We have identified three different biomarker panels (7 clinical parameters, 32 blood lymphocyte-specific genes, 13 urine peptides) and developed an integrated algorithm to accurately diagnose KD.
The clinical data we used in the multivariate analysis are routinely obtained during the evaluation of fever. However, clinicians have not used scoring systems derived by multivariate techniques for KD diagnosis. Although the clinical score correctly classified only 80% of febrile patients, patients with either low or high KD clinical scores were diagnosed as FC or KD respectively with 95% accuracy. For febrile patients with the confident diagnosis of KD, timely administration of IVIG can thus be feasible to prevent the development of coronary artery dilatation or aneurysms. For febrile patients with intermediate clinical scores for whom confident diagnosis is not feasible, we developed a sequential algorithm, integrating clinical and molecular findings to improve KD diagnosis. Both the peripheral blood cell type-specific analysis and the urine peptidome biomarker analysis yielded sensitive and specific classifiers, which performed well in the diagnosis of KD. Prospective testing of these biomarker panels will be necessary to confirm their diagnostic utilities.
The csSAM-derived lymphocyte-specific gene markers and their mapped canonical pathways, for example PI3K signaling in B cells and T cell receptor signaling, provide insight into the host response in KD. Confirmation of our de-convolution observations on independent samples will establish the role of these genes as KD biomarkers. Validation of these markers may help to focus the search for the etiology of KD on agents that suppress specific lymphocyte gene expression.
The overlapping sequences of the two COL1A1 and four UMOD peptides suggests that these peptide biomarkers reflect differential activities of disease-related proteases or their inhibitors such as TIMP1 or matrix metalloproteinases in KD [6, 27–32]. Serum peptide biomarker analysis of cancer subjects  has demonstrated overlapping peptide biomarkers generated by disease-specific exo-peptidase activity. We have also observed tight clusters of urine peptide biomarkers in renal allograft dysfunction  and SJIA . Therefore, the discovery of multiple overlapping collagen and uromodulin peptides suggests that the pathophysiology of KD involves the active degradation of proteins including collagen and uromodulin.
With respect to the concern regarding incomplete KD cases hidden among the FC, we agree that inaccurate diagnosis is always one of the limitations in the absence of a gold standard diagnostic test. However, FC in this study included only patients whose illness resolved within three days of blood sampling OR for whom a definite diagnosis was established (for example osteomyelitis, JIA). None of the FC included here had peeling in the convalescent phase. As for the KD patients, we have maintained a stable rate of coronary artery aneurysms from year to year (approximately 9%) suggesting that our diagnostic practices are stable. All the KD patients in this study were evaluated by one of two experienced clinicians at a single medical center. In this study, most of the FCs were enrolled by our team member, thus assuring consistency in diagnosis and sample collection. Our study is unique in focusing on a clinically relevant control group of children with fever who were actually being evaluated to rule in or rule out KD. All FC were evaluated with a standardized set of clinical laboratory tests that was also used to evaluate our KD patients. Our study also differs from many previous investigations on KD that used samples collected from a large number of hospitals that cared for only a few KD patients each. Therefore, a big problem with consistency in these studies was expected for comparative studies between KD and FC.
Although all FC subjects in this study had laboratory testing for KD as recommended by the American Heart Association (AHA), very few FC had echocardiographic studies done. This is indeed a limitation. Although we acknowledge the potential inaccurate diagnosis of incomplete KD, our status as the sole freestanding children's hospital, sole KD referral center, and sole pediatric emergency department in San Diego County (catchment area of 5 million people) maximizes the likelihood that FC with persistent or progressive illness confused with KD would be captured during a return visit.
We recognize several limitations to the current molecular study for future translations of these biomarkers into bedside practice. First, the small sample sizes limit the power of our biomarker analyses to validate statistically significant associations and to avoid spurious discovery. Future prospective studies with larger sample sizes will be needed to validate our cell type-specific gene expression and urine peptide biomarkers. A second limitation of our study was the lack of formal assessment by clinicians of the pre-test probability of KD in the subjects included in this study. While a large proportion of the febrile controls were referred to our emergency department by physicians for evaluation of possible KD, this was not uniformly true as some febrile controls likely had a low pre-test probability of KD. Since the pre-test probability is an important consideration in evaluating the performance of a diagnostic test, collection of this information will be critical in the next testing phase of a KD diagnostic test. Third, the application of both the cell-specific transcript patterns and urine peptide biomarkers for the diagnosis of KD will require development of technology for the rapid identification of both whole blood transcripts and urine protein fragments in the clinical laboratory.
Our flexible clinical scoring metric is amenable to automation to develop data-driven predictive systems. Consistent with the current mandate to improve electronic medical record (EMR) use  and future interoperability between the hospital EMR and our predictive algorithm based applications consisting of demographic, clinical and genomic/proteomic data can serve an effective platform to allow interfacing between interdisciplinary teams (bed and bench side; what is known and what is practiced) for productive translational medicine.