Search

BACKGROUND: Although early discharge after colectomy has garnered significant interest, contemporary, large-scale analyses are lacking. OBJECTIVE: The present study utilized a national cohort of patients undergoing colectomy to examine costs and readmissions following early discharge. METHODS: All adults undergoing elective colectomy for primary colon cancer were identified in the 2016-2019 Nationwide Readmissions Database. Patients with perioperative complications or prolonged length of stay (>8 days) were excluded to enhance cohort homogeneity. Patients discharged by postoperative day 3 were classified as Early, and others as Routine. Entropy balancing and multivariable regression were used to assess the risk-adjusted association of early discharge with costs and non-elective readmissions. Importantly, we compared 90-day stroke rates to examine whether our results were influenced by preferential early discharge of healthier patients. RESULTS: Of an estimated 153,996 patients, 45.5% comprised the Early cohort. Compared to Routine, the Early cohort was younger and more commonly male. Patients in the Early group more commonly underwent left-sided colectomy and laparoscopic operations. Following multivariable adjustment, expedited discharge was associated with a $4,500 reduction in costs as well as lower 30-day (adjusted odds ratio [AOR] 0.74, p

BACKGROUND: Expedited discharge following esophagectomy is controversial due to concerns for higher readmissions and financial burden. The present study aimed to evaluate the association of expedited discharge with hospitalization costs and unplanned readmissions following esophagectomy for malignant lesions. METHODS: Adults undergoing elective esophagectomy for cancer were identified in the 2014-2019 Nationwide Readmissions Database. Patients discharged by postoperative day 7 were considered Expedited and others as Routine. Patients who did not survive to discharge or had major perioperative complications were excluded. Multivariable regression models were constructed to assess association of expedited discharge with index hospitalization costs as well as 30- and 90-day non-elective readmissions. RESULTS: Of 9,886 patients who met study criteria, 34.6% comprised the Expedited cohort. After adjustment, female sex (adjusted odds ratio [AOR] 0.71, p = 0.001) and increasing Elixhauser Comorbidity Index (AOR 0.88/point, p

BackgroundDifferential diagnosis of elevated high sensitive Troponin T (hsTnT) in acute ischemic stroke includes myocardial infarction (MI) and neurogenic stunned myocardium (NSM). The aim of this study was to identify factors associated with baseline hsTnT levels and MI or NSM in acute ischemic stroke.MethodsWe studied 204 consecutive patients of the prospective acquired Bern Stroke Database with acute ischemic stroke diagnosed by brain MR. All patient histories and cardiac examinations were reviewed retrospectively. Volumetry of lesions on diffusion and perfusion weighted brain imaging (circular singular value decomposition, Tmax >6sec) was performed. Voxel based analysis was performed to identify brain areas associated with hsTnT elevation. Linear regression analysis was used to identify predictors of baseline hsTnT levels and myocardial infarction.ResultsElevated hsTnT was observed in 58 of the 204 patients (28.4%). The mean age was 68.3 years in the normal hsTnT group and 69.7 years in the elevated hsTnT group. Creatinine (pConclusionElevated baseline baseline hsTnT was associated with NIHSS, creatinine, ST segment depression and inverted T waves, but not with stroke location or size. None of the factors was helpful to differentiate MI and NSM. Therefore, ancillary investigations such as coronary angiography, cardiac MRI or both may be needed to solve the differential diagnosis.

Cucumis melo L. that belongs to Cucurbitaceae family ranks among one of the highest valued horticulture crops being cultivated across the globe. Besides its economical and medicinal importance, Cucumis melo L. is a valuable resource and model system for the evolutionary studies of cucurbit family. However, very limited numbers of molecular markers were reported for Cucumis melo L. so far that limits the pace of functional genomic research in melon and other similar horticulture crops. We developed the first whole genome based microsatellite DNA marker database of Cucumis melo L. and comprehensive web resource that aids in variety identification and physical mapping of Cucurbitaceae family. The Cucumis melo L. microsatellite database (CmMDb: http://65.181.125.102/cmmdb2/index.html) encompasses 39,072 SSR markers along with its motif repeat, motif length, motif sequence, marker ID, motif type and chromosomal locations. The database is featured with novel automated primer designing facility to meet the needs of wet lab researchers. CmMDb is a freely available web resource that facilitates the researchers to select the most appropriate markers for marker-assisted selection in melons and to improve breeding strategies.

