Clinical characteristics of autoimmune encephalitis with co-existence of multiple anti-neuronal antibodies.

BACKGROUND: An increasing number of cases of autoimmune encephalitis (AE) with co-existing multiple anti-neuronal antibodies have been reported in recent years. However, the clinical significance of the concurrent presence of multiple anti-neuronal antibodies in patients with AE remains unclear. METHODS: We retrospectively enrolled AE patients with multiple anti-neuronal antibodies treated at our center between August 2019 and February 2022. We also reviewed cases reported in multiple literature databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed on selection process. And then the clinical and laboratory data of these cases were collected for review and summary. RESULTS: A total of 83 AE cases with multiple antibodies (9 cases from our center and 74 cases from the literatures reviewed) were identified. In our center, nine patients presented with encephalitis symptoms, clinically characterized as disturbed consciousness, seizures, cognitive impairment, and psychiatric disorders. Of the 83 cases, 73 cases had co-existence of 2 types of antibodies, 8 cases had 3 types, and 2 cases had 4 types. Thirty-nine cases (39/83, 46.9%) were confirmed or suspected of also having a tumor, of which the most common was lung cancer (28/83, 33.7%). Partial or complete recovery was achieved in 57 cases (57/83, 68.6%), while 26 cases (26/83, 31.3%) died during treatment or follow-up. CONCLUSIONS: AE with co-existing multiple anti-neuronal antibodies is a specific subgroup, that is increasingly recognized in clinical practice. The co-existence of multiple anti-neuronal antibodies has a major impact on clinical features, disease progression, and prognosis.

Assessment of Endoscopy-Based Scoring Systems for Prognostication in Ulcerative Colitis: A Comparative Analysis.

BACKGROUND AND OBJECTIVE: Endoscopy-based scoring systems, including Mayo Endoscopic Score (MES), Modified Mayo Endoscopic Score (MMES), and Degree of Ulcerative Colitis Burden of Luminal Inflammation (DUBLIN) Score, have been introduced to evaluate UC prognosis. This study aims to compare their predictive capacity for clinical outcomes in UC patients. METHODS: Consecutive UC patients from a tertiary hospital were included. The primary outcome was acute severe ulcerative colitis (ASUC), and secondary outcomes were UC-related admission, medication treatment escalation, disease extension and surgery. Predictive performance was assessed using receiver operating characteristic (ROC) curves. RESULTS: Among 300 patients, 15.3% developed ASUC. Robust correlations were observed among the three scoring systems and were with elevated serum inflammatory markers. The DUBLIN score exhibited superior predictive ability for UC-related admission (AUC 0.751; 95%CI 0.698-0.799) and medication treatment escalation (AUC 0.735; 95% CI 0.681-0.784). No statistical differences were found among three scoring systems for predicting ASUC, disease extension, and surgery. Employing respective cut-offs of 2, 11.25, and 3, higher MES (HR = 3.859, 95% CI 1.636-9.107, p = 0.002), MMES (HR = 3.352, 95% CI 1.879-5.980, p < 0.001), and DUBLIN score (HR = 5.619, 95% CI 2.378-13.277, p < 0.001) were associated with an increased risk of developing ASUC. CONCLUSION: The DUBLIN score, assessing the overall inflammatory burden of the intestinal tract, outperforms the MMES in predicting admission and medication treatment escalation related to UC. Its integration into clinical practice has the potential to enhance risk stratification for patients with UC.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

INTRODUCTION: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS: We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS: Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION: The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS: Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

Utility of new image-derived biomarkers for autosomal dominant polycystic kidney disease prognosis using automated instance cyst segmentation.

