Characteristics and outcomes of prisoners hospitalized due to COVID-19 disease.

BACKGROUND: Correctional facilities have faced unique challenges during the COVID-19 pandemic. A COVID-19 outbreak was reported in the Federal Medical Center (FMC) in Lexington, Kentucky, a prison for inmates requiring medical and mental care. The main objective of this study was to examine clinical characteristics and outcomes of prisoners vs. non-prisoners admitted to the hospital due to COVID-19 disease. MATERIALS AND METHODS: We did a retrospective, comparative cohort study of 86 consecutive COVID-19 patients admitted to the University of Kentucky hospital between March 1 and June 1, 2020. Among these, 37 patients were inmates from a single local FMC and 49 were non-inmates. RESULTS: Mean (SD) age of the cohort was 59.1 (14.5) years, 68.6% were male and 61.6% white. All inmates were men. No significant differences in age or race were observed between inmates and non-inmates. Hypertension (81%), obesity (62%), COPD/asthma (43%), diabetes (41%), coronary artery diseases (38%), and chronic kidney disease (22%) were among the most common comorbidities prevalent in inmates. Inmates had overall higher serum creatinine and C-reactive protein, more proteinuria, and lower platelet counts at the time of hospital admission when compared to non-inmates. Incidence of acute kidney injury (AKI) was more frequent in inmates (68 vs. 38% in non-inmates, p = 0.008). Overall, patients who developed AKI had higher acuity of illness with more requirement of ICU care and mechanical ventilation. Kidney replacement therapy (KRT) was provided to 12.8% of patients. Inpatient mortality occurred in 15.1% of patients and was not different in inmates vs. non-inmates (13.5 vs. 16.3%, p = 0.862). All survivors became independent of KRT, and ~ 1 of 10 survivors had a reduction of eGFR ≥ 25% from baseline by the time of discharge, which was more frequent in inmates vs. non-inmates, 15.6 vs. 2.4%, p = 0.042, respectively. CONCLUSION: Inmates represent a vulnerable population with prevalent comorbidity and susceptibility to COVID-19. When compared to non-inmates with COVID-19, inmates exhibited higher incidence of AKI and, for survivors, less kidney recovery by the time of hospital discharge. Surveillance of long-term sequela of COVID-19 is warranted in this susceptible inmate population.

Bone Quality in Chronic Kidney Disease Patients: Current Concepts and Future Directions - Part II.

BACKGROUND: Patients with chronic kidney disease (CKD) have an increased risk of osteoporotic fractures, which is due not only to low bone volume and mass but also poor microarchitecture and tissue quality. The pharmacological and nonpharmacological interventions detailed, herein, are potential approaches to improve bone health in CKD patients. Various medications build up bone mass but also affect bone tissue quality. Antiresorptive therapies strikingly reduce bone turnover; however, they can impair bone mineralization and negatively affect the ability to repair bone microdamage and cause an increase in bone brittleness. On the other hand, some osteoporosis therapies may cause a redistribution of bone structure that may improve bone strength without noticeable effect on BMD. This may explain why some drugs can affect fracture risk disproportionately to changes in BMD. SUMMARY: An accurate detection of the underlying bone abnormalities in CKD patients, including bone quantity and quality abnormalities, helps in institution of appropriate management strategies. Here in this part II, we are focusing on advancements in bone therapeutics that are anticipated to improve bone health and decrease mortality in CKD patients. KEY MESSAGES: Therapeutic interventions to improve bone health can potentially advance life span. Emphasis should be given to the impact of various therapeutic interventions on bone quality.

Bone Quality in CKD Patients: Current Concepts and Future Directions - Part I.

BACKGROUND: There is ample evidence that patients with CKD have an increased risk of osteoporotic fractures. Bone fragility is not only influenced by low bone volume and mass but also by poor microarchitecture and tissue quality. More emphasis has been given to the quantitative rather than qualitative assessment of bone health, both in general population and CKD patients. Although bone mineral density (BMD) is a very useful clinical tool in assessing bone strength, it may underestimate the fracture risk in CKD patients. Serum and urinary bone biomarkers have been found to be reflective of bone activities and predictive of fractures independently of BMD in CKD patients. Bone quality and fracture risk in CKD patients can be better assessed by utilizing new technologies such as trabecular bone score and high-resolution imaging studies. Additionally, invasive assessments such as bone histology and micro-indentation are useful counterparts in the evaluation of bone quality. SUMMARY: A precise diagnosis of the underlying skeletal abnormalities in CKD patients is crucial to prevent further bone loss and fractures. We must consider bone quantity and quality abnormalities for management of CKD patients. Here in this part I, we are focusing on advances in bone quality diagnostics that are expected to help in proper understanding of the bone health in CKD patients. KEY MESSAGES: Assessment of bone quality and quantity in CKD patients is essential. Both noninvasive and invasive techniques for the assessment of bone quality are available.

Pathogenesis and management of vascular calcification in CKD and dialysis patients.

Patients with chronic kidney disease (CKD) have a predisposition to develop vascular calcification due to dysregulated homeostatic mechanisms, which lead to an imbalance in the circulatory promoters and inhibitors of vascular calcification, leading to a net calcification stress. These factors promote ectopic calcification and induce vascular smooth muscle cells to undergo osteogenic differentiation and actively calcify the vascular media. The article summarizes clinically relevant pathogenic mechanisms of vascular calcification in patients with CKD and in dialysis patients and summarizes novel therapeutic interventions. In addition to the management of traditional cardiovascular risk factors, patients with CKD-mineral and bone disorder need close attention in the management of the mineral metabolism to prevent adverse effects on the bone and vascular compartments. This article reviews current evidence and therapeutic guidelines in the management of mineral metabolism in CKD and dialysis.

Long-term outcomes and management considerations after parathyroidectomy in the dialysis patient.

Parathyroidectomy (PTX) remains an important intervention for dialysis patients with poorly controlled secondary hyperparathyroidism (SHPT), though there are only retrospective and observational data that show a mortality benefit to this procedure. Potential consequences that we seek to avoid after PTX include persistent or recurrent hyperparathyroidism, and parathyroid insufficiency. There is considerable subjectivity in defining and diagnosing these conditions, given that we poorly understand the optimal PTH targets (particularly post PTX) needed to maintain bone and vascular health. While lowering PTH after PTX decreases bone turnover, long-term changes in bone activity have been poorly explored. High turnover bone disease, usually present at the time a PTX is considered, often swings to a state of low turnover in the setting of sufficiently low PTH levels. It remains unclear if all low bone turnover equate with disease. However, such changes in bone turnover appear to predispose to vascular calcification, with positive calcium balance after PTX being a potential contributor. We know little of how the post-PTX state resets calcium balance, how calcium and VDRA requirements change or what kind of adjustments are needed to avoid calcium loading. The current consensus cautions against excessive reduction of PTH although there is insufficient evidence-based guidance regarding the management of chronic kidney disease - mineral bone disease (CKD-MBD) parameters in the post-PTX state. This article aims to compile existing research, provide an overview of current practice with regard to PTX and post-PTX chronic management. It highlights gaps and controversies and aims to re-orient the focus to clinically relevant contemporary priorities in CKD-MBD management after PTX.

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.

BACKGROUND: Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. OBJECTIVE: To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. METHODS: We used 7000 treats and 2918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80-20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. RESULTS: Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. CONCLUSIONS: We employed semantic graph patterns connecting pairs of candidate biomedical entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.