BackgroundDespite the known advantages of minimally invasive surgery (MIS) for diverticular disease, the impact of conversions to open (CtO) colectomy remains understudied. The present study used a nationally representative database to characterize risk factors and outcomes associated with CtO in patients with diverticular disease.MethodsAll elective adult hospitalizations entailing colectomy for diverticulitis were identified in the 2017-2019 Nationwide Readmissions Database. Annual institutional caseloads of MIS and open colectomy were independently tabulated. Restricted cubic splines were utilized to non-linearly estimate the risk-adjusted association between hospital volumes and CtO. Additional regression models were developed to evaluate the association of CtO with outcomes of interest.ResultsOf an estimated 110,281 patients with diverticulitis who met study criteria, 39.3% underwent planned open colectomy, 53.3% completed MIS, and 7.4% had a CtO. Following adjustment, an inverse relationship between hospital MIS volume and risk of CtO was observed. In contrast, increasing hospital open volume was positively associated with greater risk of CtO. On multivariable analysis, CtO was associated with lower odds of mortality (AOR 0.3, p = 0.001) when compared to open approach, and similar risk of mortality when compared to completed MIS (AOR 0.7, p = 0.436).ConclusionIn the present study, institutional MIS volume exhibited inverse correlation with adjusted rates of CtO, independent of open colectomy volume. CtO was associated with decreased rates of mortality compared to planned open approach but equivalence risk relative to completed MIS. Our findings highlight the importance of MIS experience and suggest that MIS may be safely pursued as the initial surgical approach among diverticulitis patients.

BackgroundEmergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored.ObjectiveThe present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort.MethodsNon-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016-18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R2). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models.ResultsOf 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899-0.901 vs 0.894, 95% CI 0.862-0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R2: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8-11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0-3.2) and $11,900 (95% CI 11,600-12,300) in costs.ConclusionsLogistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.

4-ethyl phenyl sulfate (EPS), a protein-bound uremic toxin found in serum of patients suffering from autism spectrum disorders (ASD) and chronic kidney disease (CKD). As per recent advances in the field, gut metabolites after their formation goes to blood stream crosses blood brain barrier and causes neuro related problems. Increased levels of 4-EPS in human body causes anxiety in patients and its role remains elusive. 4-EPS interacts with serum albumin in human body and thus, a model study of interaction of BSA with 4-EPS is presented in support of it. Absorption spectroscopy result demonstrated decrease in bovine serum albumin (BSA) absorption upon interaction with increasing concentration of EPS in a range from 2 μM to 100 μM. Moreover, this interaction was confirmed by the fluorescence quenching in presence of metabolite. The change in secondary structure was demonstrated by circular dichroism, synchronous fluorescence and Fourier transform infra-red spectroscopy. Docking studies reveals binding score of −5.28 Kcal mol−1, demarking that 4-EPS is involved in interaction with BSA via amino acid residues, forming the stable complex. This interaction study may be helpful in devising strategies for the treatment of chronic kidney disease and other neuro related diseases, by producing synthetic compound that competes with albumin binding sites to allow 4-EPS clearance from the body. [ABSTRACT FROM AUTHOR]

We consider competition between antibiotic producing bacteria, non-producers (or cheaters), and sensitive cells in a two-dimensional lattice model. Previous work has shown that these three cell types can survive in spatial models due to the presence of spatial patterns, whereas coexistence is not possible in a well-mixed system. We extend this to consider the evolution of the antibiotic production rate, assuming that the cost of antibiotic production leads to a reduction in growth rate of the producers. We find that coexistence occurs for an intermediate range of antibiotic production rate. If production rate is too high or too low, only sensitive cells survive. When evolution of production rate is allowed, a mixture of cell types arises in which there is a dominant producer strain that produces sufficient to limit the growth of sensitive cells and which is able to withstand the presence of cheaters in its own species. The mixture includes a range of low-rate producers and non-producers, none of which could survive without the presence of the dominant producer strain. We also consider the case of evolution of antibiotic resistance within the sensitive species. In order for the resistant cells to survive, they must grow faster than both the non-producers and the producers. However, if the resistant cells grow too rapidly, the producing species is eliminated, after which the resistance mutation is no longer useful, and sensitive cells take over the system. We show that there is a range of growth rates of the resistant cells where the two species coexist, and where the production mechanism is maintained as a polymorphism in the producing species and the resistance mechanism is maintained as a polymorphism in the sensitive species.