New image-derived biomarkers for patients affected by autosomal dominant polycystic kidney disease are needed to improve current clinical management. The measurement of total kidney volume (TKV) provides critical information for clinicians to drive care decisions. However, patients with similar TKV may present with very different phenotypes, often requiring subjective decisions based on other factors (e.g., appearance of healthy kidney parenchyma, a few cysts contributing significantly to overall TKV, etc.). In this study, we describe a new technique to individually segment cysts and quantify biometric parameters including cyst volume, cyst number, parenchyma volume, and cyst parenchyma surface area. Using data from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study the utility of these new parameters was explored, both quantitatively as well as visually. Total cyst number and cyst parenchyma surface area showed superior prediction of the slope of estimated glomerular filtration rate decline, kidney failure and chronic kidney disease stages 3A, 3B, and 4, compared to TKV. In addition, presentations such as a few large cysts contributing significantly to overall kidney volume were shown to be much better stratified in terms of outcome predictions. Thus, these new image biomarkers, which can be obtained automatically, will have great utility in future studies and clinical care for patients affected by autosomal dominant polycystic kidney disease.

Heart Failure-Type Symptom Score Trajectories in CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: Quality of life in chronic kidney disease (CKD) is impaired by a large burden of symptoms including some that overlap with the symptoms of heart failure (HF). We studied a group of individuals with CKD to understand the patterns and trajectories of HF-type symptoms in this setting. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: 3,044 participants in the Chronic Renal Insufficiency Cohort (CRIC) without prior diagnosis of HF. PREDICTORS: Sociodemographics, medical history, medications, vital signs, laboratory values, echocardiographic and electrocardiographic parameters. OUTCOME: Trajectory over 5.5 years of a HF-type symptom score (modified Kansas City Cardiomyopathy Questionnaire [KCCQ] Overall Summary Score with a range of 0-100 where<75 reflects clinically significant symptoms). ANALYTICAL APPROACH: Latent class mixed models were used to model trajectories. Multinomial logistic regression was used to model relationships of predictors with trajectory group membership. RESULTS: Five trajectories of KCCQ score were identified in the cohort of 3,044 adults, 45% of whom were female, and whose median age was 61 years. Group 1 (41.7%) had a stable high score (minimal symptoms, average score of 96); groups 2 (35.6%) and 3 (15.6%) had stable but lower scores (mild symptoms [average of 81] and clinically significant symptoms [average of 52], respectively). Group 4 (4.9%) had a substantial worsening in symptoms over time (mean 31-point decline), and group 5 (2.2%) had a substantial improvement (mean 33-point increase) in KCCQ score. A majority of group 1 was male, without diabetes or obesity, and this group had higher baseline kidney function. A majority of groups 2 and 3 had diabetes and obesity. A majority of group 4 was male and had substantial proteinuria. Group 5 had the highest proportion of baseline cardiovascular disease (CVD). LIMITATIONS: No validation cohort available, CKD management changes in recent years may alter trajectories, and latent class models depend on the missing at random assumption. CONCLUSIONS: Distinct HF-type symptom burden trajectories were identified in the setting of CKD, corresponding to different baseline characteristics. These results highlight the diversity of HF-type symptom experiences in individuals with CKD.

Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning.

BACKGROUND: Established prognostic models of idiopathic membranous nephropathy (IMN) were limited to traditional modeling methods and did not comprehensively consider clinical and pathological patient data. Based on the electronic medical record (EMR) system, machine learning (ML) was used to construct a risk prediction model for the prognosis of IMN. METHODS: Data from 418 patients with IMN were diagnosed by renal biopsy at the Fifth Clinical Medical College of Shanxi Medical University. Fifty-nine medical features of the patients could be obtained from EMR, and prediction models were established based on five ML algorithms. The area under the curve, recall rate, accuracy, and F1 were used to evaluate and compare the performances of the models. Shapley additive explanation (SHAP) was used to explain the results of the best-performing model. RESULTS: One hundred and seventeen patients (28.0%) with IMN experienced adverse events, 28 of them had compound outcomes (ESRD or double serum creatinine (SCr)), and 89 had relapsed. The gradient boosting machine (LightGBM) model had the best performance, with the highest AUC (0.892 ± 0.052, 95% CI 0.840-0.945), accuracy (0.909 ± 0.016), recall (0.741 ± 0.092), precision (0.906 ± 0.027), and F1 (0.905 ± 0.020). Recursive feature elimination with random forest and SHAP plots based on LightGBM showed that anti-phospholipase A2 receptor (anti-PLA2R), immunohistochemical immunoglobulin G4 (IHC IgG4), D-dimer (D-DIMER), triglyceride (TG), serum albumin (ALB), aspartate transaminase (AST), ß2-microglobulin (BMG), SCr, and fasting plasma glucose (FPG) were important risk factors for the prognosis of IMN. Increased risk of adverse events in IMN patients was correlated with high anti-PLA2R and low IHC IgG4. CONCLUSIONS: This study established a risk prediction model for the prognosis of IMN using ML based on clinical and pathological patient data. The LightGBM model may become a tool for personalized management of IMN patients.

