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1.
Blood Purif ; : 1-7, 2021 Feb 25.
Artigo em Inglês | MEDLINE | ID: mdl-33631752

RESUMO

BACKGROUND/AIMS: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. METHODS: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. RESULTS: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52-84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67-0.73), followed by MLP 0.59 (0.54-0.64), LR 0.57 (0.52-0.62), SVM 0.51 (0.46-0.56), AdaBoost 0.51 (0.46-0.55), RF 0.44 (0.39-0.48), and XGBoost 0.43 (CI 0.38-0.47). CONCLUSIONS: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

2.
PLoS One ; 16(2): e0247366, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33626098

RESUMO

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney; however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI. We explored the association of SARS-CoV-2 VL at admission to AKI in a large diverse cohort of hospitalized patients with COVID-19. METHODS AND FINDINGS: We included patients hospitalized between March 13th and May 19th, 2020 with SARS-CoV-2 in a large academic healthcare system in New York City (N = 1,049) with available VL at admission quantified by real-time RT-PCR. We extracted clinical and outcome data from our institutional electronic health records (EHRs). AKI was defined by KDIGO guidelines. We fit a Fine-Gray competing risks model (with death as a competing risk) using demographics, comorbidities, admission severity scores, and log10 transformed VL as covariates and generated adjusted hazard ratios (aHR) and 95% Confidence Intervals (CIs). VL was associated with an increased risk of AKI (aHR = 1.04, 95% CI: 1.01-1.08, p = 0.02) with a 4% increased hazard for each log10 VL change. Patients with a viral load in the top 50th percentile had an increased adjusted hazard of 1.27 (95% CI: 1.02-1.58, p = 0.03) for AKI as compared to those in the bottom 50th percentile. CONCLUSIONS: VL is weakly but significantly associated with in-hospital AKI after adjusting for confounders. This may indicate the role of VL in COVID-19 associated AKI. This data may inform future studies to discover the mechanistic basis of COVID-19 associated AKI.

3.
Sci Transl Med ; 13(576)2021 Jan 13.
Artigo em Inglês | MEDLINE | ID: mdl-33441424

RESUMO

More than 800 million people in the world suffer from chronic kidney disease (CKD). Genome-wide association studies (GWAS) have identified hundreds of loci where genetic variants are associated with kidney function; however, causal genes and pathways for CKD remain unknown. Here, we performed integration of kidney function GWAS and human kidney-specific expression quantitative trait analysis and identified that the expression of beta-mannosidase (MANBA) was lower in kidneys of subjects with CKD risk genotype. We also show an increased incidence of renal failure in subjects with rare heterozygous loss-of-function coding variants in MANBA using phenome-wide association analysis of 40,963 subjects with exome sequencing data. MANBA is a lysosomal gene highly expressed in kidney tubule cells. Deep phenotyping revealed structural and functional lysosomal alterations in human kidneys from subjects with CKD risk alleles and mice with genetic deletion of Manba Manba heterozygous and knockout mice developed more severe kidney fibrosis when subjected to toxic injury induced by cisplatin or folic acid. Manba loss altered multiple pathways, including endocytosis and autophagy. In the absence of Manba, toxic acute tubule injury induced inflammasome activation and fibrosis. Together, these results illustrate the convergence of common noncoding and rare coding variants in MANBA in kidney disease development and demonstrate the role of the endolysosomal system in kidney disease development.

4.
Nat Med ; 27(1): 66-72, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33432171

RESUMO

The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease1. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations (P < 10-6), we replicated 26 of these in the Penn Medicine Biobank, as well as in three other medical biobanks and the population-based UK Biobank. Of these 26 genes, five had associations that have been previously reported and represented positive controls, whereas 21 had phenotype associations not previously reported, among which were genes implicated in glaucoma, aortic ectasia, diabetes mellitus, muscular dystrophy and hearing loss. These findings show the value of aggregating rare predicted loss-of-function variants into 'gene burdens' for identifying new gene-disease associations using EHR phenotypes in a medical biobank. We suggest that application of this approach to even larger numbers of individuals will provide the statistical power required to uncover unexplored relationships between rare genetic variation and disease phenotypes.