BackgroundThere are few established predictors of the clinical course of PD. Prognostic markers would be useful for clinical care and research.ObjectiveTo identify predictors of long-term motor and cognitive outcomes and rate of progression in PD.MethodsNewly diagnosed PD participants were followed for 7 years in a prospective study, conducted at 55 centers in the United States and Canada. Analyses were conducted in 244 participants with complete demographic, clinical, genetic, and dopamine transporter imaging data. Machine learning dynamic Bayesian graphical models were used to identify and simulate predictors and outcomes. The outcomes rate of cognition changes are assessed by the Montreal Cognitive Assessment scores, and rate of motor changes are assessed by UPDRS part-III.ResultsThe most robust and consistent longitudinal predictors of cognitive function included older age, baseline Unified Parkinson's Disease Rating Scale (UPDRS) parts I and II, Schwab and England activities of daily living scale, striatal dopamine transporter binding, and SNP rs11724635 in the gene BST1. The most consistent predictor of UPDRS part III was baseline level of activities of daily living (part II). Key findings were replicated using long-term data from an independent cohort study.ConclusionsBaseline function near the time of Parkinson's disease diagnosis, as measured by activities of daily living, is a consistent predictor of long-term motor and cognitive outcomes. Additional predictors identified may further characterize the expected course of Parkinson's disease and suggest mechanisms underlying disease progression. The prognostic model developed in this study can be used to simulate the effects of the prognostic variables on motor and cognitive outcomes, and can be replicated and refined with data from independent longitudinal studies.

Purpose: This study investigates the accuracy of either computerized tomography (CT) or magnetic resonance imaging (MRI) for the evaluation of various orbital diseases. Methods: We collected 126 CT scans and 65 MRI scans from 144 subjects and asked two radiologists to interpret the images without clinical information. Images included 14 with a clinical diagnosis of orbital infection, 144 with orbital inflammation, and 33 with orbital neoplasm. The inflammatory diseases included thyroid eye disease (TED, n = 69), non-specific orbital inflammation (NSOI, n = 44), IgG4-related disease (IgG4-RD, n = 15), sarcoidosis (Sarcoid, n = 9), granulomatosis with polyangiitis (GPA, n = 5), and Erdheim-Chester disease (ECD, n = 2). Results: The balanced accuracy (BA) for the two radiologists ranged from 0.87 to 0.90 for cellulitis, 0.81 to 0.86 for inflammation, and 0.82 to 0.85 for neoplasm. Radiologists were excellent at recognizing GPA (BA = 0.98 to 0.99) and very good for TED (BA = 0.80 to 0.86). They also did well identifying IgG4-RD (BA = 0.75 to 0.77), but slightly less well for NSOI (BA = 0.69 to 0.75) and poorly for Sarcoid (BA = 0.48 to 0.50). Conclusions: CT or MRI scanning contributes to the evaluation of patients with orbital disease, but accuracy does varies based depending on the diagnosis. We could not evaluate issues such as determination of disease activity, variability based on the unit used for imaging or the skills beyond those of our two specialized neuroradiologists. Future studies should directly compare the two imaging modalities and assess the utility of imaging to determine disease activity. [ABSTRACT FROM AUTHOR]