Revisiting the Natural History of Pulmonary Tuberculosis: A Bayesian Estimation of Natural Recovery and Mortality Rates.

BACKGROUND: Tuberculosis (TB) natural history remains poorly characterized, and new investigations are impossible as it would be unethical to follow up TB patients without treatment. METHODS: We considered the reports identified in a previous systematic review of studies from the prechemotherapy era, and extracted detailed data on mortality over time. We used a Bayesian framework to estimate the rates of TB-induced mortality and self-cure. A hierarchical model was employed to allow estimates to vary by cohort. Inference was performed separately for smear-positive TB (SP-TB) and smear-negative TB (SN-TB). RESULTS: We included 41 cohorts of SP-TB patients and 19 cohorts of pulmonary SN-TB patients in the analysis. The median estimates of the TB-specific mortality rates were 0.389 year-1 (95% credible interval [CrI], .335-.449) and 0.025 year-1 (95% CrI, .017-.035) for SP-TB and SN-TB patients, respectively. The estimates for self-recovery rates were 0.231 year-1 (95% CrI, .177-.288) and 0.130 year-1 (95% CrI, .073-.209) for SP-TB and SN-TB patients, respectively. These rates correspond to average durations of untreated TB of 1.57 years (95% CrI, 1.37-1.81) and 5.35 years (95% CrI, 3.42-8.23) for SP-TB and SN-TB, respectively, when assuming a non-TB-related mortality rate of 0.014 year-1 (ie, a 70-year life expectancy). CONCLUSIONS: TB-specific mortality rates are around 15 times higher for SP-TB than for SN-TB patients. This difference was underestimated dramatically in previous TB modeling studies, raising concerns about the accuracy of the associated predictions. Despite being less infectious, SN-TB may be responsible for equivalent numbers of secondary infections as SP-TB due to its much longer duration.

Validation of a Histologic Scoring Index for C3 Glomerulopathy.

RATIONALE & OBJECTIVE: A previous study that evaluated associations of kidney biopsy findings with disease progression in patients with C3 glomerulopathy (C3G) proposed a prognostic histologic index (C3G-HI) that has not yet been validated. Our objective was to validate the performance of the C3G-HI in a new patient population. STUDY DESIGN: Multicenter, retrospective cohort study. SETTING & PARTICIPANTS: 111 patients fulfilling diagnostic criteria of C3G between January 1995 and December 2019, from 33 nephrology departments belonging to the Spanish Group for the Study of Glomerular Diseases (GLOSEN). PREDICTORS: Demographic, clinical parameters, C3G-HI total activity score, and the C3G-HI total chronicity score. OUTCOME: Time to kidney failure. ANALYTICAL APPROACH: Intraclass correlation coefficients and κ statistic were used to summarize inter-rater reproducibility for assessment of histopathology in kidney biopsies. The nonlinear relationships of risk of kidney failure with the total activity score and total chronicity score were modeled using Cox proportional hazards analysis that incorporated cubic splines. RESULTS: The study group included 93 patients with C3 glomerulonephritis and 18 with dense-deposit disease. Participants had an overall meanage of 35±22 (SD) years. Forty-eight patients (43%) developed kidney failure after a mean follow-up of 65±27 months. The overall inter-rater reproducibility was very good for the total activity score (intraclass correlation coefficient [ICC]=0.63) and excellent for total chronicity score (ICC=0.89). Baseline estimated glomerular filtration rate (eGFR), 24-hour proteinuria, and treatment with immunosuppression were the main determinants of kidney failure in a model with only clinical variables. Only tubular atrophy and interstitial fibrosis were identified as predictors in a model with histological variables. When the total activity score and total chronicity score were added to the model, only the latter was identified as an independent predictor of kidney failure. LIMITATIONS: Only a subset of the kidney biopsies was centrally reviewed. Residual confounding. CONCLUSIONS: We validated the performance of C3G-HI as a predictor of kidney failure in patients with C3G. The total chronicity score was the principal histologic correlate of kidney failure.

An inter-correlated cytokine network identified at the center of cytokine storm predicted COVID-19 prognosis.

The hyper-inflammatory response is thought to be a major cause of acute respiratory distress syndrome (ARDS) in patients with COVID-19. Although multiple cytokines are reportedly associated with disease severity, the key mediators of SARS-CoV-2 induced cytokine storm and their predictive values have not been fully elucidated. The present study analyzed maximal and early (within 10 days after disease onset) concentrations of 12-plex cytokines in plasma. We found consistently elevated plasma levels of IL-6, IL-8 and IL-5 in patients who were deceased compared with those who had mild/moderate or severe disease. The early plasma concentrations of IFN-a and IL-2 positively correlated with the length of the disease course. Moreover, correlation network analysis showed that IL-6, IL-8, and IL-5 located at the center of an inter-correlated cytokine network. These findings suggested that IL-8, IL-6, IL-5 might play central roles in cytokine storms associated with COVID-19 and that the early detection of multiple plasma cytokines might help to predict the prognosis of this disease.

Impact of Subthalamic Deep Brain Stimulation on Impulse Control Disorders in Parkinson's Disease: A Prospective Study.

BACKGROUND: Impact of subthalamic deep brain stimulation (DBS) on impulse control disorders (ICD) in Parkinson's disease (PD) remains controversial. OBJECTIVES: The objectives of this study were to analyze the natural history of ICD between baseline and 1 year after subthalamic DBS in patients with PD and to identify predictive factors, taking into account the positions of the active contact and stimulation parameters. METHODS: We analyzed postoperative modifications of ICD based on the multicentric, prospective Predictive Factors and Subthalamic Stimulation in Parkinson's Disease cohort. ICD status and Ardouin Scale of Behaviour in PD were assessed at baseline and 1 year following subthalamic DBS. Location of active contacts within the 3 subthalamic nucleus functional territories was investigated. RESULTS: A total of 217 were patients included. Of the patients, 10.6% had ICD at baseline of which 95.6% improved at 1 year following subthalamic DBS; 3.6% of the patients experienced de novo ICD at 1 year following subthalamic DBS. Dopamine agonist dose reduction (from 309.8 to 109.3 mg) was the main driver of ICD regression (P = 0.05). Higher preoperative dyskinesias were associated with poorer ICD evolution (P = 0.04). Whereas baseline apathy was a risk factor of de novo ICD (P = 0.02), ICD improvement correlated with postoperative apathy (P = 0.004). Stimulation power and position of active contacts-mainly located within the sensorimotor part of the subthalamic nucleus-did not influence ICD. CONCLUSIONS: This 1-year, postoperative follow-up study showed ICD regression and dopaminergic drug reduction with optimal position of the active contacts within the subthalamic nucleus. Whereas patients with PD with preoperative ICD were prone to postoperative apathy, we also showed that those with preoperative apathy had a higher risk to experience postoperative de novo ICD, further highlighting the meaningful influence of postoperative management of dopaminergic medication on outcome and the continuum between apathy and ICD. © 2020 International Parkinson and Movement Disorder Society.

Epigenomic biomarkers for prognostication and diagnosis of gastrointestinal cancers.

Altered epigenetic regulation is central to many human diseases, including cancer. Over the past two decade, major advances have been made in our understanding of the role of epigenetic alterations in carcinogenesis, particularly for DNA methylation, histone modifications and non-coding RNAs. Aberrant hypermethylation of DNA at CpG islands is a well-established phenomenon that mediates transcriptional silencing of tumor suppressor genes, and it is an early event integral to gastrointestinal cancer development. As such, detection of aberrant DNA methylation is being developed as biomarkers for prognostic and diagnostic purposes in gastrointestinal cancers. Diverse tissue types are suitable for the analyses of methylated DNA, such as tumor tissues, blood, plasma, and stool, and some of these markers are already utilized in the clinical setting. Recent advances in the genome-wide epigenomic approaches are enabling the comprehensive mapping of the cancer methylome, thus providing new avenues for mining novel biomarkers for disease prognosis and diagnosis. Here, we review the current knowledge on DNA methylation biomarkers for the prognostication and non-invasive diagnosis of gastrointestinal cancers and highlight their clinical application.

Immunorelated gene polymorphisms associated with acute myeloid leukemia.

Although the pathogenesis of acute myeloid leukemia (AML) is still unknown, accumulating evidence has revealed that immune response plays a vital part in the pathogenesis. Here, we investigated the involvement of 21 single nucleotide polymorphisms (SNPs) of immunorelated genes, including cytokines [interleukin (IL)-2, IL-4, IL-9, IL-12A, IL-22, interferon (IFN-α) and transforming growth factor (TGF)-ß1], transcriptional regulatory genes (TBX21, STAT1, STAT3, STAT5B, STAT6, GATA3, FOXP3 and IRF4) and others (IL2RA, IL6R, NFKBIA) in 269 AML in-patients and 200 healthy controls. Furthermore, we analyzed the relationship between the SNPs and clinical characteristics. Immunorelated SNP genotyping was performed on the Sequenom MassARRAY iPLEX platform. All the SNPs in healthy controls were consistent with Hardy-Weinberg equilibrium. All final P-values were adjusted by Bonferroni multiple testing. Our results showed that IL-22 (rs2227491) was significantly associated with the white blood cell (WBC) counts. Signal transducer and activator of transcription 5B (STAT-5B) (rs6503691) showed a close relationship with the recurrent genetic abnormalities in patients with AML. We verified the negatively independent effect of age and risk of cytogenetics on overall survival (OS). More importantly, the GG genotype of IL-12A (rs6887695) showed a negative impact on AML prognosis independently. Furthermore, the relative expression of IL-12 was decreased in GG genotype, no matter under a co-dominant or recessive model. However, no correlation was observed between the SNPs mentioned above and disease susceptibility, risk stratification and survival. Our findings suggest that immunorelated gene polymorphisms are associated with prognosis in AML, which may perform as novel inspection targets for AML patients.

Machine learning analysis of motor evoked potential time series to predict disability progression in multiple sclerosis.

BACKGROUND: Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years. Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabiliation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients. METHODS: We extracted a large number of time series features from the motor EPs with the highly comparative time series analysis software package. Mutual information with the target and the Boruta method are used to find features which contain information not included in the features studied in the literature. We use random forests (RF) and logistic regression (LR) classifiers to predict disability progression after two years. Statistical significance of the performance increase when adding extra features is checked. RESULTS: Including extra time series features in motor EPs leads to a statistically significant improvement compared to using only the known features, although the effect is limited in magnitude (ΔAUC = 0.02 for RF and ΔAUC = 0.05 for LR). RF with extra time series features obtains the best performance (AUC = 0.75±0.07 (mean and standard deviation)), which is good considering the limited number of biomarkers in the model. RF (a nonlinear classifier) outperforms LR (a linear classifier). CONCLUSIONS: Using machine learning methods on EPs shows promising predictive performance. Using additional EP time series features beyond those already in use leads to a modest increase in performance. Larger datasets, preferably multi-center, are needed for further research. Given a large enough dataset, these models may be used to support clinicians in their decision making process regarding future treatment.

