INTRODUCTION
The International IgA Nephropathy Network developed a tool (IINN-PT) for predicting the risk of End-Stage Renal Disease (ESRD) or a 50% decline in the estimated filtration rate (eGFR). We aimed to validate this tool in a French cohort with longer follow-up than previously published validation studies.


METHODS
The predicted survival of patients with biopsy-proven IgAN from the Saint Etienne University Hospital cohort was computed with IINN-PT models with or without ethnicity. The primary outcome was the occurrence of either ESRD or a 50% decline in eGFR. The models' performances were evaluated through c-statistics, discrimination, and calibration analysis.


RESULTS
There were 473 patients with biopsy-proven IgAN, with a median follow-up of 12.4 years. Models with and without ethnicity showed AUCs [95%CI] of 0.817 [0.765;0.869] and 0.833 [0.791;0.875] and R2D of 0.28 and 0.29, respectively, and an excellent discrimination of groups of increasing predicted risk (p < 0.001). The calibration analysis was good for both models up to 15 years after diagnosis. The model without ethnicity exhibited a mathematical issue of survival function after 15 years.


DISCUSSION
The IINN-PT provided good performances even after 10 years post-biopsy as showed by our study based on a cohort with a longer follow-up than previous cohorts (12.4 versus less than 6 years). The model without ethnicity exhibited better performances up to 15 years but became aberrant beyond this point due to a mathematical issue affecting the survival function. Our study sheds light on the usefulness of integrating ethnicity as a covariable for prediction of IgAN course.

Validation of the international IgA nephropathy prediction tool in a French cohort beyond 10 years after diagnosis.

BackgroundThe incidence of concurrent immunoglobulin A nephropathy and membranous nephropathy (cIgAN/MN) is low and rarely reported, and the prognosis of patients with cIgAN/MN remains unclear. This study was designed to compare the clinical and prognostic characteristics of cIgAN/MN with IgAN and MN and to identify crucial factors influencing the outcomes of patients with cIgAN/MN.MethodsWe included biopsy-proven cIgAN/MN patients between December 2012 and December 2020 at Xijing Hospital. In the same period, propensity score matching was employed to select an equal number of IgAN and MN patients according to the following criteria: age, sex, and follow-up time. The primary endpoint was defined as a composite of eGFR decline ≥30 %, end-stage renal disease, or death. The patient survival rate was examined using Kaplan–Meier survival curves. Univariate and multivariate Cox regression analysis models were utilized to identify the risk factors affecting renal prognosis.ResultsA total of 135 patients were finally included in this study. The median follow-up period was 45.5 (29.7, 64.7) months. Among the patients, 37 (27.4 %) reached the primary endpoint. Compared to the IgAN group, the cIgAN/MN group exhibited a lower cumulative incidence rate of composite renal endpoints (P = 0.044), while no significant difference was found between MN and cIgAN/MN patients (P = 0.211). Univariate Cox analysis revealed that mean arterial pressure, serum potassium, blood urea nitrogen, serum IgA, segmental glomerulosclerosis (S1), and MN staging were associated with an increased risk of renal composite endpoints. The multivariate Cox regression analysis of clinical variables plus histological lesion scoring demonstrated that potassium (HR = 14.350, 95 % CI 2.637–78.090, P = 0.002), serum IgA (HR = 1.870, 95 % CI 1.109–3.153, P = 0.019), and S1 (HR = 11.965, 95 % CI 2.166–66.105, P = 0.004) were independent risk factors influencing renal outcomes in cIgAN/MN patients.ConclusionThe prognosis of cIgAN/MN patients may exhibit an intermediate pattern between IgAN and MN, leaning towards being more similar to MN in certain aspects. Within the cIgAN/MN cohort, potassium, and serum IgA may be more predictive of rapid progression of renal endpoints, and S1 may indicate a more aggressive disease course. 

Prognostic factors affecting long-term outcomes in patients with concurrent IgA nephropathy and membranous nephropathy

Renal biopsy is the gold standard for Immunoglobulin A nephropathy (IgAN) but poses several problems. Thus, we aimed to establish a noninvasive model for predicting the risk probability of IgAN by analyzing routine and serological parameters. A total of 519 biopsy-diagnosed IgAN and 211 non-IgAN patients were recruited retrospectively. Artificial neural networks and logistic modeling were used. The receiver operating characteristic (ROC) curve and performance characteristics were determined to compare the diagnostic value between the two models. The training and validation sets did not differ significantly in terms of any variables. There were 19 significantly different parameters between the IgAN and non-IgAN groups. After multivariable logistic regression analysis, age, serum albumin, serum IgA, serum immunoglobulin G, estimated glomerular filtration rate, serum IgA/C3 ratio, and hematuria were found to be independently associated with the presence of IgAN. A backpropagation network model based on the above parameters was constructed and applied to the validation cohorts, revealing a sensitivity of 82.68% and a specificity of 84.78%. The area under the ROC curve for this model was higher than that for logistic regression model (0.881 vs. 0.839). The artificial neural network model based on routine markers can be a valuable noninvasive tool for predicting IgAN in screening practice. 

