"Glucometrics"--assessing the quality of inpatient glucose management

The Specialist Treatment of Inpatients: Caring for Diabetes in Surgery (STOIC-D Surgery) Trial: A Randomized Controlled Trial of Early Intervention With an Electronic Specialist-Led Model of Diabetes Care

OBJECTIVE

To investigate the effect of early intervention with an electronic specialist-led "proactive" model of care on glycemic and clinical outcomes.

RESEARCH DESIGN AND METHODS

The Specialist Treatment of Inpatients: Caring for Diabetes in Surgery (STOIC-D Surgery) randomized controlled trial was performed at the Royal Melbourne Hospital. Eligible participants were adults admitted to a surgical ward during the study with either known diabetes or newly detected hyperglycemia (at least one random blood glucose result ≥11.1 mmol/L). Participants were randomized 1:1 to standard diabetes care or the intervention consisting of an early consult by a specialist inpatient diabetes team using electronic tools for patient identification, communication of recommendations, and therapy intensification. The primary outcome was median patient-day mean glucose (PDMG). The key secondary outcome was incidence of health care-associated infection (HAI).

RESULTS

Between 12 February 2021 and 17 December 2021, 1,371 admissions met inclusion criteria, with 680 assigned to early intervention and 691 to standard diabetes care. Baseline characteristics were similar between groups. The early intervention group achieved a lower median PDMG of 8.2 mmol/L (interquartile range [IQR] 6.9-10.0 mmol/L) compared with 8.6 mmol/L (IQR 7.2-10.3 mmol/L) in the control group for an estimated difference of -0.3 mmol/L (95% CI -0.4 to -0.2 mmol/L, P < 0.0001). The incidence of HAI was lower in the intervention group (77 [11%] vs. 110 [16%]), for an absolute risk difference of -4.6% (95% CI -8.2 to -1.0, P = 0.016).

CONCLUSIONS

In surgical inpatients, early diabetes management intervention with an electronic specialist-led diabetes model of care reduces glucose and HAI.

Longitudinal Digital Glucometric Benchmarking to Evaluate the Impact of Institutional Diabetes Care Initiatives in Adults With Diabetes Mellitus Over the 2016-2020 Period

BACKGROUND

While glucometric benchmarking has been used to compare glucose management between institutions, the value of longitudinal intra-institution benchmarking to assess quality improvement changes is not established.

METHODS

A prospective six-month observational study (October 2019-March 2020 inclusive) of inpatients with diabetes or newly detected hyperglycemia admitted to eight medical and surgical wards at the Royal Melbourne Hospital. Networked blood glucose (BG) meters were used to collect capillary BG levels. Outcomes were measures of glycemic control assessed by mean and threshold glucometric measures and comparison with published glucometric benchmarks. Intra-institution comparison was over the 2016-2020 period.

RESULTS

In all, 620 admissions (588 unique individuals) met the inclusion criteria, contributing 15 164 BG results over 4023 admission-days. Compared with the 2016 cohort from the same institution, there was increased BG testing (3.8 [SD = 2.2) vs 3.3 [SD = 1.7] BG measurements per patient-day, P < .001), lower mean patient-day mean glucose (PDMG; 8.9 mmol/L [SD = 3.2] vs 9.5 mmol/L [SD = 3.3], P < .001), and reduced mean and threshold measures of hyperglycemia (P < .001 for all). Comparison with institutions across the United States revealed lower incidence of mean PDMG >13.9 or >16.7 mmol/L, and reduced hypoglycemia (<3.9, <2.8, and <2.2 mmol/L), when compared with published benchmarks from an earlier period (2009-2014).

CONCLUSIONS

Comprehensive digital-based glucometric benchmarking confirmed institutional quality improvement changes were followed by reduced hyperglycemia and hypoglycemia in a five-year comparison. Longitudinal glucometric benchmarking enables evaluation and validation of changes to institutional diabetes care management practices.

