In this retrospective analysis, we explored the correlation between measured average glucose (mAG) and A1C-estimated average glucose (eAG) in hospitalized patients with diabetes and identified factors associated with discordant mAG and eAG at the transition from home to hospital. Having mAG lower than eAG was associated with Black race, other race, increasing length of stay, community hospital setting, surgery, fever, metformin use, certain inpatient diets, home antihyperglycemic treatment, and coded type 1 or type 2 diabetes. Having mAG higher than eAG was associated with certain discharge services (e.g., intensive care unit), higher BMI, hypertension, tachycardia, higher albumin, higher potassium, anemia, inpatient glucocorticoid use, and treatment with home insulin, secretagogues, and glucocorticoids. These factors should be considered when using patients’ A1C as an indicator of outpatient glycemic control to determine the inpatient antihyperglycemic regimens.

The prevalence of diabetes in hospitalized patients is high, with the condition being listed as a diagnosis in ∼8 million hospital discharges annually (1). Although diabetes often is not the primary reason for admission, glycemic management plays an important role in hospitalization because dysglycemia has been linked to various adverse outcomes (e.g., longer length of stay [LOS], readmission, mortality, and morbidity) (26). Determining an antihyperglycemic regimen at the transition from home to hospital can be challenging because of evolving patient factors (e.g., acute illness and nutritional status) and systems factors (e.g., timing of insulin delivery with meals and availability of decision support tools).

Current clinical practice guidelines recommend obtaining an A1C measurement at admission for all patients with diabetes or hyperglycemia if one is not available from within the past 90 days (7). As an estimate of recent glycemic control, A1C allows clinicians to assess the quality of outpatient glycemic control and can inform the selection of an inpatient antihyperglycemic regimen. However, although A1C is an important indicator of the quality of outpatient glycemic control, several factors may influence A1C measurements, including race/ethnicity, anemia, hemolysis, hemoglobinopathy, renal failure, chronic kidney disease (CKD), albumin levels, and acute corticosteroid use (823). Recognizing factors that may result in spurious A1C values is important at the transition from home to hospital because incorrect assumptions about the quality of outpatient glycemic control could lead to ineffective or dangerous inpatient treatment regimens (24,25).

The objective of this study was to identify factors that may be associated with highly discordant observed and expected inpatient blood glucose values, recognizing that there may be various reasons for such discordance, including conditions that affect the accuracy of A1C, sampling frequency of point-of-care (POC) glucose readings, changes in nutritional status, differences in inpatient versus outpatient antihyperglycemic regimens, effects of acute or critical illness, and the use of glucocorticoids. We sought to explore the association between clinical characteristics and the glycemic gap, defined as the difference in measured average glucose (mAG) of all POC and serum glucose values during admission and A1C-estimated average glucose (eAG) derived from a published conversion equation (26).

We hypothesized that lower-than-expected inpatient glucose values would be observed in admissions when A1C is spuriously high because of low albumin, anemia, and Black race, or in patients who are on NPO nutrition status. Conversely, we hypothesized that higher-than-expected inpatient glucose values would be observed in admissions when A1C is spuriously low because of high albumin, hemolytic anemia, acute infection, critical illness, or corticosteroid treatment. A secondary objective was to identify other clinical factors associated with discordance between mAG and eAG. To our knowledge, no previous studies have explored this relationship in a large cohort of noncritically ill and critically ill hospitalized patients.

Design

This was a retrospective analysis of 17,903 unique adult patients hospitalized at five hospitals within the Johns Hopkins Health System who were discharged between 1 December 2014 and 30 May 2019. We were interested in factors associated with discordance between mAG and eAG at the patient level, so we selected the last admission per patient during the study period, removing all prior admissions to exclude duplicates. The study flowchart is shown in Figure 1. Data were abstracted from charts of hospitalized adults ≥18 years of age who had at least four blood glucose measurements during their hospitalization. Included patients had diabetes based on at least one of the following criteria: International Classification of Diseases, 10th revision, code for diabetes mellitus on their problem list, medical history, or discharge diagnosis; at least two blood glucose measurements ≥200 mg/dL during admission; or an A1C measurement ≥6.5% obtained during the admission. Admissions in which there was no A1C measurement obtained were excluded. The study was approved by the institutional review board of the Johns Hopkins School of Medicine.

FIGURE 1

Study flowchart.

FIGURE 1

Study flowchart.

Close modal

Data Sources

Demographics, vital signs, admission and discharge diagnoses, discharge service, laboratory data, diet orders, and medications were extracted from our electronic health record (EHR) system (EpicCare) by Clarity-certified analysts.

A1C was measured from venous samples in the Johns Hopkins Hospital laboratory using the Bio-Rad Variant II hemoglobin testing system. Both serum and POC measurements were used to determine blood glucose levels. The Roche Cobas hexokinase method was used for serum glucose measurements, and POC measurements were performed using the Nova StatStrip glucose meter. The source (venous, arterial, or capillary fingerstick) used for POC measurements was not recorded. POC test results are corrected for hematocrit by the Nova StatStrip device, and there is excellent correlation between the Nova StatStrip and plasma hexokinase methods (27,28), so POC measurements were deemed to be equivalent to serum measurements. POC measurements have also demonstrated accuracy and reliability in critically ill patients (28). The same devices and analytic methods were used at all hospitals in our dataset during the study period.

