Machine learning progressive CKD risk prediction model is associated with CKD-mineral bone disorder.

Background: Recently, we developed the machine learning (ML)-based Progressive CKD Risk Classifier (PCRC), which accurately predicts CKD progression within 5 years. While its performance is robust, it is unknown whether PCRC categorization is associated with CKD-mineral bone disorder (CKD-MBD), a critical, yet under-recognized, downstream consequence. Therefore, we aimed to 1) survey real-world testing utilization data for CKD-MBD and 2) evaluate ML-based PCRC categorization with CKD-MBD. Methods: The cohort study utilized deidentified data from a US laboratory outpatient network, composed of 330,238 outpatients, over 5 years. The main outcomes were: 1) Laboratory testing utilization of eGFR, urine albumin creatinine ratio (UACR), parathyroid hormone (PTH), calcium, phosphate; and 2) PCRC categorization and biochemical abnormalities associated with CKD-MBD over 5 years. Results: We identified significant under-utilization of laboratory testing for UACR, phosphate and PTH, which ranged from -40 % to -100 % against the minimum standard-of-care. At five years, the CKD progression group, as predicted by the PCRC, was associated with 15.5 % increase in phosphate (P value <<0.01) and 94.9 % increase in PTH (P value <<0.01), consistent with CKD-MBD. Conclusions: We identified significant under-utilization of laboratory testing for CKD-MBD. Moreover, we demonstrated that CKD progression, as predicted by the PCRC, is associated with CKD-MBD, several years in advance of disease. To our knowledge, this investigation is the first to examine the role of predictive analytics for CKD progression on mineral bone disorder. While further studies are required, these findings have the potential to advance AI/ML-based risk stratification and treatment of CKD and CKD-MBD.

Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model.

Objective: To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit from targeted treatment and management in order to preserve glucose metabolism and prevent adverse outcomes. The objective of this study was to utilize readily available laboratory data to train and test the performance of ML-based predictive risk models for progression from prediabetes to diabetes. Methods: The study population was composed of laboratory information services data procured from a large U.S. outpatient laboratory network. The retrospective dataset was composed of 15,029 adults over a 5-year period with initial hemoglobin A1C (A1C) values between 5.0% and 6.4%. ML models were developed using random forest survival methods. The ground truth outcome was progression to A1C values indicative of diabetes (i.e., ≥6.5%) within 5 years. Results: The prediabetes risk classifier model accurately predicted A1C ≥6.5% within 5 years and achieved an area under the receiver-operator characteristic curve of 0.87. The most important predictors of progression from prediabetes to diabetes were initial A1C, initial serum glucose, A1C slope, serum glucose slope, initial HDL, HDL slope, age, and sex. Conclusions: Leveraging readily obtainable laboratory data, our ML risk classifier accurately predicts elevation in A1C associated with progression from prediabetes to diabetes. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition, risk stratification, and optimal management for patients with prediabetes.

CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model.

Rationale & Objective: Chronic kidney disease (CKD) is a major cause of morbidity and mortality. To date, there are no widely used machine-learning models that can predict progressive CKD across the entire disease spectrum, including the earliest stages. The objective of this study was to use readily available demographic and laboratory data from Sonic Healthcare USA laboratories to train and test the performance of machine learning-based predictive risk models for CKD progression. Study Design: Retrospective observational study. Setting & Participants: The study population was composed of deidentified laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set included 110,264 adult patients over a 5-year period with initial estimated glomerular filtration rate (eGFR) values between 15-89 mL/min/1.73 m2. Predictors: Patient demographic and laboratory characteristics. Outcomes: Accelerated (ie, >30%) eGFR decline associated with CKD progression within 5 years. Analytical Approach: Machine-learning models were developed using random forest survival methods, with laboratory-based risk factors analyzed as potential predictors of significant eGFR decline. Results: The 7-variable risk classifier model accurately predicted an eGFR decline of >30% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.85. The most important predictor of progressive decline in kidney function was the eGFR slope. Other key contributors to the model included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex. Limitations: The cohort study did not evaluate the role of clinical variables (eg, blood pressure) on the performance of the model. Conclusions: Our progressive CKD classifier accurately predicts significant eGFR decline in patients with early, mid, and advanced disease using readily obtainable laboratory data. Although prospective studies are warranted, our results support the clinical utility of the model to improve timely recognition and optimal management for patients at risk for CKD progression. Plain-Language Summary: Defined by a significant decrease in estimated glomerular filtration rate (eGFR), chronic kidney disease (CKD) progression is strongly associated with kidney failure. However, to date, there are no broadly used resources that can predict this clinically significant event. Using machine-learning techniques on a diverse US population, this cohort study aimed to address this deficiency and found that a 5-year risk prediction model for CKD progression was accurate. The most important predictor of progressive decline in kidney function was the eGFR slope, followed by the urine albumin-creatinine ratio and serum albumin slope. Although further study is warranted, the results showed that a machine-learning model using readily obtainable laboratory information accurately predicts CKD progression, which may inform clinical diagnosis and management for this at-risk population.

Clinical and pathologic features of secondary acute promyelocytic leukemia.

Acute promyelocytic leukemia (APL) is a relatively common form of acute myeloid leukemia (AML) that has an excellent prognosis. In contrast, secondary acute myeloid leukemias, including therapy-related AML and AML with myelodysplasia-related changes, have a relatively poor prognosis. We identified 9 cases of APL at our institution in which there was a history of chemotherapy, radiotherapy, chronic immunosuppression, or antecedent myelodysplastic syndrome. The clinical and pathologic findings in these cases of secondary APL were compared with the clinical and pathologic findings in cases of de novo APL. We found that secondary and de novo APL had abnormal promyelocytes with similar morphologic and immunophenotypic features, comparable cytogenetic findings, comparable rates of FMS-like tyrosine kinase mutations, and similar rates of recurrent disease and death. These data suggest that secondary APL is similar to de novo APL and, thus, should be considered distinct from other secondary acute myeloid neoplasms.

Systemic autoimmunity and defective Fas ligand secretion in the absence of the Wiskott-Aldrich syndrome protein.

Autoimmunity is a surprisingly common complication of primary immunodeficiencies, yet the molecular mechanisms underlying this clinical observation are not well understood. One widely known example is provided by Wiskott-Aldrich syndrome (WAS), an X-linked primary immunodeficiency disorder caused by mutations in the gene encoding the WAS protein (WASp) with a high incidence of autoimmunity in affected patients. WASp deficiency affects T-cell antigen receptor (TCR) signaling and T-cell cytokine production, but its role in TCR-induced apoptosis, one of the mechanisms of peripheral immunologic tolerance, has not been investigated. We find that WASp-deficient mice produce autoantibodies and develop proliferative glomerulonephritis with immune complex deposition as they age. We also find that CD4(+) T lymphocytes from WASp-deficient mice undergo reduced apoptosis after restimulation through the TCR. While Fas-induced cell death is normal, WASp deficiency affects TCR-induced secretion of Fas ligand (FasL) and other components of secretory granules by CD4(+) T cells. These results describe a novel role of WASp in regulating TCR-induced apoptosis and FasL secretion and suggest that WASp-deficient mice provide a good model for the study of autoimmune manifestations of WAS and the development of more specific therapies for these complications.

Impaired in vitro regulatory T cell function associated with Wiskott-Aldrich syndrome.

Wiskott-Aldrich syndrome (WAS) is a primary immunodeficiency characterized by the contradictory coexistence of impaired T-cell function and exaggerated T-cell-mediated pathology, including autoimmunity and eczema. WAS protein (WASp)-deficient mice are also immunodeficient and can develop autoimmune disease. Since defects in regulatory T-cells (Treg) are associated with autoimmunity, we examined the presence and function of these cells in WAS patients and WASp-deficient mice. We found that CD4(+)CD25(+)FOXP3(+) Treg cells can develop in the absence of WASp expression. However, Treg cells both from WASp-deficient mice and from four out of five WAS patients studied showed impaired in vitro suppressor function. In WASp-deficient mice, this defect could be partially rescued by pre-activation with IL-2, suggesting that inadequate cell activation may play a role in WASp-deficient Treg dysfunction. These findings may provide insights into the complex pathophysiology and paradoxical phenotypes of WAS and suggest new therapeutic modalities for autoimmunity in these patients.