Glycaemic outcomes in hospital with IDegAsp versus BIAsp30 premixed insulins

BACKGROUND AND AIMS

IDegAsp (Ryzodeg 70/30), a unique premixed formulation of long-acting insulin degludec and rapid-acting insulin aspart, is increasing in use. Management of IDegAsp during hospitalisation is challenging because of degludec's ultra-long duration of action. We investigated inpatient glycaemia in patients treated with IDegAsp compared to biphasic insulin aspart (BIAsp30; Novomix30).

METHODS

We performed a retrospective observational study at two hospitals assessing inpatients with type 2 diabetes treated with IDegAsp or BIAsp30 prior to and during hospital admission. Standard inpatient glycaemic outcomes were analysed based on capillary blood glucose (BG) measurements.

RESULTS

We assessed 88 individuals treated with IDegAsp and 88 HbA1c-matched individuals treated with BIAsp30. Patient characteristics, including insulin dose at admission, were well matched, but the IDegAsp group had less frequent twice-daily insulin dosing than the BIAsp30 group (49% vs 87%, P < 0.001). Patient-days with BG <4 mmol/L were not different (10.6% vs 9.9%, P = 0.7); however, the IDegAsp group had a higher patient-day mean BG (10.4 (SD 3.4) vs 10.0 (3.4) mmol/L, P < 0.001), and more patient-days with mean BG >10 mmol/L (48% vs 38%, P < 0.001) compared to the BIAsp30 group. Glucose was higher in the IDegAsp group in the evening (4 PM to midnight) (11.6 (SD 4.0) vs 10.9 (4.6) mmol/L, P = 0.004), but not different at other times during the day.

CONCLUSIONS

Inpatients treated with IDegAsp compared to BIAsp30 had similar hypoglycaemia incidence, but higher hyperglycaemia incidence, potentially related to less frequent twice-daily dosing. With the increasing use of IDegAsp in the community, development of hospital management guidelines for this insulin formulation is needed.

Comparison of Glycemic Control Between In-Person and Virtual Diabetes Consults in Hospitalized Patients With Diabetes

BACKGROUND

There is limited evidence that the diabetes in-person consult in hospitalized patients can be replaced by a virtual consult. During COVID-19 pandemic, the diabetes in-person consult service at the University of Miami and Miami Veterans Affairs Healthcare System transitioned to a virtual model. The aim of this study was to assess the impact of telemedicine on glycemic control after this transition.

METHODS

We retrospectively analyzed glucose metrics from in-person consults (In-person) during January 16 to March 14, 2020 and virtual consults during March 15 to May 14, 2020. Data from virtual consults were analyzed by separating patients infected with COVID-19, who were seen only virtually (Virtual-COVID-19-Pos), and patients who were not infected (Virtual-COVID-19-Neg), or by combining the two groups (Virtual-All).

RESULTS

Patient-day-weighted blood glucose was not significantly different between In-person, Virtual-All, and Virtual-COVID-19-Neg, but Virtual-COVID-19-Pos had significantly higher mean ± SD blood glucose (mg/dL) compared with others (206.7 ± 49.6 In-person, 214.6 ± 56.2 Virtual-All, 206.5 ± 57.2 Virtual-COVID-19-Neg, 229.7 ± 51.6 Virtual-COVID-19-Pos; P = .015). A significantly less percentage of patients in this group also achieved a mean ± SD glucose target of 140 to 180 mg/dL (23.8 ± 22.5 In-person, 21.5 ± 20.5 Virtual-All, 25.3 ± 20.8 Virtual-COVID-19-Neg, and 14.4±18.1 Virtual-COVID-19-Pos, P = .024), but there was no significant difference between In-person, Virtual-All, and Virtual-COVID-19-Neg. The occurrence of hypoglycemia was not significantly different among groups.

CONCLUSIONS

In-person and virtual consults delivered by a diabetes team at an academic institution were not associated with significant differences in glycemic control. These real-world data suggest that telemedicine could be used for in-patient diabetes management, although additional studies are needed to better assess clinical outcomes and safety.

Glucometrics: Where Are We Now?

