Temporal trends of hemoglobin A1c testing

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System

Importance

Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs.

Objective

To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS.

Design

One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups.

Setting

Two large U.S. health systems.

Participants

Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders.

Intervention

The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members.

Main Outcomes and Measures

Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission.

Results

A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group.

Conclusions and Relevance

A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow.

Trial Registration

ClinicalTrials.gov Identifier: NCT03911687.

Comparative Effectiveness of Aspirin Dosing in Cardiovascular Disease and Diabetes Mellitus: A Subgroup Analysis of the ADAPTABLE Trial

OBJECTIVE

Patients with diabetes mellitus (DM) and concomitant atherosclerotic cardiovascular disease (ASCVD) must be on the most effective dose of aspirin to mitigate risk of future adverse cardiovascular events.

RESEARCH DESIGN AND METHODS

ADAPTABLE, an open-label, pragmatic study, randomized patients with stable, chronic ASCVD to 81 mg or 325 mg of daily aspirin. The effects of aspirin dosing was assessed on the primary effectiveness outcome, a composite of all-cause death, hospitalization for myocardial infarction, or hospitalization for stroke, and the primary safety outcome of hospitalization for major bleeding. In this prespecified analysis, we used Cox proportional hazards models to compare aspirin dosing in patients with and without DM for the primary effectiveness and safety outcome.

RESULTS

Of 15,076 patients, 5,676 (39%) had DM of whom 2,820 (49.7%) were assigned to 81 mg aspirin and 2,856 (50.3%) to 325 mg aspirin. Patients with versus without DM had higher rates of the composite cardiovascular outcome (9.6% vs. 5.9%; P < 0.001) and bleeding events (0.78% vs. 0.50%; P < 0.001). When comparing 81 mg vs. 325 mg of aspirin, patients with DM had no difference in the primary effectiveness outcome (9.3% vs. 10.0%; hazard ratio [HR] 0.98 [95% CI 0.83-1.16]; P = 0.265) or safety outcome (0.87% vs. 0.69%; subdistribution HR 1.25 [95% CI 0.72-2.16]; P = 0.772).

CONCLUSIONS

This study confirms the inherently higher risk of patients with DM irrespective of aspirin dosing. Our findings suggest that a higher dose of aspirin yields no added clinical benefit, even in a more vulnerable population.

Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research

OBJECTIVES

The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods.

MATERIALS AND METHODS

We conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data.

RESULTS

When missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression.

DISCUSSION AND CONCLUSION

Missing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.

Potential Overtreatment and Overtesting Among Older Adults With Type 2 Diabetes Across Canada: An Observational, Retrospective Cohort Study

OBJECTIVE

Our aim in this study was to assess potential overtreatment and overtesting among older adults with type 2 diabetes across Canada.

METHODS

An observational, population-based cohort study was conducted using data available through the Canadian Primary Care Sentinel Surveillance Network. All patients included in the study were seen by a primary care provider between 2010 and 2017, ≥65 years with type 2 diabetes and had at least one glycated hemoglobin (A1C) measurement. Potential overtreatment was defined as an index A1C of <7% and being prescribed antidiabetes medications other than metformin within 1 year of the index A1C. Testing ≥3 times/year in patients with A1C <7% was considered potential overtesting. Analyses were performed/compared within 2 cross-sectional cohorts (2012 and 2016). A subcohort analysis was performed on those with advanced age and dementia.

RESULTS

An overall cohort of 41,032 patients (mean age, 76.6 years) was identified. Proportions of potential overtreatment were 7.0% (2012) and 6.9% (2016) (difference in rate in %: 0.1; 95% confidence interval [CI], -0.32 to 0.52]). Overall, 19.2% (2012) and 19.0% (2016) of patients were potentially overtested (difference in rate in %: 0.2; 95% CI, -0.45 to 0.85), whereas 2.4% (2012) and 2.3% (2016) were potentially undertested (difference in rate in %: 0.1; 95% CI, -0.15 to 0.35). Among patients with dementia and advanced age, proportions of patients potentially overtreated were 14.5% and 12.1%, and those overtested were 29.2% and 25.0% in 2012 and 2016, respectively.

CONCLUSIONS

Potential overtreatment and overtesting exists among older adults with diabetes in Canadian primary care practices with minimal change over time. Higher proportions of potentially unnecessary care were observed in those with advanced age and dementia. Our study highlights an opportunity for primary care clinicians to improve testing and treatment practices considering the individual patient, context and potential for net benefit.

Hemoglobin A1C testing frequency among patients with type 2 diabetes within a US payer system: a retrospective observational study

OBJECTIVE

The American Diabetes Association (ADA) guidelines recommend A1C testing schedules for patients with type 2 diabetes; however, level of real-world guideline adherence remains unclear. The current study evaluated A1C testing frequency and its association with glycemic control and cardiovascular outcomes.

METHODS

A retrospective study was conducted utilizing Aetna's Enterprise Data Warehouse. Adult patients with a medical claim for type 2 diabetes in 2017 (index date) were included. Patients had continuous enrollment through December 2019 and ≥1 reported A1C measurement from 2017 to 2019. Follow-up was up to 36 months post-index date.

RESULTS

Of the 112,572 eligible patients, 50.0% were female and median age was 70 years; 32.9% of patients with controlled baseline A1C (<8%, 64 mmol/mol) received less than the 2 tests/year recommended by the ADA, while 60.6% of patients with uncontrolled baseline A1C received less than the quarterly testing recommended by the ADA. More frequent testing was associated with age (65-75 years), uncontrolled baseline A1C and presence of comorbidities. In separate multivariable models, 2-3 A1C tests/year were associated with greater likelihood of A1C < 8% (64 mmol/mol) vs. <2 tests/year (OR = 1.07, 95% confidence interval [CI] 1.02-1.12), while >3 tests/year was associated with a modestly increased risk of cardiovascular events vs. <2 tests/year (OR = 1.08, 95% CI 1.01-1.15).

CONCLUSIONS

A large proportion of type 2 diabetes patients were not tested per guideline recommendations. The relationship between A1C testing frequency and glycemic control was inconsistent, though there was a significant association between more frequent testing and experiencing a CV event.

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

OBJECTIVE

There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals).

MATERIALS AND METHODS

We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories.

RESULTS

Seven themes-identified during development and simulation testing of the CONCERN model-informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual's decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework.

DISCUSSION

The HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle.

CONCLUSIONS

We propose this framework as an approach to embed clinicians' knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.

Culturally based pre-Ramadan education increased benefits and reduced hazards of Ramadan fasting for type 2 diabetic patients

Objectives

In the current study, we aimed at evaluating the effect of a culturally-based pre-Ramadan education program (PREP) on glycemic control, weight, adherence to post-sunset physical activity, perception of hypoglycemia, and anti-diabetic medication dose adjustment during Ramadan fasting in type 2 diabetics.

Study design

A total of 1008 type 2 Diabetes patients were offered a culturally-based PREP in addition to the standard of care, two months before Ramadan. A retrospective interview one month after Ramadan compared the fasting experience of PREP attendees (470 patients) with those who merely received standard of care (538 patients) (Non-PREP).

Results

Ramadan fasting improved glycemic control with a correlation between HbA1c percent reduction and the number of fasting days (r = -0.290, p = 0.007). More HbA1c and weight percent reduction were observed in PREP attendees compared to the Non-PREP group (-14.8% ± 9.3 vs. -5.4% ± 5.4; p < 0.001; and - 1.96% ± 5.4 vs. -0.39% ± 2.8; p < 0.001, respectively). More commitment to night prayers in the PREP attendees compared to the Non-PREP group, (85.5% prayed >20 nights vs 28.4%; p < 0.001) with more HbA1c and weight percent reduction in the those who performed the prayers more than 20 nights compared to those who performed no prayers (-11.69% ± 8.8 vs -6.28% ± 6.4, p < 0.001; and - 2.76% ±5.1 vs 1.35% ±1.8, p < 0.001, respectively). More perception of true hypoglycemia was associated with PREP attendance (p0.046), insulin treatment (p0.000), and reduction of antidiabetic medication dosage (p0.004). Repeated lowering of antidiabetic medications doses with sequential downsizing of meals' portions, and appetite was reported.

Conclusion

Ramadan fasting was beneficial for people with type 2 diabetes with reduction of HbA1c in correlation with the number of fasting days. Contrasting PREP with Non-PREP participants discovered better HbA1c and weight reduction in the former group even with equal number of fasting days. PREP participants performed more Taraweeh night prayers. The more the prayer nights the more decline of HbA1c and weight was observed. PREP improved perception and response to hypoglycemia with low-dosing of antidiabetic medications, especially insulin.

