Trajectory of Kidney Function: The Canary in Sepsis.
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ABSTRACT:
SUBMITTER: Bhatraju PK
PROVIDER: S-EPMC7605179 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Autosomal dominant polycystic kidney disease (ADPKD) is characterized by cyst and kidney growth, which is hypothesized to cause loss of functioning renal mass and eventually end-stage kidney disease. However, the time course of decline in glomerular filtration rate (GFR) is poorly defined. The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study is a 14-year observational cohort study of 241 adults with ADPKD. As an estimate of the rate of kidney growth, participants were stratified into 5 subclasses based on baseline age and magnetic resonance imaging measurements of total kidney volume (TKV) according to the method of Irazabal. GFR trajectories spanning over four decades of life were reconstructed and fitted using mixed polynomial models, which were validated using data from the HALT-PKD study. GFR trajectories were nonlinear, with a period of relative stability in most participants, followed by accelerating decline. The shape and slope of these trajectories were strongly associated with baseline Irazabal class. Patients with PKD1 mutations had a steeper GFR decline than patients with PKD2 mutations or with no detected mutation, largely mediated by the effect of genotype on Irazabal class. Thus, GFR decline in ADPKD is nonlinear, and its trajectory throughout adulthood can be predicted from a single measurement of kidney volume. These models can be used for clinical prognostication, clinical trial design, and patient selection for clinical interventions. Our findings support a causal link between growth in kidney volume and GFR decline, adding support for the use of TKV as a surrogate endpoint in clinical trials.
| S-EPMC6478515 | biostudies-literature
Project description:Rationale & objectiveEarly prediction of chronic kidney disease (CKD) progression to end-stage kidney disease (ESKD) currently use Cox models including baseline estimated glomerular filtration rate (eGFR) only. Alternative approaches include a Cox model that includes eGFR slope determined over a baseline period of time, a Cox model with time varying GFR, or a joint modeling approach. We studied if these more complex approaches may further improve ESKD prediction.Study designProspective cohort.Setting & participantsWe re-used data from two CKD cohorts including patients with baseline eGFR >30ml/min per 1.73m2. MASTERPLAN (N = 505; 55 ESKD events) was used as development dataset, and NephroTest (N = 1385; 72 events) for validation.PredictorsAll models included age, sex, eGFR, and albuminuria, known prognostic markers for ESKD.Analytical approachWe trained the models on the MASTERPLAN data and determined discrimination and calibration for each model at 2 years follow-up for a prediction horizon of 2 years in the NephroTest cohort. We benchmarked the predictive performance against the Kidney Failure Risk Equation (KFRE).ResultsThe C-statistics for the KFRE was 0.94 (95%CI 0.86 to 1.01). Performance was similar for the Cox model with time-varying eGFR (0.92 [0.84 to 0.97]), eGFR (0.95 [0.90 to 1.00]), and the joint model 0.91 [0.87 to 0.96]). The Cox model with eGFR slope showed the best calibration.ConclusionIn the present studies, where the outcome was rare and follow-up data was highly complete, the joint models did not offer improvement in predictive performance over more traditional approaches such as a survival model with time-varying eGFR, or a model with eGFR slope.
| S-EPMC6508737 | biostudies-literature
Project description:ObjectivesCognitive impairment is an important consequence of sepsis. We sought to determine long-term trajectories of cognitive function after sepsis.DesignProspective study of the Reasons for Geographic and Racial Differences in Stroke cohort.SettingUnited States.PatientsTwenty-one thousand eight-hundred twenty-three participants greater than or equal to 45 years, mean (sd) age 64.3 (9.2) years at first cognitive assessment, 30.9% men, and 27.1% Black.Measurements and main resultsThe main exposure was time-dependent sepsis hospitalization. The primary outcome was global cognitive function (Six-Item Screener range, 0-6). Secondary outcomes were incident cognitive impairment (Six-Item Screener score ≤ 4 [impaired] vs ≥5 [unimpaired]), new learning (Consortium to Establish a Registry for Alzheimer Disease Word List Learning range, 0-30), verbal memory (word list delayed recall range, 0-10), and executive function/semantic fluency (animal fluency test range, ≥ 30). Over a median follow-up of 10 years (interquartile range, 6-12 yr), 840 (3.8%) experienced sepsis (incidence 282 per 1,000 person-years). Sepsis was associated with faster long-term declines in Six-Item Screener (-0.02 points per year faster [95% CI, -0.01 to -0.03]; p < 0.001) and faster long-term rates of incident cognitive impairment (odds ratio 1.08 per year [95% CI, 1.02-1.15]; p = 0.008) compared with presepsis slopes. Although cognitive function acutely changed after sepsis (0.05 points [95% CI, 0.01-0.09]; p = 0.01), the odds of acute cognitive impairment (Six-Item Screener ≤ 4) immediately after sepsis was not significant (odds ratio, 0.81 [95% CI, 0.63-1.06]; p = 0.12). Sepsis hospitalization was not associated with acute changes or faster declines in word list learning, word list delayed recall, or animal fluency test.ConclusionsSepsis is associated with accelerated long-term decline in global cognitive function.
