A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score.

BACKGROUND: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling. OBJECTIVE: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia. METHODS: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO2/FiO2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori. RESULTS: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively. CONCLUSIONS: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia.

Quantification of Myocardial Contraction Fraction with Three-Dimensional Automated, Machine-Learning-Based Left-Heart-Chamber Metrics: Diagnostic Utility in Hypertrophic Phenotypes and Normal Ejection Fraction.

Aims: The differentiation of left ventricular (LV) hypertrophic phenotypes is challenging in patients with normal ejection fraction (EF). The myocardial contraction fraction (MCF) is a simple dimensionless index useful for specifically identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) when calculated by cardiac magnetic resonance. The purpose of this study was to evaluate the value of MCF measured by three-dimensional automated, machine-learning-based LV chamber metrics (dynamic heart model [DHM]) for the discrimination of different forms of hypertrophic phenotypes. Methods and Results: We analyzed the DHM LV metrics of patients with CA (n = 10), hypertrophic cardiomyopathy (HCM, n = 36), isolated hypertension (IH, n = 87), and 54 healthy controls. MCF was calculated by dividing LV stroke volume by LV myocardial volume. Compared with controls (median 61.95%, interquartile range 55.43-67.79%), mean values for MCF were significantly reduced in HCM-48.55% (43.46-54.86% p < 0.001)-and CA-40.92% (36.68-46.84% p < 0.002)-but not in IH-59.35% (53.22-64.93% p < 0.7). MCF showed a weak correlation with EF in the overall cohort (R2 = 0.136) and the four study subgroups (healthy adults, R2 = 0.039 IH, R2 = 0.089; HCM, R2 = 0.225; CA, R2 = 0.102). ROC analyses showed that MCF could differentiate between healthy adults and HCM (sensitivity 75.9%, specificity 77.8%, AUC 0.814) and between healthy adults and CA (sensitivity 87.0%, specificity 100%, AUC 0.959). The best cut-off values were 55.3% and 52.8%. Conclusions: The easily derived quantification of MCF by DHM can refine our echocardiographic discrimination capacity in patients with hypertrophic phenotype and normal EF. It should be added to the diagnostic workup of these patients.

Lithium induces mesenchymal-epithelial differentiation during human kidney development by activation of the Wnt signalling system.

Kidney function is directly linked to the number of nephrons which are generated until 32-36 weeks gestation in humans. Failure to make nephrons during development leads to congenital renal malformations, whilst nephron loss in adulthood occurs in progressive renal disease. Therefore, an understanding of the molecular processes which underlie human nephron development may help design new treatments for renal disease. Mesenchyme to epithelial transition (MET) is critical for forming nephrons, and molecular pathways which control rodent MET have been identified. However, we do not know whether they are relevant in human kidney development. In this study, we isolated mesenchymal cell lines derived from human first trimester kidneys in monolayer culture and investigated their differentiation potential. We found that the mesenchymal cells could convert into osteogenic, but not adipogenic or endothelial lineages. Furthermore, addition of lithium chloride led to MET which was accompanied by increases in epithelial (CDH1) and tubular (ENPEP) markers and downregulation of renal progenitor (SIX2, EYA1, CD133) and mesenchymal markers (HGF, CD24). Prior to phenotypic changes, lithium chloride altered Wnt signalling with elevations in AXIN2, GSK3ß phosphorylation and ß-catenin. Collectively, these studies provide the first evidence that lithium-induced Wnt activation causes MET in human kidneys. Therapies targeting Wnts may be critical in the quest to regenerate nephrons for human renal diseases.

Concise Review: Understanding the Renal Progenitor Cell Niche In Vivo to Recapitulate Nephrogenesis In Vitro.

UNLABELLED: Chronic kidney disease (CKD), defined as progressive kidney damage and a reduction of the glomerular filtration rate, can progress to end-stage renal failure (CKD5), in which kidney function is completely lost. CKD5 requires dialysis or kidney transplantation, which is limited by the shortage of donor organs. The incidence of CKD5 is increasing annually in the Western world, stimulating an urgent need for new therapies to repair injured kidneys. Many efforts are directed toward regenerative medicine, in particular using stem cells to replace nephrons lost during progression to CKD5. In the present review, we provide an overview of the native nephrogenic niche, describing the complex signals that allow survival and maintenance of undifferentiated renal stem/progenitor cells and the stimuli that promote differentiation. Recapitulating in vitro what normally happens in vivo will be beneficial to guide amplification and direct differentiation of stem cells toward functional renal cells for nephron regeneration. SIGNIFICANCE: Kidneys perform a plethora of functions essential for life. When their main effector, the nephron, is irreversibly compromised, the only therapeutic choices available are artificial replacement (dialysis) or renal transplantation. Research focusing on alternative treatments includes the use of stem cells. These are immature cells with the potential to mature into renal cells, which could be used to regenerate the kidney. To achieve this aim, many problems must be overcome, such as where to take these cells from, how to obtain enough cells to deliver to patients, and, finally, how to mature stem cells into the cell types normally present in the kidney. In the present report, these questions are discussed. By knowing the factors directing the proliferation and differentiation of renal stem cells normally present in developing kidney, this knowledge can applied to other types of stem cells in the laboratory and use them in the clinic as therapy for the kidney.