Xuefeng B Ling

Stanford University, Stanford, California, United States

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Publications (47)175.82 Total impact

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    ABSTRACT: Estimating patient risk of future emergency department (ED) revisits can guide the allocation of resources, e.g. local primary care and/or specialty, to better manage ED high utilization patient populations and thereby improve patient life qualities. We set to develop and validate a method to estimate patient ED revisit risk in the subsequent 6 months from an ED discharge date. An ensemble decision-tree-based model with Electronic Medical Record (EMR) encounter data from HealthInfoNet (HIN), Maine’s Health Information Exchange (HIE), was developed and validated, assessing patient risk for a subsequent 6 month return ED visit based on the ED encounter-associated demographic and EMR clinical history data. A retrospective cohort of 293,461 ED encounters that occurred between January 1, 2012 and December 31, 2012, was assembled with the associated patients’ 1-year clinical histories before the ED discharge date, for model training and calibration purposes. To validate, a prospective cohort of 193,886 ED encounters that occurred between January 1, 2013 and June 30, 2013 was constructed. Statistical learning that was utilized to construct the prediction model identified 152 variables that included the following data domains: demographics groups (12), different encounter history (104), care facilities (12), primary and secondary diagnoses (10), primary and secondary procedures (2), chronic disease condition (1), laboratory test results (2), and outpatient prescription medications (9). The c-statistics for the retrospective and prospective cohorts were 0.742 and 0.730 respectively. Total medical expense and ED utilization by risk score 6 months after the discharge were analyzed. Cluster analysis identified discrete subpopulations of high-risk patients with distinctive resource utilization patterns, suggesting the need for diversified care management strategies. Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. It promises to provide increased opportunity for high ED utilization identification, and optimized resource and population management.
    No preview · Article · Dec 2016 · BMC Emergency Medicine
  • Zhen Li · Zhou Tan · Shiying Hao · Bo Jin · Xiaohong Deng · Guang Hu · Xiaodan Liu · Jie Zhang · Hua Jin · Min Huang · John T Kanegaye · Adriana H Tremoulet · Jane C Burns · Jianmin Wu · Harvey J Cohen · Xuefeng B Ling
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    ABSTRACT: Objectives: Kawasaki disease (KD) is an acute pediatric vasculitis of infants and young children with unknown etiology and no specific laboratory-based test to identify. A specific molecular diagnostic test is urgently needed to support the clinical decision of proper medical intervention, preventing subsequent complications of coronary artery aneurysms. We used a simple and low-cost colorimetric sensor array to address the lack of a specific diagnostic test to differentiate KD from febrile control (FC) patients with similar rash/fever illnesses. Study design: Demographic and clinical data were prospectively collected for subjects with KD and FCs under standard protocol. After screening using a genetic algorithm, eleven compounds including metalloporphyrins, pH indicators, redox indicators and solvatochromic dye categories, were selected from our chromatic compound library (n = 190) to construct a colorimetric sensor array for diagnosing KD. Quantitative color difference analysis led to a decision-tree-based KD diagnostic algorithm. Results: This KD sensing array allowed the identification of 94% of KD subjects (receiver operating characteristic [ROC] area under the curve [AUC] 0.981) in the training set (33 KD, 33 FC) and 94% of KD subjects (ROC AUC: 0.873) in the testing set (16 KD, 17 FC). Color difference maps reconstructed from the digital images of the sensing compounds demonstrated distinctive patterns differentiating KD from FC patients. Conclusions: The colorimetric sensor array, composed of common used chemical compounds, is an easily accessible, low-cost method to realize the discrimination of subjects with KD from other febrile illness.
    No preview · Article · Feb 2016 · PLoS ONE
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    ABSTRACT: Objectives Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups. Methods Our objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients. Results A risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates. Conclusions The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.