| S-EPMC8217073 | biostudies-literature
Project description:BackgroundSepsis is a heterogeneous syndrome, and the identification of clinical subphenotypes is essential. Although organ dysfunction is a defining element of sepsis, subphenotypes of differential trajectory are not well studied. We sought to identify distinct Sequential Organ Failure Assessment (SOFA) score trajectory-based subphenotypes in sepsis.MethodsWe created 72-h SOFA score trajectories in patients with sepsis from four diverse intensive care unit (ICU) cohorts. We then used dynamic time warping (DTW) to compute heterogeneous SOFA trajectory similarities and hierarchical agglomerative clustering (HAC) to identify trajectory-based subphenotypes. Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. The model was tested on three validation cohorts. Sensitivity analyses were performed with alternative clustering methodologies.ResultsA total of 4678, 3665, 12,282, and 4804 unique sepsis patients were included in development and three validation cohorts, respectively. Four subphenotypes were identified in the development cohort: Rapidly Worsening (n = 612, 13.1%), Delayed Worsening (n = 960, 20.5%), Rapidly Improving (n = 1932, 41.3%), and Delayed Improving (n = 1174, 25.1%). Baseline characteristics, including the pattern of organ dysfunction, varied between subphenotypes. Rapidly Worsening was defined by a higher comorbidity burden, acidosis, and visceral organ dysfunction. Rapidly Improving was defined by vasopressor use without acidosis. Outcomes differed across the subphenotypes, Rapidly Worsening had the highest in-hospital mortality (28.3%, P-value < 0.001), despite a lower SOFA (mean: 4.5) at ICU admission compared to Rapidly Improving (mortality:5.5%, mean SOFA: 5.5). An overall prediction accuracy of 0.78 (95% CI, [0.77, 0.8]) was obtained at 6 h after ICU admission, which increased to 0.87 (95% CI, [0.86, 0.88]) at 24 h. Similar subphenotypes were replicated in three validation cohorts. The majority of patients with sepsis have an improving phenotype with a lower mortality risk; however, they make up over 20% of all deaths due to their larger numbers.ConclusionsFour novel, clinically-defined, trajectory-based sepsis subphenotypes were identified and validated. Identifying trajectory-based subphenotypes has immediate implications for the powering and predictive enrichment of clinical trials. Understanding the pathophysiology of these differential trajectories may reveal unanticipated therapeutic targets and identify more precise populations and endpoints for clinical trials.
| S-EPMC9250715 | biostudies-literature
Project description:BackgroundSepsis is a heterogeneous syndrome. This study aimed to identify new sepsis sub-phenotypes using plasma cortisol trajectory.MethodsThis retrospective study included patients with sepsis admitted to the intensive care unit of Zhongshan Hospital Fudan University between March 2020 and July 2022. A group-based cortisol trajectory model was used to classify septic patients into different sub-phenotypes. The clinical characteristics, biomarkers, and outcomes were compared between sub-phenotypes.ResultsA total of 258 patients with sepsis were included, of whom 186 were male. Patients were divided into two trajectory groups: the lower-cortisol group (n = 217) exhibited consistently low and slowly declining cortisol levels, while the higher-cortisol group (n = 41) showed relatively higher levels in comparison. The 28-day mortality (65.9% vs.16.1%, P < 0.001) and 90-day mortality (65.9% vs. 19.8%, P < 0.001) of the higher-cortisol group were significantly higher than the lower-cortisol group. Multivariable Cox regression analysis showed that the trajectory sub-phenotype (HR = 5.292; 95% CI 2.218-12.626; P < 0.001), APACHE II (HR = 1.109; 95% CI 1.030-1.193; P = 0.006), SOFA (HR = 1.161; 95% CI 1.045-1.291; P = 0.006), and IL-1β (HR = 1.001; 95% CI 1.000-1.002; P = 0.007) were independent risk factors for 28-day mortality. Besides, the trajectory sub-phenotype (HR = 4.571; 95% CI 1.980-10.551; P < 0.001), APACHE II (HR = 1.108; 95% CI 1.043-1.177; P = 0.001), SOFA (HR = 1.270; 95% CI 1.130-1.428; P < 0.001), and IL-1β (HR = 1.001; 95% CI 1.000-1.001; P = 0.015) were also independent risk factors for 90-day mortality.ConclusionThis study identified two novel cortisol trajectory sub-phenotypes in patients with sepsis. The trajectories were associated with mortality, providing new insights into sepsis classification.