Background: In Canada, one in seven adults has diabetes (i.e., 2.3 million) and the lifetime risk of developing diabetes is approximately 30% by age 65. Although 30% of patients admitted to the hospital have diabetes, data from inpatient hospitalizations for patients with diabetes are lacking, both in Canada and globally. Objective: To validate International Classification of Diseases 10th edition Canadian version (ICD-10-CA) codes for the identification of patients with diabetes, to create a multicenter database of patients with diabetes hospitalized under internal medicine in Ontario, and to determine their baseline characteristics, medication use, and admission characteristics. Study design: We created a database of people who had diabetes and were hospitalized between 2010 and 2020 at 8 hospitals in Ontario that were part of the General Medicine Inpatient Initiative (GEMINI) hospital data-sharing network. Patients who had diabetes were identified using chart review, based upon either (i) a previous physician diagnosis of diabetes, (ii) a recorded hemoglobin A1c ≥ 6.5% or (iii) outpatient prescription of a diabetes medication preceding the hospitalization. The test characteristics of ICD-10-CA codes for diabetes were evaluated. We compared baseline demographics, medication use and hospitalization details among patients with and without diabetes. For hospitalization details, we collected information on the admission diagnosis, comorbidity index, length of stay, receipt of ICU-level care, and inpatient mortality. Results: There were 384,588 admissions within the total study cohort, of which 118,987 (30.9%) had an ICD-10-CA diagnosis code of diabetes (E10.x, E11.x, E13.x, E14.x). The sensitivity and specificity of ICD-10-CA diagnostic codes was 95.9% and 98.8%, respectively. Most patients with an ICD-10-CA code for diabetes had a code for type 2 diabetes (93.9%) and a code for type 1 diabetes was rare (6.1%). The mean age was 66.4 years for patients without diabetes and 71.3 years for those with an ICD-10-CA diagnosis code for diabetes. Patients with diabetes had a higher prevalence of hypertension (64% vs. 37.9%), coronary artery disease (28.7% vs. 15.3%), heart failure (24.5% vs. 12.1%) and renal failure (33.8% vs. 17.3%) in comparison to those without diabetes. The most prevalent diabetes medications received in hospital were metformin (43%), DPP4 inhibitors (22.7%) and sulfonylureas (18.8%). The most common reason for admission among patients with diabetes was heart failure (9.0%), and among patients without diabetes was pneumonia (7.8%). Median length of stay was longer for patients with diabetes (5.5 vs. 4.5 days) and in-hospital mortality was similar between groups (6.8% with diabetes vs. 6.5% without diabetes). Importance: Diabetes is one of the most prevalent chronic medical conditions, affecting roughly one third of all patients hospitalized on an internal medicine ward and is associated with other comorbidities and longer hospital stays. ICD-10-CA codes were highly accurate in identifying patients with diabetes. The development of an inpatient cohort will allow for further study of in-hospital practices and outcomes among patients with diabetes. [ABSTRACT FROM AUTHOR]

Cognitive decline in Alzheimer's disease is associated with electroencephalographic (EEG) biosignatures even at early stages of mild cognitive impairment (MCI). The aim of this work is to provide a unified measure of cognitive decline by aggregating biosignatures from multiple EEG modalities and to evaluate repeatability of the composite measure at an individual level. These modalities included resting state EEG (eyes-closed) and two event-related potential (ERP) tasks on visual memory and attention. We compared individuals with MCI (n = 38) to age-matched healthy controls HC (n = 44). In resting state EEG, the MCI group exhibited higher power in Theta (3-7Hz) and lower power in Beta (13-20Hz) frequency bands. In both ERP tasks, the MCI group exhibited reduced ERP late positive potential (LPP), delayed ERP early component latency, slower reaction time, and decreased response accuracy. Cluster-based permutation analysis revealed significant clusters of difference between the MCI and HC groups in the frequency-channel and time-channel spaces. Cluster-based measures and performance measures (12 biosignatures in total) were selected as predictors of MCI. We trained a support vector machine (SVM) classifier achieving AUC = 0.89, accuracy = 77% in cross-validation using all data. Split-data validation resulted in (AUC = 0.87, accuracy = 76%) and (AUC = 0.75, accuracy = 70%) on testing data at baseline and follow-up visits, respectively. Classification scores at baseline and follow-up visits were correlated (r = 0.72, p<0.001, ICC = 0.84), supporting test-retest reliability of EEG biosignature. These results support the utility of EEG/ERP for prognostic testing, repeated assessments, and tracking potential treatment outcomes in the limited duration of clinical trials. [ABSTRACT FROM AUTHOR]

