A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm.

BACKGROUND: Fabry disease (FD) is a rare genetic disorder characterized by glycosphingolipid accumulation and progressive damage across multiple organ systems. Due to its heterogeneous presentation, the condition is likely significantly underdiagnosed. Several approaches, including provider education efforts and newborn screening, have attempted to address underdiagnosis of FD across the age spectrum, with limited success. Artificial intelligence (AI) methods present another option for improving diagnosis. These methods isolate common health history patterns among patients using longitudinal real-world data, and can be particularly useful when patients experience nonspecific, heterogeneous symptoms over time. In this study, the performance of an AI tool in identifying patients with FD was analyzed. The tool was calibrated using de-identified health record data from a large cohort of nearly 5000 FD patients, and extracted phenotypic patterns from these records. The tool then used this FD pattern information to make individual-level estimates of FD in a testing dataset. Patterns were reviewed and confirmed with medical experts. RESULTS: The AI tool demonstrated strong analytic performance in identifying FD patients. In out-of-sample testing, it achieved an area under the receiver operating characteristic curve (AUROC) of 0.82. Strong performance was maintained when testing on male-only and female-only cohorts, with AUROCs of 0.83 and 0.82 respectively. The tool identified small segments of the population with greatly increased prevalence of FD: in the 1% of the population identified by the tool as at highest risk, FD was 23.9 times more prevalent than in the population overall. The AI algorithm used hundreds of phenotypic signals to make predictions and included both familiar symptoms associated with FD (e.g. renal manifestations) as well as less well-studied characteristics. CONCLUSIONS: The AI tool analyzed in this study performed very well in identifying Fabry disease patients using structured medical history data. Performance was maintained in all-male and all-female cohorts, and the phenotypic manifestations of FD highlighted by the tool were reviewed and confirmed by clinical experts in the condition. The platform's analytic performance, transparency, and ability to generate predictions based on existing real-world health data may allow it to contribute to reducing persistent underdiagnosis of Fabry disease.

Validation of a machine learning approach to estimate Clinical Disease Activity Index Scores for rheumatoid arthritis.

OBJECTIVE: Disease activity measures, such as the Clinical Disease Activity Index (CDAI), are important tools for informing treatment decisions and monitoring patient outcomes in rheumatoid arthritis (RA). Yet, documentation of CDAI scores in electronic medical records and other real-world data sources is inconsistent, making it challenging to use these data for research. The purpose of this study was to validate a machine learning model to estimate CDAI scores for patients with RA using clinical notes. METHODS: A machine learning model was developed to estimate CDAI score values using clinical notes from a specific rheumatology visit. Data from the OM1 RA Registry were used to create a training cohort of 56 177 encounters and a separate validation cohort of 18 726 encounters, 11 985 of which passed a model-derived confidence filter; all included encounters had both a clinician-recorded CDAI score and a clinical note. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), positive predictive value (PPV) and negative predictive value (NPV), calculated using a binarised version of the outcome. The Spearman's R and Pearson's R values were also calculated. RESULTS: The model had a PPV of 0.80, NPV of 0.84 and AUC of 0.88 when evaluating performance using the binarised version of the outcome. The model had a Spearman's R value of 0.72 and a Pearson's R value of 0.69 when evaluating performance using the continuous CDAI numeric scores. CONCLUSION: A machine learning model estimates CDAI scores from clinical notes with good performance. Application of the model to real-world data sets may allow estimated CDAI scores to be used for research purposes.

Scaling of cytoskeletal organization with cell size in Drosophila.

Spatially organized macromolecular complexes are essential for cell and tissue function, but the mechanisms that organize micron-scale structures within cells are not well understood. Microtubule-based structures such as mitotic spindles scale with cell size, but less is known about the scaling of actin structures within cells. Actin-rich denticle precursors cover the ventral surface of the Drosophila embryo and larva and provide templates for cuticular structures involved in larval locomotion. Using quantitative imaging and statistical modeling, we demonstrate that denticle number and spacing scale with cell length over a wide range of cell sizes in embryos and larvae. Denticle number and spacing are reduced under space-limited conditions, and both features robustly scale over a 10-fold increase in cell length during larval growth. We show that the relationship between cell length and denticle spacing can be recapitulated by specific mathematical equations in embryos and larvae and that accurate denticle spacing requires an intact microtubule network and the microtubule minus end-binding protein, Patronin. These results identify a novel mechanism of micro-tubule-dependent actin scaling that maintains precise patterns of actin organization during tissue growth.

Combined deletion of cathepsin protease family members reveals compensatory mechanisms in cancer.

Proteases are important for regulating multiple tumorigenic processes, including angiogenesis, tumor growth, and invasion. Elevated protease expression is associated with poor patient prognosis across numerous tumor types. Several multigene protease families have been implicated in cancer, including cysteine cathepsins. However, whether individual family members have unique roles or are functionally redundant remains poorly understood. Here we demonstrate stage-dependent effects of simultaneously deleting cathepsin B (CtsB) and CtsS in a murine pancreatic neuroendocrine tumor model. Early in tumorigenesis, the double knockout results in an additive reduction in angiogenic switching, whereas at late stages, several tumorigenic phenotypes are unexpectedly restored to wild-type levels. We identified CtsZ, which is predominantly supplied by tumor-associated macrophages, as the compensatory protease that regulates the acquired tumor-promoting functions of lesions deficient in both CtsB and CtsS. Thus, deletion of multiple cathepsins can lead to stage-dependent, compensatory mechanisms in the tumor microenvironment, which has potential implications for the clinical consideration of selective versus pan-family cathepsin inhibitors in cancer.