publications | Rohit Vashisht

2024

Characterisation of digital therapeutic clinical trials: a systematic review with natural language processing

Brenda Y Miao, Madhumita Sushil, Ava Xu, and 7 more authors

Elsevier, 2024

Abs Bib HTML

Digital therapeutics (DTx) are a somewhat novel class of US Food and Drug Administration-regulated software that help patients prevent, manage, or treat disease. Here, we use natural language processing to characterise registered DTx clinical trials and provide insights into the clinical development landscape for these novel therapeutics. We identified 449 DTx clinical trials, initiated or expected to be initiated between 2010 and 2030, from ClinicalTrials.gov using 27 search terms, and available data were analysed, including trial durations, locations, MeSH categories, enrolment, and sponsor types. Topic modelling of eligibility criteria, done with BERTopic, showed that DTx trials frequently exclude patients on the basis of age, comorbidities, pregnancy, language barriers, and digital determinants of health, including smartphone or data plan access. Our comprehensive overview of the DTx development landscape
@article{rand3, title = {Characterisation of digital therapeutic clinical trials: a systematic review with natural language processing}, author = {Miao, Brenda Y and Sushil, Madhumita and Xu, Ava and Wang, Michelle and Arneson, Douglas and Berkley, Ellen and Subash, Meera and Vashisht, Rohit and Rudrapatna, Vivek and Butte, Atul J}, journal = {Elsevier}, volume = {6}, issue = {3}, pages = {e222--e229}, date = {2024-03-01}, year = {2024}, keywords = {Rohit Vashisht}, dimensions = {true}, }

2023

Real-world performance of SARS-Cov-2 serology tests in the United States, 2020

Carla V Rodriguez-Watson, Anthony M Louder, Carly Kabelac, and 24 more authors

PloS one, 2023

Abs Bib HTML

BACKGROUND: Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. METHODS: Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. RESULTS: A total of 15,615 people were observed to have at least one serology test 14-90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79-96%) compared to non-Hispanic (60-89%) patients; in those presenting with at least one COVID-19 related symptom (69-93%) as compared to no such symptoms (63-91%); and in inpatient (70-97%) and emergency department (93-99%) compared to outpatient (63-92%) settings across datasets. PPA was highest in those with diabetes (75-94%) and kidney disease (83-95%); and lowest in those with auto-immune conditions or who are immunocompromised (56-93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59-3.86), patients with diabetes (1.49-1.56), and obesity (1.63-2.23); and lower in those with immunocompromised or autoimmune conditions (0.25-0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. CONCLUSION: Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.
@article{36735683, title = {Real-world performance of SARS-Cov-2 serology tests in the United States, {2020}}, author = {Rodriguez-Watson, Carla V and Louder, Anthony M and Kabelac, Carly and Frederick, Christopher M and Sheils, Natalie E and Eldridge, Elizabeth H and Lin, Nancy D and Pollock, Benjamin D and Gatz, Jennifer L and Grannis, Shaun J and Vashisht, Rohit and Ghauri, Kanwal and Knepper, Camille and Leonard, Sandy and Embi, Peter J and Jenkinson, Garrett and Klesh, Reyna and Garner, Omai B and Patel, Ayan and Dahm, Lisa and Barin, Aiden and Cooper, Dan M and Andriola, Tom and Byington, Carrie L and Crews, Bridgit O and Butte, Atul J and Allen, Jeff}, journal = {PloS one}, publisher = {Public Library of Science}, volume = {18}, issue = {2}, pages = {e0279956}, date = {2023-02-03}, year = {2023}, doi = {10.1371/journal.pone.0279956}, pmc = {PMC9897562}, pmid = {36735683}, issn = {1932-6203}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }
Real-world utilization of SARS-CoV-2 serological testing in RNA positive patients across the United States

Carla V Rodriguez-Watson, Natalie E Sheils, Anthony M Louder, and 29 more authors

PloS one, 2023

Abs Bib HTML

BACKGROUND: As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. METHODS: Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. RESULTS: Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred 30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. CONCLUSION: Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.
@article{36763574, title = {Real-world utilization of SARS-CoV-2 serological testing in RNA positive patients across the United States}, author = {Rodriguez-Watson, Carla V and Sheils, Natalie E and Louder, Anthony M and Eldridge, Elizabeth H and Lin, Nancy D and Pollock, Benjamin D and Gatz, Jennifer L and Grannis, Shaun J and Vashisht, Rohit and Ghauri, Kanwal and Valo, Gina and Chakravarty, Aloka G and Lasky, Tamar and Jung, Mary and Lovell, Stephen L and Major, Jacqueline M and Kabelac, Carly and Knepper, Camille and Leonard, Sandy and Embi, Peter J and Jenkinson, William G and Klesh, Reyna and Garner, Omai B and Patel, Ayan and Dahm, Lisa and Barin, Aiden and Cooper, Dan M and Andriola, Tom and Byington, Carrie L and Crews, Bridgit O and Butte, Atul J and Allen, Jeff}, journal = {PloS one}, publisher = {Public Library of Science}, volume = {18}, issue = {2}, pages = {e0281365}, date = {2023-02-10}, year = {2023}, doi = {10.1371/journal.pone.0281365}, pmc = {PMC9916659}, pmid = {36763574}, issn = {1932-6203}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }
Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

Erica A Voss, Azza Shoaibi, Lana Yin Hui Lai, and 61 more authors

EClinicalMedicine, 2023

Abs Bib HTML

Background: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population. Methods: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study’s evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell’s palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases. Findings: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism. Interpretation: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term. Funding: None.
@article{37034358, title = {Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study}, author = {Voss, Erica A and Shoaibi, Azza and Yin Hui Lai, Lana and Blacketer, Clair and Alshammari, Thamir and Makadia, Rupa and Haynes, Kevin and Sena, Anthony G and Rao, Gowtham and van Sandijk, Sebastiaan and Fraboulet, Clement and Boyer, Laurent and Le Carrour, Tanguy and Horban, Scott and Morales, Daniel R and Martínez Roldán, Jordi and Ramírez-Anguita, Juan Manuel and Mayer, Miguel A and de Wilde, Marcel and John, Luis H and Duarte-Salles, Talita and Roel, Elena and Pistillo, Andrea and Kolde, Raivo and Maljković, Filip and Denaxas, Spiros and Papez, Vaclav and Kahn, Michael G and Natarajan, Karthik and Reich, Christian and Secora, Alex and Minty, Evan P and Shah, Nigam H and Posada, Jose D and Garcia Morales, Maria Teresa and Bosca, Diego and Cadenas Juanino, Honorio and Diaz Holgado, Antonio and Pedrera Jiménez, Miguel and Serrano Balazote, Pablo and García Barrio, Noelia and Şen, Selçuk and Üresin, Ali Yağız and Erdogan, Baris and Belmans, Luc and Byttebier, Geert and Malbrain, Manu L N G and Dedman, Daniel J and Cuccu, Zara and Vashisht, Rohit and Butte, Atul J and Patel, Ayan and Dahm, Lisa and Han, Cora and Bu, Fan and Arshad, Faaizah and Ostropolets, Anna and Nyberg, Fredrik and Hripcsak, George and Suchard, Marc A and Prieto-Alhambra, Dani and Rijnbeek, Peter R and Schuemie, Martijn J and Ryan, Patrick B}, journal = {EClinicalMedicine}, publisher = {Elsevier BV}, volume = {58}, issue = {101932}, pages = {101932}, date = {2023-04}, year = {2023}, doi = {10.1016/j.eclinm.2023.101932}, pmc = {PMC10072853}, pmid = {37034358}, issn = {2589-5370}, keywords = {Adverse events of special interest; COVID-19; OMOP CDM; Observational research;Rohit Vashisht}, language = {en}, dimensions = {true}, }
Second-line pharmaceutical treatments for patients with type 2 diabetes

