Article Text

Association of glucose variability at the last day of hospitalization with 30-day readmission in adults with diabetes
1. Elias K Spanakis1,2,3,
2. Lakshmi G Singh1,
3. Tariq Siddiqui2,4,
4. John D Sorkin5,
5. George Notas3,
6. Michelle F Magee6,
7. Jeffrey C Fink4,7,
8. Min Zhan8,
9. Guillermo E Umpierrez9
1. 1Division of Endocrinology, Baltimore Veterans Affairs Medical Center, Baltimore, Maryland, USA
2. 2Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, USA
3. 3Laboratory of Experimental Endocrinology, University of Crete School of Medicine, Heraklion, Greece
4. 4Division of Nephrology, University of Maryland School of Medicine, Baltimore, Maryland, USA
5. 5Baltimore Veterans Affairs Medical Center GRECC (Geriatric Research, Education, and Clinical Center), Baltimore, Maryland, USA
6. 6Georgetown University School of Medicine; MedStar Diabetes, Research and Innovation Institutes, Washington, DC, USA
7. 7Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
8. 8Department of Epidemiology and Public Health, University of Maryland, Baltimore, Maryland, USA
9. 9Division of Endocrinology, Metabolism, and Lipids, Emory University School of Medicine, Atlanta, Georgia, USA
1. Correspondence to Dr Elias K Spanakis; ispanakis{at}som.umaryland.edu

## Abstract

Objective To evaluate whether increased glucose variability (GV) during the last day of inpatient stay is associated with increased risk of 30-day readmission in patients with diabetes.

Research design and methods A comprehensive list of clinical, pharmacy and utilization files were obtained from the Veterans Affairs (VA) Central Data Warehouse to create a nationwide cohort including 1 042 150 admissions of patients with diabetes over a 14-year study observation period. Point-of-care glucose values during the last 24 hours of hospitalization were extracted to calculate GV (measured as SD and coefficient of variation (CV)). Admissions were divided into 10 categories defined by progressively increasing SD and CV. The primary outcome was 30-day readmission rate, adjusted for multiple covariates including demographics, comorbidities and hypoglycemia.

Results As GV increased, there was an overall increase in the 30-day readmission rate ratio. In the fully adjusted model, admissions with CV in the 5th–10th CV categories and admissions with SD in the 4th–10th categories had a statistically significant progressive increase in 30-day readmission rates, compared with admissions in the 1st (lowest) CV and SD categories. Admissions with the greatest CV and SD values (10th category) had the highest risk for readmission (rate ratio (RR): 1.08 (95% CI 1.05 to 1.10), p<0.0001 and RR: 1.11 (95% CI 1.09 to 1.14), p<0.0001 for CV and SD, respectively).

Conclusions Patients with diabetes who exhibited higher degrees of GV on the final day of hospitalization had higher rates of 30-day readmission.

Trial registration number NCT03508934, NCT03877068.

• epidemiology
• hospitalization
• inpatient diabetes management

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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### Significance of this Study

• Hospital readmissions represent a high-priority quality indicator for the healthcare delivery system.

• Increased glucose variability (GV) has been linked to adverse outcomes in the hospital, such as prolonged length of stay.

• Hypoglycemia in the last 24 hours of hospitalization has been associated with increased 30-day readmissions.

#### What are the new findings?

• Admissions of patients with diabetes mellitus with the highest GV in the last 24 hours of the inpatient stay were associated with an increase in 30-day readmission rate ratios.

• This association persisted even after adjustment of multiple covariates, including hypoglycemia in the last 24 hours of the inpatient stay.

#### How might these results change the focus of research or clinical practice?

• The observed association between increased GV in the last 24 hours of hospitalization with higher rates of 30-day readmission may reveal a potentially independent and modifiable factor to reduce hospital readmissions.