PURPOSE OF REVIEW

Inpatient glucose data analysis, or glucometrics, has developed alongside the growing emphasis on glycemic control in the hospital. Shortcomings in the initial capabilities for glucometrics have pushed advancements in defining meaningful units of measurement and methods for capturing glucose data. This review addresses the growth in glucometrics and ends with its promising new state.

RECENT FINDINGS

Standardization, allowing for benchmarking and purposeful comparison, has been a goal of the field. The National Quality Foundation glycemic measures and recently enacted Center for Medicare and Medicaid Services (CMS) electronic quality measures for hypo- and hyperglycemia have allowed for improved integration and consistency. Prior systems have culminated in an upcoming measure from the Center for Disease Control and Prevention's National Healthcare Safety Network. It is poised to create a new gold standard for glucometrics by expanding and refining the CMS metrics, which should empower both local improvement and benchmarking as the program matures.

Use of Cross-sectional and Perspective Mapping to Spatially and Statistically Represent Inpatient Glucose Control

BACKGROUND

The use of inpatient location for the depiction of glycemic control is an alternative approach to the traditional analysis of hospital-derived glucometric data. Our aim was to develop a method of spatial representation and to test for corresponding statistical variation in inpatient glucose control data.

METHODS

Point-of-care blood glucose data from inpatients with diabetes mellitus were extracted. Calculations included patient-day weighted means (PDWMs) and percentage of patient hospital days with hypoglycemia. Results were overlaid onto hospital floor plans, and room numbers were used as geolocators to generate cross-sectional (2-dimensional) and perspective (3-dimensional) views of the data. Linear mixed and mixed-effects logistic regression models were used to compare the location effect and to assess statistical variation in the data after adjusting for age, sex, and severity of illness.

RESULTS

Visual inspection of cross-sectional and perspective maps demonstrated variation in glucometric outcomes across areas within the hospital. Statistical analysis confirmed significant variation between some hospital wings and floors.

CONCLUSIONS

Spatial depiction of glucometric data within the hospital could yield insights into hot spots of poor glycemic control. Future studies on how to operationalize this approach, and whether this method of analysis can drive changes in glycemic management practices, need to be conducted.

The Pro-Diab Melbourne Perioperative Study: A structured pre-admission perioperative diabetes management plan to improve medication usage in elective surgery

BACKGROUND

Perioperative diabetes management has become increasingly complex; management is often inconsistent resulting in dysglycaemia and associated morbidity.

AIM

To evaluate a structured pre-admission perioperative diabetes management plan (PDMP) for safe and appropriate recommendation, prescription and administration of diabetes medications in the perioperative period for people with diabetes undergoing elective, non-cardiac surgery.

METHODS

A multidisciplinary team developed the intervention, a structured PDMP (including diabetes medication reconciliation, management guide, individualised plan) to standardise optimal perioperative diabetes management. A single centre prospective pre- and post-intervention pilot study was performed, including all individuals with diabetes medications attending the pre-admissions clinic during two 4-month periods (February to May) in 2016 (control period) and 2017 (intervention period). The primary outcome was appropriate recommendation, prescription and administration of diabetes medications (including insulin), according to the PDMP, in the perioperative period. Secondary outcomes measures were glycaemia. Analysis was by intention to treat.

RESULTS

Control and intervention groups included 131 and 133 participants, respectively; they were well matched in clinical characteristics. The PDMP was completed correctly in 100 (75%) individuals in the intervention group. The appropriate use of diabetes medications increased from 30% in the control group to 71% in the intervention group (p < 0.001). Following the PDMP implementations, glycaemia improved in the overall perioperative period (8.7 ± 2.9 vs. 9.8 ± 3.3 mmol/L, p = 0.005) and at all time points (from admission and over entire hospital stay).

CONCLUSION

A structured pre-admission perioperative diabetes management plan for elective surgery improved safe and appropriate diabetes medication use and glycaemia in the perioperative period.

Basal-bolus insulin therapy for the treatment of non-critically ill patients with type 2 diabetes in Vietnam: effectiveness and factors associated with inpatient glycemic control

Purpose

This study assessed the effectiveness of basal-bolus insulin therapy (BBIT) in non-critically ill patients with type 2 diabetes mellitus (DM) and the factors associated with optimal inpatient glycemic control (IGC) with BBIT.