    Preview · Article · Oct 2015 · PLoS ONE
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    ABSTRACT: Background: The increasing rate of health care expenditures in the United States has placed a significant burden on the nation's economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. Objective: This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. Methods: In the HealthInfoNet, Maine's health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient's next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree-based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. Results: Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. Conclusions: The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes.
    No preview · Article · Sep 2015 · Journal of Medical Internet Research
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    ABSTRACT: As Kawasaki disease (KD) shares many clinical features with other more common febrile illnesses and misdiagnosis, leading to a delay in treatment, increases the risk of coronary artery damage, a diagnostic test for KD is urgently needed. We sought to develop a panel of biomarkers that could distinguish between acute KD patients and febrile controls (FC) with sufficient accuracy to be clinically useful. Plasma samples were collected from three independent cohorts of FC and acute KD patients who met the American Heart Association definition for KD and presented within the first 10 days of fever. The levels of 88 biomarkers associated with inflammation were assessed by Luminex bead technology. Unsupervised clustering followed by supervised clustering using a Random Forest model was used to find a panel of candidate biomarkers. A panel of biomarkers commonly available in the hospital laboratory (absolute neutrophil count, erythrocyte sedimentation rate, alanine aminotransferase, gamma glutamyl transferase, concentrations of alpha-1-antitrypsin, C-reactive protein, and fibrinogen, and platelet count) accurately diagnosed 81 to 96% of KD patients in a series of three independent cohorts. After prospective validation, this 8-biomarker panel may improve the recognition of KD.Pediatric Research (2015); doi:10.1038/pr.2015.137.
    No preview · Article · Aug 2015 · Pediatric Research
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    ABSTRACT: Understanding value provides an important context for improvement. However, most health care models fail to measure value. Our objective was to categorize inpatient encounters within an academic congenital heart program based on clinical outcome and the cost to achieve the outcome (value). We aimed to describe clinical and nonclinical features associated with value. We defined hospital encounters based on outcome per resource utilized. We performed principal component and cluster analysis to classify encounters based on mortality, length of stay, hospital cost and revenue into six classes. We used nearest shrunken centroid to identify discriminant features associated with the cluster-derived classes. These features underwent hierarchical clustering and multivariate analysis to identify features associated with each class. We analyzed all patients admitted to an academic congenital heart program between September 1, 2009, and December 31, 2012. A total of 2658 encounters occurred during the study period. Six classes were categorized by value. Low-performing value classes were associated with greater institutional reward; however, encounters with higher-performing value were associated with a loss in profitability. Encounters that included insertion of a pediatric ventricular assist device (log OR 2.5 [95% CI, 1.78 to 3.43]) and acquisition of a hospital-acquired infection (log OR 1.42 [95% CI, 0.99 to 1.87]) were risk factors for inferior health care value. Among the patients in our study, institutional reward was not associated with value. We describe a framework to target quality improvement and resource management efforts that can benefit patients, institutions, and payers alike. © 2015 Wiley Periodicals, Inc.
    No preview · Article · Jul 2015 · Congenital Heart Disease
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    ABSTRACT: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHF patients. We set to identify CHF cases from both EMR codified and natural language processing (NLP) found cases. Using narrative clinical notes from all Maine Health Information Exchange (HIE) patients, the NLP case finding algorithm was retrospectively (July 1, 2012-June 30, 2013) developed with a random subset of HIE associated facilities, and blind-tested with the remaining facilities. The NLP based method was integrated into a live HIE population exploration system and validated prospectively (July 1, 2013-June 30, 2014). Total of 18,295 codified CHF patients were included in Maine HIE. Among the 253,803 subjects without CHF codings, our case finding algorithm prospectively identified 2411 uncodified CHF cases. The positive predictive value (PPV) is 0.914, and 70.1% of these 2411 cases were found to be with CHF histories in the clinical notes. A CHF case finding algorithm was developed, tested and prospectively validated. The successful integration of the CHF case findings algorithm into the Maine HIE live system is expected to improve the Maine CHF care. Copyright © 2015. Published by Elsevier Ireland Ltd.