/pdf/a-noninvasive-artificial-neural-network-model-to-predict-iga-j8pai5k6.pdf

A noninvasive artificial neural network model to predict IgA nephropathy risk in Chinese population

Abstract Prognosis determines major decisions regarding treatment for critically ill patients. Statistical models have been developed to predict the probability of survival and other outcomes of intensive care. Although they were trained on the characteristics of large patient cohorts, they often do not represent very old patients (age ≥ 80 years) appropriately. Moreover, the heterogeneity within this particular group impairs the utility of statistical predictions for informing decision-making in very old individuals. In addition to these methodological problems, the diversity of cultural attitudes, available resources as well as variations of legal and professional norms limit the generalisability of prediction models, especially in patients with complex multi-morbidity and pre-existing functional impairments. Thus, current approaches to prognosticating outcomes in very old patients are imperfect and can generate substantial uncertainty about optimal trajectories of critical care in the individual. This article presents the state of the art and new approaches to predicting outcomes of intensive care for these patients. Special emphasis has been given to the integration of predictions into the decision-making for individual patients. This requires quantification of prognostic uncertainty and a careful alignment of decisions with the preferences of patients, who might prioritise functional outcomes over survival. Since the performance of outcome predictions for the individual patient may improve over time, time-limited trials in intensive care may be an appropriate way to increase the confidence in decisions about life-sustaining treatment. 

pdf/prognosticating-the-outcome-of-intensive-care-in-older-5ebm119si1.pdf

Prognosticating the outcome of intensive care in older patients—a narrative review

Background and objectives Immunoglobulin a nephropathy (IgAN) is the most common primary glomerular disease in the world, with different clinical manifestations, varying severity of pathological changes, common complications of crescent formation in different proportions, and great individual heterogeneous in clinical outcomes. Therefore, we aim to develop a machine learning (ML) based predictive model for predicting the prognosis of IgAN with focal crescent formation and without obvious chronic renal lesions (glomerulosclerosis <25%). Materials We retrospectively reviewed biopsy-proven IgAN patients in our hospital and cooperative hospital from 2005 to 2017. The method of feature importance of random forest (RF) was applied to conduct feature exploration of feature variables to establish the characteristic variables that are closely related to the prognosis of focal crescent IgAN. Multiple ML algorithms were attempted to establish the prediction models. The area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) were applied to evaluate the predictive performance via three-fold cross validation (namely 2 training sets and 1 validation set). Results RF was used to screen the important features, the top three of which were baseline estimated glomerular filtration rate (eGFR), serum creatine and triglyceride. Ten important features were selected as important predictors for modeling on the basis of data-driven and medical selection, predictors include: age, baseline eGFR, serum creatine, serum triglycerides, complement 3(C3), proteinuria, mean arterial pressure (MAP) and Hematuria, crescents proportion of glomeruli, Global crescent proportion of glomeruli. In a variety of ML algorithms, the support vector machine (SVM) algorithm displayed better predictive performance, with Precision of 0.77, Recall of 0.77, F1-score of 0.73, accuracy of 0.77, AUROC of 79.57%, and AUPRC of 76.5%. Conclusions The SVM model is potentially useful for predicting the prognosis of IgAN patients with focal crescent shape and without obvious chronic renal lesions.

/pdf/prediction-of-prognosis-in-immunoglobulin-a-nephropathy-1hys13q5.pdf

Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning

Background Although severity scoring systems are used to support decision making and assess ICU performance, the likelihood of bias based on age, gender, and primary language has not been studied. We aimed to identify the potential bias of them such as Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation IVa (APACHE IVa) by evaluating hospital mortality across subgroups divided by age, gender, and primary language via two large intensive care unit (ICU) databases. Methods This multicenter, retrospective study was conducted using data from the Medical Information Mart for Intensive Care (MIMIC, 2001-2019) database and the electronic ICU Collaborative Research Database (eICU-CRD, 2014-2015). SOFA and APACHE IVa scores were obtained from the first 24 hours of ICU admission. Hospital mortality was the primary outcome. Patients were stratified by age (16-44, 45-64, 64-79, and 80-), gender (female and male), and primary language (English and non-English) then assessed for discrimination and calibration in all subgroups. To evaluate for discrimination, the area under receiver operating characteristic (AUROC) curve and area under precision-recall curve (AUPRC) were used. Standardized mortality ratio (SMR) and calibration belt plot were used to evaluate calibration. Findings A total of 173,930 patient encounters (78,550 MIMIC and 95,380 eICU-CRD) were studied. Measurements of discrimination performed best for the youngest age ranges and worsened with increasing age (AUROC ranging from 0.812 to 0.673 for SOFA and 0.882 to 0.754 for APACHE IVa, p <0.001). There was a significant difference in discrimination between male and female patients, with female patients performing worse. With MIMIC data, patients whose primary language was not English performed worse than English speaking patients (AUROC ranging 0.771 to 0.709 [p <0.001] for SOFA). Measurements of calibration applied to SOFA showed a statistically significant overestimation of mortality in the youngest patients (SMR 0.55-0.6) and underestimation of mortality in the oldest patients (SMR 1.54-1.57). When using SOFA, mortality is overestimated for male patients (SMR 0.92-0.97) and underestimated for female patients (SMR 1.05-1.11) while mortality is overestimated for English-speaking patients (SMR 0.85) and greatly underestimated for non-English speaking patients (SMR 1.4). In contrast, the calibration applied to APACHE-IVa shows underestimation of mortality for all age groups and genders. Interpretation The differences in discrimination and calibration with increasing age, female gender, and non-English speaking patients suggest that illness severity scores are prone to bias in their mortality predictions. Caution must be taken when using these illness severity scores for quality benchmarking across ICUs and decision-making for practices among a diverse population.

Evaluating Prognostic Bias of Critical Illness Severity Scores Based on Age, Gender, and Primary Language in the USA: A Retrospective Multicenter Study

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Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning

Evaluating Prognostic Bias of Critical Illness Severity Scores Based on Age, Gender, and Primary Language in the USA: A Retrospective Multicenter Study