| S-EPMC11370002 | biostudies-literature
Project description:The quick Sequential Organ Failure Assessment (qSOFA) system identifies an individual's risk to progress to poor sepsis-related outcomes using minimal variables. We used Support Vector Machine, Learning Using Concave and Convex Kernels, and Random Forest to predict an increase in qSOFA score using electronic health record (EHR) data, electrocardiograms (ECG), and arterial line signals. We structured physiological signals data in a tensor format and used Canonical Polyadic/Parallel Factors (CP) decomposition for feature reduction. Random Forests trained on ECG data show improved performance after tensor decomposition for predictions in a 6-h time frame (AUROC 0.67 ± 0.06 compared to 0.57 ± 0.08, p=0.01 ). Adding arterial line features can also improve performance (AUROC 0.69 ± 0.07, p<0.01 ), and benefit from tensor decomposition (AUROC 0.71 ± 0.07, p=0.01 ). Adding EHR data features to a tensor-reduced signal model further improves performance (AUROC 0.77 ± 0.06, p<0.01 ). Despite reduction in performance going from an EHR data-informed model to a tensor-reduced waveform data model, the signals-informed model offers distinct advantages. The first is that predictions can be made on a continuous basis in real-time, and second is that these predictions are not limited by the availability of EHR data. Additionally, structuring the waveform features as a tensor conserves structural and temporal information that would otherwise be lost if the data were presented as flat vectors.
| S-EPMC11300462 | biostudies-literature
Project description:Sepsis is characterized by a severe inflammatory response to infection, and its complications, including acute kidney injury, can be fatal. Animal models that correctly mimic human disease are extremely valuable because they hasten the development of clinically useful therapeutics. Too often, however, animal models do not properly mimic human disease. In this Review, we outline a bedside-to-bench-to-bedside approach that has resulted in improved animal models for the study of sepsis - a complex disease for which preventive and therapeutic strategies are unfortunately lacking. We also highlight a few of the promising avenues for therapeutic advances and biomarkers for sepsis and sepsis-induced acute kidney injury. Finally, we review how the study of drug targets and biomarkers are affected by and in turn have influenced these evolving animal models.
| S-EPMC2752080 | biostudies-other
Project description:ObjectivesWe recently found that distinct body temperature trajectories of infected patients correlated with survival. Understanding the relationship between the temperature trajectories and the host immune response to infection could allow us to immunophenotype patients at the bedside using temperature. The objective was to identify whether temperature trajectories have consistent associations with specific cytokine responses in two distinct cohorts of infected patients.DesignProspective observational study.SettingLarge academic medical center between 2013 and 2019.SubjectsTwo cohorts of infected patients: 1) patients in the ICU with septic shock and 2) hospitalized patients with Staphylococcus aureus bacteremia.InterventionsClinical data (including body temperature) and plasma cytokine concentrations were measured. Patients were classified into four temperature trajectory subphenotypes using their temperature measurements in the first 72 hours from the onset of infection. Log-transformed cytokine levels were standardized to the mean and compared with the subphenotypes in both cohorts.Measurements and main resultsThe cohorts consisted of 120 patients with septic shock (cohort 1) and 88 patients with S. aureus bacteremia (cohort 2). Patients from both cohorts were classified into one of four previously validated temperature subphenotypes: "hyperthermic, slow resolvers" (n = 19 cohort 1; n = 13 cohort 2), "hyperthermic, fast resolvers" (n = 18 C1; n = 24 C2), "normothermic" (n = 54 C1; n = 31 C2), and "hypothermic" (n = 29 C1; n = 20 C2). Both "hyperthermic, slow resolvers" and "hyperthermic, fast resolvers" had high levels of G-CSF, CCL2, and interleukin-10 compared with the "hypothermic" group when controlling for cohort and timing of cytokine measurement (p < 0.05). In contrast to the "hyperthermic, slow resolvers," the "hyperthermic, fast resolvers" showed significant decreases in the levels of several cytokines over a 24-hour period, including interleukin-1RA, interleukin-6, interleukin-8, G-CSF, and M-CSF (p < 0.001).ConclusionsTemperature trajectory subphenotypes are associated with consistent cytokine profiles in two distinct cohorts of infected patients. These subphenotypes could play a role in the bedside identification of cytokine profiles in patients with sepsis.
| S-EPMC7837282 | biostudies-literature
Project description:PurposeThe Acute Disease Quality Initiative (ADQI) Workgroup recently released a consensus definition of sepsis-associated acute kidney injury (SA-AKI), combining Sepsis-3 and Kidney Disease Improving Global Outcomes (KDIGO) AKI criteria. This study aims to describe the epidemiology of SA-AKI.MethodsThis is a retrospective cohort study carried out in 12 intensive care units (ICUs) from 2015 to 2021. We studied the incidence, patient characteristics, timing, trajectory, treatment, and associated outcomes of SA-AKI based on the ADQI definition.ResultsOut of 84,528 admissions, 13,451 met the SA-AKI criteria with its incidence peaking at 18% in 2021. SA-AKI patients were typically admitted from home via the emergency department (ED) with a median time to SA-AKI diagnosis of 1 day (interquartile range (IQR) 1-1) from ICU admission. At diagnosis, most SA-AKI patients (54%) had a stage 1 AKI, mostly due to the low urinary output (UO) criterion only (65%). Compared to diagnosis by creatinine alone, or by both UO and creatinine criteria, patients diagnosed by UO alone had lower renal replacement therapy (RRT) requirements (2.8% vs 18% vs 50%; p < 0.001), which was consistent across all stages of AKI. SA-AKI hospital mortality was 18% and SA-AKI was independently associated with increased mortality. In SA-AKI, diagnosis by low UO only, compared to creatinine alone or to both UO and creatinine criteria, carried an odds ratio of 0.34 (95% confidence interval (CI) 0.32-0.36) for mortality.ConclusionSA-AKI occurs in 1 in 6 ICU patients, is diagnosed on day 1 and carries significant morbidity and mortality risk with patients mostly admitted from home via the ED. However, most SA-AKI is stage 1 and mostly due to low UO, which carries much lower risk than diagnosis by other criteria.
| S-EPMC10499944 | biostudies-literature
Project description:IntroductionAbout one-third of critically ill patients with acute kidney injury (AKI) develop persistently decreased kidney function, known as acute kidney disease (AKD), which may progress to chronic kidney disease (CKD). Although sepsis is the most common cause of AKI, little is known about sepsis-associated AKD.MethodsUsing data from a large randomized trial including 1341 patients with septic shock, we studied patients with stage 2 or 3 AKI on day 1 of hospitalization. We defined AKD as a persistently reduced glomerular filtration rate for >7 days. In addition to clinical data, we measured several urinary biomarkers (tissue inhibitor of metalloproteinases-2 and insulin-like growth factor-binding protein 7 [TIMP-2?IGFBP7], neutrophil gelatinase-associated lipocalin [NGAL], kidney injury molecule-1 [KIM-1], liver-type fatty acid binding protein, and type 4 collagen) at 0, 6, and 24 hours, to predict AKD.ResultsOf 598 patients, 119 (19.9%) died within 7 days, 318 (53.2%) had early reversal of AKI within the first 7 days, whereas 161 (26.9%) developed AKD. In patients with early reversal, 45 (14.2%) had relapsed AKI after early reversal, and only about one-third of these recovered. Among patients developing AKD, only 15 (9.3%) recovered renal function prior to discharge. Male sex, African American race, and underlying CKD were more predominant in patients developing AKD. None of the biomarkers tested performed well for prediction of AKD, although NGAL modestly increased the performance of a clinical model.ConclusionsAKD is common in patients with septic shock, especially among African American males and those with underlying CKD. Existing AKI biomarkers have limited utility for predicting AKD but might be useful together with clinical variables. Novel predictive biomarkers for renal recovery are needed.
| S-EPMC7270721 | biostudies-literature