Assuntos
Registros Eletrônicos de Saúde , Exoma , Genótipo , Fenótipo , Idoso , Biologia Computacional , Feminino , Estudo de Associação Genômica Ampla , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Sequenciamento Completo do Exoma
7.
Clin J Am Soc Nephrol ; 15(11): 1557-1565, 2020 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-33033164

RESUMO

BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement. RESULTS: We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4). CONCLUSIONS: Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

8.
J Am Soc Nephrol ; 2020 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-32883700

RESUMO

BACKGROUND: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described. METHODS: This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. RESULTS: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. CONCLUSIONS: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.

9.
Hemodial Int ; 24(4): 495-505, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32809268

RESUMO

INTRODUCTION: A previous study demonstrated that the surface area-normalized standard Kt/V (SAstdKt/V) was better associated with mortality than standard Kt/V (stdKt/V). This study investigates the association of SAstdKt/V and stdKt/V with mortality, anemia, and hypoalbuminemia in a larger patient cohort with a longer follow-up period. METHODS: We included adult patients on thrice-weekly hemodialysis in the USRDS database and excluded amputated patients. StdKt/V and SAstdKt/V were calculated from the available single-pool Kt/V. Patients were categorized into five groups according to their stdKt/V and SAstdKt/V: <2.00, 2.00-2.19, 2.20-2.39, 2.40-2.59, and ≥2.60. Hazard ratios (HR) and odds ratios (OR) were calculated using Cox and logistic regression analysis respectively. FINDINGS: There were 507,656 patients included in the analysis. The patients had a median age of 65.5 years with a median follow-up period of 2 years. Thirty-four percent died during follow-up. HRs for mortality progressively decreased as SAstdKt/V increased in both unadjusted and adjusted models. Unlike SAstdKt/V, HRs were the lowest in the categories with stdKt/V of 2.40-2.59 and they increased in the higher stdKt/V category. The adjusted HR for SAstdKt/V vs. stdKt/V were 0.68 vs. 0.62 in the category of 2.40-2.59, and 0.63 vs. 0.73 in the category of ≥2.60. The adjusted ORs for anemia progressively decreased as SAstdKt/V increased, whereas ORs decreased to the lowest in stdKt/V category 2.40-2.59 and increased in the ≥2.60 category. The adjusted ORs for hypoalbuminemia progressively decreased as SAstdKt/V and stdKt/V increased which were both 0.45 in 2.40-2.59 category and decreased to 0.29 and 0.42 in the ≥2.60 category. DISCUSSION: SAstdKt/V is better associated with mortality, anemia, and hypoalbuminemia than stdKt/V. SAstdKt/V is a better parameter in defining hemodialysis dosing which can be calculated by an available online tool. Further studies to determine the optimal SAstdKt/V dose required to achieve improved clinical outcomes with better cost-effectiveness are needed.

10.
medRxiv ; 2020 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-32511564

RESUMO

IMPORTANCE: Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described. OBJECTIVE: To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. DESIGN: Observational, retrospective study. SETTING: Admitted to hospital between February 27 and April 15, 2020. PARTICIPANTS: Patients aged ≥18 years with laboratory confirmed COVID-19 Exposures: AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. RESULTS: A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test. CONCLUSIONS AND RELEVANCE: AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.

11.
J Am Coll Cardiol ; 75(22): 2769-2780, 2020 Jun 09.
Artigo em Inglês | MEDLINE | ID: mdl-32498804

RESUMO

BACKGROUND: Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear. OBJECTIVES: This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention. METHODS: A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy. RESULTS: Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p < 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p < 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor. CONCLUSIONS: Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.

12.
Kidney Int ; 98(5): 1323-1330, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32540406

RESUMO

Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 - 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.