White blood cell population dynamics for risk stratification of acute coronary syndrome.

The complete blood count (CBC) provides a high-level assessment of a patient's immunologic state and guides the diagnosis and treatment of almost all diseases. Hematology analyzers evaluate CBCs by making high-dimensional single-cell measurements of size and cytoplasmic and nuclear morphology in high throughput, but only the final cell counts are commonly used for clinical decisions. Here, we utilize the underlying single-cell measurements from conventional clinical instruments to develop a mathematical model guided by cellular mechanisms that quantifies the population dynamics of neutrophil, lymphocyte, and monocyte characteristics. The dynamic model tracks the evolution of the morphology of WBC subpopulations as a patient transitions from a healthy to a diseased state. We show how healthy individuals and hospitalized patients with similar WBC counts can be robustly classified based on their WBC population dynamics. We combine the model with supervised learning techniques to risk-stratify patients under evaluation for acute coronary syndrome. In particular, the model can identify more than 70% of patients in our study population with initially negative screening tests who will be diagnosed with acute coronary syndrome in the subsequent 48 hours. More generally, our study shows how mechanistic modeling of existing clinical data can help realize the vision of precision medicine.

Transcriptional variations in the wider peritumoral tissue environment of pancreatic cancer.

Transcriptional profiling was performed on 452 RNA preparations isolated from various types of pancreatic tissue from tumour patients and healthy donors, with a particular focus on peritumoral samples. Pancreatic ductal adenocarcinomas (PDAC) and cystic tumours were most different in these non-tumorous tissues surrounding them, whereas the actual tumours exhibited rather similar transcript patterns. The environment of cystic tumours was transcriptionally nearly identical to normal pancreas tissue. In contrast, the tissue around PDAC behaved a lot like the tumour, indicating some kind of field defect, while showing far less molecular resemblance to both chronic pancreatitis and healthy tissue. This suggests that the major pathogenic difference between cystic and ductal tumours may be due to their cellular environment rather than the few variations between the tumours. Lack of correlation between DNA methylation and transcript levels makes it unlikely that the observed field defect in the peritumoral tissue of PDAC is controlled to a large extent by such epigenetic regulation. Functionally, a strikingly large number of autophagy-related transcripts was changed in both PDAC and its peritumoral tissue, but not in other pancreatic tumours. A transcription signature of 15 autophagy-related genes was established that permits a prognosis of survival with high accuracy and indicates the role of autophagy in tumour biology.

Clinical Significance of Serum Adiponectin and Resistin Levels in Liver Cirrhosis.

INTRODUCTION: Adiponectin and resistin levels are increased in patients with cirrhosis, but it prognostic significance is unknown. We sought to investigate the factors associated with adiponectin and resistin levels and its clinical significance in patients with cirrhosis. MATERIALS AND METHODS: This was a prospective cohort study that included 122 subjects with cirrhosis who attended an outpatient clinic and were initially evaluated in 2012. Serum adiponectin and resistin levels were measured in samples collected in 2012 (adiponectin and resistin) and 2014 (adiponectin). Thirty healthy subjects served as a control group. RESULTS: Higher adiponectin (21.59 µ g/mL vs. 12.52 µg/mL, P < 0.001) and resistin levels (3.83 ng/mL vs. 2.66 ng/mL, P < 0.001) were observed among patients with cirrhosis compared to controls. Patients classified as Child-Pugh B/C had higher adiponectin levels in relation to Child-Pugh A patients. At second measurement, adiponectin levels increased significantly in non-transplant patients and decreased in liver transplant recipients. Univariate Cox analysis showed that among patients with alcoholic liver disease, adiponectin levels were associated with lower transplant-free survival (HR = 1.034, 95% CI 1.006 - 1.062, P = 0.016). The transplant-free survival was significantly lower among patients with alcoholic liver disease and adiponectin ≥ 17 µg/mL (26.55 months, 95% CI 21.40-31.70) as compared to those with levels < 17 µg/mL (33.76 months, 95% CI 30.70-36.82) (P = 0.045). No relationship was found between the levels of resistin and survival. CONCLUSION: Adiponectin but not resistin levels were associated with intensity of liver dysfunction and worse prognosis in patients with alcoholic liver disease, suggesting a potential as a prognostic biomarker.

