Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79-0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC.

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

BACKGROUND: The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease. METHODS: This study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature. RESULTS: Through robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10-16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed ( https://xistance.shinyapps.io/prs-ra/ ) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs. CONCLUSIONS: These findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

Area-Under-Curve-Guided Versus Trough-Guided Monitoring of Vancomycin and Its Impact on Nephrotoxicity: A Systematic Review and Meta-Analysis.

BACKGROUND: Conventionally, vancomycin trough levels have been used for therapeutic drug monitoring (TDM). Owing to the increasing evidence of trough levels being poor surrogates of area under the curve (AUC) and the advent of advanced pharmacokinetics software, a paradigm shift has been made toward AUC-guided dosing. This study aims to evaluate the impact of AUC-guided versus trough-guided TDM on vancomycin-associated nephrotoxicity. METHODS: A systematic review was conducted using PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google scholar, and Cochrane library databases; articles published from January 01, 2009, to January 01, 2021, were retrieved and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Studies that evaluated trough-guided or AUC-guided vancomycin TDM and vancomycin-associated nephrotoxicity were included. Random-effects models were used to compare the differences in nephrotoxicity. RESULTS: Of the 1191 retrieved studies, 57 were included. Most studies included adults and older adults (n = 47, 82.45%). The pooled prevalence of nephrotoxicity was lower in AUC-guided TDM [6.2%; 95% confidence interval (CI): 2.9%-9.5%] than in trough-guided TDM (17.0%; 95% CI: 14.7%-19.2%). Compared with the trough-guided approach, the AUC-guided approach had a lower risk of nephrotoxicity (odds ratio: 0.53; 95% CI: 0.32-0.89). The risk of nephrotoxicity was unaffected by the AUC derivation method. AUC thresholds correlated with nephrotoxicity only within the first 96 hours of therapy. CONCLUSIONS: The AUC-guided approach had a lower risk of nephrotoxicity, supporting the updated American Society of Health-System Pharmacists guidelines. Further studies are needed to evaluate the optimal AUC-derivation methods and clinical utility of repeated measurements of the AUC and trough levels of vancomycin.

Cortical thickness abnormalities in attention deficit hyperactivity disorder revealed by structural magnetic resonance imaging: Newborns to young adults.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition for which we have an incomplete understanding, and so brain imaging methods, such as magnetic resonance imaging (MRI), may be able to assist in characterising and understanding the presentation of the brain in an ADHD population. Statistical and computational methods were used to compare participants with ADHD and neurotypical controls at a variety of developmental stages to assess detectable abnormal neurodevelopment potentially associated with ADHD and to assess our ability to diagnose and characterise the condition from real-world clinical MRI examinations. T1-weighted structural MRI examinations (n = 993; 0-31 years old [YO]) were obtained from neurotypical controls, and 637 examinations were obtained from patients with ADHD (0-26 YO). Measures of average (mean) regional cortical thickness were acquired, alongside the first reporting of regional cortical thickness variability (as assessed with the standard deviation [SD]) in ADHD. A comparison between the inattentive and combined (inattentive and hyperactive) subtypes of ADHD is also provided. A preliminary independent validation was also performed on the publicly available ADHD200 dataset. Relative to controls, subjects with ADHD had, on average, lowered SD of cortical thicknesses and increased mean thicknesses across several key regions potentially linked with known symptoms of ADHD, including the precuneus and supramarginal gyrus.

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature.

BACKGROUND: The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. METHODS: Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. RESULTS: A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson's disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. CONCLUSIONS: Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.

BACKGROUND: Major challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients. METHODS: Exome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set. FINDINGS: Significantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients. INTERPRETATION: Majority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways. FUNDING: Singapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.

Machine learning using genetic and clinical data identifies a signature that robustly predicts methotrexate response in rheumatoid arthritis.

OBJECTIVE: To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. METHODS: MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. RESULTS: The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855-0.916; sensitivity: 0.715-0.892; and specificity: 0.733-0.862) and the unseen test set (area under the curve: 0.751-0.826; sensitivity: 0.581-0.839; and specificity: 0.641-0.923). CONCLUSION: Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management.

