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Shaw, Stanley

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Shaw

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Stanley

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Shaw, Stanley
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Now showing 1 - 10 of 17
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    Model for end‐stage liver disease Na Score predicts incident major cardiovascular events in patients with nonalcoholic fatty liver disease
    (John Wiley and Sons Inc., 2017) Simon, Tracey; Kartoun, Uri; Zheng, Hui; Chan, Andrew; Chung, Raymond; Shaw, Stanley; Corey, Kathleen
    Cardiovascular disease (CVD) is the leading cause of mortality among adults with nonalcoholic fatty liver disease (NAFLD); however, accurate tools for identifying NAFLD patients at highest CVD risk are lacking. Using a validated algorithm, we identified a retrospective cohort of 914 NAFLD patients without known CVD. Fibrosis severity was estimated using the fibrosis‐4 index. Patients were followed for 5 years for the development of a major adverse cardiovascular event (MACE); a composite of cardiovascular death, myocardial infarction, or unstable angina; urgent coronary revascularization; or stroke. Using an adjusted Cox proportional hazard regression model, NAFLD‐specific biomarkers of CVD risk were identified. Discrimination was compared to that of the Framingham Risk Score (FRS) using the area under the receiver operating characteristic curve. Among 914 patients, the mean age was 53.4 years and 60.6% were female. Over 5 years, 288 (31.5%) experienced MACE. After adjustment for traditional cardiometabolic risk factors and underlying FIB‐4 index score, each 1‐point increase in the model for end‐stage liver disease integrating sodium (MELD‐Na) was associated with a 4.2% increased risk of MACE (hazard ratio, 1.042; 95% confidence interval, 1.009‐1.075; P = 0.011). Compared to patients in the lowest MELD‐Na quartile (<7.5), those in the highest quartile (≥13.2) had a 2.2‐fold increased risk of MACE (adjusted hazard ratio, 2.21; 95% confidence interval, 1.11‐4.40; P = 0.024; P trend = 0.004). Incorporating MELD‐Na with the FRS significantly improved discrimination of future CVD risk (combined C‐statistic 0.703 versus 0.660 for the FRS alone; P = 0.040). Conclusion:: Among patients with NAFLD, the MELD‐Na score accurately stratifies the risk for patients according to future CVD event risk. The addition of the MELD‐Na score to the FRS may further improve discrimination of NAFLD‐related CVD risk. (Hepatology Communications 2017;1:429–438)
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    Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records
    (Public Library of Science, 2013) Xia, Zongqi; Secor, Elizabeth; Chibnik, Lori; Bove, Riley; Cheng, Suchun; Chitnis, Tanuja; Cagan, Andrew; Gainer, Vivian S.; Chen, Pei J.; Liao, Katherine; Shaw, Stanley; Ananthakrishnan, Ashwin; Szolovits, Peter; Weiner, Howard; Karlson, Elizabeth; Murphy, Shawn; Savova, Guergana; Cai, Tianxi; Churchill, Susanne E.; Plenge, Robert M.; Kohane, Isaac; De Jager, Philip
    Objective: To optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings. Methods: In a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume). Results: The EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R2 = 0.38±0.05, and that between EHR-derived and true BPF has a mean R2 = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10−12). Conclusion: Incorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.
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    Disease Allele-Dependent Small-Molecule Sensitivities in Blood Cells from Monogenic Diabetes
    (Proceedings of the National Academy of Sciences, 2010) Shaw, Stanley; Blodgett, David M.; Ma, Maggie S.; Westly, Elizabeth C.; Clemons, Paul A.; Subramanian, Aravind; Schreiber, Stuart
    Even as genetic studies identify alleles that influence human disease susceptibility, it remains challenging to understand their functional significance and how they contribute to disease phenotypes. Here, we describe an approach to translate discoveries from human genetics into functional and therapeutic hypotheses by relating human genetic variation to small-molecule sensitivities. We use small-molecule probes modulating a breadth of targets and processes to reveal disease allele-dependent sensitivities, using cells from multiple individuals with an extreme form of diabetes (maturity onset diabetes of the young type 1, caused by mutation in the orphan nuclear receptor HNF4α). This approach enabled the discovery of small molecules that show mechanistically revealing and therapeutically relevant interactions with HNF4α in both lymphoblasts and pancreatic β-cells, including compounds that physically interact with HNF4α. Compounds including US Food and Drug Administration–approved drugs were identified that favorably modulate a critical disease phenotype, insulin secretion from β-cells. This method may suggest therapeutic hypotheses for other nonblood disorders.
