Discussion
The clinical presentation of SARDs is characterised by a diverse array of symptoms.2 The intricate interplay between diverse symptoms in each SARD highlights the challenges faced by clinicians in distinguishing these conditions from one another, particularly considering the potential for overlapping symptoms.29 30 Within clinical practice, skilled physicians can often arrive at a diagnosis of SARDs even with the presence of a limited number of highly informative manifestations in an individual. For instance, the presence of the classic malar rash coupled with anti-dsDNA can lead to a diagnosis of SLE. However, this diagnostic acumen is a manifestation of clinical expertise cultivated through experience.31 The data emphasise the importance of developing advanced diagnostic tools, such as ML models, to aid in the accurate and timely differentiation of SARDs.
ML, a computational analytical approach, is gaining rapid prominence in the realm of biomedicine.32 33 In the context of rheumatology, the integration of ML is witnessing a gradual upsurge, with numerous studies leveraging ML techniques to classify patients with SARDs based on diverse data sources encompassing medical records,34 35 imaging data,36 biometric measurements37 and gene expression profiles.18 20 38 The modelling indicators of these ML models primarily encompass clinical symptoms, which are relatively subjective, as well as a diverse array of complex laboratory indicators, and even biologically intricate indicators that are challenging to obtain, such as genetic polymorphisms. However, comparatively simple and easily accessible laboratory indicators, such as blood routine, and crucial autoantibodies of significant relevance in SARDs diagnosis, such as ANA profiles, are seldom incorporated into consideration. This limitation to some extent impacts the applicability of these models in clinical contexts.
In this study, we present a novel ML framework that is based on readily accessible and objective laboratory indicators, notably the ANA profile and blood routine, tailored for the early-stage classification and diagnosis of three SARDs (SLE, SS and IM). This approach holds the potential to surmount the diagnostic challenges posed by the overlapping clinical presentations commonly observed in SARDs. Through an exhaustive process encompassing techniques such as differential analysis, feature selection, weight analysis, we ultimately identified laboratory indicators anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (IIFA), Hb, PLT, NEUT% and cytoplasmic patterns (AC-19, AC-20) for inclusion as modelling factors. The inclusion of ANA profiles as modelling indicators is expected. It is widely acknowledged that certain antibodies play pivotal roles in classifying SARDs.39 For instance, the presence of anti-dsDNA and anti-Sm antibodies contributes to the classification criteria of SLE,12 while the detection of anti-SS-A60 antibodies is significant for diagnosing SS.9 Moreover, the prevalence of antichromatin antibodies is strongly associated with lupus nephritis.40 Additionally, different methods for detecting anti-dsDNA antibodies may yield varying diagnostic performance. Currently, the most specific method acknowledged for diagnosing SLE is the IIFA.41 Some indicators within the classification criteria, such as complement C3 and C4, were unexpectedly excluded, potentially due to their lack of specificity. Even more unexpectedly, certain highly specific antibodies such as anti-Jo-1, anti-Sm and anti-RibP antibodies were not included, possibly due to their low positive rates in clinic.41 The IIFA on HEp-2 cells is widely used for detection of ANA. Fluorescence patterns may also reveal clinically relevant information. Cytoplasmic patterns (AC-19, AC-20) are associated with the distinct anti-tRNA synthetase antibodies, which are significant relevant antibodies in the context of IM.42 Considering the comprehensive impact of SARDs on the blood system, factors such as Hb, PLT and NEUT% hold significant clinical significance. Reduced haemoglobin levels, known as anaemia, and thrombocytopenia, characterised by low PLT counts, are commonly observed in individuals with SARDs, arising from factors such as chronic inflammation, bone marrow suppression, renal involvement, immune-mediated destruction or impaired PLT production. Leucopenia is also a common symptom of SARDs and is one of the classification criteria for SLE. However, surprisingly, what was selected is the NEUT% rather than the total white blood cell counts. Neutrophils play a central role in the immune response, and alterations in NEUT%s can reflect the immune dysregulation present in SARDs. In recent years, emerging evidence highlights the potential of SLE-derived low-density granulocytes to contribute to vascular damage, heightened type I interferon synthesis, increased cell death and enhanced extracellular trap formation, all potentially significant in SLE pathogenesis and autoimmunity induction.43
In contrast to the prevalent binary classification methods employed by existing ML models, which primarily categorise outcomes as either ‘yes’ or ‘no’, our model represents a pioneering effort in the realm of multiclass classification, offering a novel approach where a single model can provide the risk probabilities associated with three distinct disease types for each patient which enhance clinical operability and applicability. We opted for the top-performing model using the RF Classifier model for its superior performance. The model demonstrated an accuracy of 90.0% when tested on a subset of 120 patients, randomly selected for evaluation. Notably, the accuracy rates for distinct SARDs subtypes were as follows: 98.5% (66/67) for SLE, 67.9% (19/28) for SS and 92.0% (23/25) for IM, respectively. Finally, we conducted a performance validation of the model in a separate cohort comprising 150 newly diagnosed patients, and the results consistently demonstrated a high accuracy rate (87.3%). Compared with other diagnostic models, our model demonstrates a comparable high performance. For example, Burlina et al achieved an 86.6% accuracy in diagnosing IM through AI learning of muscle ultrasound images.36 Pinal-Fernandez et al further discovered that dermatomyositis, antisynthetase syndrome, immune-mediated necrotising myopathy and inclusion body myositis can be distinguished based on their unique gene expression patterns by applying ML algorithms to muscle biopsy transcriptomic data.18 However, this study’s outcomes generated five different ML models, a limitation that significantly constrains its practicality in clinical settings. Dros et al developed an ML model based on routine healthcare data, achieving an 84.0% diagnostic accuracy for primary Sjogren’s syndrome (pSS).34 Another study, using a set of 14 signature genes that play pivotal roles in transcription regulation and disease progression in pSS, achieved an 83.2% accuracy for pSS diagnosis.38 Our model demonstrates a lower classification accuracy for SS compared with the outcomes of these two studies. This disparity could be attributed to variations in the composition of patient groups, differences in the chosen modelling indicators, and other relevant factors. Notably, our study includes a smaller proportion of SS patients in comparison to those with SLE and IM. As a result, the selected modelling indicators, aside from anti-SS-A60, contribute less significantly to the accurate classification of SS in our model. In a study by Adamichou et al, an ML model relying on 14 indicators encompassing a diverse range of clinical symptoms and laboratory parameters, including complement C3 and C4 levels, achieved a diagnostic accuracy of 94.2% for SLE.16 The research is one of the few studies that employs readily available and simple indicators for modelling, aiming to maximise the clinical applicability. However, there exists a substantial disparity in the modelling indicators used between our study and theirs. This divergence could potentially be attributed to the differences in the selected patient cohorts, as distinct patient populations often exhibit significant variations in their characteristic features, subsequently influencing the extracted feature indicators.
Our study brings forth a pioneering approach in the field of SARDs classification by introducing a novel multiclass ML model. This innovative model employs basic laboratory indicators, such as ANA profiles and blood routine results, to effectively classify patients into three distinct disease types. This approach not only offers a simplified classification process but also enhances the clinical feasibility of the model, aligning with real-world medical practices. However, it is important to acknowledge the limitation posed by our patient cohort’s predominantly Han ethnicity composition, which restricts the generalisability of our findings to diverse ethnic groups. Second, in our study, autoantibodies are treated as quantitative data. It is important to note that values of antibody levels can vary significantly between different commercial assays, which may impact the generalisability of the model. The third concern is that many of our modelling indicators, such as anti-dsDNA antibodies, are autoantibodies already used in clinical classification, posing a significant risk of circular reasoning. This is also a key reason for the high-risk assessment of outcome and predictor association in the bias risk evaluation using PROBAST. However, it is crucial to clarify that the primary aim of our model is to extract information from rich laboratory parameters through ML, rather than simply reproducing clinical classification criteria. We will emphasise more clearly that, while some antibodies may be relevant to current classification criteria, the value of the model lies in the potential associations it learns from complex data. Additionally, the relatively lower accuracy in classifying SS patients highlights the need for larger sample sizes to improve SS classification performance and ensure comprehensive applicability. Despite these limitations, our study’s promising implications for accurate and expedited SARDs diagnosis underscore the necessity for further research and validation across diverse patient populations.
In conclusion, our study introduces the application of a multiclass ML model in the realm of SARDs, demonstrates the feasibility and effectiveness of employing an ML model based on basic laboratory indicators for the accurate multiclass classification of these diseases. Certainly, the wisdom and experience of clinical physicians remain essential. The amalgamation of clinical acumen with ML-driven insights promises a more nuanced and sophisticated approach to diagnosing SARDs, ultimately paving the way for enhanced patient care and improved disease management.