14.
Kidney Int ; 97(2): 383-392, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-31883805

RESUMO

Symptoms are common in patients on maintenance hemodialysis but identification is challenging. New informatics approaches including natural language processing (NLP) can be utilized to identify symptoms from narrative clinical documentation. Here we utilized NLP to identify seven patient symptoms from notes of maintenance hemodialysis patients of the BioMe Biobank and validated our findings using a separate cohort and the MIMIC-III database. NLP performance was compared for symptom detection with International Classification of Diseases (ICD)-9/10 codes and the performance of both methods were validated against manual chart review. From 1034 and 519 hemodialysis patients within BioMe and MIMIC-III databases, respectively, the most frequently identified symptoms by NLP were fatigue, pain, and nausea/vomiting. In BioMe, sensitivity for NLP (0.85 - 0.99) was higher than for ICD codes (0.09 - 0.59) for all symptoms with similar results in the BioMe validation cohort and MIMIC-III. ICD codes were significantly more specific for nausea/vomiting in BioMe and more specific for fatigue, depression, and pain in the MIMIC-III database. A majority of patients in both cohorts had four or more symptoms. Patients with more symptoms identified by NLP, ICD, and chart review had more clinical encounters. NLP had higher specificity in inpatient notes but higher sensitivity in outpatient notes and performed similarly across pain severity subgroups. Thus, NLP had higher sensitivity compared to ICD codes for identification of seven common hemodialysis-related symptoms, with comparable specificity between the two methods. Hence, NLP may be useful for the high-throughput identification of patient-centered outcomes when using electronic health records.

15.
JAMA ; 322(22): 2191-2202, 2019 12 10.
Artigo em Inglês | MEDLINE | ID: mdl-31821430

RESUMO

Importance: Hereditary transthyretin (TTR) amyloid cardiomyopathy (hATTR-CM) due to the TTR V122I variant is an autosomal-dominant disorder that causes heart failure in elderly individuals of African ancestry. The clinical associations of carrying the variant, its effect in other African ancestry populations including Hispanic/Latino individuals, and the rates of achieving a clinical diagnosis in carriers are unknown. Objective: To assess the association between the TTR V122I variant and heart failure and identify rates of hATTR-CM diagnosis among carriers with heart failure. Design, Setting, and Participants: Cross-sectional analysis of carriers and noncarriers of TTR V122I of African ancestry aged 50 years or older enrolled in the Penn Medicine Biobank between 2008 and 2017 using electronic health record data from 1996 to 2017. Case-control study in participants of African and Hispanic/Latino ancestry with and without heart failure in the Mount Sinai BioMe Biobank enrolled between 2007 and 2015 using electronic health record data from 2007 to 2018. Exposures: TTR V122I carrier status. Main Outcomes and Measures: The primary outcome was prevalent heart failure. The rate of diagnosis with hATTR-CM among TTR V122I carriers with heart failure was measured. Results: The cross-sectional cohort included 3724 individuals of African ancestry with a median age of 64 years (interquartile range, 57-71); 1755 (47%) were male, 2896 (78%) had a diagnosis of hypertension, and 753 (20%) had a history of myocardial infarction or coronary revascularization. There were 116 TTR V122I carriers (3.1%); 1121 participants (30%) had heart failure. The case-control study consisted of 2307 individuals of African ancestry and 3663 Hispanic/Latino individuals; the median age was 73 years (interquartile range, 68-80), 2271 (38%) were male, 4709 (79%) had a diagnosis of hypertension, and 1008 (17%) had a history of myocardial infarction or coronary revascularization. There were 1376 cases of heart failure. TTR V122I was associated with higher rates of heart failure (cross-sectional cohort: n = 51/116 TTR V122I carriers [44%], n = 1070/3608 noncarriers [30%], adjusted odds ratio, 1.7 [95% CI, 1.2-2.4], P = .006; case-control study: n = 36/1376 heart failure cases [2.6%], n = 82/4594 controls [1.8%], adjusted odds ratio, 1.8 [95% CI, 1.2-2.7], P = .008). Ten of 92 TTR V122I carriers with heart failure (11%) were diagnosed as having hATTR-CM; the median time from onset of symptoms to clinical diagnosis was 3 years. Conclusions and Relevance: Among individuals of African or Hispanic/Latino ancestry enrolled in 2 academic medical center-based biobanks, the TTR V122I genetic variant was significantly associated with heart failure.