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    Development of phenotype algorithms using electronic medical records and incorporating natural language processing
    (BMJ Publishing Group Ltd., 2015) Liao, Katherine; Cai, Tianxi; Savova, Guergana K; Murphy, Shawn; Karlson, Elizabeth; Ananthakrishnan, Ashwin; Gainer, Vivian S; Shaw, Stanley; Xia, Zongqi; Szolovits, Peter; Churchill, Susanne; Kohane, Isaac
    Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.
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    The kinase DYRK1A reciprocally regulates the differentiation of Th17 and regulatory T cells
    (eLife Sciences Publications, Ltd, 2015) Khor, Bernard; Gagnon, John D; Goel, Gautam; Roche, Marly I; Conway, Kara L.; Tran, Khoa; Aldrich, Leslie N; Sundberg, Thomas B; Paterson, Alison M; Mordecai, Scott; Dombkowski, David; Schirmer, Melanie; Tan, Pauline H; Bhan, Atul; Roychoudhuri, Rahul; Restifo, Nicholas P; O'Shea, John J; Medoff, Benjamin; Shamji, Alykhan; Schreiber, Stuart; Sharpe, Arlene; Shaw, Stanley; Xavier, Ramnik
    The balance between Th17 and T regulatory (Treg) cells critically modulates immune homeostasis, with an inadequate Treg response contributing to inflammatory disease. Using an unbiased chemical biology approach, we identified a novel role for the dual specificity tyrosine-phosphorylation-regulated kinase DYRK1A in regulating this balance. Inhibition of DYRK1A enhances Treg differentiation and impairs Th17 differentiation without affecting known pathways of Treg/Th17 differentiation. Thus, DYRK1A represents a novel mechanistic node at the branch point between commitment to either Treg or Th17 lineages. Importantly, both Treg cells generated using the DYRK1A inhibitor harmine and direct administration of harmine itself potently attenuate inflammation in multiple experimental models of systemic autoimmunity and mucosal inflammation. Our results identify DYRK1A as a physiologically relevant regulator of Treg cell differentiation and suggest a broader role for other DYRK family members in immune homeostasis. These results are discussed in the context of human diseases associated with dysregulated DYRK activity. DOI: http://dx.doi.org/10.7554/eLife.05920.001
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    Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts
    (Public Library of Science, 2015) Liao, Katherine; Ananthakrishnan, Ashwin; Kumar, Vishesh; Xia, Zongqi; Cagan, Andrew; Gainer, Vivian S.; Goryachev, Sergey; Chen, Pei; Savova, Guergana; Agniel, Denis; Churchill, Susanne; Lee, Jaeyoung; Murphy, Shawn; Plenge, Robert M.; Szolovits, Peter; Kohane, Isaac; Shaw, Stanley; Karlson, Elizabeth; Cai, Tianxi
    Background: Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations. Our objectives were: (1) to develop an algorithm that would enable the study of coronary artery disease (CAD) across diverse patient populations; (2) to study the impact of adding narrative data extracted using natural language processing (NLP) in the algorithm. Additionally, we demonstrate how to implement CAD algorithm to compare risk across 3 chronic diseases in a preliminary study. Methods and Results: We studied 3 established EMR based patient cohorts: diabetes mellitus (DM, n = 65,099), inflammatory bowel disease (IBD, n = 10,974), and rheumatoid arthritis (RA, n = 4,453) from two large academic centers. We developed a CAD algorithm using NLP in addition to structured data (e.g. ICD9 codes) in the RA cohort and validated it in the DM and IBD cohorts. The CAD algorithm using NLP in addition to structured data achieved specificity >95% with a positive predictive value (PPV) 90% in the training (RA) and validation sets (IBD and DM). The addition of NLP data improved the sensitivity for all cohorts, classifying an additional 17% of CAD subjects in IBD and 10% in DM while maintaining PPV of 90%. The algorithm classified 16,488 DM (26.1%), 457 IBD (4.2%), and 245 RA (5.0%) with CAD. In a cross-sectional analysis, CAD risk was 63% lower in RA and 68% lower in IBD compared to DM (p<0.0001) after adjusting for traditional cardiovascular risk factors. Conclusions: We developed and validated a CAD algorithm that performed well across diverse patient populations. The addition of NLP into the CAD algorithm improved the sensitivity of the algorithm, particularly in cohorts where the prevalence of CAD was low. Preliminary data suggest that CAD risk was significantly lower in RA and IBD compared to DM.
