Early Diagnosis of Primary Immunodeficiency Disease Using Clinical Data and Machine Learning

piRNA associates with immune diseases

PIWI-interacting RNA (piRNA) is the most abundant small non-coding RNA in animal cells, typically 26-31 nucleotides in length and it binds with PIWI proteins, a subfamily of Argonaute proteins. Initially discovered in germ cells, piRNA is well known for its role in silencing transposons and maintaining genome integrity. However, piRNA is also present in somatic cells as well as in extracellular vesicles and exosomes. While piRNA has been extensively studied in various diseases, particular cancer, its function in immune diseases remains unclear. In this review, we summarize current research on piRNA in immune diseases. We first introduce the basic characteristics, biogenesis and functions of piRNA. Then, we review the association of piRNA with different types of immune diseases, including autoimmune diseases, immunodeficiency diseases, infectious diseases, and other immune-related diseases. piRNA is considered a promising biomarker for diseases, highlighting the need for further research into its potential mechanisms in disease pathogenesis.

Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment

Background

There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date.

Objective

We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey.

Methods

Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center's electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis.

Results

The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve > 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI.

Conclusion

Mining EHR notes with NLP holds promise for improving early IEI patient detection.

The Inborn Errors of Immunity-Virtual Consultation System Platform in Service for the Italian Primary Immunodeficiency Network: Results from the Validation Phase

PURPOSE

Inborn errors of immunity (IEI) represent a heterogeneous group of rare genetically determined diseases. In some cases, patients present with complex or atypical phenotypes, not fulfilling the accepted diagnostic criteria for IEI and, thus, at high risk of misdiagnosis or diagnostic delay. This study aimed to validate a platform that, through the opinion of immunologist experts, improves the diagnostic process and the level of care of patients with atypical/complex IEI.

METHODS

Here, we describe the functioning of the IEI-Virtual Consultation System (VCS), an innovative platform created by the Italian Immunodeficiency Network (IPINet).

RESULTS

In the validation phase, from January 2020 to June 2021, 68 cases were entered on the IEI-VCS platform. A final diagnosis was achieved in 35/68 cases (51%, 95% CI 38.7 to 64.2). In 22 out of 35 solved cases, the diagnosis was confirmed by genetic analysis. In 3/35 cases, a diagnosis of secondary immunodeficiency was made. In the remaining 10 cases, an unequivocal clinical and immunological diagnosis was obtained, even though not substantiated by genetic analysis.

CONCLUSION

From our preliminary study, the VCS represents an innovative and useful system to improve the diagnostic process of patients with complex unsolved IEI disorders, with benefits both in terms of reduction of time of diagnosis and access to the required therapies. These results may help the functioning of other international platforms for the management of complex cases.

Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies

BACKGROUND

Primary immunodeficiency (PI) is a group of heterogeneous disorders resulting from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality, co-morbidity and healthcare costs. Among PI disorders, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PI. In light of available treatments, it is critical to identify adult patients at risk for CID and CVID, before the development of serious morbidity and mortality.

METHODS

We developed a deep learning-based method (named "TabMLPNet") to analyze clinical history from nationally representative medical claims from electronic health records (Optum® data, covering all US), evaluated in the setting of identifying CID/CVID in adults. Further, we revealed the most important CID/CVID-associated antecedent phenotype combinations. Four large cohorts were generated: a total of 47,660 PI cases and (1:1 matched) controls.

RESULTS

The sensitivity/specificity of TabMLPNet modeling ranges from 0.82-0.88/0.82-0.85 across cohorts. Distinctive combinations of antecedent phenotypes associated with CID/CVID are identified, consisting of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies, which can possibly be useful to systematize the identification of CID and CVID.

CONCLUSIONS

We demonstrated an accurate method in terms of CID and CVID detection evaluated on large-scale medical claims data. Our predictive scheme can potentially lead to the development of new clinical insights and expanded guidelines for identification of adult patients at risk for CID and CVID as well as be used to improve patient outcomes on population level.

Clinical Validation of a Primary Antibody Deficiency Screening Algorithm for Primary Care

PURPOSE

The diagnostic delay of primary antibody deficiencies (PADs) is associated with increased morbidity, mortality, and healthcare costs. Therefore, a screening algorithm was previously developed for the early detection of patients at risk of PAD in primary care. We aimed to clinically validate and optimize the PAD screening algorithm by applying it to a primary care database in the Netherlands.

