Publications by authors named "Vikrant G Deshmukh"

9 Publications

  • Page 1 of 1

Understanding the Prevalence of Prediabetes and Diabetes in Patients With Cancer in Clinical Practice: A Real-World Cohort Study.

J Natl Compr Canc Netw 2021 Mar 10;19(6):709-718. Epub 2021 Mar 10.

2Huntsman Cancer Institute, and.

Background: This study aimed to understand the prevalence of prediabetes (preDM) and diabetes mellitus (DM) in patients with cancer overall and by tumor site, cancer treatment, and time point in the cancer continuum.

Methods: This cohort study was conducted at Huntsman Cancer Institute at the University of Utah. Patients with a first primary invasive cancer enrolled in the Total Cancer Care protocol between July 2016 and July 2018 were eligible. Prevalence of preDM and DM was based on ICD code, laboratory tests for hemoglobin A1c, fasting plasma glucose, nonfasting blood glucose, or insulin prescription.

Results: The final cohort comprised 3,512 patients with cancer, with a mean age of 57.8 years at cancer diagnosis. Of all patients, 49.1% (n=1,724) were female. At cancer diagnosis, the prevalence of preDM and DM was 6.0% (95% CI, 5.3%-6.8%) and 12.2% (95% CI, 11.2%-13.3%), respectively. One year after diagnosis the prevalence was 16.6% (95% CI, 15.4%-17.9%) and 25.0% (95% CI, 23.6%-26.4%), respectively. At the end of the observation period, the prevalence of preDM and DM was 21.2% (95% CI, 19.9%-22.6%) and 32.6% (95% CI, 31.1%-34.2%), respectively. Patients with myeloma (39.2%; 95% CI, 32.6%-46.2%) had the highest prevalence of preDM, and those with pancreatic cancer had the highest prevalence of DM (65.1%; 95% CI, 57.0%-72.3%). Patients who underwent chemotherapy, radiotherapy, or immunotherapy had a higher prevalence of preDM and DM compared with those who did not undergo these therapies.

Conclusions: Every second patient with cancer experiences preDM or DM. It is essential to foster interprofessional collaboration and to develop evidence-based practice guidelines. A better understanding of the impact of cancer treatment on the development of preDM and DM remains critical.
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http://dx.doi.org/10.6004/jnccn.2020.7653DOI Listing
March 2021

Long-term diabetes risk among endometrial cancer survivors in a population-based cohort study.

Gynecol Oncol 2020 01 12;156(1):185-193. Epub 2019 Dec 12.

Huntsman Cancer Institute, Salt Lake City, UT, USA; Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; Utah Cancer Registry, Salt Lake City, UT, USA. Electronic address:

Objective: The majority of endometrial cancer patients are overweight or obese at cancer diagnosis. Obesity is a shared risk factor for both endometrial cancer and diabetes, but it is unknown whether endometrial cancer patients have increased diabetes risks. The aim of our study was to investigate diabetes risk among endometrial cancer patients.

Methods: Endometrial cancer patients diagnosed between 1997 and 2012 in Utah (n = 2,314) were identified. Women from the general population (n = 8,583) were matched to the cancer patients on birth year and birth state. Diabetes diagnoses were identified from electronic medical records and statewide healthcare facility databases. Cox proportional hazards models were used to estimate hazard ratios for diabetes after cancer diagnosis.

Results: Endometrial cancer survivors had a significantly higher risk of type II diabetes when compared to women from the general population in the first year after cancer diagnosis (HR = 5.22, 95% CI = 4.05, 6.71), >1-5 years after cancer diagnosis (HR = 1.67, 95% CI = 1.31, 2.12), and >5 years after cancer diagnosis (HR = 1.65, 95% CI = 1.29, 2.11). Endometrial cancer patients who were obese at cancer diagnosis had a three-fold increase in type II diabetes risk (HR = 2.99, 95%CI = 2.59, 3.45). Although endometrial cancer patients diagnosed at distant stage had a higher risk of diabetes, cancer treatment did not appear to contribute to any diabetes risks.

Conclusions: In conclusion, endometrial cancer survivors had a higher risk of diabetes than women in the general population. These results suggest that long term monitoring for diabetes is indicated for endometrial cancer survivors.
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http://dx.doi.org/10.1016/j.ygyno.2019.10.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083523PMC
January 2020

Endocrine and Metabolic Diseases Among Colorectal Cancer Survivors in a Population-Based Cohort.