    No preview · Article · Jul 2015 · International Journal of Medical Informatics
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    ABSTRACT: An inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) peptide (QLGLPGPPDVPDHAAYHPF) was previously identified as a preterm birth biomarker from a predominantly (∼75%) African-American cohort. Because there is a single nucleotide polymorphism (SNP) at position 669 of interalpha-trypsin inhibitor heavy chain 4 (ITIH4), we hypothesized that the "L" peptide isoform (LLGLPGPPDVPDHAAYHPF) would be found in the sera of patients of different ethnic backgrounds and that reduction of the "L" isoform serum abundance would also be associated with human preterm birth (<37 weeks gestation), similar to the reported "Q" isoform. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) profiling of term pregnancies (n=14) identified the "L" isoform peptide, which remained largely unchanged throughout gestation. Subsequent quantitative proteomics based on stable isotope dilution (SID) quantified the ITIH4 "L" isoform peptide serum abundance in a cohort of uncomplicated term pregnancies (n=14) and pregnancies complicated by preterm birth (n=11) subjects.Our results showed that the reduction of the "L" peptide's serum quantity is associated with the clinical state of preterm birth (p value < 0.001). We concluded that the "L" ITIH4 peptide is a potential biomarker predictive of preterm birth and the validation of the L isoform in a prospective series of pregnancies should be performed.
    No preview · Article · Jun 2015 · Journal of Genetics and Genomics
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    ABSTRACT: To get a better understanding of the ongoing in situ environmental changes preceding the brain tumorigenesis, we assessed cerebrospinal fluid (CSF) proteome profile changes in a glioma rat model in which brain tumor invariably developed after a single in utero exposure to the neurocarcinogen ethylnitrosourea (ENU). Computationally, the CSF proteome profile dynamics during the tumorigenesis can be modeled as non-smooth or even abrupt state changes. Such brain tumor environment transition analysis, correlating the CSF composition changes with the development of early cellular hyperplasia, can reveal the pathogenesis process at network level during a time before the image detection of the tumors. In our controlled rat model study, matched ENU- and saline-exposed rats' CSF proteomics changes were quantified at approximately 30, 60, 90, 120, 150 days of age (P30, P60, P90, P120, P150). We applied our transition-based network entropy (TNE) method to compute the CSF proteome changes in the ENU rat model and test the hypothesis of the critical transition state prior to impending hyperplasia. Our analysis identified a dynamic driver network (DDN) of CSF proteins related with the emerging tumorigenesis progressing from the non-hyperplasia state. The DDN associated leading network CSF proteins can allow the early detection of such dynamics before the catastrophic shift to the clear clinical landmarks in gliomas. Future characterization of the critical transition state (P60) during the brain tumor progression may reveal the underlying pathophysiology to device novel therapeutics preventing tumor formation. More detailed method and information are accessible through our website at http://translationalmedicine.stanford.edu. Copyright © 2015. Published by Elsevier Inc.
    No preview · Article · May 2015 · Methods
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    ABSTRACT: An easily accessible real-time Web-based utility to assess patient risks of future emergency department (ED) visits can help the health care provider guide the allocation of resources to better manage higher-risk patient populations and thereby reduce unnecessary use of EDs. Our main objective was to develop a Health Information Exchange-based, next 6-month ED risk surveillance system in the state of Maine. Data on electronic medical record (EMR) encounters integrated by HealthInfoNet (HIN), Maine's Health Information Exchange, were used to develop the Web-based surveillance system for a population ED future 6-month risk prediction. To model, a retrospective cohort of 829,641 patients with comprehensive clinical histories from January 1 to December 31, 2012 was used for training and then tested with a prospective cohort of 875,979 patients from July 1, 2012, to June 30, 2013. The multivariate statistical analysis identified 101 variables predictive of future defined 6-month risk of ED visit: 4 age groups, history of 8 different encounter types, history of 17 primary and 8 secondary diagnoses, 8 specific chronic diseases, 28 laboratory test results, history of 3 radiographic tests, and history of 25 outpatient prescription medications. The c-statistics for the retrospective and prospective cohorts were 0.739 and 0.732 respectively. Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. Cluster analysis in both the retrospective and prospective analyses revealed discrete subpopulations of high-risk patients, grouped around multiple "anchoring" demographics and chronic conditions. With the Web-based population risk-monitoring enterprise dashboards, the effectiveness of the active case finding algorithm has been validated by clinicians and caregivers in Maine. The active case finding model and associated real-time Web-based app were designed to track the evolving nature of total population risk, in a longitudinal manner, for ED visits across all payers, all diseases, and all age groups. Therefore, providers can implement targeted care management strategies to the patient subgroups with similar patterns of clinical histories, driving the delivery of more efficient and effective health care interventions. To the best of our knowledge, this prospectively validated EMR-based, Web-based tool is the first one to allow real-time total population risk assessment for statewide ED visits.
    Full-text · Article · Jan 2015
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    ABSTRACT: Background Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. Methods and Findings A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns. Conclusions Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.
    Full-text · Article · Nov 2014 · PLoS ONE
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    ABSTRACT: Background: Central line-associated bloodstream infections is an important contributor of morbidity and mortality in children recovering from congenital heart surgery. The reliability of commonly used biomarkers to differentiate these patients has not been specifically studied. Methods: This was a retrospective cohort study in a university-affiliated children's hospital examining all patients with congenital or acquired heart disease admitted to the cardiovascular intensive care unit after cardiac surgery who underwent evaluation for a catheter-associated bloodstream infection. Results: Among 1260 cardiac surgeries performed, 451 encounters underwent an infection evaluation postoperatively. Twenty-five instances of central line-associated blood stream infections (CLABSI) and 227 instances of a negative infection evaluation were the subject of analysis. Patients with CLABSI tended to be younger (1.34 vs. 4.56 years, P=0.011) and underwent more complex surgery (RACHS-1 score 3.79 vs. 3.04, P=0.039). The 2 groups were indistinguishable in white blood cell, polymorphonuclears and band count at the time of their presentation. On multivariate analysis, CLABSI was associated with fever (adjusted odds ratio: 4.78; 95% CI: 1.6-5.8) and elevated C-reactive protein (CRP; adjusted odds ratio: 1.28; 95% CI: 1.09-1.68) after adjusting for differences between the 2 groups. Receiver-operating characteristic analysis demonstrated the discriminatory power of both fever and CRP (area under curve 0.7247, 95% CI: 0.42 to 0.74 and 0.58, 95% CI: 0.4208 to 0.7408). We calculated multilevel likelihood ratios for a spectrum of temperature and CRP values. Conclusions: We found commonly used serum biomarkers such as fever and CRP not to be helpful discriminators in patients after congenital heart surgery.
    No preview · Article · Sep 2014 · The Pediatric Infectious Disease Journal
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    ABSTRACT: Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. MACHINE LEARNING USING CLINICAL AND LABORATORY RESULTS AT THE TIME OF CLINICAL PRESENTATION LED TO TWO NEC MODELS: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.