Assuntos
Afro-Americanos/genética , Neuropatias Amiloides Familiares/genética , Insuficiência Cardíaca/genética , Hispano-Americanos/genética , Pré-Albumina/genética , Centros Médicos Acadêmicos , Idoso , Neuropatias Amiloides Familiares/complicações , Neuropatias Amiloides Familiares/etnologia , Bancos de Espécimes Biológicos , Estudos de Casos e Controles , Estudos Transversais , Feminino , Variação Genética , Insuficiência Cardíaca/etnologia , Humanos , Masculino , Pessoa de Meia-Idade
17.
Clin J Am Soc Nephrol ; 14(5): 656-663, 2019 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-30948456

RESUMO

BACKGROUND AND OBJECTIVES: Hypernatremia is common in hospitalized, critically ill patients. Although there are no clear guidelines on sodium correction rate for hypernatremia, some studies suggest a reduction rate not to exceed 0.5 mmol/L per hour. However, the data supporting this recommendation and the optimal rate of hypernatremia correction in hospitalized adults are unclear. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We assessed the association of hypernatremia correction rates with neurologic outcomes and mortality in critically ill patients with hypernatremia at admission and those that developed hypernatremia during hospitalization. We used data from the Medical Information Mart for Intensive Care-III and identified patients with hypernatremia (serum sodium level >155 mmol/L) on admission (n=122) and hospital-acquired (n=327). We calculated different ranges of rapid correction rates (>0.5 mmol/L per hour overall and >8, >10, and >12 mmol/L per 24 hours) and utilized logistic regression to generate adjusted odds ratios (aOR) with 95% confidence intervals (95% CIs) to examine association with outcomes. RESULTS: We had complete data on 122 patients with severe hypernatremia on admission and 327 patients who developed hospital-acquired hypernatremia. The difference in in-hospital 30-day mortality proportion between rapid (>0.5 mmol/L per hour) and slower (≤0.5 mmol/L per hour) correction rates were not significant either in patients with hypernatremia at admission with rapid versus slow correction (25% versus 28%; P=0.80) or in patients with hospital-acquired hypernatremia with rapid versus slow correction (44% versus 40%; P=0.50). There was no difference in aOR of mortality for rapid versus slow correction in either admission (aOR, 1.3; 95% CI, 0.5 to 3.7) or hospital-acquired hypernatremia (aOR, 1.3; 95% CI, 0.8 to 2.3). Manual chart review of all suspected chronic hypernatremia patients, which included all 122 with hypernatremia at admission, 128 of the 327 hospital-acquired hypernatremia, and an additional 28 patients with ICD-9 codes for cerebral edema, seizures and/or alteration of consciousness, did not reveal a single case of cerebral edema attributable to rapid hyprnatremia correction. CONCLUSIONS: We did not find any evidence that rapid correction of hypernatremia is associated with a higher risk for mortality, seizure, alteration of consciousness, and/or cerebral edema in critically ill adult patients with either admission or hospital-acquired hypernatremia.


Assuntos
Estado Terminal , Hipernatremia/terapia , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Feminino , Mortalidade Hospitalar , Humanos , Hipernatremia/complicações , Hipernatremia/mortalidade , Masculino , Pessoa de Meia-Idade , Sódio/sangue
18.
Clin Cancer Res ; 25(2): 463-472, 2019 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-30242023

RESUMO

Although driver genes in hepatocellular carcinoma (HCC) have been investigated in various previous genetic studies, prevalence of key driver genes among heterogeneous populations is unknown. Moreover, the phenotypic associations of these driver genes are poorly understood. This report aims to reveal the phenotypic impacts of a group of consensus driver genes in HCC. We used MutSigCV and OncodriveFM modules implemented in the IntOGen pipeline to identify consensus driver genes across six HCC cohorts comprising 1,494 samples in total. To access their global impacts, we used The Cancer Genome Atlas (TCGA) mutations and copy-number variations to predict the transcriptomics data, under generalized linear models. We further investigated the associations of the consensus driver genes to patient survival, age, gender, race, and risk factors. We identify 10 consensus driver genes across six HCC cohorts in total. Integrative analysis of driver mutations, copy-number variations, and transcriptomic data reveals that these consensus driver mutations and their copy-number variations are associated with a majority (62.5%) of the mRNA transcriptome but only a small fraction (8.9%) of miRNAs. Genes associated with TP53, CTNNB1, and ARID1A mutations contribute to the tripod of most densely connected pathway clusters. These driver genes are significantly associated with patients' overall survival. Some driver genes are significantly linked to HCC gender (CTNNB1, ALB, TP53, and AXIN1), race (TP53 and CDKN2A), and age (RB1) disparities. This study prioritizes a group of consensus drivers in HCC, which collectively show vast impacts on the phenotypes. These driver genes may warrant as valuable therapeutic targets of HCC.