Sentiments Regarding COVID-19 Vaccination among Graduate Students in Singapore.

As the COVID-19 pandemic rages unabated, and with more infectious variants, vaccination may offer a way to transit out of strict restrictions on physical human interactions to curb the virus spread and prevent overwhelming the healthcare system. However, vaccine hesitancy threatens to significantly impact our progress towards achieving this. It is thus important to understand the sentiments regarding vaccination for different segments of the population to facilitate the development of effective strategies to persuade these groups. Here, we surveyed the COVID-19 vaccination sentiments among a highly educated group of graduate students from the National University of Singapore (NUS). Graduate students who are citizens of 54 different countries, mainly from Asia, pursue studies in diverse fields, with 32% expressing vaccine hesitancy. Citizenship, religion, country of undergraduate/postgraduate studies, exposure risk and field of study are significantly associated with vaccine sentiments. Students who are Chinese citizens or studied in Chinese Universities prior to joining NUS are more hesitant, while students of Indian descent or studied in India are less hesitant about vaccination. Side effects, safety issues and vaccine choice are the major concerns of the hesitant group. Hence, this study would facilitate the development of strategies that focus on these determinants to enhance vaccine acceptance.

Barriers to and Facilitators of Cervical Cancer Screening among Women in Southeast Asia: A Systematic Review.

In Southeast Asia, cervical cancer is the second most common cancer in women. Low coverage for cervical cancer screening (CCS) becomes a roadblock to disease detection and treatment. Existing reviews on CCS have limited insights into the barriers and facilitators for SEA. Hence, this study aims to identify key barriers and facilitators among women living in SEA. A systematic literature review was conducted on Pubmed, Embase, PsycINFO, CINAHL, and SCOPUS. Primary qualitative and quantitative studies published in English that reported barriers and facilitators to CCS were included. The Mix Methods Appraisal Tool was used for the quality assessment of the included studies. Among the 93 included studies, pap smears (73.1%) were the most common screening modality. A majority of the studies were from Malaysia (35.5%). No studies were from Timor-Leste and the Philippines. The most common barriers were embarrassment (number of articles, n = 33), time constraints (n = 27), and poor knowledge of screening (n = 27). The most common facilitators were related to age (n = 21), receiving advice from healthcare workers (n = 17), and education status (n = 11). Findings from this review may inform health policy makers in developing effective cervical cancer screening programs in SEA countries.

Taurolidine-citrate lock solution for the prevention of central line-associated bloodstream infection in paediatric haematology-oncology and gastrointestinal failure patients with high baseline central-line associated bloodstream infection rates.

AIM: Central line-associated bloodstream infection associated bloodstream infection (CLABSI) is a serious complication of patients on central venous catheters (CVC). Taurolidine-citrate solution (TCS) is a catheter-lock solution with broad-spectrum antimicrobial action. This study's aim was to evaluate the efficacy of TCS in reducing CLABSI rates in paediatric haematology-oncology (H/O) and gastrointestinal (GI) patients with long-term CVC. METHODS: This was an open-label trial of H/O and GI inpatients with the following inclusion criteria: <17 years old, more than or equal to one previous CLABSI and a minimum TCS dwell time of ≥8 h. CLABSI per 1000 catheter-days was calculated from each patient's first CVC insertion till 14 December 2017 or until TCS discontinuation. RESULTS: Thirty-three patients were recruited with a median age of 3.5 years; H/O and GI constituted 60.6 and 39.4% respectively. CVC types were Hickman line (45.5%), implantable port (24.2%) and peripherally inserted central catheter (30.3%). Mean pre- and post-TCS CLABSI rates per 1000 catheter-days were 14.44 and 2.45 (P < 0.001) for all patients; 16.55 and 2.81 for H/O patients; and 11.21 and 1.90 for GI patients, respectively. Pre- and post-TCS rate ratio was 0.20, 0.10 and 0.30 for all, H/O and GI patients, respectively (P < 0.001). TCS also led to a reduction in CVC removal from 66.7 to 9.09% (P < 0.001). CONCLUSIONS: TCS usage was highly successful in CLABSI reduction by 80% in all patients, 90% in H/O and 70% in GI patients. In patients with high baseline CLABSI rates, TCS is an effective catheter-lock therapy to reduce CLABSI rates in paediatric patients.

Structural magnetic resonance imaging demonstrates abnormal cortical thickness in Down syndrome: Newborns to young adults.

Down syndrome (DS) is a genetic disorder caused by an extra copy of all or part of chromosome 21 and is characterized by intellectual disability. We performed a retrospective analysis of 47 magnetic resonance imaging (MRI) examinations of participants with DS (aged 5 to 22 years) and compared them with a large cohort of 854 brain MRIs obtained from neurotypical participants (aged 5 to 32 years) with the objective of assessing the clinical presentation of Down syndrome, towards better understanding the neurological development associated with the condition. An additional cohort of 26 MRI exams from patients with DS and 139 exams from neurotypical participants (aged 0-5 years) are included as part of a supplementary analysis. Regionally distributed cortical thickness measurements, including average measurements as well as standard deviations (intra-regional cortical thickness variability) were extracted from each examination. The largest effect sizes observed were associated with increased average cortical thickness in the postcentral gyrus with specific abnormalities observed in Brodmann's areas 1 and 3b in DS, which was observed across all age ranges. We also observed strong effect sizes associated with decreased cortical thickness variability in the lateral orbitofrontal gyrus, the postcentral gyrus and more in DS participants. Findings suggest regionally irregular gray matter development in DS that can be detected with MRI.

Structural Magnetic Resonance Imaging Demonstrates Abnormal Regionally-Differential Cortical Thickness Variability in Autism: From Newborns to Adults.

Autism is a group of complex neurodevelopmental disorders characterized by impaired social interaction and restricted/repetitive behavior. We performed a large-scale retrospective analysis of 1,996 clinical neurological structural magnetic resonance imaging (MRI) examinations of 781 autistic and 988 control subjects (aged 0-32 years), and extracted regionally distributed cortical thickness measurements, including average measurements as well as standard deviations which supports the assessment of intra-regional cortical thickness variability. The youngest autistic participants (<2.5 years) were diagnosed after imaging and were identified retrospectively. The largest effect sizes and the most common findings not previously published in the scientific literature involve abnormal intra-regional variability in cortical thickness affecting many (but not all) regions of the autistic brain, suggesting irregular gray matter development in autism that can be detected with MRI. Atypical developmental patterns have been detected as early as 0 years old in individuals who would later be diagnosed with autism.

Asymmetric Insular Connectomics Revealed by Diffusion Magnetic Resonance Imaging Analysis of Healthy Brain Development.

The insula has been implicated in playing important roles in various brain functions including consciousness, homeostasis, perception, self-awareness, language processing, and interpersonal experience. Abnormalities of the insula have been observed in patients suffering from addiction, deteriorating language function, anorexia, and emotional dysregulation. We analyzed typical development of insular connections in a large-scale pediatric population using 642 magnetic resonance imaging examinations. Interpreting large quantities of acquired data is one of the major challenges in connectomics. This article focuses its analysis on the connectivity observed between the insula and many other regions throughout the brain and performs a hemispheric asymmetry analysis comparing localized connectome measurements. Results demonstrate asymmetries in the pathways connecting the insula to the superior temporal region, pars opercularis, etc. that may be representative of language lateralization in the brain. Results also demonstrate multiple fiber pathways that exhibit hemispheric dominance in tract length and an inverted hemispheric dominance in tract counts, implying the presence of asymmetric lateralization of some of the brain's insular pathways. This study illustrates the investigative potential of performing connectomics-style analyses in a clinical context across a large population of children as part of routine imaging, demonstrating the feasibility of using current technologies to perform regionally focused clinical connectivity studies.