Summarizing the extent of visit irregularity in longitudinal data

Background

Observational longitudinal data often feature irregular, informative visit times. We propose descriptive measures to quantify the extent of irregularity to select an appropriate analytic outcome approach.

Methods

We divided the study period into bins and calculated the mean proportions of individuals with 0, 1, and > 1 visits per bin. Perfect repeated measures features everyone with 1 visit per bin. Missingness leads to individuals with 0 visits per bin while irregularity leads to individuals with > 1 visit per bin. We applied these methods to: 1) the TARGet Kids! study, which invites participation at ages 2, 4, 6, 9, 12, 15, 18, 24 months, and 2) the childhood-onset Systemic Lupus Erythematosus (cSLE) study which recommended at least 1 visit every 6 months.

Results

The mean proportions of 0 and > 1 visits per bin were above 0.67 and below 0.03 respectively in the TARGet Kids! study, suggesting repeated measures with missingness. For the cSLE study, bin widths of 6 months yielded mean proportions of 1 and > 1 visits per bin of 0.39, suggesting irregular visits.

Conclusions

Our methods describe the extent of irregularity and help distinguish between protocol-driven visits and irregular visits. This is an important step in choosing an analytic strategy for the outcome.

Reducing the probability of falsely elevated HbA1c results in diabetic patients by applying automated and educative HbA1c re-testing intervals

INTRODUCTION

Too frequent HbA1c measurements may lead to unnecessary treatment modifications of diabetic patients. The aim of this study was to estimate the percentage of falsely elevated HbA1c results in two hospitals, Landeskrankenhaus/Uniklinikum Salzburg (LKH) and Landesklinik St. Veit (STV), as well as to retrospectively investigate the effect of an automated and an educative 60-day re-testing interval (RTI).

METHODS

The amount of estimated falsely elevated results (eFER), based on odds calculated using the baseline and the follow-up values and the time between these measurements, the number of HbA1c re-testings within 60 days as well as the overall number of ordered and performed HbA1c analyses were calculated. In LKH, an automated algorithm cancelling inappropriate HbA1c testing was applied, and in STV, educational actions were taken.

RESULTS

Before RTI-implementation, eFER were 0.9% and 2.1% and within-60-days-re-testing were 15.0% and 7.4% of cases in LKH and STV, respectively. After RTI-implementation, these numbers decreased to 0.2% (p < .001) and 1.8% (p = .869) and within-60-days-re-testing decreased to 1.1% (p < .001) and 3.6% (p = .003) in LKH and STV, respectively. Median monthly HbA1c measurements decreased by 15.8% (p < .001) and 21.1% (p = .002) in LKH and STV, respectively.

CONCLUSION

Both the educational and the automated 60-day-RTI were proven to be efficient in reducing overall HbA1c measurements, re-testing within 60 days and eFER.

Impact of Patient Portal Use on Duplicate Laboratory Tests in Diabetes Management

Background: Patients seek care across multiple health care settings. One coordination issue is the unnecessary duplication of laboratory across different health care settings. This analysis examined the association between patient portal use and duplication of laboratory testing among Veterans who are dual users of Veterans Affairs (VA) and non-VA providers. Materials and Methods: A national sample of Veterans who were newly authenticated users of the portal during fiscal year (FY) 2013 who used Blue Button at least once were compared with a random sample of Veterans who were not registered to use the portal. From these two groups, Veterans who were also Medicare-eligible users in FY2014 were identified. Duplicate testing was defined as receipt of more than five HbA1c (hemoglobin A1c) in 1 year. Results: Use of the Blue Button decreased the odds of duplicate HbA1c testing in VA and Medicare-covered facilities across three comparisons: (1) overall between users and nonusers: portal users were less likely to have duplicate testing; (2) pre-post comparison: there was a trend toward lower duplicate testing in both groups across time; and (3) pre-post comparisons accounting for use of the portal: the trend toward lower duplicate testing was greater in Blue Button users. Conclusion: Duplicate HbA1c testing was significantly lower in dual users of VA and Medicare services who used the Blue Button feature of their VA patient portal. Non-VA providers encounter barriers to access of complete information about Veterans who also use VA health care. Provider endorsement of consumer-mediated health information exchange could help further this model of sharing information.

