METHODS: In this prospective multicentre study, consecutive CKD patients (n = 154) undergoing routine clinical cardiac magnetic resonance (CMR) imaging were compared with patients with hypertensive (HTN, n = 163) and hypertrophic cardiomyopathy (HCM, n = 158), and normotensive controls (n = 133).
RESULTS: Native T1 was significantly higher in all patient groups, whereas native T2 in CKD only (p
METHODS: Consecutive patients with established CKD and estimated glomerular filtration rate (eGFR)
METHODS: This is a prospective observational study on patients with SIRS. Plasma creatinine (pCr) and NGAL were measured on ICU admission. Patients were classified according to the occurrence of AKI and sepsis.
RESULTS: Of 225 patients recruited, 129 (57%) had sepsis of whom 67 (52%) also had AKI. 96 patients (43%) had non-infectious SIRS, of whom 20 (21%) also had AKI. NGAL concentrations were higher in AKI patients within both the sepsis and non-infectious SIRS cohorts (both P
MATERIALS: We recruited consecutively adult patients with SIRS admitted to an intensive care unit. They were divided into sepsis and noninfectious SIRS based on clinical assessment with or without positive cultures. Concentrations of PCT and IL-6 were measured daily over the first 3 days.
RESULTS: A total of 239 patients were recruited, 164 (68.6%) had sepsis, and 68 (28.5%) died in hospital. The PCT levels were higher in sepsis compared with noninfectious SIRS throughout the 3-day period (P < .0001). On admission, PCT concentration was diagnostic of sepsis (area under the curve of 0.63 [0.55-0.71]), and IL-6 was predictive of mortality, (area under the curve of 0.70 [0.62-0.78]). Peak IL-6 concentration improved the risk assessment of Sequential Organ Failure Assessment (SOFA) score for prediction of mortality among those who went on to die by an average of 5% and who did not die by 2%
CONCLUSIONS: Procalcitonin measured on intensive care unit admission was diagnostic of sepsis, and IL-6 was predictive of mortality. Addition of IL-6 concentration to SOFA score improved risk assessment for prediction of mortality. Future studies should include clinical indices, for example, SOFA score, for prognostic evaluation of biomarkers.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
OBJECTIVE: To compare the ability of the prehospital GCS and GCS-M to predict 30-day mortality and severe disability in trauma patients.
DESIGN: We used the Pan-Asia Trauma Outcomes Study registry to enroll all trauma patients >18 years of age who presented to hospitals via emergency medical services from 1 January 2016 to November 30, 2018.
SETTINGS AND PARTICIPANTS: A total of 16,218 patients were included in the analysis of 30-day mortality and 11 653 patients in the analysis of functional outcomes.
OUTCOME MEASURES AND ANALYSIS: The primary outcome was 30-day mortality after injury, and the secondary outcome was severe disability at discharge defined as a Modified Rankin Scale (MRS) score ≥4. Areas under the receiver operating characteristic curve (AUROCs) were compared between GCS and GCS-M for these outcomes. Patients with and without traumatic brain injury (TBI) were analyzed separately. The predictive discrimination ability of logistic regression models for outcomes (30-day mortality and MRS) between GCS and GCS-M is illustrated using AUROCs.
MAIN RESULTS: The primary outcome for 30-day mortality was 1.04% and the AUROCs and 95% confidence intervals for prediction were GCS: 0.917 (0.887-0.946) vs. GCS-M:0.907 (0.875-0.938), P = 0.155. The secondary outcome for poor functional outcome (MRS ≥ 4) was 12.4% and the AUROCs and 95% confidence intervals for prediction were GCS: 0.617 (0.597-0.637) vs. GCS-M: 0.613 (0.593-0.633), P = 0.616. The subgroup analyses of patients with and without TBI demonstrated consistent discrimination ability between the GCS and GCS-M. The AUROC values of the GCS vs. GCS-M models for 30-day mortality and poor functional outcome were 0.92 (0.821-1.0) vs. 0.92 (0.824-1.0) ( P = 0.64) and 0.75 (0.72-0.78) vs. 0.74 (0.717-0.758) ( P = 0.21), respectively.
CONCLUSION: In the prehospital setting, on-scene GCS-M was comparable to GCS in predicting 30-day mortality and poor functional outcomes among patients with trauma, whether or not there was a TBI.