Assuntos
Carcinoma Hepatocelular/diagnóstico , Carcinoma Hepatocelular/genética , Predisposição Genética para Doença , Neoplasias Hepáticas/diagnóstico , Neoplasias Hepáticas/genética , Oncogenes , Fenótipo , Algoritmos , Biologia Computacional/métodos , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Redes Reguladoras de Genes , Estudos de Associação Genética , Humanos , Modelos Biológicos , Mutação , Transcriptoma
19.
J Transl Med ; 16(1): 181, 2018 07 03.
Artigo em Inglês | MEDLINE | ID: mdl-29970096

RESUMO

BACKGROUND: Evidences in literature strongly advocate the potential of immunomodulatory peptides for use as vaccine adjuvants. All the mechanisms of vaccine adjuvants ensuing immunostimulatory effects directly or indirectly stimulate antigen presenting cells (APCs). While numerous methods have been developed in the past for predicting B cell and T-cell epitopes; no method is available for predicting the peptides that can modulate the APCs. METHODS: We named the peptides that can activate APCs as A-cell epitopes and developed methods for their prediction in this study. A dataset of experimentally validated A-cell epitopes was collected and compiled from various resources. To predict A-cell epitopes, we developed support vector machine-based machine learning models using different sequence-based features. RESULTS: A hybrid model developed on a combination of sequence-based features (dipeptide composition and motif occurrence), achieved the highest accuracy of 95.71% with Matthews correlation coefficient (MCC) value of 0.91 on the training dataset. We also evaluated the hybrid models on an independent dataset and achieved a comparable accuracy of 95.00% with MCC 0.90. CONCLUSION: The models developed in this study were implemented in a web-based platform VaxinPAD to predict and design immunomodulatory peptides or A-cell epitopes. This web server available at http://webs.iiitd.edu.in/raghava/vaxinpad/ will facilitate researchers in designing peptide-based vaccine adjuvants.


Assuntos
Adjuvantes Imunológicos/farmacologia , Células Apresentadoras de Antígenos/efeitos dos fármacos , Simulação por Computador , Desenho de Fármacos , Vacinas de Subunidades/farmacologia , Motivos de Aminoácidos , Sequência de Aminoácidos , Bases de Dados de Proteínas , Epitopos/metabolismo , Humanos , Fatores Imunológicos/farmacologia , Internet , Modelos Teóricos , Máquina de Vetores de Suporte , Interface Usuário-Computador , Vacinas de Subunidades/química
20.
AMIA Jt Summits Transl Sci Proc ; 2017: 197-206, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29888072

RESUMO

We propose an unsupervised multi-omics integration pipeline, using deep-learning autoencoder algorithm, to predict the survival subtypes in bladder cancer (BC). We used TCGA dataset comprising mRNA, miRNA and methylation to infer two survival subtypes. We then constructed a supervised classification model to predict the survival subgroups of any new individual sample. Our training data gave two subgroups with significant survival differences (p-value=8e-4), where high-risk survival subgroup was enriched with KRT6/14 overexpression and PI3K-Akt pathways. We tested the robustness of model by randomly splitting the main dataset into multiple training and test folds, which gave overall significant p-values. Then, we successfully inferred the subtypes for a subset of samples kept as test dataset (p-value=0.03). We further applied our pipeline to predict the survival subgroups from another validation dataset with miRNA data (p-value=0.02). Conclusively, present pipeline is an effective approach to infer the survival subtype of a new sample, exemplified by BC.

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