METHODS

The algorithm was applied to a data set of 61,172 electronic health records (EHRs). Four hundred high-scoring EHRs were screened for exclusion criteria, and remaining patients were invited for serum immunoglobulin analysis and referred if clinically necessary.

RESULTS

Of the 104 patients eligible for inclusion, 16 were referred by their general practitioner for suspected PAD, of whom 10 had a PAD diagnosis. In patients selected by the screening algorithm and included for laboratory analysis, prevalence of PAD was ~ 1:10 versus 1:1700-1:25,000 in the general population. To optimize efficiency of the screening process, we refitted the algorithm with the subset of high-risk patients, which improved the area under the curve-receiver operating characteristics curve value to 0.80 (95% confidence interval 0.63-0.97). We propose a two-step screening process, first applying the original algorithm to distinguish high-risk from low-risk patients, then applying the optimized algorithm to select high-risk patients for serum immunoglobulin analysis.

CONCLUSION

Using the screening algorithm, we were able to identify 10 new PAD patients from a primary care population, thus reducing diagnostic delay. Future studies should address further validation in other populations and full cost-effectiveness analyses.

REGISTRATION

Clinicaltrials.gov record number NCT05310604, first submitted 25 March 2022.

Who's your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry

PURPOSE

There are currently more than 480 primary immune deficiency (PID) diseases and about 7000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. In a proof-of-concept data mining study, we aimed to predict PID diagnoses using a supervised machine learning algorithm based on classification tree boosting.

METHODS

Through a data query at the USIDNET registry we obtained a database of 2396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 286 features and all 12 diagnoses to include in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. After a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight to correct for imbalanced classification.

RESULTS

The twelve PID diagnoses were CVID (1098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, PID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46-0.87, Gini 0.07-0.75, and LogLoss 0.09-8.55.

DISCUSSION

Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses.

Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms

PURPOSE

The purpose of this study is to propose an efficient coal workers' pneumoconiosis (CWP) clinical prediction system and put it into clinical use for clinical diagnosis of pneumoconiosis.

METHODS

Patients with CWP and dust-exposed workers who were enrolled from August 2021 to December 2021 were included in this study. Firstly, we chose the embedded method through using three feature selection approaches to perform the prediction analysis. Then, we performed the machine learning algorithms as the model backbone and combined them with three feature selection methods, respectively, to determine the optimal predictive model for CWP.

RESULTS

Through applying three feature selection approaches based on machine learning algorithms, it was found that AaDO₂ and some pulmonary function indicators played an important role in prediction for identifying CWP of early stage. The support vector machine (SVM) algorithm was proved as the optimal machine learning model for predicting CWP, with the ROC curves obtained from three feature selection methods using SVM algorithm whose AUC values of 97.78%, 93.7%, and 95.56%, respectively.

CONCLUSION

We developed the optimal model (SVM algorithm) through comparisons and analyses among the performances of different models for the prediction of CWP as a clinical application.

A Diagnostic Classifier Based on Circulating miRNA Pairs for COPD Using a Machine Learning Approach

Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed, and early detection is urgent to prevent advanced progression. Circulating microRNAs (miRNAs) have been diagnostic candidates for multiple diseases. However, their diagnostic value has not yet been fully established in COPD. The purpose of this study was to develop an effective model for the diagnosis of COPD based on circulating miRNAs. We included circulating miRNA expression profiles of two independent cohorts consisting of 63 COPD and 110 normal samples, and then we constructed a miRNA pair-based matrix. Diagnostic models were developed using several machine learning algorithms. The predictive performance of the optimal model was validated in our external cohort. In this study, the diagnostic values of miRNAs based on the expression levels were unsatisfactory. We identified five key miRNA pairs and further developed seven machine learning models. The classifier based on LightGBM was selected as the final model with the area under the curve (AUC) values of 0.883 and 0.794 in test and validation datasets, respectively. We also built a web tool to assist diagnosis for clinicians. Enriched signaling pathways indicated the potential biological functions of the model. Collectively, we developed a robust machine learning model based on circulating miRNAs for COPD screening.