The frequency of testing for glycated haemoglobin, HbA1c, is linked to the probability of achieving target levels in patients with suboptimally controlled diabetes mellitus

Background We previously showed, in patients with diabetes, that >50% of monitoring tests for glycated haemoglobin (HbA1c) are outside recommended intervals and that this is linked to diabetes control. Here, we examined the effect of tests/year on achievement of commonly utilised HbA1c targets and on HbA1c changes over time. Methods Data on 20,690 adults with diabetes with a baseline HbA1c of >53 mmol/mol (7%) were extracted from Clinical Biochemistry Laboratory records at three UK hospitals. We examined the effect of HbA1c tests/year on (i) the probability of achieving targets of ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) in a year using multi-state modelling and (ii) the changes in mean HbA1c using a linear mixed-effects model. Results The probabilities of achieving ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) targets within 1 year were 0.20 (95% confidence interval: 0.19-0.21) and 0.10 (0.09-0.10), respectively. Compared with four tests/year, having one test or more than four tests/year were associated with lower likelihoods of achieving either target; two to three tests/year gave similar likelihoods to four tests/year. Mean HbA1c levels were higher in patients who had one test/year compared to those with four tests/year (mean difference: 2.64 mmol/mol [0.24%], p<0.001). Conclusions We showed that ≥80% of patients with suboptimal control are not achieving commonly recommended HbA1c targets within 1 year, highlighting the major challenge facing healthcare services. We also demonstrated that, although appropriate monitoring frequency is important, testing every 6 months is as effective as quarterly testing, supporting international recommendations. We suggest that the importance HbA1c monitoring frequency is being insufficiently recognised in diabetes management.

Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding

Introduction

Electronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus.

Methods

Narrative literature review of articles, published in both the medical- and engineering-based informatics literature.

Results

There has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results.

Discussion

Electronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

Feasibility of representing adherence to blood glucose monitoring through visualizations: A pilot survey study among healthcare workers

BACKGROUND

Measuring adherence to processes is one of the established ways to quantify the quality of healthcare. Providing timely feedback to healthcare workers on the level of adherence can improve process measures. However, it is challenging to present data on adherence to repetitive time-sensitive tasks in a clear manner.

OBJECTIVES

We used inpatient glucose monitoring as a test case to explore the feasibility of using visualizations to communicate adherence to repetitive scheduled tasks to healthcare workers.

METHODS

We selected four candidate plots that represented distribution across time: histogram, probability density function plot (pdf plot), violin plot and cumulative density function plot (cdf plot). Doctors and nurses involved in inpatient diabetes care in a tertiary hospital were invited to complete a self-administered questionnaire that measured self-reported baseline knowledge, performance, and perception towards the visualizations. Performance was assessed by determining if a participant was able to correctly identify visualizations representing protocol adherence. We also assessed the perception of usability of these visualizations for monitoring protocol adherence. Binomial regression models were used to identify factors associated with overall performance and perception. Logistic regression models with generalized estimating equation were used to compare performance and perception between visualizations, and identify effect modifiers.

RESULTS

A total of 57 doctors and nurses completed the questionnaire. Participants were most familiar with histogram (87.7%), followed by cdf plot (61.4%), pdf plot (40.4%), and violin plot (7%). However, the percentages of participants who identified non-adherence using these plots were generally lower, ranging from 29.8% to 40.4%. Participants' perception of usability ranged from 14% to 17.5% across these visualizations. More favorable perceptions were found among participants with baseline knowledge for two or more visualizations (adjusted odds ratio: 3.21; 95%CI: 1.29, 7.96; p-value: 0.012) and having identified two or more non-adherent visualizations (adjusted odds ratio: 4.23; 95%CI: 1.95, 9.16; p-value: < 0.001).

CONCLUSIONS

Adherence to repetitive time-sensitive tasks can be presented in the form of visualizations. However, nurses' and doctors' knowledge and understanding of these visualizations are generally poor. This may influence their perception of usability of these plots. Therefore, these visualizations need to be implemented in tandem with training on their interpretation, to enhance the usefulness of these plots in motivating quality improvement.

When to re-order laboratory tests? Learning laboratory test shelf-life

Most laboratory results are valid for only a certain time period (laboratory tests shelf-life), after which they are outdated and the test needs to be re-administered. Currently, laboratory test shelf-lives are not centrally available anywhere but the implicit knowledge of doctors. In this work we propose an automated method to learn laboratory test-specific shelf-life by identifying prevalent laboratory test order patterns in electronic health records. The resulting shelf-lives performed well in the evaluation of internal validity, clinical interpretability, and external validity.