Methods

This prospective study included 103 patients who were admitted to the University Medical Center and were treated with BBIT. Clinical characteristics, glucose, and glycated hemoglobin (HbA1c) levels at admission, renal function tests, basal-bolus insulin dosing, and other treatments were recorded. The optimal IGC was defined and classified for the analysis.

Results

The mean age of the patients was 67.2 ± 12.0 years. The blood glucose and HbA1c levels at admission were 319.2 ± 184.8 mg/dL and 10.7 ± 2.6%, respectively. Optimal IGC was defined as patients with ≥60% of in-hospital blood glucose values within the target range (3.9–10 mmol/L). Of the 103 patients, 66 patients (64%) achieved optimal IGC and only 5 patients (4.9%) had at least one hypoglycemic episode. The number of patients consuming snacks was higher in the poor than in the optimal IGC group whereas an estimated glomerular filtration rate (eGFR) <45-mL/min/1.73 m2 was predominant in the optimal IGC group. Multivariate analysis revealed that snack consumption and glucocorticoid (GC) use were factors associated with poor IGC, while eGFR <45 mL/min/1.73 m2 was a favorable factor for optimal IGC.

Conclusion

BBIT is safe and effective for the treatment of IGC in non-critically ill patients. Moreover, eGFR <45 mL/min/1.73 m2 at admission, snack consumption, and GC therapy were independent factors associated with IGC outcomes.

The relationship between diabetes and surgical site infection following coronary artery bypass graft surgery in current-era models of care

BACKGROUND

Although diabetes is a recognized risk factor for postoperative infections, the seminal Portland Diabetic Project studies in cardiac surgery demonstrated intravenous insulin infusions following open-cardiac surgery achieved near normal glycaemia and decreased deep sternal wound infection to similar rates to those without diabetes.

AIM

We sought to examine a contemporary cohort of patients undergoing coronary artery bypass graft surgery (CABGS) to evaluate the relationship between diabetes, hyperglycaemia and risk of surgical site infection (SSI) in current-era models of care.

METHODS

Consecutive patients who underwent CABGS between 2016 and 2018 were identified through a state-wide data repository for healthcare-associated infections. Clinical characteristics and records of postoperative SSIs were obtained from individual chart review. Type 2 diabetes (T2D), perioperative glycaemia and other clinical characteristics were analysed in relation to the development of SSI.

FINDINGS

Of the 953 patients evaluated, 11% developed SSIs a median eight days post CABGS, with few cases of deep SSIs (<1%). T2D was evident in 41% and more prevalent in those who developed SSIs (51%). On multivariate analysis T2D was not significantly associated with development of SSI (odds ratio (OR) 1.35; P=0.174) but body mass index (BMI) remained a significant risk factor (OR 1.07, P<0.001). In patients with T2D, perioperative glycaemia was not significantly associated with SSI.

CONCLUSION

In a specialist cardiac surgery centre using perioperative intravenous insulin infusions and antibiotic prophylaxis, deep SSIs were uncommon; however, approximately one in 10 patients developed superficial SSIs. T2D was not independently associated with SSI yet BMI was independently associated with SSI post CABGS.

An open source tool to compute measures of inpatient glycemic control: translating from healthcare analytics research to clinical quality improvement

Objectives

The objective of this study is to facilitate monitoring of the quality of inpatient glycemic control by providing an open-source tool to compute glucometrics. To allay regulatory and privacy concerns, the tool is usable locally; no data are uploaded to the internet.

Materials and Methods

We extended code, initially developed for healthcare analytics research, to serve the clinical need for quality monitoring of diabetes. We built an application, with a graphical interface, which can be run locally without any internet connection.

Results

We verified that our code produced results identical to prior work in glucometrics. We extended the prior work by including additional metrics and by providing user customizability. The software has been used at an academic healthcare institution.

Conclusion

We successfully translated code used for research methods into an open source, user-friendly tool which hospitals may use to expedite quality measure computation for the management of inpatients with diabetes.