Regional volumetric abnormalities in pediatric autism revealed by structural magnetic resonance imaging.

Autism is a group of complex neurodevelopmental disorders characterized by impaired social interaction, restricted and repetitive behavior. We performed a large-scale retrospective analysis of 1,996 structural magnetic resonance imaging (MRI) examinations of the brain from 1,769 autistic and neurologically typically developing patients (aged 0-32 years), and extracted regional volumetric measurements distributed across 463 brain regions of each patient. The youngest autistic patients (<2.5 years) were diagnosed after imaging and identified retrospectively. Our study demonstrates corpus callosum volumetric abnormalities among autistic patients that are associated with brain overgrowth in early childhood (0-5 years old), followed by a shift towards known decreased volumes in later ages. Results confirm known increases in ventricular volumes among autistic populations and extends those findings to increased volumes of the choroid plexus. Our study also demonstrates distributed volumetric abnormalities among autistic patients that affect a variety of key regional white and grey matter areas of the brain potentially associated with known symptoms of autism.

A Sorting Statistic with Application in Neurological Magnetic Resonance Imaging of Autism.

Effect size refers to the assessment of the extent of differences between two groups of samples on a single measurement. Assessing effect size in medical research is typically accomplished with Cohen's d statistic. Cohen's d statistic assumes that average values are good estimators of the position of a distribution of numbers and also assumes Gaussian (or bell-shaped) underlying data distributions. In this paper, we present an alternative evaluative statistic that can quantify differences between two data distributions in a manner that is similar to traditional effect size calculations; however, the proposed approach avoids making assumptions regarding the shape of the underlying data distribution. The proposed sorting statistic is compared with Cohen's d statistic and is demonstrated to be capable of identifying feature measurements of potential interest for which Cohen's d statistic implies the measurement would be of little use. This proposed sorting statistic has been evaluated on a large clinical autism dataset from Boston Children's Hospital, Harvard Medical School, demonstrating that it can potentially play a constructive role in future healthcare technologies.

A pediatric structural MRI analysis of healthy brain development from newborns to young adults.

Assessment of healthy brain maturation can be useful toward better understanding natural patterns of brain growth and toward the characterization of a variety of neurodevelopmental disorders as deviations from normal growth trajectories. Structural magnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which allows for the assessment of gray and white matter in the developing brain. We performed a large-scale retrospective analysis of 993 pediatric structural brain MRI examinations of healthy subjects (n = 988, aged 0-32 years) imaged clinically at 3 T, and extracted a wide variety of measurements such as white matter volumes, cortical thickness, and gyral curvature localized to subregions of the brain. All extracted structural biomarkers were tested for their correlation with subject age at time of imaging, providing measurements that may assist in the assessment of neurological maturation. Additional analyses were also performed to assess gender-based differences in the brain at a variety of developmental stages, and to assess hemispheric asymmetries. Results add to the literature by analyzing a realistic distribution of healthy participants imaged clinically, a useful cohort toward the investigation and creation of diagnostic tests for a variety of pathologies as aberrations from healthy growth trajectories. The next generation of diagnostic tests will be responsible for identifying pathological conditions from populations of healthy clinically imaged individuals. Hum Brain Mapp 38:5931-5942, 2017. © 2017 Wiley Periodicals, Inc.

Detection and Growth Pattern of Arcuate Fasciculus from Newborn to Adult.