Methodological variations in lagged regression for detecting physiologic drug effects in EHR data

We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effect of methodological variations ((i) time series construction, (ii) temporal parameterization, (iii) intra-subject normalization, (iv) differencing (lagged rates of change achieved by taking differences between consecutive measurements), (v) explanatory variables, and (vi) regression models) on performance of lagged linear methods in this context. We generated two gold standards (one knowledge-base derived, one expert-curated) for expected pairwise relationships between 7 drugs and 4 labs, and evaluated how the 64 unique combinations of methodological perturbations reproduce the gold standards. Our 28 cohorts included patients in the Columbia University Medical Center/NewYork-Presbyterian Hospital clinical database, and ranged from 2820 to 79,514 patients with between 8 and 209 average time points per patient. The most accurate methods achieved AUROC of 0.794 for knowledge-base derived gold standard (95%CI [0.741, 0.847]) and 0.705 for expert-curated gold standard (95% CI [0.629, 0.781]). We observed a mean AUROC of 0.633 (95%CI [0.610, 0.657], expert-curated gold standard) across all methods that re-parameterize time according to sequence and use either a joint autoregressive model with time-series differencing or an independent lag model without differencing. The complement of this set of methods achieved a mean AUROC close to 0.5, indicating the importance of these choices. We conclude that time-series analysis of EHR data will likely rely on some of the beneficial pre-processing and modeling methodologies identified, and will certainly benefit from continued careful analysis of methodological perturbations. This study found that methodological variations, such as pre-processing and representations, have a large effect on results, exposing the importance of thoroughly evaluating these components when comparing machine-learning methods.

Use of a model for A1c formation to estimate average glucose operative between short-interval measurements of %A1c

BACKGROUND

Estimated average glucose (AG) is generally reported along with hemoglobin A1c measurements according to a standard calculation. Given a normal red blood cell lifetime of 120 days, serial A1c measurements at intervals <120 days are not completely independent. For short interval measurements, a change in AG (ΔAG) necessarily underestimates the change in average glucose operative during the interval (ΔG). We use a model for kinetics of HbA1c to evaluate the theoretical relationship between ΔAG and ΔG for HbA1c measurements made at intervals between 0 and 120 days.

METHODS

From any given starting point for A1c, step changes in G were simulated using model calculations to determine the extent to which A1c could change as a function of the interval of exposure. Values for ΔAG were compared to the operative ΔG as a function of the interval between A1c measurements.

RESULTS

Results of model simulations are a single graph for relationship of ΔAG to ΔG as a function of the interval between A1c measurements. ΔAG for (15, 30, 45, 60, 76, and 90) day intervals underestimated operative ΔG by (73, 51, 34, 21, 11, and 5)%, respectively.

CONCLUSIONS

Model calculations predict the relationship between changes in estimated average glucose to changes in operative glucose for serial A1c measurements made at intervals <120 days. Given that serial measurements of A1c made at short intervals are not uncommon in practice, physicians may find this information to be useful.

Efficacy and safety of evogliptin monotherapy in patients with type 2 diabetes and moderately elevated glycated haemoglobin levels after diet and exercise

AIMS

To evaluate the efficacy and safety of evogliptin, a newly developed dipeptidyl peptidase-4 inhibitor, in patients with type 2 diabetes (T2D) inadequately controlled by diet and exercise.

MATERIALS AND METHODS

In this randomized, double-blind, placebo-controlled, parallel-group, multicentre, phase III study, 160 patients with T2D were assigned to either evogliptin 5 mg or placebo for 24 weeks. The primary endpoint was the mean change in glycated haemoglobin (HbA1c) from baseline to week 24.

RESULTS

The mean baseline HbA1c levels were similar in the evogliptin and the placebo groups (7.20% ± 0.56% vs 7.20% ± 0.63%, respectively). At week 24, evogliptin significantly reduced HbA1c levels from baseline compared with placebo (-0.23% vs 0.05%, respectively, P < .0001). Additionally, the proportion of patients achieving HbA1c <6.5% was significantly higher in the evogliptin group than in the placebo group (33.3% vs 15.2%; P = .008). The overall incidence of adverse events, including hypoglycaemia, was similar in the 2 groups.

CONCLUSIONS

In this 24-week study, once-daily evogliptin monotherapy significantly improved glycaemic control and was well tolerated in patients with T2D.

