Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial.

INTRODUCTION: Spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whether artificial intelligence (AI) decision support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standard (expert interpretation). METHODS AND ANALYSIS: A parallel, two-group, statistician-blinded, randomised controlled trial of primary care clinicians in the UK, who refer for, or interpret, spirometry. People with specialist training in respiratory medicine to consultant level were excluded. A minimum target of 228 primary care clinician participants will be randomised with a 1:1 allocation to assess fifty de-identified, real-world patient spirometry sessions through an online platform either with (intervention group) or without (control group) AI decision support software report. Outcomes will cover primary care clinicians' spirometry interpretation performance including measures of technical quality assessment, spirometry pattern recognition and diagnostic prediction, compared with reference standard. Clinicians' self-rated confidence in spirometry interpretation will also be evaluated. The primary outcome is the proportion of the 50 spirometry sessions where the participant's preferred diagnosis matches the reference diagnosis. Unpaired t-tests and analysis of covariance will be used to estimate the difference in primary outcome between intervention and control groups. ETHICS AND DISSEMINATION: This study has been reviewed and given favourable opinion by Health Research Authority Wales (reference: 22/HRA/5023). Results will be submitted for publication in peer-reviewed journals, presented at relevant national and international conferences, disseminated through social media, patient and public routes and directly shared with stakeholders. TRIAL REGISTRATION NUMBER: NCT05933694.

ERS International Congress 2023: highlights from the Allied Respiratory Professionals Assembly.

This article summarises some of the outstanding sessions that were (co)organised by the Allied Respiratory Professionals Assembly during the 2023 European Respiratory Society International Congress. Two sessions from each Assembly group are outlined, covering the following topics: Group 9.01 focuses on respiratory physiology techniques, specifically on predicted values and reference equations, device development and novel applications of cardiopulmonary exercise tests; Group 9.02 presents an overview of the talks given at the mini-symposium on exercise training, physical activity and self-management at home and outlines some of the best abstracts in respiratory physiotherapy; Group 9.03 highlights the nursing role in global respiratory health and presents nursing interventions and outcomes; and Group 9.04 provides an overview of the best abstracts and recent advances in behavioural science and health psychology. This Highlights article provides valuable insight into the latest scientific data and emerging areas affecting the clinical practice of Allied Respiratory Professionals.

Global metabolomics revealed deviations from the metabolic aging clock in colorectal cancer patients.

Background: Markers of aging hold promise in the context of colorectal cancer (CRC) care. Utilizing high-resolution metabolomic profiling, we can unveil distinctive age-related patterns that have the potential to predict early CRC development. Our study aims to unearth a panel of aging markers and delve into the metabolomic alterations associated with aging and CRC. Methods: We assembled a serum cohort comprising 5,649 individuals, consisting of 3,002 healthy volunteers, 715 patients diagnosed with colorectal advanced precancerous lesions (APL), and 1,932 CRC patients, to perform a comprehensive metabolomic analysis. Results: We successfully identified unique age-associated patterns across 42 metabolic pathways. Moreover, we established a metabolic aging clock, comprising 9 key metabolites, using an elastic net regularized regression model that accurately estimates chronological age. Notably, we observed significant chronological disparities among the healthy population, APL patients, and CRC patients. By combining the analysis of circulative carcinoembryonic antigen levels with the categorization of individuals into the "hypo" metabolic aging subgroup, our blood test demonstrates the ability to detect APL and CRC with positive predictive values of 68.4% (64.3%, 72.2%) and 21.4% (17.8%, 25.9%), respectively. Conclusions: This innovative approach utilizing our metabolic aging clock holds significant promise for accurately assessing biological age and enhancing our capacity to detect APL and CRC.

Wnt/ß-catenin signaling activation promotes lipogenesis in the steatotic liver via physical mTOR interaction.

Background and aims: Wnt/ß-catenin signaling plays an important role in regulating hepatic metabolism. This study is to explore the molecular mechanisms underlying the potential crosstalk between Wnt/ß-catenin and mTOR signaling in hepatic steatosis. Methods: Transgenic mice (overexpress Wnt1 in hepatocytes, Wnt+) mice and wild-type littermates were given high fat diet (HFD) for 12 weeks to induce hepatic steatosis. Mouse hepatocytes cells (AML12) and those transfected to cause constitutive ß-catenin stabilization (S33Y) were treated with oleic acid for lipid accumulation. Results: Wnt+ mice developed more hepatic steatosis in response to HFD. Immunoblot shows a significant increase in the expression of fatty acid synthesis-related genes (SREBP-1 and its downstream targets ACC, AceCS1, and FASN) and a decrease in fatty acid oxidation gene (MCAD) in Wnt+ mice livers under HFD. Wnt+ mice also revealed increased Akt signaling and its downstream target gene mTOR in response to HFD. In vitro, increased lipid accumulation was detected in S33Y cells in response to oleic acid compared to AML12 cells reinforcing the in vivo findings. mTOR inhibition by rapamycin led to a down-regulation of fatty acid synthesis in S33Y cells. In addition, ß-catenin has a physical interaction with mTOR as verified by co-immunoprecipitation in hepatocytes. Conclusions: Taken together, our results demonstrate that ß-catenin stabilization through Wnt signaling serves a central role in lipid metabolism in the steatotic liver through up-regulation of fatty acid synthesis via Akt/mTOR signaling. These findings suggest hepatic Wnt signaling may represent a therapeutic strategy in hepatic steatosis.

High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers.

Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin's lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL). Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling. Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients. Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL.

Exploring the feasibility of using long-term stored newborn dried blood spots to identify metabolic features for congenital heart disease screening.

Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. There's currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. This study was set to assess the feasibility of using DBS to identify reliable metabolite biomarkers with clinical relevance, with the aim to screen and classify CHD utilizing the DBS. We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories. A DBS-based quantitative metabolomics method was developed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records to verify the reliability of metabolic profiling. For hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Logistic and LightGBM models were established to aid in CHD discrimination and classification. Consistent and reliable quantification of metabolites were demonstrated in DBS samples stored for up to 15 years. We discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites and twelve metabolites as potential biomarkers for CHD assessment and subtypes classifying. This study is the first to confirm the feasibility of validating metabolite profiling results using long-term stored DBS samples. Our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications.

MOTIVATION: Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer. METHODS: A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13). RESULTS: Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%. CONCLUSION: Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.

The association of cardiorespiratory fitness with spirometric small airway obstruction.

Spirometric small airway obstruction is associated with impaired ventilatory response to exercise independently of FEV1/FVC ratio https://bit.ly/3pre4sk.

European Respiratory Society/American Thoracic Society technical statement: standardisation of the measurement of lung volumes, 2023 update.

This document updates the 2005 European Respiratory Society (ERS) and American Thoracic Society (ATS) technical standard for the measurement of lung volumes. The 2005 document integrated the recommendations of an ATS/ERS task force with those from an earlier National Heart, Lung, and Blood Institute workshop that led to the publication of background papers between 1995 and 1999 and a consensus workshop report with more in-depth descriptions and discussion. Advancements in hardware and software, new research and emerging approaches have necessitated an update to the 2005 technical standard to guide laboratory directors, physiologists, operators, pulmonologists and manufacturers. Key updates include standardisation of linked spirometry, new equipment quality control and validation recommendations, generalisation of the multiple breath washout concept beyond nitrogen, a new acceptability and grading system with addition of example tracings, and a brief review of imaging and other new techniques to measure lung volumes. Future directions and key research questions are also noted.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

ERS International Congress 2022: highlights from the Allied Respiratory Professionals Assembly.

In this article, we provide a brief overview of some of the outstanding sessions that were (co)organised by the Allied Respiratory Professionals Assembly during the 2022 European Respiratory Society International Congress, which was held in a hybrid format. Early Career Members from Assembly 9 summarised the content of the sessions, with the support of the Officers from the four Assembly groups: Respiratory Function Technologists and Scientists (Group 9.01); Physiotherapists (Group 9.02); Nurses (Group 9.03); and Psychologists and Behavioural Scientists (Group 9.04). The sessions covered the following topics: recent advances in cardiopulmonary exercise and challenge testing; the role and new trends in physiotherapy, exercise and physical activity promotion interventions in chronic respiratory diseases; development of the international curriculum for respiratory nurses and nursing aspects in disease management; and treatment adherence, e-health interventions and post-coronavirus disease 2019 challenges. This Highlights article targets delegates who attended the Congress sessions, as well as those who were unable to attend, and provides valuable insight into the latest scientific data and emerging areas affecting the clinical practice of Allied Respiratory Professionals.

Prognostic role of minute ventilation/carbon dioxide production slope for perioperative morbidity and long-term survival in resectable patients with nonsmall-cell lung cancer: a prospective study using propensity score overlap weighting.

BACKGROUND: The role of minute ventilation/carbon dioxide production ( / CO 2 ) slope, a ventilation efficiency marker, in predicting short-term and long-term health outcomes for patients with nonsmall-cell lung cancer (NSCLC) undergoing lung resection has not been well investigated. MATERIAL AND METHODS: This prospective cohort study consecutively enrolled NSCLC patients who underwent a presurgical cardiopulmonary exercise test from November 2014 to December 2019. The association of / CO 2 slope with relapse-free survival (RFS), overall survival (OS), and perioperative mortality was evaluated using the Cox proportional hazards and logistic models. Covariates were adjusted using propensity score overlap weighting. The optimal cut-off point of the E/ CO 2 slope was estimated using the receiver operating characteristics curve. Internal validation was completed through bootstrap resampling. RESULTS: A cohort of 895 patients [median age (interquartile range), 59 (13) years; 62.5% male] was followed for a median of 40 (range, 1-85) months. Throughout the study, there were 247 relapses or deaths and 156 perioperative complications. The incidence rates per 1000 person-years for relapses or deaths were 108.8 and 79.6 among patients with high and low E/ CO 2 slopes, respectively [weighted incidence rate difference per 1000 person-years, 29.21 (95% CI, 7.30-51.12)]. A E/ CO 2 slope of greater than or equal to 31 was associated with shorter RFS [hazard ratio for relapse or death, 1.38 (95% CI, 1.02-1.88), P =0.04] and poorer OS [hazard ratio for death, 1.69 (1.15-2.48), P =0.02] compared to a lower / CO 2 slope. A high E/ CO 2 slope increased the risk of perioperative morbidity compared with a low E/ CO 2 slope [odds ratio, 2.32 (1.54-3.49), P <0.001]. CONCLUSIONS: In patients with operable NSCLC, a high E/ CO 2 slope was significantly associated with elevated risks of poorer RFS, OS, and perioperative morbidity.

Fecal Keratin 8 Is a Noninvasive and Specific Marker for Intestinal Injury in Necrotizing Enterocolitis.

Specific biomarkers of intestinal injury associated with necrotizing enterocolitis (NEC) are needed to diagnose and monitor intestinal mucosal injury and recovery. This study aims to develop and test a modified enzyme-linked immunosorbent assay (ELISA) protocol to detect the total keratin 8 (K8) in the stool of newborns with NEC and investigate the clinical value of fecal K8 as a marker of intestinal injury specifically associated with NEC. We collected fecal samples from five newborns with NEC and five gestational age-matched premature neonates without NEC at the Lucile Packard Children's Hospital Stanford and Washington University School of Medicine, respectively. Fecal K8 levels were measured using a modified ELISA protocol and Western blot, and fecal calprotectin was measured using a commercial ELISA kit. Clinical data, including gestational age, birth weight, Bell stage for NEC, feeding strategies, total white blood cell (WBC) count, and other pertinent clinical variables, were collected and analyzed. Fecal K8 levels were significantly higher in the pre-NEC group (1-2 days before diagnosis of NEC) and NEC group than those in the non-NEC group (p = 0.013, p = 0.041). Moreover, fecal K8 was relatively higher at the onset of NEC and declined after the resolution of the disease (p = 0.019). Results with similar trends to fecal K8 were also seen in fecal calprotectin (p = 0.046), but not seen in total WBC count (p = 0.182). In conclusion, a modified ELISA protocol for the total K8 protein was successfully developed for the detection of fecal K8 in the clinical setting of premature newborns with NEC. Fecal K8 is noted to be significantly increased in premature newborns with NEC and may, therefore, serve as a noninvasive and specific marker for intestinal epithelial injury associated with NEC.