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    Use of Chronic Oral Anticoagulation and Associated Outcomes Among Patients Undergoing Percutaneous Coronary Intervention
    (John Wiley and Sons Inc., 2016) Secemsky, Eric; Butala, Neel; Kartoun, Uri; Mahmood, Sadiqa; Wasfy, Jason; Kennedy, Kevin F.; Shaw, Stanley; Yeh, Robert
    Background: Contemporary rates of oral anticoagulant (OAC) therapy and associated outcomes among patients undergoing percutaneous coronary intervention (PCI) have been poorly described. Methods and Results: Using data from an integrated health care system from 2009 to 2014, we identified patients on OACs within 30 days of PCI. Outcomes included in‐hospital bleeding and mortality. Of 9566 PCIs, 837 patients (8.8%) were on OACs, and of these, 7.9% used non–vitamin K antagonist agents. OAC use remained stable during the study (8.1% in 2009, 9.0% in 2014; P=0.11), whereas use of non–vitamin K antagonist agents in those on OACs increased (0% in 2009, 16% in 2014; P<0.01). Following PCI, OAC‐treated patients had higher crude rates of major bleeding (11% versus 6.5%; P<0.01), access‐site bleeding (2.3% versus 1.3%; P=0.017), and non–access‐site bleeding (8.2% versus 5.2%; P<0.01) but similar crude rates of in‐hospital stent thrombosis (0.4% versus 0.3%; P=0.85), myocardial infarction (2.5% versus 3.0%; P=0.40), and stroke (0.48% versus 0.52%; P=0.88). In addition, prior to adjustment, OAC‐treated patients had longer hospitalizations (3.9±5.5 versus 2.8±4.6 days; P<0.01), more transfusions (7.2% versus 4.2%; P<0.01), and higher 90‐day readmission rates (22.1% versus 13.1%; P<0.01). In adjusted models, OAC use was associated with increased risks of in‐hospital bleeding (odds ratio 1.50; P<0.01), 90‐day readmission (odds ratio 1.40; P<0.01), and long‐term mortality (hazard ratio 1.36; P<0.01). Conclusions: Chronic OAC therapy is frequent among contemporary patients undergoing PCI. After adjustment for potential confounders, OAC‐treated patients experienced greater in‐hospital bleeding, more readmissions, and decreased long‐term survival following PCI. Efforts are needed to reduce the occurrence of adverse events in this population.
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    The MELD-Plus: A generalizable prediction risk score in cirrhosis
    (Public Library of Science, 2017) Kartoun, Uri; Corey, Kathleen; Simon, Tracey; Zheng, Hui; Aggarwal, Rahul; Ng, Kenney; Shaw, Stanley
    Background and aims Accurate assessment of the risk of mortality following a cirrhosis-related admission can enable health-care providers to identify high-risk patients and modify treatment plans to decrease the risk of mortality. Methods: We developed a post-discharge mortality prediction model for patients with a cirrhosis-related admission using a population of 314,292 patients who received care either at Massachusetts General Hospital (MGH) or Brigham and Women’s Hospital (BWH) between 1992 and 2010. We extracted 68 variables from the electronic medical records (EMRs), including demographics, laboratory values, diagnosis codes, and medications. We then used a regularized logistic regression to select the most informative variables and created a risk score that comprises the selected variables. To evaluate the potential for generalizability of our score, we applied it on all cirrhosis-related admissions between 2010 and 2015 at an independent EMR data source of more than 18 million patients, pooled from different health-care systems with EMRs. We calculated the areas under the receiver operating characteristic curves (AUROCs) to assess prediction performance. Results: We identified 4,781 cirrhosis-related admissions at MGH/BWH hospitals, of which 778 resulted in death within 90 days of discharge. Nine variables were the most effective predictors for 90-day mortality, and these included all MELD-Na’s components, as well as albumin, total cholesterol, white blood cell count, age, and length of stay. Applying our nine-variable risk score (denoted as “MELD-Plus”) resulted in an improvement over MELD and MELD-Na scores in several prediction models. On the MGH/BWH 90-day model, MELD-Plus improved the performance of MELD-Na by 11.4% (0.78 [95% CI, 0.75–0.81] versus 0.70 [95% CI, 0.66–0.73]). In the MGH/BWH approximate 1-year model, MELD-Plus improved the performance of MELD-Na by 8.3% (0.78 [95% CI, 0.76–0.79] versus 0.72 [95% CI, 0.71–0.73]). Performance improvement was similar when the novel MELD-Plus risk score was applied to an independent database; when considering 24,042 cirrhosis-related admissions, MELD-Plus improved the performance of MELD-Na by 16.9% (0.69 [95% CI, 0.69–0.70] versus 0.59 [95% CI, 0.58–0.60]). Conclusions: We developed a new risk score, MELD-Plus that accurately stratifies the short-term mortality of patients with established cirrhosis, following a hospital admission. Our findings demonstrate that using a small set of easily accessible structured variables can help identify novel predictors of outcomes in cirrhosis patients and improve the performance of widely used traditional risk scores.