## Introduction

Hospital readmissions, a high-priority quality indicator for the healthcare delivery system, has remained an important quality metric due to the significant economic burden, high prevalence and preventability.1 The Centers for Medicare and Medicaid hospital readmissions reduction program penalizes hospitals with higher readmission rates by reducing payments.2 3

Patients with diabetes mellitus (DM) have high 30-day readmission rates, ranging from 14% to 26%.1 3–14 In both 2012 and 2017, an estimated US$123 billion of healthcare costs incurred was attributed to the hospitalizations of patients with DM,15 16 with 30-day readmissions costs estimated at US$20 billion.17 Therefore, identifying underlying causes and potentially modifiable risk factors for readmissions is imperative as it may improve quality of care and reduce the cost of inpatient care in patients with DM.

Numerous risk factors for hospital readmissions have been described in patients with DM, including burden of comorbidities, ethnic/racial minority, hospital-related and socioeconomic factors, among others.1 17 However, there is limited data available regarding the impact of inpatient dysglycemia (defined as hyperglycemia, hypoglycemia and increased glucose variability) on readmission risk. Hyperglycemia at time of admission or 24 hours before admission10 and hypoglycemia during hospitalization have been associated with higher 30-day readmission rates.18

Glucose variability (GV), another marker of dysglycemia, refers to the magnitude of glucose fluctuations that occur around the mean glucose, and is increasingly considered as an indicator of poor glycemic control.19 20 Increased GV and hyperglycemia are known to be associated with adverse outcomes presumed to be secondary to their impact on oxidative stress,19 21–23 neuronal damage, mitochondrial damage and coagulation activity.23 24 Increased GV during hospitalization is associated with a higher risk for poor clinical outcomes in the non-critical care setting such as prolonged length of stay (LOS) and increased postdischarge mortality,19 as well as increased mortality in the intensive care unit (ICU) setting.22 There is presently no gold standard for measurement of GV22 25 and several indices have been described.22 23 The most widely used measure is SD22 along with the coefficient of variation (CV) another valid and frequently used measure for GV.19

The majority of the published studies have examined the effect of glucose control during the entire hospital stay on adverse outcomes. There is limited information about the relationship between glucose control during the last day of hospitalization and the risk for readmission. In a nationwide cohort study, we examined 843 978 admissions among patients with DM and we reported a strong association of lower glucose values during the last 24 hours of hospitalization with adverse clinical outcomes, including higher 30-day readmission rates.26

To our knowledge, no previous studies have examined the relationship between GV during the last day of the hospitalization and risk of hospital readmission in patients with DM. Therefore, we evaluated whether increased GV during the last 24 hours of hospitalization, a potentially modifiable risk factor, is associated with increased 30-day readmission rates.

## Methods

### Study overview and data sources

This nationwide cohort study used data obtained from the Veterans Affairs (VA) health system of patients with DM admitted between January 1, 2000 and December 31, 2014.26 27 The study ended in 2014, as this was the final year in which International Classification of Diseases (ICD)-9 codes were used. Data were obtained from the VA Central Data Warehouse, a national administrative data repository which stores comprehensive clinical, pharmacy and utilization records and VA Vital Status File for dates of death.27

The cohort was created by initially identifying all acute VA admissions among patients with DM. Patients with DM were identified by either the presence of ≥2 ICD-9 codes during the past 2 years from an inpatient stay or outpatient visit on separate days and/or had prescriptions for DM within the current year.28 We excluded admissions (figure 1) to psychiatric or long-term care settings (n=273 549) and admissions ending with transfer to a non-VA hospital (n=54 992), as follow-up data were not available, admissions with LOS ≥30 days (n=34 006) or LOS <1 day (n=59 474), and admissions with in-hospital deaths (n=30 603). We also excluded admissions where there were less than two glucose values (including only those with two or more glucose values) during the last 24 hours of the hospitalization, as neither SD or CV can be computed, and glucose values collected within 5 min of previous glucose values as previously described (n=7 72 482).19

Figure 1

Study cohort creation flow diagram. BMI, body mass index; DM, diabetes mellitus; ICU, intensive care unit; LOS, length of stay; Psych, psychiatric.