Rohit Vashisht, Ayan Patel, Lisa Dahm, and 6 more authors

JAMA network open, 2023

Abs Bib HTML

Key Points Question Can the comparative effectiveness and safety associated with second-line pharmaceutical interventions in type 2 diabetes be assessed using clinical data across a multicenter health care system? Findings This cohort study including 31 852 patients with diabetes monitored for 5 years and using clinical data analysis found that treatment with either a glucagon-like peptide-1 receptor agonist, sodium-glucose cotransporter-2 inhibitor, or dipeptidyl peptidase-4 inhibitor added to metformin monotherapy was effective compared with a sulfonylurea in maintaining glycemic control, with glucagon-like peptide-1 receptor agonist being more effective than dipeptidyl peptidase-4 inhibitor. These add-on treatments were associated with fewer diabetes-related cardiovascular and renal complications compared with a sulfonylurea and were safe, avoiding hypoglycemia. Meaning These findings suggest that clinical data across a multisite health care system could be used to assess the comparative effectiveness and safety associated with treatments in diabetes and could help guide medical decisions.
@article{Vashisht2023-jo, title = {Second-line pharmaceutical treatments for patients with type 2 diabetes}, author = {Vashisht, Rohit and Patel, Ayan and Dahm, Lisa and Han, Cora and Medders, K and Mowers, Robert and Byington, Carrie L and Koliwad, S and Butte, A}, journal = {JAMA network open}, publisher = {American Medical Association}, volume = {6}, issue = {10}, pages = {e2336613--e2336613}, date = {2023-10-01}, year = {2023}, doi = {10.1001/jamanetworkopen.2023.36613}, pmid = {37782497}, issn = {2574-3805}, keywords = {Rohit Vashisht}, dimensions = {true}, }

2022

Real-world utilization of SARS-COV-2 serological testing in RNA positive patients across the United States

Carla V Rodriguez-Watson, Natalie E Sheils, Anthony Louder, and 21 more authors

WILEY, 2022

Bib HTML

@article{seroOne,
  title = {Real-world utilization of SARS-COV-2 serological testing in RNA
                 positive patients across the United States},
  author = {Rodriguez-Watson, Carla V and Sheils, Natalie E and Louder, Anthony and Eldridge, Elizabeth H and Lin, Nancy D and Pollock, Benjamin and Gatz, Jennifer and Vashisht, Rohit and Ghauri, Kanwal and Valo, Gina A and Chakravarty, Aloka and Lasky, Tamar and Jung, Mary and Kabelac, Carly and Knepper, Camille and Leonard, Sandy and Embi, Peter J and Jenkinson, Garrett and Patel, Ayan and Dahm, Lisa and Byington, Carrie L and Crews, Bridgit and Butte, Atul J and Allen, Jeff},
  journal = {WILEY},
  volume = {31},
  pages = {435--436},
  date = {2022-09-01},
  year = {2022},
  keywords = {Rohit Vashisht},
  dimensions = {true},
}

From genes to geography, from cells to community, from biomolecules to behaviors: The importance of social determinants of health

Jaysón Davidson, Rohit Vashisht, and Atul J Butte

Biomolecules, 2022

Abs Bib HTML

Much scientific work over the past few decades has linked health outcomes and disease risk to genomics, to derive a better understanding of disease mechanisms at the genetic and molecular level. However, genomics alone does not quite capture the full picture of one’s overall health. Modern computational biomedical research is moving in the direction of including social/environmental factors that ultimately affect quality of life and health outcomes at both the population and individual level. The future of studying disease now lies at the hands of the social determinants of health (SDOH) to answer pressing clinical questions and address healthcare disparities across population groups through its integration into electronic health records (EHRs). In this perspective article, we argue that the SDOH are the future of disease risk and health outcomes studies due to their vast coverage of a patient’s overall health. SDOH data availability in EHRs has improved tremendously over the years with EHR toolkits, diagnosis codes, wearable devices, and census tract information to study disease risk. We discuss the availability of SDOH data, challenges in SDOH implementation, its future in real-world evidence studies, and the next steps to report study outcomes in an equitable and actionable way.
@article{36291658, title = {From genes to geography, from cells to community, from biomolecules to behaviors: The importance of social determinants of health}, author = {Davidson, Jaysón and Vashisht, Rohit and Butte, Atul J}, journal = {Biomolecules}, publisher = {MDPI AG}, volume = {12}, issue = {10}, pages = {1449}, date = {2022-10-09}, year = {2022}, doi = {10.3390/biom12101449}, pmc = {PMC9599320}, pmid = {36291658}, issn = {2218-273X}, urldate = {2024-09-22}, keywords = {census tract; data science; electronic health records; real-world evidence; social determinants of health;Rohit Vashisht}, language = {en}, dimensions = {true}, }
Toward a causal model of chronic back pain: Challenges and opportunities

J Russell Huie, Rohit Vashisht, Anoop Galivanche, and 5 more authors

Frontiers in computational neuroscience, 2022

Abs Bib HTML

Chronic low back pain (cLBP) afflicts 8. 2% of adults in the United States, and is the leading global cause of disability. Neuropsychiatric co-morbidities including anxiety, depression, and substance abuse- are common in cLBP patients. In particular, cLBP is a risk factor for opioid addiction, as more than 50% of opioid prescriptions in the United States are for cLBP. Misuse of these prescriptions is a common precursor to addiction. While associations between cLBP and neuropsychiatric disorders are well established, causal relationships for the most part are unknown. Developing effective treatments for cLBP, and associated co-morbidities, requires identifying and understanding causal relationships. Rigorous methods for causal inference, a process for quantifying causal effects from observational data, have been developed over the past 30 years. In this review we first discuss the conceptual model of cLBP that current treatments are based on, and how gaps in causal knowledge contribute to poor clinical outcomes. We then present cLBP as a "Big Data" problem and identify how advanced analytic techniques may close knowledge gaps and improve clinical outcomes. We will focus on causal discovery, which is a data-driven method that uses artificial intelligence (AI) and high dimensional datasets to identify causal structures, discussing both constraint-based (PC and Fast Causal Inference) and score-based (Fast Greedy Equivalent Search) algorithms.
@article{36714527, title = {Toward a causal model of chronic back pain: Challenges and opportunities}, author = {Huie, J Russell and Vashisht, Rohit and Galivanche, Anoop and Hadjadj, Constance and Morshed, Saam and Butte, Atul J and Ferguson, Adam R and O'Neill, Conor}, journal = {Frontiers in computational neuroscience}, publisher = {Frontiers Media SA}, volume = {16}, pages = {1017412}, date = {2022}, year = {2022}, doi = {10.3389/fncom.2022.1017412}, pmc = {PMC9874096}, pmid = {36714527}, issn = {1662-5188}, keywords = {back pain; causal (structural) model; clinical trials; data science; pain;Rohit Vashisht}, language = {en}, dimensions = {true}, }

2021

Identification of antiviral antihistamines for COVID-19 repurposing

Leah R Reznikov, Michael H Norris, Rohit Vashisht, and 6 more authors

Biochemical and biophysical research communications, 2021

Abs Bib HTML

There is an urgent need to identify therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. Although repurposed drugs with favorable safety profiles could have significant benefit, widely available prevention or treatment options for COVID-19 have yet to be identified. Efforts to identify approved drugs with in vitro activity against SARS-CoV-2 resulted in identification of antiviral sigma-1 receptor ligands, including antihistamines in the histamine-1 receptor binding class. We identified antihistamine candidates for repurposing by mining electronic health records of usage in population of more than 219,000 subjects tested for SARS-CoV-2. Usage of diphenhydramine, hydroxyzine and azelastine was associated with reduced incidence of SARS-CoV-2 positivity in subjects greater than age 61. We found diphenhydramine, hydroxyzine and azelastine to exhibit direct antiviral activity against SARS-CoV-2 in vitro. Although mechanisms by which specific antihistamines exert antiviral effects is not clear, hydroxyzine, and possibly azelastine, bind Angiotensin Converting Enzyme-2 (ACE2) and the sigma-1 receptor as off-targets. Clinical studies are needed to measure the effectiveness of diphenhydramine, hydroxyzine and azelastine for disease prevention, for early intervention, or as adjuvant therapy for severe COVID-19.
@article{33309272, title = {Identification of antiviral antihistamines for COVID-19 repurposing}, author = {Reznikov, Leah R and Norris, Michael H and Vashisht, Rohit and Bluhm, Andrew P and Li, Danmeng and Liao, Yan-Shin J and Brown, Ashley and Butte, Atul J and Ostrov, David A}, journal = {Biochemical and biophysical research communications}, publisher = {Elsevier BV}, volume = {538}, pages = {173--179}, date = {2021-01-29}, year = {2021}, doi = {10.1016/j.bbrc.2020.11.095}, pmc = {PMC7713548}, pmid = {33309272}, issn = {0006-291X,1090-2104}, keywords = {Angiotensin converting Enzyme-2; Docking; Repurposing; SARS-CoV-2; Sigma-1 receptor;Rohit Vashisht}, language = {en}, dimensions = {true}, }

Age- and sex-associated variations in the sensitivity of serological tests among individuals infected with SARS-CoV-2

R Vashisht, Ayan Patel, B Crews, and 4 more authors

JAMA network open, 2021

Abs Bib HTML

@article{Vashisht2021-pl,
  title = {Age- and sex-associated variations in the sensitivity of
                    serological tests among individuals infected with SARS-CoV-2},
  author = {Vashisht, R and Patel, Ayan and Crews, B and Garner, O and Dahm, Lisa and Wilson, Charles M and Butte, A},
  journal = {JAMA network open},
  publisher = {American Medical Association},
  volume = {4},
  issue = {2},
  pages = {e210337--e210337},
  date = {2021-02-01},
  year = {2021},
  doi = {10.1001/jamanetworkopen.2021.0337},
  pmid = {33576815},
  issn = {2574-3805},
  keywords = {Rohit Vashisht},
  dimensions = {true},
}

Androgen deprivation therapy and risk of SARS-CoV-2 infection in men with prostate cancer: A University of California (UC) Health System registry study

Daniel Kwon, Rohit Vashisht, Hala Borno, and 4 more authors

Journal of clinical oncology: official journal of the American Society of Clinical Oncology, 2021

Abs Bib HTML

37 Background: SARS-CoV-2 entry into host cells is facilitated by the transmembrane protease TMPRSS2. TMPRSS2 expression can be modulated by the androgen receptor. It is unclear whether androgen deprivation therapy (ADT) may partially protect from SARS-CoV-2 infection. Methods: A retrospective registry study of adult men with prostate cancer who underwent testing for SARS-CoV-2 in the UC Health System between February 1, 2020 and October 6, 2020 was performed. The University of California Health COVID Research Data Set (UC CORDS), which includes electronic health data of all patients who underwent testing for SARS-CoV-2 at 5 UC academic medical centers (UC Davis, UC Irvine, UC Los Angeles, UC San Diego, and UC San Francisco) and 12 affiliated hospitals across California, was used. Association of SARS-CoV-2 infection and receipt of ADT (GnRH agonist or antagonist) within 90 days of COVID testing was determined using the Chi-Squared test. Analyses (Chi-Squared or Fisher’s exact tests) were also performed in race/ethnicity subgroups. Results: Overall, 2,948 men with prostate cancer who underwent SARS-CoV-2 testing were identified, of whom 59 (2.0%) tested positive. Of the 2,948 men, 2,124 (72%) were White; 219 (7%) Black or African-American; 182 (6%) Asian or Native Hawaiian/Pacific-Islander; 176 (6%) Other race; and 247 (8%) Unknown race. There were 235 (8%) Hispanic or Latino men. Among the 444 men who received ADT in the entire cohort, 7 (1.6%) tested positive, and among the 2,504 men who did not receive ADT, 52 (2.1%) tested positive (OR 0.76, 95% CI 0.34-1.67, P = 0.49). No statistically significant association between ADT and SARS-CoV-2 positivity was found within race or ethnicity subgroups. Conclusions: No association between the use of ADT and the risk of testing positive for SARS-CoV-2 was identified in this study of a diverse patient population in the University of California Health System medical centers and hospitals. In this setting of an overall low prevalence of SARS-CoV-2 infection, thus far, there is no strong evidence of a protective benefit of ADT.
@article{3999, title = {Androgen deprivation therapy and risk of SARS-CoV-2 infection in men with prostate cancer: A University of California (UC) Health System registry study}, author = {Kwon, Daniel and Vashisht, Rohit and Borno, Hala and Aggarwal, Rahul Raj and Small, Eric Jay and Butte, Atul and Huang, Franklin W}, journal = {Journal of clinical oncology: official journal of the American Society of Clinical Oncology}, publisher = {American Society of Clinical Oncology (ASCO)}, volume = {39}, issue = {6\_suppl}, pages = {37--37}, date = {2021-02-20}, year = {2021}, doi = {10.1200/jco.2021.39.6_suppl.37}, issn = {0732-183X,1527-7755}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }

Androgen-deprivation therapy and SARS-CoV-2 in men with prostate cancer: findings from the University of California Health System registry

D H Kwon, R Vashisht, H T Borno, and 4 more authors

Annals of oncology, 2021

Bib HTML

@article{33571636,
  title = {Androgen-deprivation therapy and SARS-CoV-2 in men with
                    prostate cancer: findings from the University of California
                    Health System registry},
  author = {Kwon, D H and Vashisht, R and Borno, H T and Aggarwal, R R and Small, E J and Butte, A J and Huang, F W},
  journal = {Annals of oncology},
  publisher = {Elsevier BV},
  volume = {32},
  issue = {5},
  pages = {678--679},
  date = {2021-05},
  year = {2021},
  doi = {10.1016/j.annonc.2021.01.067},
  pmc = {PMC7870099},
  pmid = {33571636},
  issn = {0923-7534,1569-8041},
  keywords = {Rohit Vashisht},
  language = {en},
  dimensions = {true},
}