    Full-text · Article · Feb 2014 · PLoS ONE
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    ABSTRACT: To test the hypothesis that an exploratory proteomics analysis of urine proteins with subsequent development of validated urine biomarker panels would produce molecular classifiers for both the diagnosis and prognosis of infants with necrotizing enterocolitis (NEC). Urine samples were collected from 119 premature infants (85 NEC, 17 sepsis, 17 control) at the time of initial clinical concern for disease. The urine from 59 infants was used for candidate biomarker discovery by liquid chromatography/mass spectrometry. The remaining 60 samples were subject to enzyme-linked immunosorbent assay for quantitative biomarker validation. A panel of 7 biomarkers (alpha-2-macroglobulin-like protein 1, cluster of differentiation protein 14, cystatin 3, fibrinogen alpha chain, pigment epithelium-derived factor, retinol binding protein 4, and vasolin) was identified by liquid chromatography/mass spectrometry and subsequently validated by enzyme-linked immunosorbent assay. These proteins were consistently found to be either up- or down-regulated depending on the presence, absence, or severity of disease. Biomarker panel validation resulted in a receiver-operator characteristic area under the curve of 98.2% for NEC vs sepsis and an area under the curve of 98.4% for medical NEC vs surgical NEC. We identified 7 urine proteins capable of providing highly accurate diagnostic and prognostic information for infants with suspected NEC. This work represents a novel approach to improving the efficiency with which we diagnose early NEC and identify those at risk for developing severe, or surgical, disease.
    Full-text · Article · Jan 2014 · The Journal of pediatrics
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    ABSTRACT: Preeclampsia (PE) is a pregnancy-related vascular disorder which is the leading cause of maternal morbidity and mortality. We sought to identify novel serological protein markers to diagnose PE with a multi-'omics' based discovery approach. Seven previous placental expression studies were combined for a multiplex analysis, and in parallel, two-dimensional gel electrophoresis was performed to compare serum proteomes in PE and control subjects. The combined biomarker candidates were validated with available ELISA assays using gestational age-matched PE (n=32) and control (n=32) samples. With the validated biomarkers, a genetic algorithm was then used to construct and optimize biomarker panels in PE assessment. In addition to the previously identified biomarkers, the angiogenic and antiangiogenic factors (soluble fms-like tyrosine kinase (sFlt-1) and placental growth factor (PIGF)), we found 3 up-regulated and 6 down-regulated biomakers in PE sera. Two optimal biomarker panels were developed for early and late onset PE assessment, respectively. Both early and late onset PE diagnostic panels, constructed with our PE biomarkers, were superior over sFlt-1/PIGF ratio in PE discrimination. The functional significance of these PE biomarkers and their associated pathways were analyzed which may provide new insights into the pathogenesis of PE.
    Full-text · Article · Nov 2013 · BMC Medicine
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    ABSTRACT: Necrotising enterocolitis (NEC) is a major source of neonatal morbidity and mortality. The management of infants with NEC is currently complicated by our inability to accurately identify those at risk for progression of disease prior to the development of irreversible intestinal necrosis. We hypothesised that integrated analysis of clinical parameters in combination with urine peptide biomarkers would lead to improved prognostic accuracy in the NEC population. Infants under suspicion of having NEC (n=550) were prospectively enrolled from a consortium consisting of eight university-based paediatric teaching hospitals. Twenty-seven clinical parameters were used to construct a multivariate predictor of NEC progression. Liquid chromatography/mass spectrometry was used to profile the urine peptidomes from a subset of this population (n=65) to discover novel biomarkers of NEC progression. An ensemble model for the prediction of disease progression was then created using clinical and biomarker data. The use of clinical parameters alone resulted in a receiver-operator characteristic curve with an area under the curve of 0.817 and left 40.1% of all patients in an 'indeterminate' risk group. Three validated urine peptide biomarkers (fibrinogen peptides: FGA1826, FGA1883 and FGA2659) produced a receiver-operator characteristic area under the curve of 0.856. The integration of clinical parameters with urine biomarkers in an ensemble model resulted in the correct prediction of NEC outcomes in all cases tested. Ensemble modelling combining clinical parameters with biomarker analysis dramatically improves our ability to identify the population at risk for developing progressive NEC.