Lack of association between either outpatient or inpatient glycemic control and COVID-19 illness severity or mortality in patients with diabetes

INTRODUCTION

To evaluate whether outpatient insulin treatment, hemoglobin A1c (HbA1c), glucose on admission, or glycemic control during hospitalization is associated with SARS-CoV-2 (COVID-19) illness severity or mortality in hospitalized patients with diabetes mellitus (DM) in a geographical region with low COVID-19 prevalence.

RESEARCH DESIGN AND METHODS

A single-center retrospective study of patients hospitalized with COVID-19 from January 1 through August 31, 2020 to evaluate whether outpatient insulin use, HbA1c, glucose on admission, or average glucose during admission was associated with intensive care unit (ICU) admission, mechanical ventilation (ventilator) requirement, or mortality.

RESULTS

Among 111 patients with DM, 48 (43.2%) were on outpatient insulin and the average HbA1c was 8.1% (65 mmol/mol). The average glucose on admission was 187.0±102.94 mg/dL and the average glucose during hospitalization was 173.4±39.8 mg/dL. Use of outpatient insulin, level of HbA1c, glucose on admission, or average glucose during hospitalization was not associated with ICU admission, ventilator requirement, or mortality among patients with COVID-19 and DM.

CONCLUSIONS

Our findings in a region with relatively low COVID-19 prevalence suggest that neither outpatient glycemic control, glucose on admission, or inpatient glycemic control is predictive of illness severity or mortality in patients with DM hospitalized with COVID-19.

Insulin Requirements in Patients With Type 2 Diabetes Undergoing Bariatric Surgery in the Inpatient Setting and Upon Discharge: A Single-Center Retrospective Analysis of Insulin Management Strategies

OBJECTIVE

Rapid improvement in blood glucose (BG) after weight-loss surgery (WLS) can make postoperative glucose management challenging in patients with type 2 diabetes mellitus (T2DM). Our study examined the safety and efficacy of insulin management strategies during hospitalization and after discharge following WLS.

METHODS

This single-center retrospective cohort study included 160 adult patients with type 2 diabetes mellitus undergoing WLS. Patients with glycated hemoglobin A1C (HbA1C) level <7% (53 mmol/mol) and not on antihyperglycemic medications or metformin monotherapy were excluded. BG and insulin dosing during hospitalization and at 2-week follow-up, and impact of preoperative HbA1C level were analyzed.

RESULTS

Mean age was 46.3 years. Median preoperative HbA1C level was 8% (64 mmol/mol). Postoperatively, most patients received basal insulin plus sliding-scale insulin (SSI; 79/160, 49%) or SSI alone (77/160, 48%). The initial postoperative basal dose was 0.23 units/kg/day. The median basal insulin dose at discharge was 61% lower than preoperative dose. At 2-week follow-up, 34 of 44 patients (77%) had BG levels between 70-200 mg/dL and 1 of 44 (2.2%) had BG levels >200 mg/dL, with no hypoglycemia. Patients with HbA1C level >9% (75 mmol/mol) had higher BG on admission and during hospitalization, required higher insulin doses while hospitalized, and were more frequently discharged on insulin.

CONCLUSION

SSI is effective in managing BG in some patients immediately after WLS. However, about half of the patients may require basal insulin at doses similar to those required by other inpatients. Preoperative hyperglycemia may affect inpatient insulin needs and BG. Low-dose basal insulin appears safe and effective upon discharge for select patients.

Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations

Aim:

Evaluate forecasting models applied to smaller geographic locations within the hospital.

Materials & methods:

Damped trend models were applied to blood glucose measurements of progressively smaller inpatient geographic subpopulations. Mean absolute percentage error (MAPE) and 95% prediction intervals (PIs) assessed validity of the models to forecasts 48 weeks into the future.

Results:

MAPE values increased, and 95% PIs widened, when data from progressively smaller geographic areas were analyzed. MAPE values were highest and 95% PIs were broadest with the smallest geographic areas. In contrast, observations missed at larger geographical locations were more evident with smaller subpopulations.

Conclusion:

The utility of damped trend models to forecast inpatient glucose control diminished when applied to smaller geographic areas within the hospital.

