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Background: Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations of intensive care unit (ICU) patients (e.g patients with sepsis). Here, we study the general ICU population of the Medical Information Mart for Intensive Care III (MIMIC-III) database to corroborate these associations, and propose a supervised learning model for the prediction of hyperchloremia in ICU patients.Methods: We assessed hyperchloremia and chloride load and their associations with several outcomes (ICU mortality, new acute kidney injury [AKI] by day 7, and multiple organ dysfunction syndrome [MODS] on day 7) using regression analysis. Four predictive supervised learning classifiers were trained to predict hyperchloremia using features representative of clinical records from the first 24h of adult ICU stays.Results: Hyperchloremia was shown to have an independent association with increased odds of ICU mortality, new AKI by day 7, and MODS on day 7. High chloride load was also associated with increased odds of ICU mortality. Our best performing supervised learning model predicted second-day hyperchloremia with an AUC of 0.76 and a number needed to alert (NNA) of 7-a clinically-actionable rate.Conclusions: Our results support the use of predictive models to aid clinicians in monitoring for and preventing hyperchloremia in high-risk patients and offers an opportunity to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: HbA1c variability has been linked to retinopathy, renal disease and autonomic neuropathy in patients with type 1 diabetes mellitus (T1D) and type 2 diabetes mellitus (T2D). Although the same relationship has been demonstrated for diabetic peripheral neuropathy (DPN) in patients with T2D, data for T1D are still lacking. Methods: Patients older than 17 years of age with ≥ 10 years of T1D duration and follow-up were included. All patients underwent nerve conduction studies and neurological examination. Laboratorial data was retrospectively extracted from chart review. Mean HbA1c (mHbA1c) over 10 years was calculated, as well as HbA1c variability estimated by standard deviation (HbA1c-SD) and coefficient of variation (HbA1c-CV). Results: Fifty patients with T1D were included (30 females and 21 non-caucasians), with mean age and T1D duration of 25.6 ± 5.0 and 17.9 ± 6.1 years, respectively. The frequency of DPN was 24%. Higher mHbA1c (10.4 ± % vs 8.1 ± %; p < 0.001), HbA1c-SD (1.8 ± 0.8 vs 0.9 ± 0.4; p < 0.001), and HbA1c-CV (1.7 ± 0.8 vs 1.2 ± 1.1; p = 0.006) were observed in patients with DPN compared to others. SD-HbA1c and HbA1c-CV were associated with DPN, diagnosed by either clinical or NCS criteria, independent of mHbA1c, age and gender. Conclusions: Not only long-term glycemic control, but also its variability is associated with DPN in patients with T1D. Larger studies are required to confirm this finding. [ABSTRACT FROM AUTHOR]

Background: The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.Methods: Our objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission.Results: Utilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224 mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts.Conclusions: Experimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI. [ABSTRACT FROM AUTHOR]

Background: Despite the health concerns and nutritional importance of fatty acids, there is a relative paucity of studies in the literature that report genetic or genomic parameters, especially in the case of sheep populations. To investigate the genetic architecture of fatty acid composition of sheep, we conducted genome-wide association studies (GWAS) and estimated genomic heritabilities for fatty acid profile in Longissimus dorsi muscle of 216 male sheep. Results: Genomic heritability estimates for fatty acid content ranged from 0.25 to 0.46, indicating that substantial genetic variation exists for the evaluated traits. Therefore, it is possible to alter fatty acid profiles through selection. Twenty-seven genomic regions of 10 adjacent SNPs associated with fatty acids composition were identified on chromosomes 1, 2, 3, 5, 8, 12, 14, 15, 16, 17, and 18, each explaining ≥0.30% of the additive genetic variance. Twenty-three genes supporting the understanding of genetic mechanisms of fat composition in sheep were identified in these regions, such as DGAT2, TRHDE, TPH2, ME1, C6, C7, UBE3D, PARP14, and MRPS30. Conclusions: Estimates of genomic heritabilities and elucidating important genomic regions can contribute to a better understanding of the genetic control of fatty acid deposition and improve the selection strategies to enhance meat quality and health attributes. [ABSTRACT FROM AUTHOR]