2020

Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis

Kim Yeesuk, Tian Yuxi, Yang Jianxiao, and 17 more authors

Scientific Reports (Nature Publisher Group), 2020

Bib HTML

@article{ohdd,
  title = {Comparative safety and effectiveness of alendronate versus
                    raloxifene in women with osteoporosis},
  author = {Yeesuk, Kim and Yuxi, Tian and Jianxiao, Yang and Vojtech, Huser and Peng, Jin and Lambert, Christophe G and Hojun, Park and Chan, You Seng and Park, Rae Woong and Rijnbeek, Peter R and Reich, Christian and Rohit, Vashisht and Wu, Yonghui and Duke, Jon and Hripcsak, George and Madigan, David and Shah, Nigam H and Ryan, Patrick B and Schuemie, Martijn J and Suchard, Marc A},
  journal = {Scientific Reports (Nature Publisher Group)},
  publisher = {Nature Publishing Group},
  volume = {10},
  issue = {1},
  date = {2020},
  year = {2020},
  keywords = {Rohit Vashisht},
  dimensions = {true},
}

Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network

Qiong Wang, Jenna M Reps, Kristin Feeney Kostka, and 20 more authors

PloS one, 2020

Abs Bib HTML

BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient’s risk of HT within 30 days of initial ischemic stroke. METHODS: We utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia. RESULTS: In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78. CONCLUSIONS: A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.
@article{31910437, title = {Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network}, author = {Wang, Qiong and Reps, Jenna M and Kostka, Kristin Feeney and Ryan, Patrick B and Zou, Yuhui and Voss, Erica A and Rijnbeek, Peter R and Chen, Ruijun and Rao, Gowtham A and Morgan Stewart, Henry and Williams, Andrew E and Williams, Ross D and Van Zandt, Mui and Falconer, Thomas and Fernandez-Chas, Margarita and Vashisht, Rohit and Pfohl, Stephen R and Shah, Nigam H and Kasthurirathne, Suranga N and You, Seng Chan and Jiang, Qing and Reich, Christian and Zhou, Yi}, journal = {PloS one}, publisher = {Public Library of Science (PLoS)}, volume = {15}, issue = {1}, pages = {e0226718}, date = {2020-01-07}, year = {2020}, doi = {10.1371/journal.pone.0226718}, pmc = {PMC6946584}, pmid = {31910437}, issn = {1932-6203}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }
Treatment patterns for chronic comorbid conditions in patients with cancer using a large-scale observational data network

Ruijun Chen, Patrick Ryan, Karthik Natarajan, and 7 more authors

JCO clinical cancer informatics, 2020

Abs Bib HTML

PURPOSE: Patients with cancer are predisposed to developing chronic, comorbid conditions that affect prognosis, quality of life, and mortality. While treatment guidelines and care variations for these comorbidities have been described for the general noncancer population, less is known about real-world treatment patterns in patients with cancer. We sought to characterize the prevalence and distribution of initial treatment patterns across a large-scale data network for depression, hypertension, and type II diabetes mellitus (T2DM) among patients with cancer. METHODS: We used the Observational Health Data Sciences and Informatics network, an international collaborative implementing the Observational Medical Outcomes Partnership Common Data Model to standardize more than 2 billion patient records. For this study, we used 8 databases across 3 countries-the United States, France, and Germany-with 295,529,655 patient records. We identified patients with cancer using SNOMED (Systematized Nomenclature of Medicine) codes validated via manual review. We then characterized the treatment patterns of these patients initiating treatment of depression, hypertension, or T2DM with persistent treatment and at least 365 days of observation. RESULTS: Across databases, wide variations exist in treatment patterns for depression (n = 1,145,510), hypertension (n = 3,178,944), and T2DM (n = 886,766). When limited to 6-node (6-drug) sequences, we identified 61,052 unique sequences for depression, 346,067 sequences for hypertension, and 40,629 sequences for T2DM. These variations persisted across sites, databases, countries, and conditions, with the exception of metformin (73.8%) being the most common initial T2DM treatment. The most common initial medications were sertraline (17.5%) and escitalopram (17.5%) for depression and hydrochlorothiazide (20.5%) and lisinopril (19.6%) for hypertension. CONCLUSION: We identified wide variations in the treatment of common comorbidities in patients with cancer, similar to the general population, and demonstrate the feasibility of conducting research on patients with cancer across a large-scale observational data network using a common data model.
@article{32134687, title = {Treatment patterns for chronic comorbid conditions in patients with cancer using a large-scale observational data network}, author = {Chen, Ruijun and Ryan, Patrick and Natarajan, Karthik and Falconer, Thomas and Crew, Katherine D and Reich, Christian G and Vashisht, Rohit and Randhawa, Gurvaneet and Shah, Nigam H and Hripcsak, George}, journal = {JCO clinical cancer informatics}, publisher = {American Society of Clinical Oncology (ASCO)}, volume = {4}, issue = {4}, pages = {171--183}, date = {2020-03}, year = {2020}, doi = {10.1200/CCI.19.00107}, pmc = {PMC7113074}, pmid = {32134687}, issn = {2473-4276}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }
Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis

Yeesuk Kim, Yuxi Tian, Jianxiao Yang, and 18 more authors

Scientific Reports, 2020

Abs Bib HTML

Alendronate and raloxifene are among the most popular anti-osteoporosis medications. However, there is a lack of head-to-head comparative effectiveness studies comparing the two treatments. We conducted a retrospective large-scale multicenter study encompassing over 300 million patients across nine databases encoded in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The primary outcome was the incidence of osteoporotic hip fracture, while secondary outcomes were vertebral fracture, atypical femoral fracture (AFF), osteonecrosis of the jaw (ONJ), and esophageal cancer. We used propensity score trimming and stratification based on an expansive propensity score model with all pre-treatment patient characteritistcs. We accounted for unmeasured confounding using negative control outcomes to estimate and adjust for residual systematic bias in each data source
@article{11115, title = {Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis}, author = {Kim, Yeesuk and Tian, Yuxi and Yang, Jianxiao and Huser, Vojtech and Jin, Peng and Lambert, Christophe G and Park, Hojun and You, Seng Chan and Park, Rae Woong and Rijnbeek, Peter R and Van Zandt, Mui and Reich, Christian and Vashisht, Rohit and Wu, Yonghui and Duke, Jon and Hripcsak, George and Madigan, David and Shah, Nigam H and Ryan, Patrick B and Schuemie, Martijn J and Suchard, Marc A}, journal = {Scientific Reports}, publisher = {Nature Publishing Group UK}, volume = {10}, issue = {1}, pages = {11115}, date = {2020-07-06}, year = {2020}, keywords = {Rohit Vashisht}, dimensions = {true}, }
CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties

Douglas Arneson, Matthew Elliott, Arman Mosenia, and 7 more authors

Scientific data, 2020

Abs Bib HTML

Management of the COVID-19 pandemic has proven to be a significant challenge to policy makers. This is in large part due to uneven reporting and the absence of open-access visualization tools to present local trends and infer healthcare needs. Here we report the development of CovidCounties.org, an interactive web application that depicts daily disease trends at the level of US counties using time series plots and maps. This application is accompanied by a manually curated dataset that catalogs all major public policy actions made at the state-level, as well as technical validation of the primary data. Finally, the underlying code for the site is also provided as open source, enabling others to validate and learn from this work.
@article{33199721, title = {CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties}, author = {Arneson, Douglas and Elliott, Matthew and Mosenia, Arman and Oskotsky, Boris and Solodar, Samuel and Vashisht, Rohit and Zack, Travis and Bleicher, Paul and Butte, Atul J and Rudrapatna, Vivek A}, journal = {Scientific data}, publisher = {Springer Science and Business Media LLC}, volume = {7}, issue = {1}, pages = {405}, date = {2020-11-16}, year = {2020}, doi = {10.1038/s41597-020-00731-8}, pmc = {PMC7669883}, pmid = {33199721}, issn = {2052-4463}, urldate = {2024-09-22}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }

2019

Impact of integrated psycho-socio-economic support on treatment outcome in drug resistant tuberculosis - A retrospective cohort study

Rama Bhatt, Kamal Chopra, and Rohit Vashisht

The Indian journal of tuberculosis, 2019

Abs Bib HTML

OBJECTIVE: To assess the impact of providing integrated psycho-socio-economic support to drug resistant tuberculosis (DRTB) patients on the treatment outcome under programmatic conditions. STUDY DESIGN: Retrospective cohort study. SETTING: An urban district TB centre in India under the Revised National Tuberculosis Control Programme. PARTICIPANTS: A cohort of 123 patients who started DRTB treatment between June 2010 and May 2013. METHODS: Patients started on treatment for DRTB between June 2010 and May 2013 who were provided with the integrated support package for at least 3 months formed the supported group while the other patients of the cohort formed the non-supported group. The treatment outcomes and sputum culture conversion rates were compared between the two groups. RESULTS: The supported group consisted of 60 patients and the non-supported group of 63 patients. The treatment success rate was found to be significantly higher in the supported group (65% vs 46.03%; p=0.0349). Support duration was significantly associated with lower incidence of death [HR 0.876, 95% CI 0.811-0.947; p=0.0009] and loss to follow up [OR: 0.752, 95% CI 0.597-0.873; p=0.0023]. The treatment failure rate was higher in the supported group (16.66% vs 4.76%) with 60% of the failures in the supported group occurring after 24 months of compliant treatment. There was no significant association found between support duration and treatment failure or sputum culture conversion. CONCLUSION: Integrated support seems to significantly increase the treatment success rate and improve survival and treatment adherence of DRTB patients. However, early diagnosis and effective pharmacotherapy are crucial for reducing treatment failures.
@article{30797265, title = {Impact of integrated psycho-socio-economic support on treatment outcome in drug resistant tuberculosis - A retrospective cohort study}, author = {Bhatt, Rama and Chopra, Kamal and Vashisht, Rohit}, journal = {The Indian journal of tuberculosis}, publisher = {Elsevier BV}, volume = {66}, issue = {1}, pages = {105--110}, date = {2019-01}, year = {2019}, doi = {10.1016/j.ijtb.2018.05.020}, pmid = {30797265}, issn = {0019-5707,2589-1278}, keywords = {Drug resistant TB; Psycho-socio-economic support; RNTCP; TB; Tuberculosis;Rohit Vashisht}, language = {en}, dimensions = {true}, }
Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures

Hayley Warsinske, Rohit Vashisht, and Purvesh Khatri

PLoS medicine, 2019

Abs Bib HTML

BACKGROUND: The World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been implemented in clinical settings. The focus of this study is to directly compare published gene signatures for diagnosis of patients with ATB across a large, diverse list of publicly available gene expression datasets, and evaluate their performance against the WHO/FIND TPPs. METHODS AND FINDINGS: We searched PubMed, Gene Expression Omnibus (GEO), and ArrayExpress in June 2018. We included all studies irrespective of study design and enrollment criteria. We found 16 gene signatures for the diagnosis of ATB compared to other clinical conditions in PubMed. For each signature, we implemented a classification model as described in the corresponding original publication of the signature. We identified 24 datasets containing 3,083 transcriptome profiles from whole blood or peripheral blood mononuclear cell samples of healthy controls or patients with ATB, LTBI, or other diseases from 14 countries in GEO. Using these datasets, we calculated weighted mean area under the receiver operating characteristic curve (AUROC), specificity at 90% sensitivity, and negative predictive value (NPV) for each gene signature across all datasets. We also compared the diagnostic odds ratio (DOR), heterogeneity in DOR, and false positive rate (FPR) for each signature using bivariate meta-analysis. Across 9 datasets of patients with culture-confirmed diagnosis of ATB, 11 signatures had weighted mean AUROC > 0.8, and 2 signatures had weighted mean AUROC ≤ 0.6. All but 2 signatures had high NPV (>98% at 2% prevalence). Two gene signatures achieved the minimal WHO TPP for a non-sputum-based triage test. When including datasets with clinical diagnosis of ATB, there was minimal reduction in the weighted mean AUROC and specificity of all but 3 signatures compared to when using only culture-confirmed ATB data. Only 4 signatures had homogeneous DOR and lower FPR when datasets with clinical diagnosis of ATB were included; other signatures either had heterogeneous DOR or higher FPR or both. Finally, 7 of 16 gene signatures predicted progression from LTBI to ATB 6 months prior to sputum conversion with positive predictive value > 6% at 2% prevalence. Our analyses may have under- or overestimated the performance of certain ATB diagnostic signatures because our implementation may be different from the published models for those signatures. We re-implemented published models because the exact models were not publicly available. CONCLUSIONS: We found that host-response-based diagnostics could accurately identify patients with ATB and predict individuals with high risk of progression from LTBI to ATB prior to sputum conversion. We found that a higher number of genes in a signature did not increase the accuracy of the signature. Overall, the Sweeney3 signature performed robustly across all comparisons. Our results provide strong evidence for the potential of host-response-based diagnostics in achieving the WHO goal of ending tuberculosis by 2035, and host-response-based diagnostics should be pursued for clinical implementation.
@article{31013272, title = {Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures}, author = {Warsinske, Hayley and Vashisht, Rohit and Khatri, Purvesh}, journal = {PLoS medicine}, publisher = {Public Library of Science (PLoS)}, volume = {16}, issue = {4}, pages = {e1002786}, date = {2019-04}, year = {2019}, doi = {10.1371/journal.pmed.1002786}, pmc = {PMC6478271}, pmid = {31013272}, issn = {1549-1277,1549-1676}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }

2018

Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis

Yuxi Tian, Yeesuk Kim, Jianxiao Yang, and 18 more authors

WILEY, 2018

Bib HTML

@article{Tian2018-wi,
  title = {Comparative safety and effectiveness of alendronate versus
                 raloxifene in women with osteoporosis},
  author = {Tian, Yuxi and Kim, Yeesuk and Yang, Jianxiao and Huser, Vojtech and Jin, Peng and Lambert, Christophe and Park, Hojun and Park, Rae Woong and Rijnbeek, Peter and Van Zandt, Mui and Vashisht, Rohit and Wu, Yonghui and You, Seng Chan and Duke, Jon and Hripcsak, George and Madigan, David and Reich, Christian and Shah, Nigam and Ryan, Patrick and Schuemie, Martijn and Suchard, Marc},
  journal = {WILEY},
  volume = {27},
  pages = {184--184},
  date = {2018-08-01},
  year = {2018},
  keywords = {Rohit Vashisht},
  dimensions = {true},
}

Association of hemoglobin A1c levels with use of sulfonylureas, dipeptidyl peptidase 4 inhibitors, and thiazolidinediones in patients with type 2 diabetes treated with metformin: Analysis from the Observational Health Data Sciences and Informatics initiative

Rohit Vashisht, Kenneth Jung, Alejandro Schuler, and 20 more authors

JAMA network open, 2018

Abs Bib HTML

Importance: Consensus around an efficient second-line treatment option for type 2 diabetes (T2D) remains ambiguous. The availability of electronic medical records and insurance claims data, which capture routine medical practice, accessed via the Observational Health Data Sciences and Informatics network presents an opportunity to generate evidence for the effectiveness of second-line treatments. Objective: To identify which drug classes among sulfonylureas, dipeptidyl peptidase 4 (DPP-4) inhibitors, and thiazolidinediones are associated with reduced hemoglobin A1c (HbA1c) levels and lower risk of myocardial infarction, kidney disorders, and eye disorders in patients with T2D treated with metformin as a first-line therapy. Design, Setting, and Participants: Three retrospective, propensity-matched, new-user cohort studies with replication across 8 sites were performed from 1975 to 2017. Medical data of 246 558 805 patients from multiple countries from the Observational Health Data Sciences and Informatics (OHDSI) initiative were included and medical data sets were transformed into a unified common data model, with analysis done using open-source analytical tools. Participants included patients with T2D receiving metformin with at least 1 prior HbA1c laboratory test who were then prescribed either sulfonylureas, DPP-4 inhibitors, or thiazolidinediones. Data analysis was conducted from 2015 to 2018. Exposures: Treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones starting at least 90 days after the initial prescription of metformin. Main Outcomes and Measures: The primary outcome is the first observation of the reduction of HbA1c level to 7% of total hemoglobin or less after prescription of a second-line drug. Secondary outcomes are myocardial infarction, kidney disorder, and eye disorder after prescription of a second-line drug. Results: A total of 246 558 805 patients (126 977 785 women [51.5%]) were analyzed. Effectiveness of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones prescribed after metformin to lower HbA1c level to 7% or less of total hemoglobin remained indistinguishable in patients with T2D. Patients treated with sulfonylureas compared with DPP-4 inhibitors had a small increased consensus hazard ratio of myocardial infarction (1.12; 95% CI, 1.02-1.24) and eye disorders (1.15; 95% CI, 1.11-1.19) in the meta-analysis. Hazard of observing kidney disorders after treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones was equally likely. Conclusions and Relevance: The examined drug classes did not differ in lowering HbA1c and in hazards of kidney disorders in patients with T2D treated with metformin as a first-line therapy. Sulfonylureas had a small, higher observed hazard of myocardial infarction and eye disorders compared with DPP-4 inhibitors in the meta-analysis. The OHDSI collaborative network can be used to conduct a large international study examining the effectiveness of second-line treatment choices made in clinical management of T2D.
@article{Vashisht2018-yd, title = {Association of hemoglobin A1c levels with use of sulfonylureas, dipeptidyl peptidase 4 inhibitors, and thiazolidinediones in patients with type 2 diabetes treated with metformin: Analysis from the Observational Health Data Sciences and Informatics initiative}, author = {Vashisht, Rohit and Jung, Kenneth and Schuler, Alejandro and Banda, Juan M and Park, Rae Woong and Jin, Sanghyung and Li, Li and Dudley, Joel T and Johnson, Kipp W and Shervey, Mark M and Xu, Hua and Wu, Yonghui and Natrajan, Karthik and Hripcsak, George and Jin, Peng and Van Zandt, Mui and Reckard, Anthony and Reich, Christian G and Weaver, James and Schuemie, Martijn J and Ryan, Patrick B and Callahan, Alison and Shah, Nigam H}, journal = {JAMA network open}, publisher = {American Medical Association}, volume = {1}, issue = {4}, pages = {e181755}, date = {2018-08-03}, year = {2018}, doi = {10.1001/jamanetworkopen.2018.1755}, pmc = {PMC6324274}, pmid = {30646124}, issn = {2574-3805}, urldate = {2024-09-22}, keywords = {hemoglobin a; hemoglobin a, glycosylated; sulfonylurea compounds; thiazolidinedione; dipeptidyl-peptidase iv inhibitors; second line treatment; metformin; diabetes mellitus, type 2; myocardial infarction; eye diseases; kidney diseases; dipeptidyl-peptidase iv; informatics; data science;Rohit Vashisht}, language = {en}, dimensions = {true}, }

2017

Integrated molecular, clinical, and ontological analysis identifies overlooked disease relationships

Winston A Haynes, Rohit Vashisht, Francesco Vallania, and 8 more authors

bioRxiv, 2017

Abs Bib HTML

AbstractExisting knowledge of human disease relationships is incomplete. To establish a comprehensive understanding of disease, we integrated transcriptome profiles of 41,000 human samples with clinical profiles of 2 million patients, across 89 diseases. Based on transcriptome data, autoimmune diseases clustered with their specific infectious triggers, and brain disorders clustered by disease class. Clinical profiles clustered diseases according to the similarity of their initial manifestation and later complications, identifying disease relationships absent in prior co-occurrence analyses. Our integrated analysis of transcriptome and clinical profiles identified overlooked, therapeutically actionable disease relationships, such as between myositis and interstitial cystitis. Our improved understanding of disease relationships will identify disease mechanisms, offer novel therapeutic targets, and create synergistic research opportunities.
@article{214833, title = {Integrated molecular, clinical, and ontological analysis identifies overlooked disease relationships}, author = {Haynes, Winston A and Vashisht, Rohit and Vallania, Francesco and Liu, Charles and Gaskin, Gregory L and Bongen, Erika and Lofgren, Shane and Sweeney, Timothy E and Utz, Paul J and Shah, Nigam H and Khatri, Purvesh}, journal = {bioRxiv}, institution = {bioRxiv}, pages = {214833}, date = {2017-11-06}, year = {2017}, doi = {10.1101/214833}, keywords = {Rohit Vashisht}, dimensions = {true}, }

2016

Learning effective treatment pathways for type-2 diabetes from a clinical data warehouse

Rohit Vashisht, Ken Jung, and Nigam Shah

AMIA Annual Symposium Proceedings, 2016

Abs Bib HTML

Treatment guidelines for management of type-2 diabetes mellitus (T2DM) are controversial because existing evidence from randomized clinical trials do not address many important clinical questions. Data from Electronic Medical Records (EMRs) has been used to profile first line therapy choices, but this work did not elucidate the factors underlying deviations from current treatment guidelines and the relative efficacy of different treatment options. We have used data from the Stanford Hospital to attempt to address these issues. Clinical features associated with the initial choice of treatment were effectively re-discovered using a machine learning approach. In addition, the efficacies of first and second line treatments were evaluated using Cox proportional hazard models for control of Hemoglobin A 1c. Factors such as acute kidney disorder and liver disorder were predictive of first line therapy choices. Sitagliptin was
@article{Vashisht2016-og, title = {Learning effective treatment pathways for type-2 diabetes from a clinical data warehouse}, author = {Vashisht, Rohit and Jung, Ken and Shah, Nigam}, journal = {AMIA Annual Symposium Proceedings}, publisher = {American Medical Informatics Association}, volume = {2016}, pages = {2036}, date = {2016}, year = {2016}, pmc = {PMC5333256}, keywords = {Rohit Vashisht}, dimensions = {true}, }

2015

Metformin as a potential combination therapy with existing front-line antibiotics for Tuberculosis

Rohit Vashisht, and Samir K Brahmachari

Journal of translational medicine, 2015

Abs Bib HTML

Tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb) remains a global health concern. The evolution of various multi-drug resistant strains through genetic mutations or drug tolerant strains through bacterial persistence renders existing antibiotics ineffective. Hence there is need for the development of either new antibiotics or rationalizing approved drugs that can be utilized in combination with existing antibiotics as a therapeutic strategy. A comprehensive systems level mapping of metabolic complexity in Mtb revels a putative role of NDH-I in the formation of bacterial persistence under the influence of front-line antibiotics. Possibilities of targeting bacterial NDH-I with existing FDA approved drug for type-II diabetes, Metformin, along with existing front-line antibiotics is discussed and proposed as a potential combination therapy for TB.
@article{Vashisht2015-xc, title = {Metformin as a potential combination therapy with existing front-line antibiotics for Tuberculosis}, author = {Vashisht, Rohit and Brahmachari, Samir K}, journal = {Journal of translational medicine}, publisher = {Springer Nature}, volume = {13}, issue = {1}, pages = {83}, date = {2015-03-07}, year = {2015}, doi = {10.1186/s12967-015-0443-y}, pmc = {PMC4359515}, pmid = {25880846}, issn = {1479-5876}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }

2014

Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets

Rohit Vashisht, Ashwini G Bhat, Shreeram Kushwaha, and 3 more authors

Journal of translational medicine, 2014

Abs Bib HTML

BACKGROUND: The effectiveness of current therapeutic regimens for Mycobacterium tuberculosis (Mtb) is diminished by the need for prolonged therapy and the rise of drug resistant/tolerant strains. This global health threat, despite decades of basic research and a wealth of legacy knowledge, is due to a lack of systems level understanding that can innovate the process of fast acting and high efficacy drug discovery. METHODS: The enhanced functional annotations of the Mtb genome, which were previously obtained through a crowd sourcing approach was used to reconstruct the metabolic network of Mtb in a bottom up manner. We represent this information by developing a novel Systems Biology Spindle Map of Metabolism (SBSM) and comprehend its static and dynamic structure using various computational approaches based on simulation and design. RESULTS: The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways. By accounting for static and dynamic analysis of SBSM in Mtb we identified various critical proteins required for the growth and survival of bacteria. Further, we assessed the potential of these proteins as putative drug targets that are fast acting and less toxic. Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence. Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy. We propose such MPG’s as potential combination of drug targets for existing antibiotics that can improve their efficacy and efficiency for drug tolerant bacteria. CONCLUSION: The systems level framework formulated by us to identify potential non-toxic drug targets and strategies to circumvent the issue of bacterial persistence can substantially aid in the process of TB drug discovery and translational research.
@article{Vashisht2014-fp, title = {Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets}, author = {Vashisht, Rohit and Bhat, Ashwini G and Kushwaha, Shreeram and Bhardwaj, Anshu and {OSDD Consortium} and Brahmachari, Samir K}, journal = {Journal of translational medicine}, publisher = {Springer Science and Business Media LLC}, volume = {12}, issue = {1}, pages = {263}, date = {2014-10-11}, year = {2014}, doi = {10.1186/s12967-014-0263-5}, pmc = {PMC4201925}, pmid = {25304862}, issn = {1479-5876}, keywords = {Rohit Vashisht}, language = {en}, dimensions = {true}, }

2013

Software Platform for Metabolic Network Reconstruction of Mycobacterium tuberculosis

Samik Ghosh, Yukiko Matsuoka, Yoshiyuki Asai, and 15 more authors

, New York, NY, 2013

Bib HTML

@inbook{9781461449652,
  title = {Software Platform for Metabolic Network Reconstruction of
                 Mycobacterium tuberculosis},
  year = {2013},
  author = {Ghosh, Samik and Matsuoka, Yukiko and Asai, Yoshiyuki and Kitano, Hiroaki and Bhardwaj, Anshu and Scaria, Vinod and Vashisht, Rohit and Shah, Anup and Mondal, Anupam Kumar and Vishnoi, Priti and Sonal, Kumari and Jain, Akanksha and Priyadarshini, Priyanka and Bhattacharyya, Kausik and Kumar, Vikas and Passi, Anurag and Sharma, Pratibha and Brahmachari, Samir},
  booktitle = {Systems Biology of Tuberculosis},
  publisher = {Springer New York},
  location = {New York, NY},
  pages = {21--35},
  date = {2013},
  doi = {10.1007/978-1-4614-4966-9_2},
  isbn = {9781461449652,9781461449669},
  keywords = {Rohit Vashisht},
  dimensions = {true},
}

Social networks to biological networks: systems biology of Mycobacterium tuberculosis

Rohit Vashisht, Anshu Bhardwaj, Samir K Brahmachari, and 1 more author

Royal Society of Chemistry, 2013

Abs Bib HTML

Contextualizing relevant information to construct a network that represents a given biological process presents a fundamental challenge in the network science of biology. The quality of network for the organism of interest is critically dependent on the extent of functional annotation of its genome. Mostly the automated annotation pipelines do not account for unstructured information present in volumes of literature and hence large fraction of genome remains poorly annotated. However, if used, this information could substantially enhance the functional annotation of a genome, aiding the development of a more comprehensive network. Mining unstructured information buried in volumes of literature often requires manual intervention to a great extent and thus becomes a bottleneck for most of the automated pipelines. In this review, we discuss the potential of scientific social networking as a solution for systematic
@article{Vashisht2013-nc, title = {Social networks to biological networks: systems biology of Mycobacterium tuberculosis}, author = {Vashisht, Rohit and Bhardwaj, Anshu and Brahmachari, Samir K and {Osdd Consortium}}, journal = {Royal Society of Chemistry}, volume = {9}, issue = {7}, pages = {1584-1593}, year = {2013}, date = {2013}, keywords = {Rohit Vashisht}, dimensions = {true}, }

2012

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis

Rohit Vashisht, Anupam Kumar Mondal, Akanksha Jain, and 51 more authors

PloS one, 2012

Abs Bib HTML

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ’Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ’interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

@article{Vashisht2012-mr,
  title = {Crowd sourcing a new paradigm for interactome driven drug
                    target identification in Mycobacterium tuberculosis},
  year = {2012},
  author = {Vashisht, Rohit and Mondal, Anupam Kumar and Jain, Akanksha and Shah, Anup and Vishnoi, Priti and Priyadarshini, Priyanka and Bhattacharyya, Kausik and Rohira, Harsha and Bhat, Ashwini G and Passi, Anurag and Mukherjee, Keya and Choudhary, Kumari Sonal and Kumar, Vikas and Arora, Anshula and Munusamy, Prabhakaran and Subramanian, Ahalyaa and Venkatachalam, Aparna and Gayathri, S and Raj, Sweety and Chitra, Vijaya and Verma, Kaveri and Zaheer, Salman and Balaganesh, J and Gurusamy, Malarvizhi and Razeeth, Mohammed and Raja, Ilamathi and Thandapani, Madhumohan and Mevada, Vishal and Soni, Raviraj and Rana, Shruti and Ramanna, Girish Muthagadhalli and Raghavan, Swetha and Subramanya, Sunil N and Kholia, Trupti and Patel, Rajesh and Bhavnani, Varsha and Chiranjeevi, Lakavath and Sengupta, Soumi and Singh, Pankaj Kumar and Atray, Naresh and Gandhi, Swati and Avasthi, Tiruvayipati Suma and Nisthar, Shefin and Anurag, Meenakshi and Sharma, Pratibha and Hasija, Yasha and Dash, Debasis and Sharma, Arun and Scaria, Vinod and Thomas, Zakir and {OSDD Consortium} and Chandra, Nagasuma and Brahmachari, Samir K and Bhardwaj, Anshu},
  journal = {PloS one},
  publisher = {Public Library of Science (PLoS)},
  volume = {7},
  issue = {7},
  pages = {e39808},
  date = {2012-07-11},
  doi = {10.1371/journal.pone.0039808},
  pmc = {PMC3395720},
  pmid = {22808064},
  issn = {1932-6203},
  keywords = {Rohit Vashisht},
  language = {en},
  dimensions = {true},
}

2010

Protein-protein interaction networks suggest different targets have different propensities for triggering drug resistance

Jyothi Padiadpu, Rohit Vashisht, and Nagasuma Chandra

Systems and synthetic biology, 2010

Abs Bib HTML

Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem from a wholistic perspective, it is essential to understand the molecular mechanisms by which bacteria acquire drug resistance using a systems approach. Availability of genome-scale data of expression profiles under different drug exposed conditions and protein-protein interactions, makes it feasible to reconstruct and analyze systems-level models. A number of proteins involved in different resistance mechanisms, referred to as the resistome are identified from literature. The interaction of the drug directly with the resistome is unable to explain most resistance processes adequately, including that of increased mutations in the target’s binding site. We recently hypothesized that some communication might exist from the drug environment to the resistome to trigger emergence of drug resistance. We report here a network based approach to identify most plausible paths of such communication in Mycobacterium tuberculosis. Networks capturing both structural and functional linkages among various proteins were weighted based on gene expression profiles upon exposure to specific drugs and betweenness centrality of the interactions. Our analysis suggests that different drug targets and hence different drugs could trigger the resistome to different extents and through different routes. The identified paths correlate well with the mechanisms known through experiment. Some examples of the top ranked hubs in multiple drug specific networks are PolA, FadD1, CydA, a monoxygenase and GltS, which could serve as co-targets, that could be inhibited in order to retard resistance related communication in the cell.
@article{22132058, title = {Protein-protein interaction networks suggest different targets have different propensities for triggering drug resistance}, year = {2010}, author = {Padiadpu, Jyothi and Vashisht, Rohit and Chandra, Nagasuma}, journal = {Systems and synthetic biology}, publisher = {Springer Science and Business Media LLC}, volume = {4}, issue = {4}, pages = {311-322}, date = {2010-12}, doi = {10.1007/s11693-011-9076-5}, pmc = {PMC3065591}, pmid = {22132058}, issn = {1872-5325,1872-5333}, keywords = {Emergence of resistance; Network analysis; Resistome;Rohit Vashisht}, language = {en}, dimensions = {true}, }
Modeling metabolic adjustment in Mycobacterium tuberculosis upon treatment with isoniazid

Ashwini G Bhat, Rohit Vashisht, and Nagasuma Chandra

Systems and synthetic biology, 2010

Abs Bib HTML

UNLABELLED: Complex biological systems exhibit a property of robustness at all levels of organization. Through different mechanisms, the system tries to sustain stress such as due to starvation or drug exposure. To explore whether reconfiguration of the metabolic networks is used as a means to achieve robustness, we have studied possible metabolic adjustments in Mtb upon exposure to isoniazid (INH), a front-line clinical drug. The redundancy in the genome of M. tuberculosis (Mtb) makes it an attractive system to explore if alternate routes of metabolism exist in the bacterium. While the mechanism of action of INH is well studied, its effect on the overall metabolism is not well characterized. Using flux balance analysis, inhibiting the fluxes flowing through the reactions catalyzed by Rv1484, the target of INH, significantly changes the overall flux profiles. At the pathway level, activation or inactivation of certain pathways distant from the target pathway, are seen. Metabolites such as NADPH are shown to reduce drastically, while fatty acids tend to accumulate. The overall biomass also decreases with increasing inhibition levels. Inhibition studies, pathway level clustering and comparison of the flux profiles with the gene expression data indicate the activation of folate metabolism, ubiquinone metabolism, and metabolism of certain amino acids. This analysis provides insights useful for target identification and designing strategies for combination therapy. Insights gained about the role of individual components of a system and their interactions will also provide a basis for reconstruction of whole systems through synthetic biology approaches. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11693-011-9075-6) contains supplementary material, which is available to authorized users.
@article{22132057, title = {Modeling metabolic adjustment in Mycobacterium tuberculosis upon treatment with isoniazid}, year = {2010}, author = {Bhat, Ashwini G and Vashisht, Rohit and Chandra, Nagasuma}, journal = {Systems and synthetic biology}, publisher = {Springer Science and Business Media LLC}, volume = {4}, issue = {4}, pages = {299--309}, date = {2010-12}, doi = {10.1007/s11693-011-9075-6}, pmc = {PMC3065594}, pmid = {22132057}, issn = {1872-5325,1872-5333}, keywords = {Applications of flux balance analysis; Flux profiles; Genome scale metabolic networks; Incorporating gene expression profiles; Robustness through metabolic adjustment;Rohit Vashisht}, language = {en}, dimensions = {true}, }

2009

Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis

Karthik Raman, R Vashisht, and N Chandra

Molecular bioSystems, 2009

Abs Bib HTML

Tuberculosis continues to be a major health challenge, warranting the need for newer strategies for therapeutic intervention and newer approaches to discover them. Here, we report the identification of efficient metabolism disruption strategies by analysis of a reactome network. Protein-protein dependencies at a genome scale are derived from the curated metabolic network, from which insights into the nature and extent of inter-protein and inter-pathway dependencies have been obtained. A functional distance matrix and a subsequent nearness index derived from this information, helps in understanding how the influence of a given protein can pervade to the metabolic network. Thus, the nearness index can be viewed as a metabolic disruptability index, which suggests possible strategies for achieving maximal metabolic disruption by inhibition of the least number of proteins. A greedy approach has been used to identify the most influential singleton, and its combination with the other most pervasive proteins to obtain highly influential pairs, triplets and quadruplets. The effect of deletion of these combinations on cellular metabolism has been studied by flux balance analysis. An obvious outcome of this study is a rational identification of drug targets, to efficiently bring down mycobacterial metabolism.
@article{19593474, title = {Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis}, author = {Raman, Karthik and Vashisht, R and Chandra, N}, journal = {Molecular bioSystems}, publisher = {Royal Society of Chemistry}, volume = {5}, issue = {12}, pages = {1740-1751}, date = {2009-11-12}, doi = {10.1039/B905817F}, pmid = {19593474}, issn = {1742-206X,1742-2051}, year = {2009}, keywords = {Rohit Vashisht}, dimensions = {true}, }