Hyperglycemia (high blood sugar) is associated with worse outcomes in hospitalized patients. Predicting future glucose control could identify problems sooner and lead to earlier quality improvement changes. This study examined whether a forecasting tool used across an entire hospital might be useful in smaller geographic areas of the facility. The models became less reliable when tested with progressively smaller geographic subpopulations.

Investigation of Daily Glucose Profile of Inpatients in Non-endocrinology Departments in Chinese Population

Background: Inpatient hyperglycemia is associated with poor prognosis and increased hospitalization expenses. China has a large population of inpatients with hyperglycemia, but their glucose monitoring states (including preprandial, postprandial and bedtime glucose) are unknown, especially in non-endocrinology departments.

Methods: In this cross-sectional study, 5,790 patients with hyperglycemia from 31 non-endocrinology departments were enrolled, and a total of 1,22,032 point-of-care blood glucose (POC-BG) records were collected. The “patient-day” unit of measure was used as a metric for the inpatient glucose. A total of 2,763 patients from endocrinology wards were included for the comparison of the improvement of glycemic management during hospitalization in non-endocrinology wards.

Results: A total of 61.16% of patient-days had <4 POC-BG tests. Postprandial POC-BG was tested significantly less frequently than preprandial POC-BG (10.60% vs. 58.85% of all records, P < 0.001). The patient-day-weighted mean BG was higher in non-ICU wards than in the ICU (9.72 ± 3.37 vs. 9.00 ± 3.19 mmol/L, P < 0.001). The rate of hyperglycemia (BG >10 mmol/L) was 37.60% in all non-endocrinology wards (ICU vs. non-ICU: 33.19% vs. 39.17%, P < 0.001). In non-ICU wards, the rate of hyperglycemia (BG >10 mmol/L) was significantly higher in surgical wards than in medical wards (40.30% vs. 36.90%, P < 0.001). ICU had a significantly higher rate of achieving the blood glucose target than the non-ICU wards (32.50% vs. 26.38%, P < 0.001). In the non-ICU departments, medical wards had higher rate of achieving the blood glucose target than surgical wards (39.70% vs. 19.08%, P < 0.001). With increasing days of hospitalization, there was no improvement in glycemic control in non-endocrinology wards. The ICU had a significantly higher rate of hypoglycemia than non-ICU wards (4.62% vs. 3.73%, P < 0.05). In non-ICU wards, medical wards had a significantly higher rate of hypoglycemia than surgical wards (5.71% vs. 2.75%, P < 0.05).

Conclusions: Both the frequency of BG monitoring and the daily glucose profile of inpatients in Chinese non-endocrinology departments were less than ideal and need to be urgently improved.

Evaluating the effectiveness of a multi-faceted inpatient diabetes management program among hospitalised patients with diabetes mellitus

BACKGROUND

Diabetes mellitus (DM) is one of the most common chronic diseases. Individuals with DM are more likely to be hospitalised and stay longer than those without DM. Inpatient hypoglycemia and hyperglycemia, which are associated with adverse outcomes, are common, but can be prevented through hospital quality improvement programs.

METHODS

We designed a multi-faceted intervention program with the aim of reducing inpatient hypoglycemia and hyperglycemia. This was implemented over seven phases between September 2013 to January 2016, and covered all the non-critical care wards in a tertiary hospital. The program represented a pragmatic approach that leveraged on existing resources and infrastructure within the hospital. We calculated glucometric outcomes in June to August 2016 and compared them with those in June to August 2013 to assess the overall effectiveness of the program. We used regression models with generalised estimating equations to adjust for potential confounders and account for correlations of repeated outcomes within patients and admissions.

RESULTS

We observed significant reductions in patient-days affected by hypoglycemia (any glucose reading < 4 mmol/L: OR = 0.71, 95% CI: 0.61 to 0.83, p <  0.001), and hyperglycemia (any glucose reading > 14 mmol/L: OR = 0.84, 95% CI: 0.71 to 0.99, p = 0.041). Similar findings were observed for admission-level hypoglycemia and hyperglycemia. Further analyses suggested that these reductions started to occur four to 6 months post-implementation.

CONCLUSIONS

Our program was associated with sustained improvements in clinically relevant outcomes. Our described intervention could be feasibly implemented by other secondary and tertiary care hospitals by leveraging on existing infrastructure and work force.

Subcutaneous Insulin Dosing Calculators for Inpatient Glucose Control

PURPOSE OF REVIEW

The goal of this review is to summarize information about insulin dosing software and calculators used as computerized decision support systems or electronic glucose management systems (eGMS). These are used for hospitalized, insulin-treated patients with diabetes. We describe the advantages and disadvantages and the rationale for their use.

RECENT FINDINGS

We compared commercially available insulin dosing software, namely, Glucommander™, EndoTool®, GlucoStabilizer®, and GlucoTab®, in addition to computerized order entry systems that are available in electronic health records. The common feature among these eGMS is their ability to limit occurrences of hypoglycemia while achieving and maintaining patients at target blood glucose level. More research needs to be done examining the efficacy of eGMS in disease-specific states and their benefits and utility in preventing adverse outcomes. Their long-term benefits to health care systems are beginning to emerge in cost-saving benefits and prevention of readmissions.

Benchmarking blood sugar control in the small rural intensive care unit

Objective: We sought to determine a benchmark for our blood glucose monitoring and compare our data to published data.Methods: Natividad Medical Center is a 172-bed rural hospital located in Salinas, California.Point of care blood glucose (POC-BG) data was extracted from our EMR for all ICU patients greater than 18 years of age between January 2014 and May 2018. Patient day-weighted mean POC-BGs were calculated for each patient by calculating the average POC-BG per day for each patient. Proportion measurements for each of our measurements groups were recorded (>180 mg/dL, <70 mg/dL, >250 mg/dL and <50 mg/dL). Monthly averages were plotted for visual comparison. Benchmarks were calculated by using 2x Standard Deviation for each measurement group.Results: A total of 3164 patients were found with 21,006 POC-BG measurements. The average POC-BG was 136 mg/dL and median 119 mg/dL. Proportion measurements of monthly day-weighted mean POC-BGs ranged from 0-1.2%, 5.3-44.8%, 0-0.3% and 0.6-16.5%, respectively for less than 70 mg/dL, greater than 180 mg/dL, less than 50 mg/dL and greater than 250 mg/dL. A 2x Standard Deviation was used to calculate our benchmark cut offs which provides a 95% confidence interval and includes 97.5% when neglecting the lower range. Our calculated benchmark values are 1.2, 38.2, 0.19, and 13.1% respectively for measurement groups less than 70 mg/dL, greater than 180 mg/dL, less than 50 mg/dL and greater than 250 mg/dL.Conclusion: Here we present data from a small rural hospital in the Western United States. We calculated benchmarks that could be used to track our ongoing hyper/hypoglycemia improvement projects. We found that when compared to published data, our hyper/hypoglycemia data was comparable to national data.

Impact of measurement error on predicting population-based inpatient glucose control

Aim:

Instrument measurement error (ME) may affect ability of damped trend analysis to forecast inpatient glycemic control.

Materials & methods:

A statistical approach was developed to introduce ME into damped trend analysis algorithm. Point-of-care blood glucose device data were extracted from the laboratory system. Forecasts were generated from various inpatient subgroups and time intervals.

Results:

ME produced differences in damped trend model during the forecast learning cycle. However, forecast trajectory stayed identical regardless of ME in 85% (119/140) of studied scenarios. Forecasts did not change with greater ME.

Conclusion:

ME inherent in the point-of-care blood glucose device had little effect on trajectory of damped trend exponential forecasts and apparently would not influence decision making in inpatient glycemic control algorithms.

High blood glucose (sugar) levels can lead to complications for hospitalized patients, including more surgical infections or longer hospital stays. The ability to forecast glucose control through trend analysis could identify problems sooner and allow earlier care to keep levels in the recommended range. However, measurement error (ME) is inherent in the glucometer used to check point-of-care glucose values and could limit the usefulness of forecasting methods. This study examined how ME affects forecasting. It showed little effect on glucose forecasts and showed potential robustness of trend analysis in assessment of inpatient glucose control.

