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BackgroundEarly hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.AimsWe leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.MethodsA national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.ResultsWe analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.ConclusionOur model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.

OBJECTIVE: Cirrhotic patients are at high hospitalisation risk with subsequent high mortality. Current risk prediction models have varied performances with methodological room for improvement. We used current analytical techniques using automatically extractable variables from the electronic health record (EHR) to develop and validate a posthospitalisation mortality risk score for cirrhotic patients and compared performance with the model for end-stage liver disease (MELD), model for end-stage liver disease with sodium (MELD-Na), and the CLIF Consortium Acute Decompensation (CLIF-C AD) models. DESIGN: We analysed a retrospective cohort of 73 976 patients comprising 247 650 hospitalisations between 2006 and 2013 at any of 123 Department of Veterans Affairs hospitals. Using 45 predictor variables, we built a time-dependent Cox proportional hazards model with all-cause mortality as the outcome. We compared performance to the three extant models and reported discrimination and calibration using bootstrapping. Furthermore, we analysed differential utility using the net reclassification index (NRI). RESULTS: The C-statistic for the final model was 0.863, representing a significant improvement over the MELD, MELD-Na, and the CLIF-C AD, which had C-statistics of 0.655, 0.675, and 0.679, respectively. Multiple risk factors were significant in our model, including variables reflecting disease severity and haemodynamic compromise. The NRI showed a 24% improvement in predicting survival of low-risk patients and a 30% improvement in predicting death of high-risk patients. CONCLUSION: We developed a more accurate mortality risk prediction score using variables automatically extractable from an EHR that may be used to risk stratify patients with cirrhosis for targeted postdischarge management.

The present study aimed at identifying risk factors associated with periodontitis development and periodontal health disparities with emphasis on differential oral microbiota. The prevalence of periodontitis is recently rising dentate adults in the US, which presents a challenge to oral health and overall health. The risk of developing periodontitis is higher in African Americans (AAs), and Hispanic Americans (HAs) than in Caucasian Americans (CAs). To identify potentially microbiological determinations of periodontal health disparities, we examined the distribution of several potentially beneficial and pathogenic bacteria in the oral cavities of AA, CA, and HA study participants. Dental plaque samples from 340 individuals with intact periodontium were collected prior to any dental treatment, and levels of some key oral bacteria were quantitated using qPCR, and the medical and dental histories of participants were obtained retrospectively from axiUm. Data were analyzed statistically using SAS 9.4, IBM SPSS version 28, and R/RStudio version 4.1.2. Amongst racial/ethnic groups: 1) neighborhood medium incomes were significantly higher in the CA participants than the AA and the HA participants; 2) levels of bleeding on probing (BOP) were higher in the AAs than in the CAs and HAs; 3) Porphyromonas gingivalis levels were higher in the HAs compared to that in the CAs; 4) most P. gingivalis detected in the AAs were the fimA genotype II strain that was significantly associated with higher BOP indexes along with the fimA type IV strain. Our results suggest that socioeconomic disadvantages, higher level of P. gingivalis, and specific types of P. gingivalis fimbriae, particularly type II FimA, contribute to risks for development of periodontitis and periodontal health disparities. [ABSTRACT FROM AUTHOR]

This paper presents a semiautomatic method for the registration of images acquired during surgery with a tracked laser range scanner (LRS). This method, which relies on the registration of vessels that can be visualized in the pre- and the postresection images, is a component of a larger system designed to compute brain shift that occurs during tumor resection cases. Because very large differences between pre- and postresection images are typically observed, the development of fully automatic methods to register these images is difficult. The method presented herein is semiautomatic and requires only the identification of a number of points along the length of the vessels. Vessel segments joining these points are then automatically identified using an optimal path finding algorithm that relies on intensity features extracted from the images. Once vessels are identified, they are registered using a robust point-based nonrigid registration algorithm. The transformation computed with the vessels is then applied to the entire image. This permits establishment of a complete correspondence between the pre- and post-3-D LRS data. Experiments show that the method is robust to operator errors in localizing homologous points and a quantitative evaluation performed on ten surgical cases shows submillimetric registration accuracy. Index Terms--Brain shift, feature extraction, image-guided neurosurgery (IGNS), image registration, laser range scan (LRS).

Abstract Background One hypothesis for the social deficits that characterize autism spectrum disorders (ASD) is diminished neural reward response to social interaction and attachment. Prior research using established monetary reward paradigms as a test of non-social reward to compare with social reward may involve confounds in the ability of individuals with ASD to utilize symbolic representation of money and the abstraction required to interpret monetary gains. Thus, a useful addition to our understanding of neural reward circuitry in ASD includes a characterization of the neural response to primary rewards. Method We asked 17 children with ASD and 18 children without ASD to abstain from eating for at least four hours before an MRI scan in which they viewed images of high-calorie foods. We assessed the neural reward network for increases in the blood oxygenation level dependent (BOLD) signal in response to the food images Results We found very similar patterns of increased BOLD signal to these images in the two groups; both groups showed increased BOLD signal in the bilateral amygdala, as well as in the nucleus accumbens, orbitofrontal cortex, and insula. Direct group comparisons revealed that the ASD group showed a stronger response to food cues in bilateral insula along the anterior-posterior gradient and in the anterior cingulate cortex than the control group, whereas there were no neural reward regions that showed higher activation for controls than for ASD. Conclusion These results suggest that neural response to primary rewards is not diminished but in fact shows an aberrant enhancement in children with ASD.

This paper presents a semiautomatic method for the registration of images acquired during surgery with a tracked laser range scanner (LRS). This method, which relies on the registration of vessels that can be visualized in the pre- and the postresection images, is a component of a larger system designed to compute brain shift that occurs during tumor resection cases. Because very large differences between pre- and postresection images are typically observed, the development of fully automatic methods to register these images is difficult. The method presented herein is semiautomatic and requires only the identification of a number of points along the length of the vessels. Vessel segments joining these points are then automatically identified using an optimal path finding algorithm that relies on intensity features extracted from the images. Once vesselsare identified, they are registered using a robust point-based nonrigid registration algorithm. The transformation computed with the vessels is then applied to the entire image. This permits establishment of a complete correspondence between the pre- and post-3-D LRS data. Experiments show that the method is robust to operator errors in localizing homologous points and a quantitative evaluation performed on ten surgical cases shows submillimetric registration accuracy. [ABSTRACT FROM AUTHOR]

Intra-operative brain shift limits the usefulness of image-guided neurosurgery systems (IGNS), which are based on pre-operative images. Methods that are being developed to address this problem need intra-operative measurements as input. In this work, we present an intra-operative surface shift measurement technique that relies on a tracked 3D laser range scanner. This scanner acquires both 3D range data and 2D images, which are co-registered. We compare two methods to derive displacements at every point in the field of view. The first one relies on the registration of the 2D images; the second relies on the direct 3D registration of the 3D range data. Our results, based on five data sets, show that the 2D method is preferable.

Nucleotide sequences contain motifs that preserved through evolution because they are important to the structure or function of the molecules. DNA binding site analysis is an important issue in biology experiments as well as in computational methods. To find DNA binding sites that bind to specific transcription factors, we develop a robust mixed effect mixture model (RMEMM). The DNA sequences are represented as mixed effect model of position specific frequency, considering the relationship of frequency between positions. The results show that the mean effect is similar to position-specific scoring matrices (PSSM), providing a new view of the sequence. This model is robust to outliers or data with a bit large tails on distribution.