Additionally, 399 duplicate admissions were also excluded. Patients in the ICU were excluded as this represents a different population than those admitted to non-critical care settings (n=13 071).19 Finally, we excluded admissions with missing body mass index (BMI) or with BMI <14 or >120 kg/m2 (n=20 835), hospitalizations where it was not possible to determine the admitting service (medicine or surgery, n=1) or the hospital where the patients were admitted (n=85).26 The final cohort sample used for analysis was 1 042 150 admissions.

### Covariates

Independent variables analyzed for this study included age, gender, race, BMI, income, year of admission, admission source (whether patients were admitted from home or other facilities), admitting service (medicine or surgery), hemoglobin A1c (A1c) obtained 90 days prior to the admission, DM medications used during the last 24 hours of hospitalization and several comorbid conditions as previously defined by Elixhauser et al (table 1).29 Hypoglycemia in the hospital was defined as glucose values <70 mg/dL.30 Length of hospital stay was calculated by subtracting the discharge day and time from the admission day and time, to ascertain the last 24 hours of the hospitalization.

Table 1

Characteristics of admissions of patients with diabetes mellitus

### Exposures and outcomes

Exposures of interest for this study were measures of GV, measured by the CV and SD. CV and SD were calculated from point-of-care (POC) glucose values measured (minimum ≥2 values) during the last 24 hours of hospitalization.19 We divided CV and SD values into 10 different groups (deciles) with the 1st decile and the 10th decile having the lowest and the highest measurements, respectively. Our outcome measure was the 30-day readmission, defined as a rehospitalization which occurred within 30 days from the discharge date of the index admission.27 31 As patients with DM are at risk for multiple admissions (and therefore readmissions),32 we did not include the first readmission, as this approach would have led to exclusion of a significant number of rehospitalizations. All readmissions that occurred >30 days from the index hospitalization were considered new index admissions, as previously described.26 27 31

### Statistical methods

The event rates were computed for every decile group of CV and SD. General estimating equations (GEE) with a Poisson distribution and an exchangeable covariance structure were used to compute adjusted rate ratios of the 30-day readmission while accounting for the correlation of repeated admissions obtained from the same patient and clustering in each center.33 34

We considered three models based on CV and SD deciles: (1) the minimally adjusted model, which controls for age, gender and race; (2) the second model which controls for all the variables collected (table 1), except for hypoglycemia (age, gender, including income, BMI, admission source (whether patients were admitted from home or other facilities), admitting service, DM medications, year of admission and multiple comorbidities); (3) the third model which controls for all the variables in the second model including also hypoglycemia. We did not adjust for A1c as only 35.3% of patient-admissions had an A1c obtained within 90 days of the hospitalization. Statistical analyses were performed using SAS software, V.9.4 (SAS Institute). Bonferroni corrected p values adjusting for multiple testing were used to compare the covariates among three CV categories based on the calculated CV values (admissions with CV deciles 1–4, CV deciles 5–7 and CV deciles 8–10) in table 1. A two-tailed p<0.05 was considered to be statistically significant.

## Results

Table 2

Event RR of the 30-day readmission by CV decile category, obtained in the last 24 hours of inpatient stay

Table 3

Event RR of the 30-day readmission by SD decile category, obtained in the last 24 hours of inpatient stay

Similarly, when we used SD as a measurement of glucose variability (table 3), admissions with SD in the 3rd–10th categories (models 1 and 2) and admissions with SD in the 4th–10th categories (model 3) had a higher 30-day readmission rate compared with the 1st SD category. After adjusting for all the covariates including hypoglycemia (model 3), admissions with the SD values in the 10th category had the highest 30-day readmission rate (RR: 1.11 (95% CI 1.09 to 1.14), p<0.0001).

## Discussion

In this study, we evaluated the association of GV during the last 24 hours of hospitalization with 30-day readmission rates among a large cohort of adults with diabetes admitted in the non-critical care setting. We identified that admissions of patients with DM with the highest GV during the last 24 hours of the inpatient stay, using CV and SD measurements, had an increased risk for 30-day readmission. This association persisted despite adjustment for multiple covariates, notably including adjustment for hypoglycemia during the last 24 hours of hospitalization.