Exposure Variables

Exposure variables were selected based on clinical knowledge and published literature. Patient admissions with data missing for key exposure variables were excluded, as shown in the study flowchart in Figure 1. Definitions and data sources for these variables are listed in Supplementary Table S1.

Outcome Variables

mAG was calculated as the mean of all serum and POC blood glucose measurements during hospitalization, and eAG was calculated from A1C using the following formula:
formula

This formula was derived from a weighted combination of continuous glucose monitoring (CGM) and seven-point self-monitoring of blood glucose (SMBG) (26); however, in the hospital setting, blood glucose values are typically measured four times daily (before meals and at bedtime) or every 4–6 hours for patients with NPO status. Thus, this formula was not expected to perfectly correlate with average glucose in the hospital because of differences in sampling frequency.

The primary outcome variable was the glycemic gap, defined as the difference (Δ) between mAG and eAG. We defined the glycemic gap in two discordant phenotypes based on the SD of the mean of the difference between the mAG and eAG. Compared with the concordant phenotype (defined as Δ within 1 SD), patients with Δ in the ≤2.5th percentile were determined to have a discordant-low glycemic gap (mAG < eAG; lower-than-expected glucose), and patients with Δ in the ≥97.5th percentile were determined to have a discordant-high glycemic gap (mAG > eAG; higher-than-expected glucose).

Statistical Analysis

Descriptive statistics were used to summarize characteristics of the overall population and to demonstrate differences among the three phenotypic groups (concordant, discordant-low, and discordant-high). Normality of data were assessed using histograms and tests for skewness and kurtosis. All continuous variables were nonnormally distributed, so medians and interquartile ranges (IQRs) are reported.

Univariate logistic regression analyses were performed to identify clinical factors significantly associated with the two discordant phenotypes using the concordant phenotype as the reference group. Covariates identified from the univariate analyses to either have a P value <0.05 or to be established clinical factors known to influence the relationship between A1C and glucose were then included in multivariate logistic regression analyses to identify factors that are independently associated with discordance. All analyses were conducted at the patient level based on the last admission during the study period. Statistical analyses were performed using Stata statistical software, v. 15 (StataCorp., College Station, TX). P <0.05 was considered statistically significant.

Table 1 shows the baseline characteristics of the overall study population and by phenotype of concordance between mAG and eAG. The full cohort consisted of older, overweight, predominantly White patients with a slight male majority. The majority of patients (53.3%) were diagnosed with type 2 diabetes. The median LOS was 5.8 days, and most patients (60.6%) were treated at an academic hospital. There was a high prevalence of hypertension (31.8%). Acute illness or infection were suggested by a high prevalence of tachycardia (55.5%), fever (40.1%), and leukocytosis (median white blood cell [WBC] count 11,600). Reduced renal function (estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m2) was observed in 18.7% of patients. Patients received a median total daily dose (TDD) of insulin of 5.5 units, and a large percentage of patients (43.3%) were on a carbohydrate-controlled diet.

TABLE 1

Baseline Characteristics of the Study Population Overall and by Phenotype of Concordance Between mAG and eAG

FactorFull CohortConcordantDiscordant-Low (mAG < eAG)P*Discordant-High (mAG > eAG)P*
Patients, n 17,903 17,009 447  447  
Age, years 65 (55–75) 66 (55–75) 54 (40–62) <0.001* 62 (52–71) <0.001* 
Female sex 8,398 (46.9) 7,981 (46.9) 200 (44.7) 0.36 217 (48.5) 0.50 
Race    <0.001*  0.096 
 White or Caucasian 9,102 (50.8) 8,703 (51.2) 146 (32.7)  253 (56.6)  
 Black or African American 6,457 (36.1) 6,059 (35.6) 250 (55.9)  148 (33.1)  
 Asian 828 (4.6) 791 (4.7) 19 (4.3)  18 (4.0)  
 Other 1,516 (8.5) 1,456 (8.6) 32 (7.2)  28 (6.3)  
Diabetes diagnosis    <0.001*  <0.001* 
 None 7,098 (39.6) 6,767 (39.8) 88 (19.7)  243 (54.4)  
 Type 1 902 (5.0) 774 (4.6) 111 (24.8)  17 (3.8)  
 Type 2 9,546 (53.3) 9,131 (53.7) 237 (53.0)  178 (39.8)  
 Other 357 (2.0) 337 (2.0) 11 (2.5)  9 (2.0)  
Discharge service    <0.001*  0.070 
 Medicine/pediatrics 9,530 (53.2) 8,984 (52.8) 301 (67.3)  245 (54.8)  
 ICU 2,209 (12.3) 2,100 (12.3) 40 (8.9)  69 (15.4)  
 IMC 3,906 (21.8) 3,767 (22.1) 50 (11.2)  89 (19.9)  
 Psychiatry 448 (2.5) 425 (2.5) 11 (2.5)  12 (2.7)  
 Other 1,810 (10.1) 1,733 (10.2) 45 (10.1)  32 (7.2)  
LOS, days 5.8 (3.3–10.5) 5.9 (3.4–10.7) 5.1 (3.3–8.8) 0.005* 4.4 (2.8–7.6) <0.001* 
Hospital type    <0.001*  0.016* 
 Academic 10,857 (60.6) 10,342 (60.8) 218 (48.8)  297 (66.4)  
 Community 7,046 (39.4) 6,667 (39.2) 229 (51.2)  150 (33.6)  
Surgery during hospitalization 4,499 (25.1) 4,338 (25.5) 98 (21.9) 0.086 63 (14.1) <0.001* 
BMI, kg/m2 28.7 (24.3–34.3) 28.7 (24.4–34.3) 27.2 (23.0–32.3) <0.001* 29.2 (24.3–35.6) 0.23 
Systolic blood pressure, mmHg    <0.001*  0.022* 
 Normotensive (90–119) 4,329 (24.2) 4,082 (24.0) 153 (34.2)  94 (21.0)  
 Hypotensive (<90) 80 (0.4) 76 (0.4) 1 (0.2)  3 (0.7)  
 Elevated and stage I HTN (120–139) 7,800 (43.6) 7,400 (43.5) 174 (38.9)  226 (50.6)  
 Stage II or more HTN (≥140) 5,694 (31.8) 5,451 (32.0) 119 (26.6)  124 (27.7)  
Tachycardia 9,932 (55.5) 9,372 (55.1) 281 (62.9) 0.001* 279 (62.4) 0.002* 
Tachypnea 3,176 (17.7) 3,034 (17.8) 50 (11.2) <0.001* 92 (20.6) 0.14 
Fever 7,171 (40.1) 6,863 (40.3) 173 (38.7) 0.48 135 (30.2) <0.001* 
Mean albumin, g/dL 3.4 (3.0–3.8) 3.4 (3.0–3.8) 3.5 (3.0–4.0) 0.051 3.5 (3.0–3.9) 0.42 
CKD stage    <0.001*  0.016* 
 1 10,818 (60.4) 10,207 (60.0) 325 (72.7)  286 (64.0)  
 2 3,735 (20.9) 3,570 (21.0) 82 (18.3)  83 (18.6)  
 3A 1,026 (5.7) 995 (5.8) 11 (2.5)  20 (4.5)  
 3B 1,032 (5.8) 981 (5.8) 14 (3.1)  37 (8.3)  
 4 811 (4.5) 783 (4.6) 12 (2.7)  16 (3.6)  
 5 481 (2.7) 473 (2.8) 3 (0.7)  5 (1.1)  
Anemia 13,602 (76.0) 12,972 (76.3) 293 (65.5) <0.001* 337 (75.4) 0.67 
 Maximum potassium, mEq/L 4.7 (4.3–5.2) 4.7 (4.3–5.2) 4.7 (4.3–5.1) 0.71 4.8 (4.4–5.3) 0.002* 
Mean sodium, mEq/L 138.6 (136.4–140.8) 138.6 (136.4–140.8) 137.4 (135.5–139.6) <0.001* 136.8 (134.6–139.6) <0.001* 
Maximum WBC count (× 10311.6 (8.4–16.4) 11.5 (8.4–16.4) 12.0 (8.2–16.2) 0.85 12.3 (8.3–16.9) 0.2 
Medical history       
 CAD 697 (3.9) 678 (4.0) 11 (2.5) 0.10 8 (1.8) 0.018* 
 CHF 996 (5.6) 968 (5.7) 10 (2.2) 0.002* 18 (4.0) 0.13 
 CVD 5 (<1) 4 (<1) 1 (0.2) 0.014* 0 (0.0) 0.75 
Mean insulin TDD, units 5.5 (0.0–21.7) 5 (0.0–20.0) 32.2 (16.1–47.8) <0.001* 17.3 (4.0–35.4) <0.001* 
Metformin during hospitalization 1,572 (8.8) 1,478 (8.7) 65 (14.5) <0.001* 29 (6.5) 0.10 
Sulfonylurea during hospitalization 847 (4.7) 814 (4.8) 15 (3.4) 0.16 18 (4.0) <0.001* 
Hydrocortisone equivalent dose    <0.001*  <0.001* 
 None (Ref) 15,340 (85.7) 14,639 (86.1) 428 (95.7)  273 (61.1)  
 Low 463 (2.6) 445 (2.6) 5 (1.1)  13 (2.9)  
 Medium 571 (3.2) 540 (3.2) 4 (0.9)  27 (6.0)  
 High 1,529 (8.5) 1,385 (8.1) 10 (2.2)  134 (30.0)  
Diet    <0.001*  0.65 
 Regular 6,488 (36.2) 6,272 (36.9) 52 (11.6)  164 (36.7)  
 Carbohydrate-controlled 7,746 (43.3) 7,218 (42.4) 327 (73.2)  201 (45.0)  
 NPO/clear liquid/full liquid 2,898 (16.2) 2,779 (16.3) 54 (12.1)  65 (14.5)  
 Tube feeding 373 (2.1) 363 (2.1) 4 (0.9)  6 (1.3)  
 Other 62 (0.3) 60 (0.4) 1 (0.2)  1 (0.2)  
 Unknown 336 (1.9) 317 (1.9) 9 (2.0)  10 (2.2)  
Home medications       
 Insulin 4,792 (26.8) 4,496 (26.4) 158 (35.3) <0.001* 138 (30.9) 0.036* 
 Meglitinide or sulfonylurea 2,701 (15.1) 2,594 (15.3) 36 (8.1) <0.001* 71 (15.9) 0.71 
 Other antihyperglycemics 5,615 (31.4) 5,376 (31.6) 126 (28.2) 0.12 113 (25.3) 0.004* 
 Glucocorticoids 1,053 (5.9) 1,004 (5.9) 6 (1.3) <0.001* 43 (9.6) 0.001* 
FactorFull CohortConcordantDiscordant-Low (mAG < eAG)P*Discordant-High (mAG > eAG)P*
Patients, n 17,903 17,009 447  447  
Age, years 65 (55–75) 66 (55–75) 54 (40–62) <0.001* 62 (52–71) <0.001* 
Female sex 8,398 (46.9) 7,981 (46.9) 200 (44.7) 0.36 217 (48.5) 0.50 
Race    <0.001*  0.096 
 White or Caucasian 9,102 (50.8) 8,703 (51.2) 146 (32.7)  253 (56.6)  
 Black or African American 6,457 (36.1) 6,059 (35.6) 250 (55.9)  148 (33.1)  
 Asian 828 (4.6) 791 (4.7) 19 (4.3)  18 (4.0)  
 Other 1,516 (8.5) 1,456 (8.6) 32 (7.2)  28 (6.3)  
Diabetes diagnosis    <0.001*  <0.001* 
 None 7,098 (39.6) 6,767 (39.8) 88 (19.7)  243 (54.4)  
 Type 1 902 (5.0) 774 (4.6) 111 (24.8)  17 (3.8)  
 Type 2 9,546 (53.3) 9,131 (53.7) 237 (53.0)  178 (39.8)  
 Other 357 (2.0) 337 (2.0) 11 (2.5)  9 (2.0)  
Discharge service    <0.001*  0.070 
 Medicine/pediatrics 9,530 (53.2) 8,984 (52.8) 301 (67.3)  245 (54.8)  
 ICU 2,209 (12.3) 2,100 (12.3) 40 (8.9)  69 (15.4)  
 IMC 3,906 (21.8) 3,767 (22.1) 50 (11.2)  89 (19.9)  
 Psychiatry 448 (2.5) 425 (2.5) 11 (2.5)  12 (2.7)  
 Other 1,810 (10.1) 1,733 (10.2) 45 (10.1)  32 (7.2)  
LOS, days 5.8 (3.3–10.5) 5.9 (3.4–10.7) 5.1 (3.3–8.8) 0.005* 4.4 (2.8–7.6) <0.001* 
Hospital type    <0.001*  0.016* 
 Academic 10,857 (60.6) 10,342 (60.8) 218 (48.8)  297 (66.4)  
 Community 7,046 (39.4) 6,667 (39.2) 229 (51.2)  150 (33.6)  
Surgery during hospitalization 4,499 (25.1) 4,338 (25.5) 98 (21.9) 0.086 63 (14.1) <0.001* 
BMI, kg/m2 28.7 (24.3–34.3) 28.7 (24.4–34.3) 27.2 (23.0–32.3) <0.001* 29.2 (24.3–35.6) 0.23 
Systolic blood pressure, mmHg    <0.001*  0.022* 
 Normotensive (90–119) 4,329 (24.2) 4,082 (24.0) 153 (34.2)  94 (21.0)  
 Hypotensive (<90) 80 (0.4) 76 (0.4) 1 (0.2)  3 (0.7)  
 Elevated and stage I HTN (120–139) 7,800 (43.6) 7,400 (43.5) 174 (38.9)  226 (50.6)  
 Stage II or more HTN (≥140) 5,694 (31.8) 5,451 (32.0) 119 (26.6)  124 (27.7)  
Tachycardia 9,932 (55.5) 9,372 (55.1) 281 (62.9) 0.001* 279 (62.4) 0.002* 
Tachypnea 3,176 (17.7) 3,034 (17.8) 50 (11.2) <0.001* 92 (20.6) 0.14 
Fever 7,171 (40.1) 6,863 (40.3) 173 (38.7) 0.48 135 (30.2) <0.001* 
Mean albumin, g/dL 3.4 (3.0–3.8) 3.4 (3.0–3.8) 3.5 (3.0–4.0) 0.051 3.5 (3.0–3.9) 0.42 
CKD stage    <0.001*  0.016* 
 1 10,818 (60.4) 10,207 (60.0) 325 (72.7)  286 (64.0)  
 2 3,735 (20.9) 3,570 (21.0) 82 (18.3)  83 (18.6)  
 3A 1,026 (5.7) 995 (5.8) 11 (2.5)  20 (4.5)  
 3B 1,032 (5.8) 981 (5.8) 14 (3.1)  37 (8.3)  
 4 811 (4.5) 783 (4.6) 12 (2.7)  16 (3.6)  
 5 481 (2.7) 473 (2.8) 3 (0.7)  5 (1.1)  
Anemia 13,602 (76.0) 12,972 (76.3) 293 (65.5) <0.001* 337 (75.4) 0.67 
 Maximum potassium, mEq/L 4.7 (4.3–5.2) 4.7 (4.3–5.2) 4.7 (4.3–5.1) 0.71 4.8 (4.4–5.3) 0.002* 
Mean sodium, mEq/L 138.6 (136.4–140.8) 138.6 (136.4–140.8) 137.4 (135.5–139.6) <0.001* 136.8 (134.6–139.6) <0.001* 
Maximum WBC count (× 10311.6 (8.4–16.4) 11.5 (8.4–16.4) 12.0 (8.2–16.2) 0.85 12.3 (8.3–16.9) 0.2 
Medical history       
 CAD 697 (3.9) 678 (4.0) 11 (2.5) 0.10 8 (1.8) 0.018* 
 CHF 996 (5.6) 968 (5.7) 10 (2.2) 0.002* 18 (4.0) 0.13 
 CVD 5 (<1) 4 (<1) 1 (0.2) 0.014* 0 (0.0) 0.75 
Mean insulin TDD, units 5.5 (0.0–21.7) 5 (0.0–20.0) 32.2 (16.1–47.8) <0.001* 17.3 (4.0–35.4) <0.001* 
Metformin during hospitalization 1,572 (8.8) 1,478 (8.7) 65 (14.5) <0.001* 29 (6.5) 0.10 
Sulfonylurea during hospitalization 847 (4.7) 814 (4.8) 15 (3.4) 0.16 18 (4.0) <0.001* 
Hydrocortisone equivalent dose    <0.001*  <0.001* 
 None (Ref) 15,340 (85.7) 14,639 (86.1) 428 (95.7)  273 (61.1)  
 Low 463 (2.6) 445 (2.6) 5 (1.1)  13 (2.9)  
 Medium 571 (3.2) 540 (3.2) 4 (0.9)  27 (6.0)  
 High 1,529 (8.5) 1,385 (8.1) 10 (2.2)  134 (30.0)  
Diet    <0.001*  0.65 
 Regular 6,488 (36.2) 6,272 (36.9) 52 (11.6)  164 (36.7)  
 Carbohydrate-controlled 7,746 (43.3) 7,218 (42.4) 327 (73.2)  201 (45.0)  
 NPO/clear liquid/full liquid 2,898 (16.2) 2,779 (16.3) 54 (12.1)  65 (14.5)  
 Tube feeding 373 (2.1) 363 (2.1) 4 (0.9)  6 (1.3)  
 Other 62 (0.3) 60 (0.4) 1 (0.2)  1 (0.2)  
 Unknown 336 (1.9) 317 (1.9) 9 (2.0)  10 (2.2)  
Home medications       
 Insulin 4,792 (26.8) 4,496 (26.4) 158 (35.3) <0.001* 138 (30.9) 0.036* 
 Meglitinide or sulfonylurea 2,701 (15.1) 2,594 (15.3) 36 (8.1) <0.001* 71 (15.9) 0.71 
 Other antihyperglycemics 5,615 (31.4) 5,376 (31.6) 126 (28.2) 0.12 113 (25.3) 0.004* 
 Glucocorticoids 1,053 (5.9) 1,004 (5.9) 6 (1.3) <0.001* 43 (9.6) 0.001* 

Data are median (IQR) or n (%) unless otherwise noted.

*

P value for comparison of the discordant to concordant phenotype. CAD, coronary artery disease; CHF, congestive heart failure; CVD, cerebrovascular disease; HTN, hypertension; Ref, reference category.

There were several differences in patient characteristics between the concordant group and each of the two discordant phenotypes. Of note, the discordant-low group (mAG < eAG) consisted of younger, predominantly Black (55.9%) patients with a higher prevalence of type 1 diabetes (24.8 vs. 4.6%) and lower prevalence of hypertension (65.5 vs. 75.5%) and differences in CKD stages. This group was more aggressively treated for diabetes with inpatient insulin (median of mean TDD 32.2 vs. 5.0 units), inpatient metformin (14.5 vs. 8.7%), a carbohydrate-controlled diet (73.2 vs. 42.4%), and insulin use at home (35.3 vs. 26.4%). The discordant-high group (mAG > eAG) had similar demographic characteristics to the concordant group but consisted predominantly of patients without coded diabetes (54.4 vs. 39.8%). LOS was shorter (4.4 vs. 5.9 days), and a smaller proportion of patients (14.1 vs. 25.5%) had undergone surgery. This group was treated more aggressively with inpatient insulin (median of mean TDD 17.3 vs. 5.0 units), home insulin (30.9 vs. 26.4%), inpatient high-dose glucocorticoids (30.0 vs. 8.1%), and home glucocorticoids (9.6 vs. 5.9%).

Figure 2 shows the relationship between mAG and eAG in a fitted line plot. There was a large amount of variation between mAG and eAG, with a coefficient of determination (R2) of 37.3%. Figure 3 shows the discordance between mAG and eAG in a Bland-Altman plot, with Δ plotted against the average of mAG and eAG. There was a negative correlation between the difference and average, suggesting that mAG is higher than eAG when the average inpatient glucose value is lower, whereas mAG is lower than eAG when the average inpatient glucose value is higher. This model suggests that blood glucose values of ∼200 mg/dL have the smallest difference and thus the highest level of concordance.

FIGURE 2

Scatterplot showing the relationship between mAG and eAG (i.e., the glycemic gap). R2, coefficient of determination. RMSE, root-mean-square error.

FIGURE 2

Scatterplot showing the relationship between mAG and eAG (i.e., the glycemic gap). R2, coefficient of determination. RMSE, root-mean-square error.

Close modal
FIGURE 3

Bland-Altman plot showing agreement between the average of mAG and eAG (x-axis) and the difference (Δ) between mAG and eAG values (y-axis). LoA, limit of association; r, correlation coefficient.

FIGURE 3

Bland-Altman plot showing agreement between the average of mAG and eAG (x-axis) and the difference (Δ) between mAG and eAG values (y-axis). LoA, limit of association; r, correlation coefficient.

Close modal

Figure 4 demonstrates the fully adjusted odds ratios (ORs) for patient characteristics associated with both discordant phenotypes, with results for univariate and multivariable regression analysis provided in Supplementary Tables S2 and S3, respectively. Qualitatively, higher odds of mAG < eAG would be expected to correspond to lower odds of mAG > eAG and vice versa.

FIGURE 4

Forest plots showing the full adjusted ORs for patient characteristics associated with each discordant phenotype. ORs for the discordant-low phenotype are shown on the left; those for the discordant-high phenotype are shown on the right. For sex, the reference group was male sex. For race, the reference group was White race. For diagnosis of diabetes, the reference group did not have coded diabetes. For service, the reference group was medicine/pediatrics. For hospital type, the reference group was academic hospital. For systolic blood pressure, the reference group was normotension (90–119 mmHg). For tachycardia, tachypnea, and fever status, the reference groups were not tachycardic, not tachypneic, and not febrile, respectively. For CKD, the reference group was stage 1 CKD (eGFR ≥90 mL/min/1.73 m2). For anemia, the reference group was not anemic. For inpatient metformin or sulfonylurea use, the reference groups did not use inpatient metformin or sulfonylureas, respectively. For inpatient glucocorticoid use, the reference group did not receive inpatient glucocorticoids. For diet, the reference group was regular diet. For home medications, the reference groups did not use insulin, secretagogues, other antihyperglycemic medications, or glucocorticoids at home, respectively. Carb, carbohydrate; DM, diabetes; GC, glucocorticoid; max, maximum; SBP, systolic blood pressure. T1DM, type 1 diabetes; T2DM, type 2 diabetes.

FIGURE 4

Forest plots showing the full adjusted ORs for patient characteristics associated with each discordant phenotype. ORs for the discordant-low phenotype are shown on the left; those for the discordant-high phenotype are shown on the right. For sex, the reference group was male sex. For race, the reference group was White race. For diagnosis of diabetes, the reference group did not have coded diabetes. For service, the reference group was medicine/pediatrics. For hospital type, the reference group was academic hospital. For systolic blood pressure, the reference group was normotension (90–119 mmHg). For tachycardia, tachypnea, and fever status, the reference groups were not tachycardic, not tachypneic, and not febrile, respectively. For CKD, the reference group was stage 1 CKD (eGFR ≥90 mL/min/1.73 m2). For anemia, the reference group was not anemic. For inpatient metformin or sulfonylurea use, the reference groups did not use inpatient metformin or sulfonylureas, respectively. For inpatient glucocorticoid use, the reference group did not receive inpatient glucocorticoids. For diet, the reference group was regular diet. For home medications, the reference groups did not use insulin, secretagogues, other antihyperglycemic medications, or glucocorticoids at home, respectively. Carb, carbohydrate; DM, diabetes; GC, glucocorticoid; max, maximum; SBP, systolic blood pressure. T1DM, type 1 diabetes; T2DM, type 2 diabetes.

Close modal

The results of the multivariable logistic regression analyses identified several patterns of association between patient characteristics and the discordant phenotypes. These patterns include no association (OR 1 for both discordant-low and discordant-high), single positive or negative association (OR >1 or OR <1 for either phenotype), dual consistent associations (OR >1 for one phenotype and OR <1 for opposite phenotype), or conflicting associations (OR <1 or OR >1 for both phenotypes).

Figure 5 summarizes these qualitative inferences. Lower-than-expected glucose was observed with both type 1 and type 2 diabetes, which were found to have a dual association favoring mAG < eAG. Factors that had a single measure of association observed for this discordant phenotype were Black or other race, increasing LOS, surgery, fever, community hospital, inpatient metformin use, outpatient use of other antihyperglycemic agents, and certain diets (i.e., carbohydrate-controlled, NPO, clear liquid, and unknown diet).

FIGURE 5

Associations of mAG and eAG with clinical factors. Bold type indicates a dual association observed for lower-than-expected glucose (*) or higher-than-expected glucose (**). CCD, carbohydrate-controlled diet; DM, diabetes mellitus; HTN, hypertension.

FIGURE 5

Associations of mAG and eAG with clinical factors. Bold type indicates a dual association observed for lower-than-expected glucose (*) or higher-than-expected glucose (**). CCD, carbohydrate-controlled diet; DM, diabetes mellitus; HTN, hypertension.

Close modal

Higher-than-expected glucose was observed with intensive care unit (ICU) service, stage I hypertension, anemia, and high-dose glucocorticoid use in the hospital, which had dual associations favoring mAG > eAG. Factors that had a single measure of association observed for this discordant phenotype were care in an intermediate medical care (IMC) unit, psychiatry or other service, stage II hypertension, BMI, tachycardia, mean albumin, outpatient glucocorticoid use, maximum potassium level, medium-dose glucocorticoid use in the hospital, and use of insulin or insulin secretagogue before admission.

For some patient factors, a conflicting pattern of association was observed with respect to the discordant phenotypes. Age, CKD stage 5, and increasing sodium levels were associated with lower odds of both discordant phenotypes. Increasing insulin TDD was associated with higher odds for both discordant phenotypes.

In this retrospective cohort study using a large EHR database, we identified several patient characteristics associated with discordance between mAG and eAG in hospitalized patients. We found a weak correlation between mAG and eAG and an overall negative glycemic gap (mAG < eAG), which suggests that patients overall have improved glycemic control in the hospital. This observation may be explained by changes in glycemic management at the transition from home to hospital, the impact of glucose sampling frequency on average glucose calculations, conditions that affect A1C accuracy, and other clinical or patient factors that may predispose patients to hypoglycemia. Understanding when an A1C measurement may not accurately predict inpatient glycemic control is important for clinicians because it could affect selection of an initial antihyperglycemic regimen.

Current practice guidelines recommend obtaining an A1C during admission for hospitalized patients if this test has not been performed in the 2–3 months before admission (29). This recommendation is based on previous research showing that A1C levels strongly predict inpatient glycemic control in most hospitalized patients (30). Although A1C is a generally useful indicator of glycemic control, various factors can influence the accuracy of this test. Our study found that, in addition to factors known to influence A1C accuracy (e.g., anemia), several clinical factors were also associated with discordance between A1C-estimated and observed glucose levels.

Lower-than-expected glucose levels were most strongly associated with type 1 and type 2 diabetes compared with no coded diabetes or other diabetes type on the problem list. This finding may be explained by more intensive glucose management when a patient’s care is shifted to the hospital team, as well as relatively lower carbohydrate intake because of a prescribed inpatient diet. It is also possible that under-coding of diabetes could account for delayed recognition in a subset of patients who have diabetes but do not have the problem listed on their problem list, which could result in higher glucose values in those admissions (31).

The association of higher A1C in Black patients is consistent with published literature, but the clinical relevance of this difference is unclear (22). Increasing LOS and fever may reflect sicker patients, in whom hypoglycemia may simply be a marker of severe illness, and surgical patients are often NPO status, which may predispose to relative hypoglycemia.

We found that hospitalization in a community hospital was associated with lower-than-expected glucose values, which could reflect differences in the patient populations served or differences in practice patterns compared with academic hospitals. Because many patients on noninsulin medications at home are transitioned to insulin in the hospital, this practice may explain why patients receiving metformin and other home antihyperglycemic medications had lower-than-expected glucose levels. Furthermore, use of some antihyperglycemic medications (e.g., weekly glucagon-like peptide 1 receptor agonists, ultra-long-acting insulins, and sodium–glucose cotransporter 2 inhibitors) before admission may influence glycemic control because of the long duration of action of these medications. Thus, use of these medications could have contributed to lower inpatient glucose levels as the effects of the medications persisted.

Higher-than-expected glucose levels were most strongly associated with anemia, high-dose glucocorticoid treatment, hypertension, and ICU status. Anemia (i.e., hemolysis; treatment for anemia from iron, vitamin B12, and folate deficiency; or treatment with erythropoietin) is known to result in underestimation of A1C (812,16). Glucocorticoid treatment would be expected to result in acute elevations in glucose. ICU status likely reflects stress hyperglycemia in the setting of critical illness. Other factors less strongly associated with this phenotype were admission to IMC (severity of illness), psychiatry, or other services (potentially related to less aggressive inpatient glucose management on these services or a direct effect of mental illness on glucose control [32]); higher BMI (increased insulin resistance [33]); higher albumin levels (consistent with a previous study [19]); and higher potassium levels (potentially because of underlying hyporeninemic hypoaldosteronism [34]).

We found conflicting patterns of association between certain factors (i.e., age, CKD stage 5, higher sodium levels, and increasing insulin TDD) and the discordant phenotypes. Advancing age is thought to be associated with increasing A1C in patients with normal glucose tolerance, but we found age to be associated with lower odds of both discordant phenotypes in our cohort (35). We also found that CKD stage 5 (eGFR <15 mL/min/1.73 m2) was associated with lower odds of both discordant phenotypes, but other stages of CKD were not. This finding is not unexpected because advanced CKD, dialysis, and erythropoietin treatment have all been associated with spurious A1C values, often in an unpredictable direction (1318). Finally, we found that higher insulin TDD was associated with higher odds of both discordant phenotypes, which may reflect more advanced diabetes. Because insulin is a relatively narrow therapeutic index medication, it is not surprising that there is greater glycemic variability in patients receiving higher insulin doses (36).

To our knowledge, there have been no previous studies evaluating the relationship between mAG and eAG in both noncritically and critically ill hospitalized patients with diabetes. Several studies have explored this relationship in critically ill patients as a measure of stress hyperglycemia to predict adverse outcomes and mortality (3745). Overall, the literature suggests that greater discordance between observed and expected glucose levels is associated with worse prognosis and potentially increased mortality in critically ill patients with diabetes. However, these studies used initial glucose values at ICU admission or a mean value for the first week, whereas we calculated a mean value throughout patients’ hospital stays. Thus, our study presents a more complete picture of this relationship and is generalizable to a broader group of patients.

These findings may have implications for the use of A1C as a predictor of inpatient glycemic control in some patients, which could potentially influence selection of an initial antihyperglycemic regimen in ways that could result in ineffective or unsafe outcomes. For example, patients who are expected to have higher glucose values may be started on more aggressive initial insulin doses, which could predispose to hypoglycemia. Indeed, we observed a nearly double mean insulin TDD in the discordant-low compared with discordant-high phenotype (32 vs. 17 units, P <0.001); however, further studies are needed to determine whether discordant mAG and eAG levels lead directly to differences in management that affect glycemic outcomes. Although no single clinical factor identified in this study would be sufficient for clinicians to dismiss patients’ A1C in clinical decision-making, the presence of multiple factors (e.g., glucocorticoid use, anemia, critical illness, known type 1 or type 2 diabetes, race, and CKD) should alert clinicians to the possibility that A1C may not be indicative of the glycemic control that patients’ will experience while hospitalized.

The major strength of this study is its large sample size and inclusion of a broad number of clinical predictor variables. Because we evaluated all hospitalized adult patients with diabetes in a large health system in both critical and noncritical care settings, our findings are widely generalizable. The main limitation of this retrospective study was the influence of sampling frequency on the correlation between blood glucose and A1C. The accuracy of eAG is dependent on the sampling of SMBG, with increased accuracy expected as the number of SMBG measurements increases (4648). Although eAG based on CGM data has traditionally been thought to have a good correlation with A1C (46,49,50), a recent study reported a high frequency of discordance between A1C and the CGM-derived glucose management indicator—an estimated A1C metric— in 641 patients with diabetes who use CGM (51). Additionally, the equation used to calculate eAG from A1C was derived from both CGM and seven-point SMBG, but blood glucose was measured approximately four times per day for the majority of patients in this study (26). Thus, we expected to have some inherent discordance between mAG and eAG. Regardless, we suspect that most providers are not aware of these issues when using eAG/A1C conversion calculators, which are readily available online and are likely being applied in clinical practice to diverse groups of patients in various settings, including hospitals. Furthermore, we believe that selection of the most extreme discordant phenotypes minimizes the contribution of sampling frequency on the observed discordance.

We identified several factors associated with discordance between mAG and eAG in the hospital, including Black race, strict inpatient antihyperglycemic control through medications and diets, signs of critical illness such as ICU service and abnormal vital signs, anemia, CKD, electrolyte abnormalities, and inpatient glucocorticoid doses. These factors should be considered when appraising A1C to determine an initial antihyperglycemic regimen at the transition from home to hospital.

Funding

The work of M.S.A. and N.M. was supported by a K23 grant (K23DK111986) from the National Institute of Diabetes and Digestive and Kidney Diseases.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

S.W. contributed to statistical analysis and interpretation of data and drafted the manuscript. M.S.A. contributed to study concept and design, data assessment, data synthesis, and statistical analysis. W.C. contributed to data analysis and reviewed the manuscript. N.M. contributed to study concept and design, data synthesis, statistical analysis, and interpretation of data and drafted the manuscript. All authors reviewed and edited the manuscript. N.M. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

This article contains supplementary material online at https://doi.org/10.2337/figshare.20981752.

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