[Antimicrobial resistance of uropathogens in patients with acute obstructive pyelonephritis].

RELEVANCE: Acute pyelonephritis is known to be the most complicated and severe urinary tract infection occurring in all age groups and accounting for 14% of all kidney diseases. The generally recognized standard antibiotic therapy cannot completely prevent the progression of the disease to its chronic form after relief of its acute manifestations thus leading to a high incidence of relapses. The aim of our study was to investigate the spectrum of uropathogens and their antibiotic sensitivity in acute obstructive pyelonephritis. MATERIALS AND METHODS: The study comprised 72 patients who underwent semi-rigid ureteroscopy and ultrasonic lithotripsy for ureteral stones. In all patients, bladder urine samples collected by a transurethral catheter were tested bacteriologically using an extended set of culture media within 3 hours after hospital admission. Antibiotics used in antibiotic sensitivity testing for all uropathogens, were grouped into 4 classes (carbapenems, fluoroquinolones, cephalosporins, penicillins). Etiotropic treatment was started upon the availability of the spectrum of microbial patterns, the level of bacteriuria and antibioticogram of uropathogens, 5-6 days after administering initial empirical antibiotic therapy. RESULTS: The study patients had a high detection rate (83.3%) of canonical uropathogens in the bladder urine identified due to using an extended set of culture media, with a bacteriuria of more or equal 103 CFU/mL. Given the results of local antibiograms, a rational antimicrobial therapy should include carbapenems, namely ertapenem or meropenem as initial empirical antibiotics. Using fluoroquinolones as the first line treatment can lead to an inadequate effect in 15.0 to 67.0% of the cases. The findings of the antibiotic resistance testing of uropathogens to cephalosporins and semisynthetic penicillins showed that they should not be used as initial empirical antibiotic therapy for acute obstructive pyelonephritis in the given department of urology.

A Meta-analysis of the Association of Estimated GFR, Albuminuria, Age, Race, and Sex With Acute Kidney Injury.

BACKGROUND: Acute kidney injury (AKI) is a serious global public health problem. We aimed to quantify the risk of AKI associated with estimated glomerular filtration rate (eGFR), albuminuria (albumin-creatinine ratio [ACR]), age, sex, and race (African American and white). STUDY DESIGN: Collaborative meta-analysis. SETTING & POPULATION: 8 general-population cohorts (1,285,049 participants) and 5 chronic kidney disease (CKD) cohorts (79,519 participants). SELECTION CRITERIA FOR STUDIES: Available eGFR, ACR, and 50 or more AKI events. PREDICTORS: Age, sex, race, eGFR, urine ACR, and interactions. OUTCOME: Hospitalized with or for AKI, using Cox proportional hazards models to estimate HRs of AKI and random-effects meta-analysis to pool results. RESULTS: 16,480 (1.3%) general-population cohort participants had AKI over a mean follow-up of 4 years; 2,087 (2.6%) CKD participants had AKI over a mean follow-up of 1 year. Lower eGFR and higher ACR were strongly associated with AKI. Compared with eGFR of 80mL/min/1.73m(2), the adjusted HR of AKI at eGFR of 45mL/min/1.73m(2) was 3.35 (95% CI, 2.75-4.07). Compared with ACR of 5mg/g, the risk of AKI at ACR of 300mg/g was 2.73 (95% CI, 2.18-3.43). Older age was associated with higher risk of AKI, but this effect was attenuated with lower eGFR or higher ACR. Male sex was associated with higher risk of AKI, with a slight attenuation in lower eGFR but not in higher ACR. African Americans had higher AKI risk at higher levels of eGFR and most levels of ACR. LIMITATIONS: Only 2 general-population cohorts could contribute to analyses by race; AKI identified by diagnostic code. CONCLUSIONS: Reduced eGFR and increased ACR are consistent strong risk factors for AKI, whereas associations of AKI with age, sex, and race may be weaker in more advanced stages of CKD.