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    Normalization of Plasma 25-Hydroxy Vitamin D Is Associated with Reduced Risk of Surgery in Crohn’s Disease
    (Oxford University Press (OUP), 2013-08-01) Ananthakrishnan, Ashwin; Cagan, Andrew; Gainer, Vivian S.; Cai, Tianxi; Cheng, Su-Chun; Savova, Guergana; Chen, Pei; Szolovits, Peter; Xia, Zongqi; De Jager, Philip; Shaw, Stanley; Churchill, Susanne; Karlson, Elizabeth; Kohane, Isaac; Plenge, Robert; Murphy, Shawn; Liao, Katherine
    Introduction Vitamin D may have an immunological role in Crohn’s disease (CD) and ulcerative colitis (UC). Retrospective studies suggested a weak association between vitamin D status and disease activity but have significant limitations. Methods Using a multi-institution inflammatory bowel disease (IBD) cohort, we identified all CD and UC patients who had at least one measured plasma 25-hydroxy vitamin D [25(OH)D]. Plasma 25(OH)D was considered sufficient at levels ≥ 30ng/mL. Logistic regression models adjusting for potential confounders were used to identify impact of measured plasma 25(OH)D on subsequent risk of IBD-related surgery or hospitalization. In a subset of patients where multiple measures of 25(OH)D were available, we examined impact of normalization of vitamin D status on study outcomes. Results Our study included 3,217 patients (55% CD, mean age 49 yrs). The median lowest plasma 25(OH)D was 26ng/ml (IQR 17–35ng/ml). In CD, on multivariable analysis, plasma 25(OH)D < 20ng/ml was associated with an increased risk of surgery (OR 1.76 (1.24 – 2.51) and IBD-related hospitalization (OR 2.07, 95% CI 1.59 – 2.68) compared to those with 25(OH)D ≥ 30ng/ml. Similar estimates were also seen for UC. Furthermore, CD patients who had initial levels < 30ng/ml but subsequently normalized their 25(OH)D had a reduced likelihood of surgery (OR 0.56, 95% CI 0.32 – 0.98) compared to those who remained deficient. Conclusion Low plasma 25(OH)D is associated with increased risk of surgery and hospitalizations in both CD and UC and normalization of 25(OH)D status is associated with a reduction in the risk of CD-related surgery.
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    Improving Case Definition of Crohnʼs Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing
    (Oxford University Press (OUP), 2013-06) Ananthakrishnan, Ashwin; Cai, Tianxi; Savova, Guergana; Cheng, Su-Chun; Chen, Pei; Guzman, Raul; Gainer, Vivian S.; Murphy, Shawn; Szolovits, Peter; Xia, Zongqi; Shaw, Stanley; Churchill, Susanne; Karlson, Elizabeth; Kohane, Isaac; Plenge, Robert M.; Liao, Katherine
    Introduction Prior studies identifying patients with inflammatory bowel disease (IBD) utilizing administrative codes have yielded inconsistent results. Our objective was to develop a robust electronic medical record (EMR) based model for classification of IBD leveraging the combination of codified data and information from clinical text notes using natural language processing (NLP). Methods Using the EMR of 2 large academic centers, we created data marts for Crohn’s disease (CD) and ulcerative colitis (UC) comprising patients with ≥ 1 ICD-9 code for each disease. We utilized codified (i.e. ICD9 codes, electronic prescriptions) and narrative data from clinical notes to develop our classification model. Model development and validation was performed in a training set of 600 randomly selected patients for each disease with medical record review as the gold standard. Logistic regression with the adaptive LASSO penalty was used to select informative variables. Results We confirmed 399 (67%) CD cases in the CD training set and 378 (63%) UC cases in the UC training set. For both, a combined model including narrative and codified data had better accuracy (area under the curve (AUC) for CD 0.95; UC 0.94) than models utilizing only disease ICD-9 codes (AUC 0.89 for CD; 0.86 for UC). Addition of NLP narrative terms to our final model resulted in classification of 6–12% more subjects with the same accuracy. Conclusion Inclusion of narrative concepts identified using NLP improves the accuracy of EMR case-definition for CD and UC while simultaneously identifying more subjects compared to models using codified data alone.