Prognostic Factors in Adult Patients with Idiopathic IgA Nephropathy

Introduction. IgA nephropathy is the dominant primary glomerular disease found throughout the majority of the world’s developed countries. Accurately identifying patients who are at risk of progressive disease is challenging. We aimed to characterise clinical and histological features that predict poor prognosis in adults. Patients and Methods. We performed a single-centre retrospective observational study of biopsy-proven IgA nephropathy. The primary outcome was renal survival and death from any cause, and the secondary outcome was proteinuria remission. Results. Data from 49 cases were available for analysis with a median follow-up of 4 years. There were no deaths. Univariable analyses identified acute renal failure, low estimated glomerular filtration rate for ≥3 months (low eGFR), arterial hypertension, baseline proteinuria, glomerular sclerosis >50% and interstitial fibrosis >50% as poor prognostic markers. Low eGFR persisted significant by multivariable model that used only clinical parameters. Multivariable models with histopathologic parameters observed that tubular atrophy/interstitial fibrosis >50% was independently associated with the primary outcome. Proteinuria remission throughout follow-up had no prognostic value in our revision. Conclusions. Two independent predictors of poor renal survival at time of biopsy were found: low eGFR and tubular atrophy/interstitial fibrosis >50%.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.