Procedure prediction from symbolic Electronic Health Records via time intervals analytics

Prediction of medical events, such as clinical procedures, is essential for preventing disease, understanding disease mechanism, and increasing patient quality of care. Although longitudinal clinical data from Electronic Health Records provides opportunities to develop predictive models, the use of these data faces significant challenges. Primarily, while the data are longitudinal and represent thousands of conceptual events having duration, they are also sparse, complicating the application of traditional analysis approaches. Furthermore, the framework presented here takes advantage of the events duration and gaps. International standards for electronic healthcare data represent data elements, such as procedures, conditions, and drug exposures, using eras, or time intervals. Such eras contain both an event and a duration and enable the application of time intervals mining - a relatively new subfield of data mining. In this study, we present Maitreya, a framework for time intervals analytics in longitudinal clinical data. Maitreya discovers frequent time intervals related patterns (TIRPs), which we use as prognostic markers for modelling clinical events. We introduce three novel TIRP metrics that are normalized versions of the horizontal-support, that represents the number of TIRP instances per patient. We evaluate Maitreya on 28 frequent and clinically important procedures, using the three novel TIRP representation metrics in comparison to no temporal representation and previous TIRPs metrics. We also evaluate the epsilon value that makes Allen's relations more flexible with several settings of 30, 60, 90 and 180days in comparison to the default zero. For twenty-two of these procedures, the use of temporal patterns as predictors was superior to non-temporal features, and the use of the vertically normalized horizontal support metric to represent TIRPs as features was most effective. The use of the epsilon value with thirty days was slightly better than the zero.

Utilizing distributional analytics and electronic records to assess timeliness of inpatient blood glucose monitoring in non-critical care wards

Background

Regular and timely monitoring of blood glucose (BG) levels in hospitalized patients with diabetes mellitus is crucial to optimizing inpatient glycaemic control. However, methods to quantify timeliness as a measurement of quality of care are lacking. We propose an analytical approach that utilizes BG measurements from electronic records to assess adherence to an inpatient BG monitoring protocol in hospital wards.

Methods

We applied our proposed analytical approach to electronic records obtained from 24 non-critical care wards in November and December 2013 from a tertiary care hospital in Singapore. We applied distributional analytics to evaluate daily adherence to BG monitoring timings. A one-sample Kolmogorov-Smirnov (1S-KS) test was performed to test daily BG timings against non-adherence represented by the uniform distribution. This test was performed among wards with high power, determined through simulation. The 1S-KS test was coupled with visualization via the cumulative distribution function (cdf) plot and a two-sample Kolmogorov-Smirnov (2S-KS) test, enabling comparison of the BG timing distributions between two consecutive days. We also applied mixture modelling to identify the key features in daily BG timings.

Results

We found that 11 out of the 24 wards had high power. Among these wards, 1S-KS test with cdf plots indicated adherence to BG monitoring protocols. Integrating both 1S-KS and 2S-KS information within a moving window consisting of two consecutive days did not suggest frequent potential change from or towards non-adherence to protocol. From mixture modelling among wards with high power, we consistently identified four components with high concentration of BG measurements taken before mealtimes and around bedtime. This agnostic analysis provided additional evidence that the wards were adherent to BG monitoring protocols.

Conclusions

We demonstrated the utility of our proposed analytical approach as a monitoring tool. It provided information to healthcare providers regarding the timeliness of daily BG measurements. From the real data application, there were empirical evidences suggesting adherence of BG timings to protocol among wards with adequate power for assessing timeliness. Our approach is extendable to other areas of healthcare where timeliness of patient care processes is important.

HbA1c overtesting and overtreatment among US adults with controlled type 2 diabetes, 2001-13: observational population based study

STUDY QUESTION

What is the extent and effect of excessive testing for glycated hemoglobin (HbA1c) among adults with controlled type 2 diabetes?

METHODS

A retrospective analysis of data from a national administrative claims database included commercially insured individuals in the USA, 2001-13. Study patients were aged 18 years or older, had type 2 diabetes with stable glycemic control (two consecutive tests showing HbA1c<7.0% within 24 months), did not use insulin, had no history of severe hypoglycemia or hyperglycemia, and were not pregnant. HbA1c testing frequency was measured within 24 months after the second (index) HbA1c test, and classified as guideline recommended (≤ 2 times/year), frequent (3-4 times/year), and excessive (≥ 5 times/year). Changes in treatment regimen were ascertained within three months of the index test.

STUDY ANSWER AND LIMITATIONS

Of 31,545 patients in the study cohort (mean age 58 years; mean index HbA1c 6.2%), HbA1c testing frequency was excessive in 6% and frequent in 55%. Despite good glycemic control at baseline, treatment was further intensified by addition of glucose lowering drugs or insulin in 8.4% of patients (comprising 13%, 9%, and 7% of those tested excessively, frequently, and per guidelines, respectively; P<0.001). Compared with guideline recommended testing, excessive testing was associated with treatment intensification (odds ratio 1.35 (95% confidence interval 1.22 to 1.50)). Excessive testing rates remained unchanged in 2001-08, but fell significantly after 2009. The odds of excessive testing was 46% lower in 2011 than in 2001-02. The study population is not representative of all US patients with type 2 diabetes because it was restricted to commercially insured adults with stable and controlled diabetes not receiving insulin treatment. The study design did not capture the underuse of HbA1c testing.

WHAT THIS STUDY ADDS

In this US cohort of adults with stable and controlled type 2 diabetes, more than 60% received too many HbA1c tests, a practice associated with potential overtreatment with hypoglycemic drugs. Excessive testing contributes to the growing problem of waste in healthcare and increased patient burden in diabetes management.

FUNDING, COMPETING INTERESTS, DATA SHARING

NDS and RGM are funded partly by the Agency for Healthcare Research and Quality (R18HS18339) and AcademyHealth Delivery System Science Fellowship (2013), respectively. No competing interests declared. Additional data are available from mccoy.rozalina@mayo.edu.

Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer

BACKGROUND

Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed.

OBJECTIVE

The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer.

METHODS

Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways.

RESULTS

The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information.

CONCLUSIONS

Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals.

Predictability Bounds of Electronic Health Records

The ability to intervene in disease progression given a person's disease history has the potential to solve one of society's most pressing issues: advancing health care delivery and reducing its cost. Controlling disease progression is inherently associated with the ability to predict possible future diseases given a patient's medical history. We invoke an information-theoretic methodology to quantify the level of predictability inherent in disease histories of a large electronic health records dataset with over half a million patients. In our analysis, we progress from zeroth order through temporal informed statistics, both from an individual patient's standpoint and also considering the collective effects. Our findings confirm our intuition that knowledge of common disease progressions results in higher predictability bounds than treating disease histories independently. We complement this result by showing the point at which the temporal dependence structure vanishes with increasing orders of the time-correlated statistic. Surprisingly, we also show that shuffling individual disease histories only marginally degrades the predictability bounds. This apparent contradiction with respect to the importance of time-ordered information is indicative of the complexities involved in capturing the health-care process and the difficulties associated with utilising this information in universal prediction algorithms.

Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation

BACKGROUND

National electronic health records (EHR) are increasingly used for research but identifying disease cases is challenging due to differences in information captured between sources (e.g. primary and secondary care). Our objective was to provide a transparent, reproducible model for integrating these data using atrial fibrillation (AF), a chronic condition diagnosed and managed in multiple ways in different healthcare settings, as a case study.

METHODS

Potentially relevant codes for AF screening, diagnosis, and management were identified in four coding systems: Read (primary care diagnoses and procedures), British National Formulary (BNF; primary care prescriptions), ICD-10 (secondary care diagnoses) and OPCS-4 (secondary care procedures). From these we developed a phenotype algorithm via expert review and analysis of linked EHR data from 1998 to 2010 for a cohort of 2.14 million UK patients aged ≥ 30 years. The cohort was also used to evaluate the phenotype by examining associations between incident AF and known risk factors.

RESULTS

The phenotype algorithm incorporated 286 codes: 201 Read, 63 BNF, 18 ICD-10, and four OPCS-4. Incident AF diagnoses were recorded for 72,793 patients, but only 39.6% (N = 28,795) were recorded in primary care and secondary care. An additional 7,468 potential cases were inferred from data on treatment and pre-existing conditions. The proportion of cases identified from each source differed by diagnosis age; inferred diagnoses contributed a greater proportion of younger cases (≤ 60 years), while older patients (≥ 80 years) were mainly diagnosed in SC. Associations of risk factors (hypertension, myocardial infarction, heart failure) with incident AF defined using different EHR sources were comparable in magnitude to those from traditional consented cohorts.

CONCLUSIONS

A single EHR source is not sufficient to identify all patients, nor will it provide a representative sample. Combining multiple data sources and integrating information on treatment and comorbid conditions can substantially improve case identification.