A Meta-analysis of the Association of Estimated GFR, Albuminuria, Diabetes Mellitus, and Hypertension With Acute Kidney Injury.

BACKGROUND: Diabetes mellitus and hypertension are risk factors for acute kidney injury (AKI). Whether estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (ACR) remain risk factors for AKI in the presence and absence of these conditions is uncertain. STUDY DESIGN: Meta-analysis of cohort studies. SETTING & POPULATION: 8 general-population (1,285,045 participants) and 5 chronic kidney disease (CKD; 79,519 participants) cohorts. SELECTION CRITERIA FOR STUDIES: Cohorts participating in the CKD Prognosis Consortium. PREDICTORS: Diabetes and hypertension status, eGFR by the 2009 CKD Epidemiology Collaboration creatinine equation, urine ACR, and interactions. OUTCOME: Hospitalization with AKI, using Cox proportional hazards models to estimate HRs of AKI and random-effects meta-analysis to pool results. RESULTS: During a mean follow-up of 4 years, there were 16,480 episodes of AKI in the general-population and 2,087 episodes in the CKD cohorts. Low eGFRs and high ACRs were associated with higher risks of AKI in individuals with or without diabetes and with or without hypertension. When compared to a common reference of eGFR of 80mL/min/1.73m(2) in nondiabetic patients, HRs for AKI were generally higher in diabetic patients at any level of eGFR. The same was true for diabetic patients at all levels of ACR compared with nondiabetic patients. The risk gradient for AKI with lower eGFRs was greater in those without diabetes than with diabetes, but similar with higher ACRs in those without versus with diabetes. Those with hypertension had a higher risk of AKI at eGFRs>60mL/min/1.73m(2) than those without hypertension. However, risk gradients for AKI with both lower eGFRs and higher ACRs were greater for those without than with hypertension. LIMITATIONS: AKI identified by diagnostic code. CONCLUSIONS: Lower eGFRs and higher ACRs are associated with higher risks of AKI among individuals with or without either diabetes or hypertension.

Eighty-six cases of clinical characteristics and outcomes of systemic lupus erythematosus-associated macrophage activation syndrome: A meta-analysis study.

OBJECTIVE: To improve our understanding of systemic lupus erythematosus (SLE)-macrophage activation syndrome (MAS). METHODS: A systematic review was performed, to retrieve all those papers on patients with SLE-MAS, in individual or aggregated form. The data in each of these medical records were extracted and analyzed to identify the characteristics of SLE-MAS. RESULTS: A total of 86 SLE-MAS patients were included (25 males and 61 females. The mean (±standard error of the mean) age was 31.21 ± 1.694 years. MAS occurred as the initial presentation of SLE in 47 people (54.65%) and during the course of SLE in 39 (45.35%). A coinfection was reported in 23 (26.74%) patients. The mean Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) score was 16.54 ± 0.9462. Overall, 10 patients (11.63%) died. The SLEDAI-2K score was higher in patients with MAS as an initial manifestation of SLE than in those where MAS occurred during the course of SLE. The proportion of patients receiving steroid pulse therapy was lower in patients with coinfections. The deceased group demonstrated lower platelet and ferritin levels. Multiple regression analysis revealed that age and thrombocytopenia were independent factors associated with poor prognosis. In receiver operating characteristic analysis, a platelet count cutoff value of ≤47 × 109/L was a predictor of poor outcome. CONCLUSIONS: SLE-MAS patients demonstrated high lupus activity, and lupus activity was especially higher in patients with MAS as an initial manifestation. Lupus activity was the predominant trigger of lupus MAS. Thrombocytopenia was an independent factor for poor prognosis.