Biomarkers of axonal damage to favor early diagnosis in variant transthyretin amyloidosis (A-ATTRv).

Early identification of ATTRv amyloidosis disease onset is still often delayed due to the lack of validated biomarkers of this disease. Light chain neurofilament (NfL) have shown promising results in early diagnosis in this disease, but data is still needed, including with alternative measuring methods. Our aim was to study the levels of NfL measured by ELISA. Furthermore, interstitial matrix metalloproteinase type 1 (MMP-1) serum levels were measured as a potential new biomarker in ATTRv. Serum NfL and MMP-1 were measured using ELISA assays in 90 participants (29 ATTR-V30M patients, 31 asymptomatic V30M-TTR variant carriers and 30 healthy controls). Median NfL levels among ATTRv amyloidosis patients were significantly higher (116 pg/mL vs 0 pg/mL in both comparison groups). The AUC comparing ATTRv amyloidosis patients and asymptomatic carriers was 0.90 and the NfL concentration of 93.55 pg/mL yielded a sensitivity of 79% and a specificity of 87%. NfL levels had a significant positive correlation with NIS values among patients. We found a negative significant correlation between eGFR and NfL levels. Finally, MMP1 levels were not different between groups. Evidence of NfL use for early diagnosis of ATTR-PN amyloidosis is growing. ELISA seems a reliable and available technique for it quantification. Decreased GFR could influence NfL plasma levels.

Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models.

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting models.

Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation.

BACKGROUND: The COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain's health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others. OBJECTIVE: This study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease. METHODS: After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model. RESULTS: After data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95% CI 0.92-0.95) for accuracy, 0.77 (95% CI 0.72-0.83) for the F-score, 0.93 (95% CI 0.89-0.98) for sensitivity, and 0.91 (95% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels. CONCLUSIONS: Our predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality.