Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems

<div><p>The major challenge in the diagnosis of disseminated intravascular coagulation (DIC) comes from the lack of specific biomarkers, leading to developing composite scoring systems. DIC scores are simple and rapidly applicable. However, optimal fibrin-related markers and their cut-off values remain to be defined, requiring optimization for use. The aim of this study is to optimize the use of DIC-related parameters through machine learning (ML)-approach. Further, we evaluated whether this approach could provide a diagnostic value in DIC diagnosis. For this, 46 DIC-related parameters were investigated for both clinical findings and laboratory results. We retrospectively reviewed 656 DIC-suspected cases at an initial order for full DIC profile and labeled their evaluation results (Set 1; DIC, n = 228; non-DIC, n = 428). Several ML algorithms were tested, and an artificial neural network (ANN) model was established via independent training and testing using 32 selected parameters. This model was externally validated from a different hospital with 217 DIC-suspected cases (Set 2; DIC, n = 80; non-DIC, n = 137). The ANN model represented higher AUC values than the three scoring systems in both set 1 (ANN 0.981; ISTH 0.945; JMHW 0.943; and JAAM 0.928) and set 2 (AUC ANN 0.968; ISTH 0.946). Additionally, the relative importance of the 32 parameters was evaluated. Most parameters had contextual importance, however, their importance in ML-approach was different from the traditional scoring system. Our study demonstrates that ML could optimize the use of clinical parameters with robustness for DIC diagnosis. We believe that this approach could play a supportive role in physicians’ medical decision by integrated into electrical health record system. Further prospective validation is required to assess the clinical consequence of ML-approach and their clinical benefit.</p></div

Diagnostic scoring systems for disseminated intravascular coagulation (DIC) used in this study.

<p>Diagnostic scoring systems for disseminated intravascular coagulation (DIC) used in this study.</p

Heat map presentation of the datasets used in this study.

<p>The x-axis denotes individual cases and the y-axis corresponds to the clinical variables. Each cell shows values of variables for each case. All cases are sorted horizontally by the labeled DIC status and predicted ANN model values. Rows 2–5 (ANN model, ISTH, JMHW, and JAAM criteria) show predictions of different DIC diagnostic classifiers based on the cut-off values (0.501 for ANN) or points (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195861#pone.0195861.t001" target="_blank">Table 1</a>).</p

Patient demographics and clinical characteristics.

<p>Patient demographics and clinical characteristics.</p

Schematic representation of patient enrollment and development of the artificial neural network (ANN) model.

<p>(A) Full DIC profile was defined as all laboratory results including complete blood count with differential counts, global coagulation tests (PT, PT % activity, international normalized ratio [INR], activated partial thromboplastin time [aPTT], and thrombin time), fibrinogen, D-dimer, FDP, and anti-thrombin III. The external validation hospital used different DIC profile: protein C was included instead of FDP, and RUO parameters were not provided. (B) ANN model for DIC diagnosis. In the training phase, the development set (n = 656) was randomly split into training and test sets in 80:20 ratio and hyper-parameters were determined for an optimal modeling. All layers have 32 nodes with an input-layer and two-hidden layers. The relative importance of input features was calculated based on the ‘Connection Weight’ approach, after the ANN model was established.</p

Diagnostic performance of ANN model and scoring systems with receiver operating characteristic curve analysis and density plot.

<p>(A) Training (Set 1): ANN model shows the best performance among the four diagnostic classifiers. The area under curve (AUC) values: ANN (0.981), ISTH (0.945), JMHW (0.943), and JAAM (0.928). (B) External validation (Set 2): four variables were unavailable owing to the different hematologic analyzers, therefore the AUC value was compromised compared to the development set in the ANN model; ANN (0.968), ISTH (0.946). (C, D) Density plots of two represented diagnostic classifiers (ANN model, ISTH criteria) shows that the ANN model far obviously differentiates two groups (DIC and non-DIC). The cut-off value for the ANN model is determined at 0.501.</p

DataSheet1_Systematic analysis of inheritance pattern determination in genes that cause rare neurodevelopmental diseases.docx

Despite recent advancements in our understanding of genetic etiology and its molecular and physiological consequences, it is not yet clear what genetic features determine the inheritance pattern of a disease. To address this issue, we conducted whole exome sequencing analysis to characterize genetic variants in 1,180 Korean patients with neurological symptoms. The diagnostic yield for definitive pathogenic variant findings was 50.8%, after including 33 cases (5.9%) additionally diagnosed by reanalysis. Of diagnosed patients, 33.4% carried inherited variants. At the genetic level, autosomal recessive-inherited genes were characterized by enrichments in metabolic process, muscle organization and metal ion homeostasis pathways. Transcriptome and interactome profiling analyses revealed less brain-centered expression and fewer protein-protein interactions for recessive genes. The majority of autosomal recessive genes were more tolerant of variation, and functional prediction scores of recessively-inherited variants tended to be lower than those of dominantly-inherited variants. Additionally, we were able to predict the rates of carriers for recessive variants. Our results showed that genes responsible for neurodevelopmental disorders harbor different molecular mechanisms and expression patterns according to their inheritance patterns. Also, calculated frequency rates for recessive variants could be utilized to pre-screen rare neurodevelopmental disorder carriers.</p

DataSheet2_Systematic analysis of inheritance pattern determination in genes that cause rare neurodevelopmental diseases.xlsx

Despite recent advancements in our understanding of genetic etiology and its molecular and physiological consequences, it is not yet clear what genetic features determine the inheritance pattern of a disease. To address this issue, we conducted whole exome sequencing analysis to characterize genetic variants in 1,180 Korean patients with neurological symptoms. The diagnostic yield for definitive pathogenic variant findings was 50.8%, after including 33 cases (5.9%) additionally diagnosed by reanalysis. Of diagnosed patients, 33.4% carried inherited variants. At the genetic level, autosomal recessive-inherited genes were characterized by enrichments in metabolic process, muscle organization and metal ion homeostasis pathways. Transcriptome and interactome profiling analyses revealed less brain-centered expression and fewer protein-protein interactions for recessive genes. The majority of autosomal recessive genes were more tolerant of variation, and functional prediction scores of recessively-inherited variants tended to be lower than those of dominantly-inherited variants. Additionally, we were able to predict the rates of carriers for recessive variants. Our results showed that genes responsible for neurodevelopmental disorders harbor different molecular mechanisms and expression patterns according to their inheritance patterns. Also, calculated frequency rates for recessive variants could be utilized to pre-screen rare neurodevelopmental disorder carriers.</p