    Full-text · Article · Sep 2013 · Gut
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    ABSTRACT: Although AKI is common among hospitalized children, comprehensive epidemiologic data are lacking. This study characterizes pediatric AKI across the United States and identifies AKI risk factors using high-content/high-throughput analytic techniques. For the cross-sectional analysis of the 2009 Kids Inpatient Database, AKI events were identified using International Classification of Diseases, Ninth Revision, Clinical Modification codes. Demographics, incident rates, and outcome data were analyzed and reported for the entire AKI cohort as well as AKI subsets. Statistical learning methods were applied to the highly imbalanced dataset to derive AKI-related risk factors. Of 2,644,263 children, 10,322 children developed AKI (3.9/1000 admissions). Although 19% of the AKI cohort was ≤1 month old, the highest incidence was seen in children 15-18 years old (6.6/1000 admissions); 49% of the AKI cohort was white, but AKI incidence was higher among African Americans (4.5 versus 3.8/1000 admissions). In-hospital mortality among patients with AKI was 15.3% but higher among children ≤1 month old (31.3% versus 10.1%, P<0.001) and children requiring critical care (32.8% versus 9.4%, P<0.001) or dialysis (27.1% versus 14.2%, P<0.001). Shock (odds ratio, 2.15; 95% confidence interval, 1.95 to 2.36), septicemia (odds ratio, 1.37; 95% confidence interval, 1.32 to 1.43), intubation/mechanical ventilation (odds ratio, 1.2; 95% confidence interval, 1.16 to 1.25), circulatory disease (odds ratio, 1.47; 95% confidence interval, 1.32 to 1.65), cardiac congenital anomalies (odds ratio, 1.2; 95% confidence interval, 1.13 to 1.23), and extracorporeal support (odds ratio, 2.58; 95% confidence interval, 2.04 to 3.26) were associated with AKI. AKI occurs in 3.9/1000 at-risk US pediatric hospitalizations. Mortality is highest among neonates and children requiring critical care or dialysis. Identified risk factors suggest that AKI occurs in association with systemic/multiorgan disease more commonly than primary renal disease.
    Preview · Article · Jul 2013 · Clinical Journal of the American Society of Nephrology
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    ABSTRACT: We sought to identify serological markers capable of diagnosing preeclampsia (PE). We performed serum peptide analysis (liquid chromatography mass spectrometry) of 62 unique samples from 31 PE patients and 31 healthy pregnant controls, with two-thirds used as a training set and the other third as a testing set. Differential serum peptide profiling identified 52 significant serum peptides, and a 19-peptide panel collectively discriminating PE in training sets (n = 21 PE, n = 21 control; specificity = 85.7% and sensitivity = 100%) and testing sets (n = 10 PE, n = 10 control; specificity = 80% and sensitivity = 100%). The panel peptides were derived from 6 different protein precursors: 13 from fibrinogen alpha (FGA), 1 from alpha-1-antitrypsin (A1AT), 1 from apolipoprotein L1 (APO-L1), 1 from inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), 2 from kininogen-1 (KNG1), and 1 from thymosin beta-4 (TMSB4). We concluded that serum peptides can accurately discriminate active PE. Measurement of a 19-peptide panel could be performed quickly and in a quantitative mass spectrometric platform available in clinical laboratories. This serum peptide panel quantification could provide clinical utility in predicting PE or differential diagnosis of PE from confounding chronic hypertension.
    Full-text · Article · Jun 2013 · PLoS ONE
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    ABSTRACT: Background Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). “Turnkey” solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation. Findings Here we present an efficient and effective solution, which provides experimental biologists easy access to “cloud” computing capabilities to analyze MS data. The web portal can be accessed at http://transmed.stanford.edu/ssa/. Conclusions Presented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS.
    Full-text · Article · Mar 2013 · BMC Research Notes

  • No preview · Article · Jan 2013

Publication Stats

739 Citations
175.82 Total Impact Points

Institutions

  • 2009-2015
    • Stanford University
      • • Department of Surgery
      • • Department of Pediatrics
      Stanford, California, United States
  • 2013
    • Stanford Medicine
      Stanford, California, United States