Fractional anisotropy (FA) threshold is commonly used to perform diffusion MRI tractography. However, FA threshold may be one aspect of tractography that needs additional scrutiny in accurately assessing pathways in immature, developing brains, as well as in adult brains. Using high-angular resolution diffusion MRI (HARDI) tractography without an FA threshold, we identified the arcuate fasciculus (AF) of 83 healthy subjects ranging in age from 40 gestational weeks (GW) (newborns) to 28-year-old adults. The AF was identified in both hemispheres in all subjects with high inter-rater reliability. The detected AF included regions with very low FA values. The entire AF was segmented into anterior, posterior, and long tracts. Growth and laterality patterns were investigated using tract count (number of detected streamlines), total volume of imaging voxels (touched by the detected streamlines), mean length, mean FA, and mean apparent diffusion coefficient (ADC). Comparison of subjects under 3 years old, to those that were older, revealed the three AF tracts that took different developmental courses. As expected, the anterior and long tracts showed lower ADC values in subjects over 3 years old, while the posterior tract showed higher ADC in that same age range. The posterior tract did not show age-related effect in terms of FA, tract count, length, and volume. These results suggest that the posterior AF tract shows a matured state, indexed by most of the used measurements in early postnatal developmental ages, and ADC is a measurement that can detect further maturation of the posterior tract. Interestingly, in all tracts, hemispheric asymmetries were found in raw (leftright) tract count, as well as in raw volume (left

Impact of a Carbapenem Antimicrobial Stewardship Program on Patient Outcomes.

Antimicrobial stewardship programs (ASPs) aim to improve appropriate antimicrobial use. However, concerns of the negative consequences from accepting ASP interventions exist, particularly when deescalation or discontinuation of broad-spectrum antibiotics is recommended. Hence, we sought to evaluate the impact on clinical outcomes when ASP interventions for inappropriate carbapenem use were accepted or rejected by primary providers. We retrospectively reviewed all carbapenem prescriptions deemed inappropriate according to institutional guidelines with ASP interventions between July 2011 and December 2014. Intervention acceptance and outcomes, including carbapenem utilization, length of stay, hospitalization charges, 30-day readmission, and mortality rates were reviewed. Data were analyzed in two groups, one in which physicians accepted all interventions ("accepted") and one in which interventions were rejected ("rejected"). A total of 158 ASP interventions were made. These included carbapenem discontinuation (35%), change to narrower-spectrum antibiotic (32%), dose optimization (17%), further investigations (including imaging and procalcitonin) (11%), infectious diseases referral (3%), antibiotic discontinuation (other than carbapenem) (1%), and source control (1%). Of 220 unique patients, carbapenem use was inappropriate in 101 (45.9%) patients. A significant reduction in carbapenem utilization was observed in the accepted group versus rejected group (median defined daily doses, 0.224 versus 0.668 per 1,000 patient-days, respectively; P < 0.001). There was a significant reduction in 30-day mortality in the accepted (none) versus rejected group (10 deaths, P = 0.015), but there were no differences in length of stay, hospitalization charge, or 30-day readmission rates. Hypotension was independently associated with mortality in multivariate analysis (odds ratio, 5.25; 95% confidence interval, 1.34 to 20.6). In our institution, acceptance of carbapenem ASP interventions did not compromise patient safety in terms of clinical outcomes while reducing consumption.

High-angular resolution diffusion imaging tractography of cerebellar pathways from newborns to young adults.

INTRODUCTION: Many neurologic and psychiatric disorders are thought to be due to, or result in, developmental errors in neuronal cerebellar connectivity. In this connectivity analysis, we studied the developmental time-course of cerebellar peduncle pathways in pediatric and young adult subjects. METHODS: A cohort of 80 subjects, newborns to young adults, was studied on a 3T MR system with 30 diffusion-weighted measurements with high-angular resolution diffusion imaging (HARDI) tractography. RESULTS: Qualitative and quantitative results were analyzed for age-based variation. In subjects of all ages, the superior cerebellar peduncle pathway (SCP) and two distinct subpathways of the middle cerebellar peduncle (MCP), as described in previous ex vivo studies, were identified in vivo with this technique: pathways between the rostral pons and inferior-lateral cerebellum (MCP cog), associated predominantly with higher cognitive function, and pathways between the caudal pons and superior-medial cerebellum (MCP mot), associated predominantly with motor function. DISCUSSION: Our findings showed that the inferior cerebellar peduncle pathway (ICP), involved primarily in proprioception and balance appears to have a later onset followed by more rapid development than that exhibited in other tracts. We hope that this study may provide an initial point of reference for future studies of normal and pathologic development of cerebellar connectivity.