnetic Resonance Imaging , Algorithms , Magnetic Resonance SpectroscopyABSTRACT
INTRODUCTION: Patients with prediabetes who contract SARS-CoV-2 infection (COVID-19) could be at higher risk of developing frank diabetes compared those who do not. This study aims to investigate the incidence of new-onset diabetes in patients with prediabetes after COVID-19 and if it differs from those not infected. RESEARCH DESIGN AND METHODS: Using electronic medical record data, 42 877 patients with COVID-19, 3102 were identified as having a history of prediabetes in the Montefiore Health System, Bronx, New York. During the same time period, 34 786 individuals without COVID-19 with history of prediabetes were identified and 9306 were propensity matched as controls. SARS-CoV-2 infection status was determined by a real-time PCR test between March 11, 2020 and August 17, 2022. The primary outcomes were new-onset in-hospital diabetes mellitus (I-DM) and new-onset persistent diabetes mellitus (P-DM) at 5 months after SARS-CoV-2 infection. RESULTS: Compared with hospitalized patients without COVID-19 with history of prediabetes, hospitalized patients with COVID-19 with history of prediabetes had a higher incidence of I-DM (21.9% vs 6.02%, p<0.001) and of P-DM 5 months postinfection (14.75% vs 7.51%, p<0.001). Non-hospitalized patients with and without COVID-19 with history of prediabetes had similar incidence of P-DM (4.15% and 4.1%, p>0.05). Critical illness (HR 4.6 (95% CI 3.5 to 6.1), p<0.005), in-hospital steroid treatment (HR 2.88 (95% CI 2.2 to 3.8), p<0.005), SARS-CoV-2 infection status (HR 1.8 (95% CI 1.4 to 2.3), p<0.005), and hemoglobin A1c (HbA1c) (HR 1.7 (95% CI 1.6 to 1.8), p<0.005) were significant predictors of I-DM. I-DM (HR 23.2 (95% CI 16.1 to 33.4), p<0.005), critical illness (HR 2.4 (95% CI 1.6 to 3.8), p<0.005), and HbA1c (HR 1.3 (95% CI 1.1 to 1.4), p<0.005) were significant predictors of P-DM at follow-up. CONCLUSIONS: SARS-CoV-2 infection confers a higher risk for developing persistent diabetes 5 months post-COVID-19 in patients with prediabetes who were hospitalized for COVID-19 compared with COVID-19-negative counterparts with prediabetes. In-hospital diabetes, critical illness, and elevated HbA1c are risk factors for developing persistent diabetes. Patients with prediabetes with severe COVID-19 disease may need more diligent monitoring for developing P-DM postacute SARS-CoV-2 infection.
Subject(s)
COVID-19 , Diabetes Mellitus , Prediabetic State , Humans , Prediabetic State/complications , Prediabetic State/epidemiology , COVID-19/complications , COVID-19/epidemiology , Glycated Hemoglobin , Retrospective Studies , Critical Illness , SARS-CoV-2 , Diabetes Mellitus/epidemiologyABSTRACT
AIMS: This study characterized incidence, patient profiles, risk factors and outcomes of in-hospital diabetic ketoacidosis (DKA) in patients with COVID-19 compared with influenza and pre-pandemic data. METHODS: This study consisted of 13 383 hospitalized patients with COVID-19 (March 2020-July 2022), 19 165 hospitalized patients with influenza (January 2018-July 2022) and 35 000 randomly sampled hospitalized pre-pandemic patients (January 2017-December 2019) in Montefiore Health System, Bronx, NY, USA. Primary outcomes were incidence of in-hospital DKA, in-hospital mortality, and insulin use at 3 and 6 months post-infection. Risk factors for developing DKA were identified. RESULTS: The overall incidence of DKA in patients with COVID-19 and influenza, and pre-pandemic were 2.1%, 1.4% and 0.5%, respectively (p < .05 pairwise). Patients with COVID-19 with DKA had worse acute outcomes (p < .05) and higher incidence of new insulin treatment 3 and 6 months post-infection compared with patients with influenza with DKA (p < .05). The incidence of DKA in patients with COVID-19 was highest among patients with type 1 diabetes (12.8%), followed by patients with insulin-dependent type 2 diabetes (T2D; 5.2%), non-insulin dependent T2D (2.3%) and, lastly, patients without T2D (1.3%). Patients with COVID-19 with DKA had worse disease severity and higher mortality [odds ratio = 6.178 (4.428-8.590), p < .0001] compared with those without DKA. Type 1 diabetes, steroid therapy for COVID-19, COVID-19 status, black race and male gender were associated with increased risk of DKA. CONCLUSIONS: The incidence of DKA was higher in COVID-19 cohort compared to the influenza and pre-pandemic cohort. Patients with COVID-19 with DKA had worse outcomes compared with those without. Many COVID-19 survivors who developed DKA during hospitalization became insulin dependent. Identification of risk factors for DKA and new insulin-dependency could enable careful monitoring and timely intervention.
Subject(s)
COVID-19 , Diabetes Mellitus, Type 1 , Diabetes Mellitus, Type 2 , Diabetic Ketoacidosis , Influenza, Human , Humans , Male , Diabetic Ketoacidosis/epidemiology , Diabetic Ketoacidosis/therapy , Diabetic Ketoacidosis/etiology , Diabetes Mellitus, Type 1/complications , Diabetes Mellitus, Type 1/drug therapy , Diabetes Mellitus, Type 1/epidemiology , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/drug therapy , Diabetes Mellitus, Type 2/epidemiology , Incidence , Pandemics , Influenza, Human/complications , Influenza, Human/epidemiology , Retrospective Studies , COVID-19/complications , COVID-19/epidemiology , Risk Factors , Insulin/therapeutic use , Insulin, Regular, HumanABSTRACT
Purpose: To investigate the evolution of COVID-19 patient characteristics and multiorgan injury across the pandemic. Methods: This retrospective cohort study consisted of 40,387 individuals tested positive for SARS-CoV-2 in the Montefiore Health System in Bronx, NY, between March 2020 and February 2022, of which 11,306 were hospitalized. Creatinine, troponin, and alanine aminotransferase were used to define acute kidney injury (AKI), acute cardiac injury (ACI) and acute liver injury, respectively. Demographics, comorbidities, emergency department visits, hospitalization, intensive care utilization, and mortality were analyzed across the pandemic. Results: COVID-19 positive cases, emergency department visits, hospitalization and mortality rate showed four distinct waves with a large first wave in April 2020, two small (Alpha and Delta) waves, and a large Omicron wave in December 2021. Omicron was more infectious but less lethal (p = 0.05). Among hospitalized COVID-19 patients, age decreased (p = 0.014), female percentage increased (p = 0.023), Hispanic (p = 0.028) and non-Hispanic Black (p = 0.05) percentages decreased, and patients with pre-existing diabetes (p = 0.002) and hypertension (p = 0.04) decreased across the pandemic. More than half (53.1%) of hospitalized patients had major organ injury. Patients with AKI, ACI and its combinations were older, more likely males, had more comorbidities, and consisted more of non-Hispanic Black and Hispanic patients (p = 0.005). Patients with AKI and its combinations had 4-9 times higher adjusted risk of mortality than those without. Conclusions: There were shifts in demographics toward younger age and proportionally more females with COVID-19 across the pandemic. While the overall trend showed improved clinical outcomes, a substantial number of COVID-19 patients developed multi-organ injuries over time. These findings could bring awareness to at-risk patients for long-term organ injuries and help to better inform public policy and outreach initiatives.