Importance The established chronic kidney disease (CKD) progression endpoint, end-stage renal disease (ESRD) or doubling of serum creatinine (corresponding to a change in estimated glomerular filtration rate (eGFR) of −57% or greater) is a late event, limiting feasibility of nephrology clinical trials. Objective To characterize the association of decline in eGFR with subsequent progression to ESRD, with implications for using lesser declines in eGFR as potential alternative endpoints for CKD progression. Since most people with CKD die before reaching ESRD, we also investigated mortality risk. Data Sources Individual meta-analysis of up to 1.7 million participants with 12,344 ESRD events and 223,944 deaths from 35 cohorts. Study Selection Cohorts in the CKD Prognosis Consortium with a repeated measure of serum creatinine over 1-3 years and outcome data. Data Extraction and Synthesis Transfer of individual participant data or standardized analysis of outputs for random effects meta-analysis took place between July 2012 and September 2013 with baseline eGFRs during 1975-2012. Main Outcomes and Measures ESRD (initiation of dialysis or transplantation) or all-cause mortality risk related to percent change in eGFR over 2 years adjusted for potential confounders and first eGFR. Results The adjusted hazard ratios (HR) of ESRD and mortality were exponentially higher with larger eGFR decline. Among participants with baseline eGFR <60 ml/min/1.73m2, the adjusted HRs for ESRD were 32.1 (95% CI 22.3-46.3) and 5.4 (4.5-6.4) for −57% and −30% eGFR changes, respectively. However, changes of −30% or greater were much more common than changes of −57% (6.9% (6.4-7.4%) vs. 0.79% (0.52-1.06%) in the whole consortium). This association was strong and consistent across length of baseline (1 or 3 years), baseline eGFR, age, diabetes status, or albuminuria. Average adjusted 10-year risk of ESRD for eGFR changes of −57%, −40%, −30% and 0% were 99% (95-100%), 83% (71-93%), 64% (52-77%), vs. 18% (15-22%) respectively at baseline eGFR of 35 ml/min/1.73m2. Corresponding mortality risks were 77% (71-82%), 60% (56-63%), 50% (47-52%), vs. 32% (31-33%), showing a similar but weaker pattern. Conclusions and Relevance Declines in eGFR smaller than doubling of serum creatinine occur more commonly and are strongly and consistently associated with the risk of ESRD and mortality, supporting consideration of lesser declines in eGFR, such as 30% reduction over 2 years, as an alternative endpoint for CKD progression.

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Outpatient potassium levels both above and below the normal range are consistently associated with adverse outcomes, with similar risk relationships across eGFR and albuminuria.

Endometriosis diagnosed in 1% of women, according to a large population-based study in a community setting.

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As the COVID-19 pandemic progresses, obtaining information on symptoms dynamics is of essence. Here, we extracted data from primary-care electronic health records and nationwide distributed surveys to assess the longitudinal dynamics of symptoms prior to and throughout SARS-CoV-2 infection. Information was available for 206,377 individuals, including 2471 positive cases. The two datasources were discordant, with survey data capturing most of the symptoms more sensitively. The most prevalent symptoms included fever, cough and fatigue. Loss of taste and smell 3 weeks prior to testing, either self-reported or recorded by physicians, were the most discriminative symptoms for COVID-19. Additional discriminative symptoms included self-reported headache and fatigue and a documentation of syncope, rhinorrhea and fever. Children had a significantly shorter disease duration. Several symptoms were reported weeks after recovery. By a unique integration of two datasources, our study shed light on the longitudinal course of symptoms experienced by cases in primary care.

Alterations in the gut microbiome have been implicated in the pathogenesis of several immune‐mediated inflammatory diseases such as psoriatic arthritis. This work aimed to characterize the gut microbial signature of patients with active psoriasis as compared with age‐, body mass index‐ and comorbidity‐matched non‐psoriatic controls and to correlate them with differential expression of metabolic pathways. Fecal samples were processed and 16S rRNA was sequenced. PICRUSt was used to perform an analysis of metabolic pathways. Of the 46 participants, 52% (n = 24) suffered from psoriasis. There was a significant difference in β‐diversity between the two groups. Psoriatic patients had a significant increase in the Firmicutes and Actinobacteria phyla as compared with matched controls. At the genus level, psoriatic patients had a unique bacterial composition. At the species level, the psoriatic patients showed significant increases in the relative proportions of (false discovery rate, <0.05) in Ruminoccocus gnavus, Dorea formicigenerans and Collinsella aerofaciens, while Prevotella copri and Parabacteroides distasonis were significantly decreased as compared with controls. PICRUSt analysis revealed increases in metabolic pathways related to lipopolysaccharide function in the psoriatic cohort. These data demonstrate unique fecal microbial and metabolic signatures in psoriatic patients.

Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed "empirically", in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10year longitudinal dataset of over 700,000 community-acquired UTIs with over 5,000,000 individually-resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a one-year test period, we find that they much reduce the risk of mismatched treatment compared to the current standard-of-care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: