A case of a pulmonary-renal syndrome caused by streptococcal infection.

Pulmonary-renal syndrome (PRS) is defined as a combination of diffuse alveolar haemorrhage and glomerulonephritis. An 18-year-old woman visited our hospital with a 2-day history of fever, dyspnoea, and leg edema. Laboratory investigations revealed an elevated inflammatory reaction, increased serum creatinine levels, and normocytic anaemia. Additionally, the anti-streptolysin-O titre was positive, and complement component-3 levels were decreased. Urinalysis revealed proteinuria and hematuria. Bronchoalveolar lavage aliquots were progressively more hemorrhagic. These findings supported a diagnosis of PRS secondary to streptococcal infection. The patient was treated with high-dose methylprednisolone and antibiotics. After 4 days of treatment, her respiratory symptoms and serum creatinine levels improved. Steroid tapering was performed over 15 days. The findings in this case indicate that streptococcal infection is a potential cause of PRS, and that short-term steroid therapy is an effective treatment.

Intravenous Tranexamic Acid in Percutaneous Kidney Biopsy: A Randomized Controlled Trial.

BACKGROUND: Tranexamic acid is frequently reported to reduce bleeding-related complications in major surgery and trauma. We aimed to investigate whether tranexamic acid reduced hematoma size after percutaneous kidney biopsy. METHODS: We conducted a double-blind, parallel three-group, randomized placebo-controlled trial at a teaching hospital in Japan between January 2016 and July 2018. Adult patients with clinical indication for ultrasound-guided percutaneous biopsy of a native kidney were included. Participants were randomly assigned into three groups: high-dose tranexamic acid (1,000 mg in total), low-dose tranexamic acid (500 mg in total), or placebo (counterpart saline). Intervention drugs were intravenously administered twice, as a bolus just before the biopsy and as a continuous infusion initiated just after the biopsy. Primary outcome was post-biopsy perirenal hematoma size as measured by ultrasound on the morning after the biopsy. RESULTS: We assessed 90 adult patients for study eligibility, of whom 56 were randomly allocated into the three groups: 20 for high-dose tranexamic acid, 19 for low-dose tranexamic acid, and 17 for placebo. The median size of perirenal hematoma was 200 mm2 (interquartile range, 21-650) in the high-dose tranexamic acid group, 52 mm2 (0-139) in the low-dose tranexamic acid group, and 0 mm2 (0-339) in the placebo group (p = 0.048 for high-dose tranexamic acid vs. placebo). CONCLUSION: In this trial, the median size of post-kidney biopsy hematoma was unexpectedly larger in the high-dose tranexamic acid group than in the placebo group. Although our results do not support the routine use of tranexamic acid in percutaneous kidney biopsy at present, further studies are needed to confirm the results.

Association between serum alkaline phosphatase and bacteraemia in haemodialysis outpatients: a multicentre retrospective cross-sectional study.

OBJECTIVES: Elevated baseline serum alkaline phosphatase (ALP) may correlate with higher medium-term to long-term mortality in the general population and in patients with chronic kidney disease. However, few data are available on the association between serum ALP and the short-term prognosis of patients on haemodialysis (HD). We verified the association of ALP levels and bacteraemia or death in maintenance HD patients suspected of bacteraemia in an outpatient setting. DESIGN: We analysed 315 consecutive HD patients suspected of having bacteraemia with two sets of blood culture drawn on admission. SETTING: Admission to two tertiary-care university medical centres from January 2013 to December 2015. PARTICIPANTS: Consecutive cases on maintenance HD aged≥18 years. Cases of hospitalised patients who had been transferred from another hospital, had a dialysis vintage<2 months, were also undergoing peritoneal dialysis, and/or were receiving HD less than once a week were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measure was bacteraemia and secondary outcome was in-hospital death. RESULTS: Among 315 cases included in the study, 187 had baseline-measured ALP levels, with a cut-off value on ROC analysis of 360 U/L (Area Under the Curve (AUC) 0.60, sensitivity 0.49, specificity 0.76). In multivariate analysis, there was a statistically significant association between a higher ALP in hospital visit and bacteraemia (OR: 2.37, 95% CI: 1.17 to 4.83). However, there were no statistically significant associations between higher ALP and in-hospital death (OR: 1.20, 95% CI: 0.57 to 2.54). A sensitivity analysis of 187 patients with no missing ALP values also demonstrated a significant association between elevated ALP and bacteraemia, but no significant association between ALP and in-hospital death. CONCLUSIONS: Elevated ALP is a predictor of bacteraemia. In HD patients suspected of bacteraemia in outpatient settings, increased ALP levels were associated with increased likelihood of confirmed disease.

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.

BACKGROUND: Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE. METHODS: In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001. RESULTS: The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-ß pathway-were also upregulated. CONCLUSIONS: This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-ß) pathways differentially regulated in patients who subsequently experience MACE.

Integrated relationship of nasopharyngeal airway host response and microbiome associates with bronchiolitis severity.

Bronchiolitis is a leading cause of infant hospitalizations but its immunopathology remains poorly understood. Here we present data from 244 infants hospitalized with bronchiolitis in a multicenter prospective study, assessing the host response (transcriptome), microbial composition, and microbial function (metatranscriptome) in the nasopharyngeal airway, and associate them with disease severity. We investigate individual associations with disease severity identify host response, microbial taxonomical, and microbial functional modules by network analyses. We also determine the integrated relationship of these modules with severity. Several modules are significantly associated with risks of positive pressure ventilation use, including the host-type I interferon, neutrophil/interleukin-1, T cell regulation, microbial-branched-chain amino acid metabolism, and nicotinamide adenine dinucleotide hydrogen modules. Taken together, we show complex interplays between host and microbiome, and their contribution to disease severity.

Prediction of Vitamin D Deficiency in Older Adults: The Role of Machine Learning Models.

CONTEXT: Conventional prediction models for vitamin D deficiency have limited accuracy. BACKGROUND: Using cross-sectional data, we developed models based on machine learning (ML) and compared their performance with those based on a conventional approach. METHODS: Participants were 5106 community-resident adults (50-84 years; 58% male). In the randomly sampled training set (65%), we constructed 5 ML models: lasso regression, elastic net regression, random forest, gradient boosted decision tree, and dense neural network. The reference model was a logistic regression model. Outcomes were deseasonalized serum 25-hydroxyvitamin D (25(OH)D) <50 nmol/L (yes/no) and <25 nmol/L (yes/no). In the test set (the remaining 35%), we evaluated predictive performance of each model, including area under the receiver operating characteristic curve (AUC) and net benefit (decision curves). RESULTS: Overall, 1270 (25%) and 91 (2%) had 25(OH)D <50 and <25 nmol/L, respectively. Compared with the reference model, the ML models predicted 25(OH)D <50 nmol/L with similar accuracy. However, for prediction of 25(OH)D <25 nmol/L, all ML models had higher AUC point estimates than the reference model by up to 0.14. AUC was highest for elastic net regression (0.93; 95% CI 0.90-0.96), compared with 0.81 (95% CI 0.71-0.91) for the reference model. In the decision curve analysis, ML models mostly achieved a greater net benefit across a range of thresholds. CONCLUSION: Compared with conventional models, ML models predicted 25(OH)D <50 nmol/L with similar accuracy but they predicted 25(OH)D <25 nmol/L with greater accuracy. The latter finding suggests a role for ML models in participant selection for vitamin D supplement trials.

Nasopharyngeal lipidomic endotypes of infants with bronchiolitis and risk of childhood asthma: a multicentre prospective study.

BACKGROUND: Bronchiolitis is the leading cause of hospitalisation of US infants and an important risk factor for childhood asthma. Recent evidence suggests that bronchiolitis is clinically heterogeneous. We sought to derive bronchiolitis endotypes by integrating clinical, virus and lipidomics data and to examine their relationship with subsequent asthma risk. METHODS: This is a multicentre prospective cohort study of infants (age <12 months) hospitalised for bronchiolitis. We identified endotypes by applying clustering approaches to clinical, virus and nasopharyngeal airway lipidomic data measured at hospitalisation. We then determined their longitudinal association with the risk for developing asthma by age 6 years by fitting a mixed-effects logistic regression model. To account for multiple comparisons of the lipidomics data, we computed the false discovery rate (FDR). To understand the underlying biological mechanism of the endotypes, we also applied pathway analyses to the lipidomics data. RESULTS: Of 917 infants with bronchiolitis (median age, 3 months), we identified clinically and biologically meaningful lipidomic endotypes: (A) cinicalclassiclipidmixed (n=263), (B) clinicalseverelipidsphingolipids-high (n=281), (C) clinicalmoderatelipidphospholipids-high (n=212) and (D) clinicalatopiclipidsphingolipids-low (n=161). Endotype A infants were characterised by 'classic' clinical presentation of bronchiolitis. Profile D infants were characterised by a higher proportion of parental asthma, IgE sensitisation and rhinovirus infection and low sphingolipids (eg, sphingomyelins, ceramides). Compared with endotype A, profile D infants had a significantly higher risk of asthma (22% vs 50%; unadjusted OR, 3.60; 95% CI 2.31 to 5.62; p<0.001). Additionally, endotype D had a significantly lower abundance of polyunsaturated fatty acids (eg, docosahexaenoic acid; FDR=0.01). The pathway analysis revealed that sphingolipid metabolism pathway was differentially expressed in endotype D (FDR=0.048). CONCLUSIONS: In this multicentre prospective cohort study of infants with bronchiolitis, integrated clustering of clinical, virus and lipidomic data identified clinically and biologically distinct endotypes that have a significantly differential risk for developing asthma.Delete.

Educational Approaches That Enhance Online Clinical Clerkship during the COVID-19 Pandemic.

Objective This cross-sectional national study determined which educational approaches are associated with the effectiveness of online clerkship for medical students. Method A survey was conducted for medical students at 78 medical schools in Japan from May 29 to June 14, 2020. It comprised the following aspects: (a) participants' profiles, (b) number of opportunities to learn from each educational approach (lecture, medical quiz, assignment, oral presentation, observation of a physician's practice, clinical skill practice, participation in interprofessional meetings, and interactive discussions with physicians) in online clerkship, (c) frequency of technical problems, and (d) educational outcome measurement (satisfaction, motivation, knowledge acquisition, skill acquisition, change in self-study time, and understanding of the importance of medical care team). Results Of the 2,640 respondents, 2,594 (98.3%) agreed to cooperate. Ultimately, 1,711 matched our inclusion criteria. All educational approaches but assignments were positively associated with satisfaction and motivation. All educational approaches excluding assignment submission and interprofessional meeting were positively associated with knowledge acquisition. Observation, practice, and interprofessional meeting were positively associated with skill acquisition. Only assignment submission was positively associated with the change in self-study time. Educational approaches excluding medical quizzes were positively associated with understanding the importance of the medical care team. Technical problems were negatively associated with motivation, knowledge acquisition, and skill acquisition. Conclusions Educators should implement various educational approaches, especially observation and practice, even in online clinical clerkship. They also need to minimize the technical problems associated with the Internet, as they reduce the effectiveness of online clerkship.

Proteomics endotyping of infants with severe bronchiolitis and risk of childhood asthma.

BACKGROUND: Bronchiolitis is the leading cause of hospitalization in U.S. infants and a major risk factor for childhood asthma. Growing evidence supports clinical heterogeneity within bronchiolitis. We aimed to identify endotypes of infant bronchiolitis by integrating clinical, virus, and serum proteome data, and examine their relationships with asthma development. METHODS: This is a multicenter prospective cohort study of infants hospitalized for physician-diagnosis of bronchiolitis. We identified bronchiolitis endotypes by applying unsupervised machine learning (clustering) approaches to integrated clinical, virus (respiratory syncytial virus [RSV], rhinovirus [RV]), and serum proteome data measured at hospitalization. We then examined their longitudinal association with the risk for developing asthma by age 6 years. RESULTS: In 140 infants hospitalized with bronchiolitis, we identified three endotypes: (1) clinicalatopic virusRV proteomeNFκB-dysregulated , (2) clinicalnon-atopic virusRSV/RV proteomeTNF-dysregulated , and (3) clinicalclassic virusRSV proteomeNFκB/TNF-regulated endotypes. Endotype 1 infants were characterized by high proportion of IgE sensitization and RV infection. These endotype 1 infants also had dysregulated NFκB pathways (FDR < 0.001) and significantly higher risks for developing asthma (53% vs. 22%; adjOR 4.04; 95% CI, 1.49-11.0; p = 0.006), compared with endotype 3 (clinically resembling "classic" bronchiolitis). Likewise, endotype 2 infants were characterized by low proportion of IgE sensitization and high proportion of RSV or RV infection. These endotype 2 infants had dysregulated tumor necrosis factor (TNF)-mediated signaling pathway (FDR <0.001) and significantly higher risks for developing asthma (44% vs. 22%; adjOR 2.71; 95% CI, 1.03-7.11, p = 0.04). CONCLUSION: In this multicenter cohort, integrated clustering of clinical, virus, and proteome data identified biologically distinct endotypes of bronchiolitis that have differential risks of asthma development.

Nasopharyngeal airway dual-transcriptome of infants with severe bronchiolitis and risk of childhood asthma: A multicenter prospective study.

BACKGROUND: Severe bronchiolitis (ie, bronchiolitis requiring hospitalization) during infancy is a major risk factor for childhood asthma. However, the exact mechanism linking these common conditions remains unclear. OBJECTIVES: This study sought to examine the integrated role of airway microbiome (both taxonomy and function) and host response in asthma development in this high-risk population. METHODS: This multicenter prospective cohort study of 244 infants with severe bronchiolitis (median age, 3 months) examined the infants' nasopharyngeal metatranscriptomes (microbiomes) and transcriptomes (hosts), as well as metabolomes at hospitalization. The longitudinal relationships investigated include (1) major bacterial species (Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis), (2) microbial function, and (3) host response with risks of developing asthma by age 6 years. RESULTS: First, the abundance of S pneumoniae was associated with greater risks of asthma (P = .01), particularly in infants with nonrhinovirus infection (Pinteraction = .04). Second, of 328 microbial functional pathways that are differentially enriched by asthma development, the top pathways (eg, fatty acid and glycolysis pathways; false discovery rate [FDR] < 1 × 10-12) were driven by these 3 major species (eg, positive association of S pneumoniae with glycolysis; FDR < 0.001). These microbial functional pathways were validated with the parallel metabolome data. Third, 104 transcriptome pathways were differentially enriched (FDR < .05)-for example, downregulated interferon-α and -γ and upregulated T-cell activation pathways. S pneumoniae was associated with most differentially expressed transcripts (eg, DAGLB; FDR < 0.05). CONCLUSIONS: By applying metatranscriptomic, transcriptomic, and metabolomic approaches to a multicenter cohort of infants with bronchiolitis, this study found an interplay between major bacterial species, their function, and host response in the airway, and their longitudinal relationship with asthma development.

Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy.

OBJECTIVE: Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease, likely encompassing several subtypes of disease with distinct biological mechanisms (ie, molecular subtypes). Current models based solely on clinical data have yielded limited accuracy in predicting the risk of major adverse cardiovascular events (MACE). Our aim in this study was to derive molecular subtypes in our multicentre prospective cohort of patients with HCM using proteomics profiling and to examine their longitudinal associations with MACE. METHODS: We applied unsupervised machine learning methods to plasma proteomics profiling data of 1681 proteins from 258 patients with HCM who were prospectively followed for a median of 2.8 years. The primary outcome was MACE, defined as a composite of arrhythmia, heart failure, stroke and sudden cardiac death. RESULTS: We identified four molecular subtypes of HCM. Time-to-event analysis revealed significant differences in MACE-free survival among the four molecular subtypes (plogrank=0.007). Compared with the reference group with the lowest risk of MACE (molecular subtype A), patients in molecular subtype D had a higher risk of subsequently developing MACE, with an HR of 3.41 (95% CI 1.54 to 7.55, p=0.003). Pathway analysis of proteins differentially regulated in molecular subtype D demonstrated an upregulation of the Ras/mitogen-activated protein kinase and associated pathways, as well as pathways related to inflammation and fibrosis (eg, transforming growth factor-ß pathway). CONCLUSIONS: Our prospective plasma proteomics study not only exhibited the presence of HCM molecular subtypes but also identified pathobiological mechanisms associated with a distinct high-risk subtype of HCM.

Metabolome subtyping of severe bronchiolitis in infancy and risk of childhood asthma.

BACKGROUND: Infants with bronchiolitis are at increased risk for developing asthma. Growing evidence suggests bronchiolitis is a heterogeneous condition. OBJECTIVES: We sought to identify biologically distinct subgroups based on the metabolome signatures (metabotypes) in infants with severe bronchiolitis and to examine the longitudinal relationships of metabotypes with asthma development. METHODS: In a multicenter prospective cohort study of infants (age, <12 months) hospitalized for bronchiolitis, the nasopharyngeal airway metabolome was profiled at hospitalization. Using a clustering approach, this study identified mutually exclusive metabotypes. This study also examined their longitudinal association with the risk of developing asthma by 5 years of age. RESULTS: Of 918 infants hospitalized for bronchiolitis (median age, 3 months), this study identified 5 distinct metabotypes-characterized by their nasopharyngeal metabolome profile: A, glycerophosphocholine-high; B, amino acid-high, polyunsaturated fatty acid-low; C, amino acid-high, glycerophospholipid-low; D, glycerophospholipid-high; and E, mixed. Compared with infants with metabotype A (who clinically resembled "classic" bronchiolitis), infants with metabotype B had a significantly higher risk for developing asthma (23% vs 41%; adjusted odds ratio, 2.22; 95% CI, 1.07-4.69). The pathway analysis showed that metabotype B had enriched amino acid (eg, methionine, histidine, glutathione) and α-linolenic/linoleic acid metabolism pathways (false discovery rate, <5 × 10-14 for all). Finally, the transcriptome analysis revealed that infants with metabotype B had upregulated IFN-α and IL-6/JAK/STAT3 pathways and downregulated fatty acid metabolism pathways (false discovery rate, <0.05 for both). CONCLUSIONS: In this multicenter prospective cohort study of infants with severe bronchiolitis, the clustering analysis of metabolome data identified biologically distinct metabotypes, including a metabotype characterized by high inflammatory amino acids and low polyunsaturated fatty acids that is at significantly increased risk for developing asthma.

Association of Growth Trajectory Profiles with Asthma Development in Infants Hospitalized with Bronchiolitis.

BACKGROUND: Little is known about the relationship of longitudinal growth trajectory in early life with asthma development, particularly in infants with bronchiolitis (a high-risk population). OBJECTIVE: Among infants with bronchiolitis, we aimed to identify growth trajectory profiles and determine their longitudinal relationship with the risk for developing childhood asthma. METHODS: A multicenter prospective study enrolled infants (aged <1 year) hospitalized for bronchiolitis. We identified growth trajectory profiles-derived from body mass index-for-age at ages 0, 6, 12, 15, 18, 24, and 36 months by using a longitudinal clustering method. We examined associations between growth trajectory profiles and asthma development by age 5 years. RESULTS: The analytic cohort consists of 880 infants hospitalized for bronchiolitis (median age, 3 months). Overall, 26% developed asthma by age 5 years. The longitudinal clustering identified 5 distinct profiles: persistent low growth (27%), normative growth (33%), transient overweight (21%), late-onset overweight (16%), and persistent obesity (3%) profiles. In multivariable model, compared with children with a normative profile, those with a persistent obesity profile had significantly higher risks of developing asthma (24% vs 38%, odds ratio [OR]: 2.55, 95% confidence interval [CI]: 1.07-6.09, P = .03). Among children with a persistent obesity profile, those without allergic predisposition had significantly higher risks of asthma (OR: 3.02, 95% CI: 1.05-8.64, P = .04 in the nonparental allergic history group; OR: 3.18, 95% CI: 1.02-9.92, P = .047 in the non-IgE sensitization group), whereas those with allergic predisposition were not at increased risk. CONCLUSIONS: This multicenter cohort study of infants with bronchiolitis demonstrated distinct growth trajectory profiles that have differential risks for developing asthma.

Nasopharyngeal metatranscriptome profiles of infants with bronchiolitis and risk of childhood asthma: a multicentre prospective study.

BACKGROUND: Bronchiolitis is not only the leading cause of hospitalisation in US infants but also a major risk factor for asthma development. Growing evidence supports clinical heterogeneity within bronchiolitis. Our objectives were to identify metatranscriptome profiles of infant bronchiolitis, and to examine their relationship with the host transcriptome and subsequent asthma development. METHODS: As part of a multicentre prospective cohort study of infants (age <1 year) hospitalised for bronchiolitis, we integrated virus and nasopharyngeal metatranscriptome (species-level taxonomy and function) data measured at hospitalisation. We applied network-based clustering approaches to identify metatranscriptome profiles. We then examined their association with the host transcriptome at hospitalisation and risk for developing asthma. RESULTS: We identified five metatranscriptome profiles of bronchiolitis (n=244): profile A: virusRSVmicrobiomecommensals; profile B: virusRSV/RV-Amicrobiome H.influenzae ; profile C: virusRSVmicrobiome S.pneumoniae ; profile D: virusRSVmicrobiome M.nonliquefaciens ; and profile E: virusRSV/RV-Cmicrobiome M.catarrhalis . Compared with profile A, profile B infants were characterised by a high proportion of eczema, Haemophilus influenzae abundance and enriched virulence related to antibiotic resistance. These profile B infants also had upregulated T-helper 17 and downregulated type I interferon pathways (false discovery rate (FDR) <0.005), and significantly higher risk for developing asthma (17.9% versus 38.9%; adjusted OR 2.81, 95% CI 1.11-7.26). Likewise, profile C infants were characterised by a high proportion of parental asthma, Streptococcus pneumoniae dominance, and enriched glycerolipid and glycerophospholipid metabolism of the microbiome. These profile C infants had an upregulated RAGE signalling pathway (FDR <0.005) and higher risk of asthma (17.9% versus 35.6%; adjusted OR 2.49, 95% CI 1.10-5.87). CONCLUSIONS: Metatranscriptome and clustering analysis identified biologically distinct metatranscriptome profiles that have differential risks of asthma.

Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy.

Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models-the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the p-value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79-0.93] vs. 0.72 [0.61-0.82]; p < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76-0.92] vs. 0.75 [0.65-0.85]; p = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM.

Big Data, Data Science, and Causal Inference: A Primer for Clinicians.

Clinicians handle a growing amount of clinical, biometric, and biomarker data. In this "big data" era, there is an emerging faith that the answer to all clinical and scientific questions reside in "big data" and that data will transform medicine into precision medicine. However, data by themselves are useless. It is the algorithms encoding causal reasoning and domain (e.g., clinical and biological) knowledge that prove transformative. The recent introduction of (health) data science presents an opportunity to re-think this data-centric view. For example, while precision medicine seeks to provide the right prevention and treatment strategy to the right patients at the right time, its realization cannot be achieved by algorithms that operate exclusively in data-driven prediction modes, as do most machine learning algorithms. Better understanding of data science and its tasks is vital to interpret findings and translate new discoveries into clinical practice. In this review, we first discuss the principles and major tasks of data science by organizing it into three defining tasks: (1) association and prediction, (2) intervention, and (3) counterfactual causal inference. Second, we review commonly-used data science tools with examples in the medical literature. Lastly, we outline current challenges and future directions in the fields of medicine, elaborating on how data science can enhance clinical effectiveness and inform medical practice. As machine learning algorithms become ubiquitous tools to handle quantitatively "big data," their integration with causal reasoning and domain knowledge is instrumental to qualitatively transform medicine, which will, in turn, improve health outcomes of patients.

Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM. METHODS: We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive. RESULTS: The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-ß pathway-were also upregulated. CONCLUSIONS: This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-ß) pathways differentially regulated in HCM.

Integrated omics endotyping of infants with respiratory syncytial virus bronchiolitis and risk of childhood asthma.

Respiratory syncytial virus (RSV) bronchiolitis is not only the leading cause of hospitalization in U.S. infants, but also a major risk factor for asthma development. While emerging evidence suggests clinical heterogeneity within RSV bronchiolitis, little is known about its biologically-distinct endotypes. Here, we integrated clinical, virus, airway microbiome (species-level), transcriptome, and metabolome data of 221 infants hospitalized with RSV bronchiolitis in a multicentre prospective cohort study. We identified four biologically- and clinically-meaningful endotypes: A) clinicalclassicmicrobiomeM. nonliquefaciensinflammationIFN-intermediate, B) clinicalatopicmicrobiomeS. pneumoniae/M. catarrhalisinflammationIFN-high, C) clinicalseveremicrobiomemixedinflammationIFN-low, and D) clinicalnon-atopicmicrobiomeM.catarrhalisinflammationIL-6. Particularly, compared with endotype A infants, endotype B infants-who are characterized by a high proportion of IgE sensitization and rhinovirus coinfection, S. pneumoniae/M. catarrhalis codominance, and high IFN-α and -γ response-had a significantly higher risk for developing asthma (9% vs. 38%; OR, 6.00: 95%CI, 2.08-21.9; P = 0.002). Our findings provide an evidence base for the early identification of high-risk children during a critical period of airway development.