Evidence from studies performed in the outpatient setting has shown that higher GV increases risk of adverse clinical outcomes. The effect of GV on oxidative stress and endothelial dysfunction is thought to be equal or greater than that attributed to persistent hyperglycemia, and is postulated to contribute to the development of microvascular and macrovascular DM complications.23 Within the inpatient setting several studies have examined increased GV with adverse outcomes, revealing associations with prolonged length of stay19 and increased mortality in both ICU and non-critical care settings.19 35–38

To our knowledge, this is the first nationwide study aiming to determine whether increased GV is associated with increased risk for hospital readmissions. Hospital readmissions within 30 days postdischarge has drawn national attention with federal policy reform due to the rising healthcare expenditure, high prevalence and preventability.1 Therefore, research focused on exploring potentially modifiable factors to reduce readmissions is of utmost importance. The effect of glucose control at admission or throughout the inpatient stay on readmission has been evaluated,18 but not during the final day of the hospitalization. Recently, we have reported an association of lower glucose values on the last inpatient day with increased rate of 30-day readmission.26 In the present analysis, we have additionally showed an independent association of higher GV on the last day of hospitalization with increased 30-day readmission. The results from our cohort studies may shed light on two potentially modifiable risk factors for reducing 30-day readmissions.

It is unknown how higher GV on the last day of hospitalization may contribute to increased risk of 30-day readmission. Although we did not have access to postdischarge glycemic values, one potential explanation is that high GV predisposes patients to postdischarge hypoglycemia or to significant hyperglycemia, which may lead to readmission. In our study, we found that those admissions of patients with the highest GV also had the highest incidence of hypoglycemia during the last 24 hours of the hospital stay, which is consistent with previous observations. It is known that blood glucose disturbances precede severe hypoglycemia39 and increased GV has been previously identified as a predictor of hypoglycemia.25 40 41 The transition of care from inpatient to the outpatient setting signifies a vulnerable and challenging time with greater risk of dysglycemia, as well as healthcare utilization such as emergency room visits or readmissions.26 42 43 As patients with DM have multiple and often suboptimally controlled comorbidities, they have an inherently higher risk for frequent readmissions.32 44

Despite the growing evidence of an association of GV with poor clinical outcomes, it remains debatable whether GV should be considered a treatment target.45–47 Lack of standardization for the definition and method of measurement of GV contributes to this uncertainty. Within the literature there is significant heterogeneity of GV indices reported,24 therefore the consolidation of the available evidence to drive changes in clinical practice is difficult. SD, a simple method for assessing GV, represents the distribution of data around the mean blood glucose,23 and is useful for analysis of intraday variation of POC glucose values. Other metrics of variability have been proposed including but not limited to J-index, mean amplitude of glucose excursion, mean absolute glucose, continuous overlapping net glycemic action, the high and low blood glucose index and mean of daily differences.22 23 25 A criticism of the CV is that the mean is used in its calculation and as a result, violations of normality of the distribution of glucose values or extreme concentrations can exaggerate the CV measurement. Although this is a valid criticism, each of the other metrics have their own limitations.23 We used the CV which has been frequently reported.19 35 Although an imperfect metric of variability, our study shows that the CV is related to 30-day readmission and suggests further studies should be conducted to determine if reducing variability lowers the rate of readmission. Recently, it has been advocated that continuous glucose monitoring in the hospital may offer advantages over POC glucose testing in managing and evaluating glycemic control in hospitalized patients.48 Future studies using this technology will help us to confirm our findings on the importance of GV on hospital outcome and readmission risk.

Our study has several strengths. To our knowledge, this cohort is one of the largest studies evaluating readmission rates in patients with DM using national data. Additionally, the VA Healthcare System is a ‘closed’ health system where most veterans receive all of their healthcare including treatment during hospitalizations thus assuring a robust method to accurately measure readmissions. Given the comprehensive and extensive nature of the Veterans Health Administration data sources, we were able to include data for >1 million admissions of patients with diabetes and a broad set of covariates and risk factors in this analysis (table 1).

There are limitations to our study that should be considered. Consistent with previously published studies using Veterans Health Administration data sources, our analysis is restricted to this single healthcare system.27 Although we included nationwide data, it excluded readmissions to non-VA hospitals. Additionally, our patient population may not be representative of the general population given veterans are more likely to be male, elderly and have multiple comorbid conditions. However, since we adjusted for social-demographic data and comorbidities, we have minimized the impact of these differences and we believe the results can be extrapolated to the general DM population. Glucose variability can be influenced by the nutritional intake during an inpatient stay, which was not collected in our study, and perhaps is a limitation. We did not distinguish the preventable readmissions from other readmissions. Although preventability of readmissions has been evaluated using administrative data previously, it is subjective and therefore may not represent the most optimal method to study this objective.27 49 Identifying high GV as a potentially modifiable risk factor in the last 24 hours using POC glucose values alone may be challenging. Methods in detecting high GV in the inpatient setting in a reliable and efficient way need to be explored. Consideration of CGM use in the hospital setting would ensure collection of accurate glucometric data, including CV and SD. Current ongoing prospective studies using CGM technology are investigating the use of CGM in the hospital setting and after discharge, and should provide more comprehensive information regarding GV on clinical outcomes and readmission risk. Lastly, we limited our analysis on the effect of GV during the last day of the hospitalization on readmission and did not examine the overall effect during the entire hospital stay.

In conclusion, the results of this VA nationwide cohort observation study including 1 042 150 admissions of patients with DM indicate that patients with higher GV on the last day of hospitalization were at a higher risk for 30-day readmission. Although the increased 30-day readmission risk could also be secondary to underlying medical conditions, inpatient diabetes medications or other risk factors unrelated to glucose variability, our extensive analyses, adjusting for multiple covariates indicate that increased GV during the last day of the hospitalization can be considered as a potential risk factor for early readmission. Further prospective studies are needed to fully explore whether reducing GV can decrease the risk for 30-day readmission.

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## Footnotes

• Contributors EKS and MZ had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. EKS conceived and designed the study, provided guidance for the statistical analysis and wrote the manuscript. GEU made critical revisions to the study design and manuscript and takes responsibility for the integrity of the data and the accuracy of the data analysis. MZ performed the statistical analyses and made critical revisions to the manuscript for important intellectual content. LGS, TS, JDS, GN, JCF and MFM made critical revisions to the manuscript for important intellectual content. The authors had access to all the study data and take full responsibility of the accuracy of the analysis. All authors approved the manuscript.

• Funding This work was supported in part by the VA MERIT award (#1I01CX001825-01) from the US Department of Veterans Affairs Clinical Sciences Research and Development Service (EKS) and by the Baltimore VA Patient Safety Center of Inquiry (JCF). MFM has received research support from the National Institutes of Health for an inpatient diabetes education study (R34 Planning Grant for Pragmatic Research in Healthcare settings, DK-109503, entitled Diabetes To Go Inpatient). JDS was supported by National Institute of Aging (NIA P30-AG028747), National Institute of Diabetes and Digestive and Kidney Disease (NIDDK P30-DK072488) and the Baltimore VA Geriatric Research, Education and Clinical Center. GEU is partly supported by research grants from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378, from the Clinical and Translational Science Award program and from the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK P30DK11102).

• Disclaimer The funders had no role in the design or the conduction of the study; collection, analysis, interpretation of the data; preparation, review, approval of the manuscript and decision to submit the manuscript for publication.

• Competing interests EKS has received research support from DEXCOM for the conduction of clinical studies, which did not support this work monetarily or in kind. MFM has recently completed an Eli Lilly funded outpatient Pharma trial, which did not support this work monetarily or in kind. GEU has received unrestricted research support for inpatient studies (to Emory University) from Sanofi, Novo Nordisk, and Dexcom, which did not support the work monetarily or in kind.

• Patient consent for publication Not required.

• Ethics approval This study was approved by the University of Maryland Institutional Review Board and the Baltimore VA Research and Development Committee.

• Provenance and peer review Not commissioned; externally peer reviewed.

• Data availability statement Data are available in a public, open access repository. Data are available on reasonable request. This nationwide cohort study used data obtained from the Veterans Affairs (VA) health system, data that are publicly available on request.

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