Data-driven longitudinal characterization of neonatal health and morbidity.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Cardiorespiratory Optimisation By Arteriovenous fistula Ligation after renal Transplantation (COBALT): study protocol for a multicentre randomised interventional feasibility trial.

INTRODUCTION: Cardiovascular events are a major cause of mortality following successful kidney transplantation.Arteriovenous fistulas (AVFs) are considered the best option for haemodialysis, but may contribute to this excess mortality because they promote adverse cardiac remodelling and ventricular hypertrophy. This raises the question whether recipients with a well-functioning kidney transplant should undergo elective AVF ligation. METHODS AND ANALYSIS: The COBALT feasibility study is a multicentre interventional randomised controlled trial (RCT) that will randomise renal transplant patients with stable graft function and a working AVF on a 1:1 basis to standard care (continued conservative management) or to AVF ligation. All patients will perform cardiopulmonary exercise testing (CPET) on recruitment and 6 months later. Daily functioning and quality of life will be additionally assessed by questionnaire completion and objective measure of physical activity. The primary outcome-the proportion of approached patients who complete the study (incorporating rates of consent, receipt of allocated intervention and completion of both CPETs without withdrawal)-will determine progression to a full-scale RCT. Design of the proposed RCT will be informed by an embedded qualitative assessment of participant and healthcare professional involvement. ETHICS AND DISSEMINATION: This study has been approved by the East Midlands-Derby Research Ethics Committee (22/EM/0002) and the Health Research Authority. The results of this work will be disseminated academically through presentation at national and international renal meetings and via open access, peer-reviewed outputs. Existing networks of renal patient groups will also be used to disseminate the study findings to other key stakeholders. TRIAL REGISTRATION NUMBER: ISRCTN49033491.

Butyrate induces development-dependent necrotizing enterocolitis-like intestinal epithelial injury via necroptosis.

BACKGROUND: The accumulation of short-chain fatty acids (SCFAs) from bacterial fermentation may adversely affect the under-developed gut as observed in premature newborns at risk for necrotizing enterocolitis (NEC). This study explores the mechanism by which specific SCFA fermentation products may injure the premature newborn intestine mucosa leading to NEC-like intestinal cell injury. METHODS: Intraluminal injections of sodium butyrate were administered to 14- and 28-day-old mice, whose small intestine and stool were harvested for analysis. Human intestinal epithelial stem cells (hIESCs) and differentiated enterocytes from preterm and term infants were treated with sodium butyrate at varying concentrations. Necrosulfonamide (NSA) and necrostatin-1 (Nec-1) were used to determine the protective effects of necroptosis inhibitors on butyrate-induced cell injury. RESULTS: The more severe intestinal epithelial injury was observed in younger mice upon exposure to butyrate (p = 0.02). Enterocytes from preterm newborns demonstrated a significant increase in sensitivity to butyrate-induced cell injury compared to term newborn enterocytes (p = 0.068, hIESCs; p = 0.038, differentiated cells). NSA and Nec-1 significantly inhibited the cell death induced by butyrate. CONCLUSIONS: Butyrate induces developmental stage-dependent intestinal injury that resembles NEC. A primary mechanism of cell injury in NEC is necroptosis. Necroptosis inhibition may represent a potential preventive or therapeutic strategy for NEC. IMPACT: Butyrate induces developmental stage-dependent intestinal injury that resembles NEC. A primary mechanism of cell injury caused by butyrate in NEC is necroptosis. Necroptosis inhibitors proved effective at significantly ameliorating the enteral toxicity of butyrate and thereby suggest a novel mechanism and approach to the prevention and treatment of NEC in premature newborns.

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.

Background: Kawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort. Methods: A single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan. Findings: Our diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks. Interpretation: This work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.