J Natl Cancer Inst 2020 01;112(1):78-86

Division of Public Health, Department of Family and Preventive Medicine, University of Utah School of Medicine, and Huntsman Cancer Institute, Salt Lake City, UT.

Background: There are an estimated 1.4 million colorectal cancer (CRC) survivors in the United States. Research on endocrine and metabolic diseases over the long term in CRC survivors is limited. Obesity is a risk factor for CRC; thus it is of interest to investigate diseases that may share this risk factor, such as diabetes, for long-term health outcomes among CRC survivors.

Methods: A total of 7114 CRC patients were identified from the Utah Population Database and matched to a general population cohort of 25 979 individuals on birth year, sex, and birth state. Disease diagnoses (assessed over three time periods of 1-5 years, 5-10 years, and >10 years) were identified using electronic medical records and statewide ambulatory and inpatient discharge data. Cox proportional hazard models were used to estimate the risk of endocrine and metabolic disease.

Results: Across all three time periods, risks for endocrine and metabolic diseases were statistically significantly greater for CRC survivors compared with the general population cohort. At 1-5 years postdiagnosis, CRC survivors' risk for diabetes mellitus with complications was statistically significantly elevated (hazard ratio [HR] = 1.36, 99% confidence interval [CI] = 1.09 to 1.70). CRC survivors also experienced a 40% increased risk of obesity at 1-5 years postcancer diagnosis (HR= 1.40, 99% CI= 1.66 to 2.18) and a 50% increased risk at 5-10 years postdiagnosis (HR = 1.50, 99% CI= 1.16 to 1.95).

Conclusions: Endocrine and metabolic diseases were statistically significantly higher in CRC survivors throughout the follow-up periods of 1-5 years, 5-10 years, and more than 10 years postdiagnosis. As the number of CRC survivors increases, understanding the long-term trajectory is critical for improved survivorship care.
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http://dx.doi.org/10.1093/jnci/djz040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7489083PMC
January 2020

Impact of computerized provider order entry (CPOE) on length of stay and mortality.

J Am Med Inform Assoc 2017 03;24(2):303-309

Hospital Information Technology Services, Enterprise Data Warehouse, University of Utah Hospital and Clinics, Salt Lake City, UT, USA.

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE).

Materials And Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome.

Results: After controlling for covariates, CPOE remained a significant statistical predictor of decreased LOS and mortality. LOS decreased by 0.90 days, P  < .0001. Mortality decrease varied by model: 1 death per 1000 admissions (pre = 0.006, post = 0.0005, P  < .001) or 3 deaths (pre = 0.008, post = 0.005, P  < .01). Mortality and LOS decreased in medical and surgical units but increased in intensive care units.

Discussion: This study examined CPOE at multiple levels. Given the inability to randomize CPOE assignment, these results may only be applicable to the local setting. Temporal trends found in this study suggest that hospital-wide implementations may have impacted nursing staff and new residents. Differences in the results were noted at the patient care unit and room levels. These differences may partly explain the mixed results from previous studies.

Conclusion: Controlling for confounders, CPOE implementation remained a statistically significant predictor of LOS and mortality at this site. Mortality appears to be a sensitive outcome indicator with regard to hospital-wide implementations and should be further studied.
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http://dx.doi.org/10.1093/jamia/ocw091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391723PMC
March 2017

Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes.

J Am Med Inform Assoc 2015 Jan 16;22(1):223-35. Epub 2014 Oct 16.

University of Utah, Salt Lake City, Utah, USA.

Objective: To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes).

Materials And Methods: In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement.

Results: A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care.

Conclusions: The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value.
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http://dx.doi.org/10.1136/amiajnl-2013-002511DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433359PMC
January 2015

Integrating historical clinical and financial data for pharmacological research.

BMC Med Res Methodol 2011 Nov 18;11:151. Epub 2011 Nov 18.

Department of Biomedical Informatics, School of Medicine, University of Utah, 26 South 2000 East Room 5775, Salt Lake City, UT 84112, USA.

Background: Retrospective research requires longitudinal data, and repositories derived from electronic health records (EHR) can be sources of such data. With Health Information Technology for Economic and Clinical Health (HITECH) Act meaningful use provisions, many institutions are expected to adopt EHRs, but may be left with large amounts of financial and historical clinical data, which can differ significantly from data obtained from newer systems, due to lack or inconsistent use of controlled medical terminologies (CMT) in older systems. We examined different approaches for semantic enrichment of financial data with CMT, and integration of clinical data from disparate historical and current sources for research.

Methods: Snapshots of financial data from 1999, 2004 and 2009 were mapped automatically to the current inpatient pharmacy catalog, and enriched with RxNorm. Administrative metadata from financial and dispensing systems, RxNorm and two commercial pharmacy vocabularies were used to integrate data from current and historical inpatient pharmacy modules, and the outpatient EHR. Data integration approaches were compared using percentages of automated matches, and effects on cohort size of a retrospective study.

Results: During 1999-2009, 71.52%-90.08% of items in use from the financial catalog were enriched using RxNorm; 64.95%-70.37% of items in use from the historical inpatient system were integrated using RxNorm, 85.96%-91.67% using a commercial vocabulary, 87.19%-94.23% using financial metadata, and 77.20%-94.68% using dispensing metadata. During 1999-2009, 48.01%-30.72% of items in use from the outpatient catalog were integrated using RxNorm, and 79.27%-48.60% using a commercial vocabulary. In a cohort of 16304 inpatients obtained from clinical systems, 4172 (25.58%) were found exclusively through integration of historical clinical data, while 15978 (98%) could be identified using semantically enriched financial data.

Conclusions: Data integration using metadata from financial/dispensing systems and pharmacy vocabularies were comparable. Given the current state of EHR adoption, semantic enrichment of financial data and integration of historical clinical data would allow the repurposing of these data for research. With the push for HITECH meaningful use, institutions that are transitioning to newer EHRs will be able to use their older financial and clinical data for research using these methods.
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http://dx.doi.org/10.1186/1471-2288-11-151DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3252280PMC
November 2011

Evaluating the informatics for integrating biology and the bedside system for clinical research.

BMC Med Res Methodol 2009 Oct 28;9:70. Epub 2009 Oct 28.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA.

Background: Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive.

Methods: Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone.

Results: We found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre- or post-processing, modifications to the hive metadata, etc.

Conclusion: The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data.
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http://dx.doi.org/10.1186/1471-2288-9-70DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2779809PMC
October 2009

Efficiency of CYP2C9 genetic test representation for automated pharmacogenetic decision support.

Methods Inf Med 2009 31;48(3):282-90. Epub 2009 Mar 31.

University of Utah, School of Medicine, Department of Biomedical Informatics, Salt Lake City, UT 84112, USA.

Objectives: We investigated the suitability of representing discrete genetic test results in the electronic health record (EHR) as individual single nucleotide polymorphisms (SNPs) and as alleles, using the CYP2C9 gene and its polymorphic states, as part of a pilot study. The purpose of our investigation was to determine the appropriate level of data abstraction when reporting genetic test results in the EHR that would allow meaningful interpretation and clinical decision support based on current knowledge, while retaining sufficient information in order to enable reinterpretation of the results in the context of future discoveries.

Methods: Based on the SNP & allele models, we designed two separate lab panels within the laboratory information system, one containing SNPs and the other containing alleles, built separate rules in the clinical decision support system based on each model, and evaluated the performance of these rules in an EHR simulation environment using real-world scenarios.

Results: Although decision-support rules based on allele model required significantly less computational time than rules based on SNP model, no difference was observed on the total time taken to chart medication orders between rules based on these two models.

Conclusions: Both, SNP- and allele-based models, can be used effectively for representing genetic test results in the EHR without impacting clinical decision support systems. While storing and reporting genetic test results as alleles allow for the construction of simpler decision-support rules, and make it easier to present these results to clinicians, SNP-based model can retain a greater amount of information that could be useful for future reinterpretation.
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http://dx.doi.org/10.3414/ME0570DOI Listing
July 2009

A clinical use case to evaluate the i2b2 Hive: predicting asthma exacerbations.

AMIA Annu Symp Proc 2009 Nov 14;2009:442-6. Epub 2009 Nov 14.

Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.

To evaluate the i2b2 Hive as a tool to query, visualize, and extract clinical data, we selected a use case from the i2b2 airways diseases driving biology project: asthma exacerbations prediction. We analyzed the cohort selection and the extraction of the clinical data used by this asthma exacerbations prediction study. The structured data included the asthma diagnosis, birthdate, age, race, sex, height, weight, and BMI. The smoking status is typically only mentioned in clinical notes, and we evaluated the Natural Language Processing (NLP) application embedded in the i2b2 NLP cell to extract the smoking status from history and physical exam reports.Querying structured data was possible with the i2b2 workbench for about half the clinical data elements. The remaining had to be queried using a commercial database management system client. The automated extraction of the smoking status reached a mean precision of 0.79 and a mean specificity of 0.90.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815458PMC
November 2009
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