Objective: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. Design: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. Setting: Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010—March 2015 (primary cohort) and 2015–2019 (secondary cohort). Patients: We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. Measurements and main results: The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015–2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56–83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015–2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. Conclusions: An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications. [ABSTRACT FROM AUTHOR]

The pathogenic spirochete Leptospira interrogans disseminates throughout its hosts via the bloodstream, then invades and colonizes a variety of host tissues. Infectious leptospires are resistant to killing by their hosts' alternative pathway of complement-mediated killing, and interact with various host extracellular matrix (ECM) components. The LenA outer surface protein (formerly called LfhA and Lsa24) was previously shown to bind the host ECM component laminin and the complement regulators factor H and factor H-related protein-1. We now demonstrate that infectious L. interrogans contain five additional paralogs of lenA, which we designated lenB, lenC, lenD, lenE and lenF. All six genes encode domains predicted to bear structural and functional similarities with mammalian endostatins. Sequence analyses of genes from seven infectious L. interrogans serovars indicated development of sequence diversity through recombination and intragenic duplication. LenB was found to bind human factor H, and all of the newly-described Len proteins bound laminin. In addition, LenB, LenC, LenD, LenE and LenF all exhibited affinities for fibronectin, a distinct host extracellular matrix protein. These characteristics suggest that Len proteins together facilitate invasion and colonization of host tissues, and protect against host immune responses during mammalian infection.

Background: There is limited evidence on the reliability and validity of the 12-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) in an Asian patient population with mental and physical disorders. The current study aimed to examine the psychometric properties of the WHODAS 2.0 among patients with schizophrenia, depression, anxiety, and diabetes. Methods: A total of 1076 patients (M = 40.9 years, SD = 14.7) were recruited from the outpatient clinics of a tertiary psychiatric hospital and a primary care clinic. Internal consistency and test-retest reliability, structural validity, convergent validity, agreement, and floor and ceiling effects were examined. Results: Our confirmatory factor analysis (CFA) showed that the 1-factor model fits our data. Multigroup CFA demonstrated metric and scalar invariance, indicating the scores can be compared across the four conditions. The WHODAS 2.0 scale had excellent reliability in the overall sample and good to excellent reliability across conditions. The test-retest reliability and agreement between self-administered and interviewer-administered modes were good. The WHODAS 2.0 scores had moderate to strong correlations with the Social and Occupational Functioning Scale and the Sheehan Disability Scale scores in the overall sample and across four conditions. Conclusion: Findings suggest that the WHODAS 2.0 is a valid tool to measure functioning and disability in those with schizophrenia, anxiety, depression, and diabetes in an Asian patient population. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is a multifactorial disease which involves number of molecular factors that are critically involved in proliferation of breast cancer cells. MEN1 gene that is traditionally known for its germline mutations in neuroendocrine tumors is associated with high risk of developing breast cancer in females with MEN1 syndrome. However, the paradoxical role of MEN1 is reported in sporadic breast cancer cases. The previous studies indicate the functional significance of MEN1 in regulating breast cells proliferation but its relevance in development and progression of breast cancer is still not known. Our study targets to find the role of MEN1 gene aberration and its clinical significance in breast cancer. Methods: Breast tumor and adjacent normal tissue of 142 sporadic breast cancer patients were collected at the time of surgery. The expression analysis of MEN1 mRNA and protein was done through RT-PCR, immunohistochemistry and western blotting. Further to find the genetic and epigenetic alterations, automated sequencing and MS-PCR was performed respectively. Correlation between our findings and clinical parameters was determined using appropriate statistical tests. Results: MEN1 expression was found to be significantly increased in the breast tumor tissue with its predominant nuclear localization. The elevated expression of MEN1 mRNA (63.38% cases) and protein (60.56% cases) exhibited a significant association with ER status of the patients. Most of the cases had unmethylated (53.52%) MEN1 promoter region, which can be a key factor responsible for dysregulated expression of MEN1 in breast cancer cases. Our findings also revealed the significant association of MEN1 mRNA overexpression with Age and lymph node status of the patients. Conclusion: Our results indicate upregulated expression of MEN1 in sporadic breast cancer patients and it could be critically associated with development and advancement of the disease. [ABSTRACT FROM AUTHOR]