Glucometric benchmarking in an Australian hospital enabled by networked glucose meter technology

OBJECTIVE

To assess glucometric outcomes and to estimate the incidence of hypo- and hyperglycaemia among non-critical care inpatients in a major Australian hospital.

DESIGN, SETTING AND PARTICIPANTS

A prospective 10-week observational study (7 March - 22 May 2016) of consecutive inpatients with diabetes or newly detected hyperglycaemia admitted to eight medical and surgical wards at the Royal Melbourne Hospital. Point-of-care blood glucose (BG) data were collected with networked glucose meters.

MAIN OUTCOME MEASURES

Glycaemic control, as assessed with three glucometric models (by population, by patient, by patient-day); incidence of adverse glycaemic days (AGDs; patient-days with BG levels below 4 mmol/L or above 15 mmol/L).

RESULTS

During the study period, there were 465 consecutive admissions of 441 patients with diabetes or newly detected hyperglycaemia, and 9817 BG measurements over 2953 patient-days. The mean patient-day BG level was 9.5 mmol/L (SD, 3.3 mmol/L). The incidence of hyperglycaemia was higher than for a United States hospital benchmark (patient-days with mean BG level above 10 mmol/L, 37% v 32), and that of hypoglycaemia lower (proportion of patient-days with mean BG level below 3.9 mmol/L, 4.1% v 6.1%). There were 260 (95% CI, 245-277) AGDs per 1000 patient-days; the incidence was higher in medical than surgical ward patients (290 [CI, 270-310] v 206 [CI, 181-230] per 1000 patient-days). 604 AGDs (79%) were linked with 116 patients (25%). Episodes of hyperglycaemia (BG above 15 mmol/L) were more frequent before lunch, dinner, and bedtime; 94 of 187 episodes of hypoglycaemia (BG below 4 mmol/L) occurred between 11 pm and 8 am.

DISCUSSION

Glucometric analysis supported by networked glucose meter technology provides detailed inpatient data that could enable local benchmarking for promoting safe diabetes care in Australian hospitals.

Perioperative Management of the Diabetic Patient Referred to Cardiac Surgery

Currently there is a progressive increase in the prevalence of diabetes in a referred for cardiovascular surgery. Benefits of glycemic management (< 180 mg/dL) in diabetic patients compared to patients without diabetes in perioperative cardiac surgery. The purpose of this study is to present recommendations based on international evidence and adapted to our clinical practice for the perioperative management of hyperglycemia in adult patients with and without diabetes undergoing cardiovascular surgery. This update is based on the latest current literature derived from articles and guidelines regarding perioperative management of diabetic patients to cardiovascular surgery.

Impact of an inpatient multidisciplinary glucose control management program

OBJECTIVE

Glycemic control has been increasingly recognized as a critical element in inpatient care, but optimal management of blood glucose in the hospital setting remains challenging. The aims of this study were to describe and evaluate the impact of the implementation of an inpatient multidisciplinary glucose control management program on glucose control in hospitalized patients.

MATERIALS AND METHODS

Retrospective analysis of medical records and glucose monitoring data obtained by point- of-care testing (POCT) in hospitalized patients before (May 2014) and after (June 2015 and May 2017) the implementation of the program.

RESULTS

We analyzed 6888, 7290, and 7669 POCTs from 389, 545, and 475 patients in May 2014, June 2015, and May 2017, respectively. Hyperglycemia (≥ 180 mg/ dL) occurred in 23.5%, 19.6%, and 19.3% POCTs in May 2014, June 2015, and May/2017, respectively (p < 0.001), while severe hyperglycemia (≥ 300 mg/dL) was observed in 2.5%, 2.2%, and 1.8% of them, respectively (p = 0.003). Hyperglycemia (≥ 180 mg/dL) reduced significantly from May 2014 to June 2015 (16.3%, p < 0.001) and from May 2014 to May 2017 (178%, p < 0.001). No significant changes occurred in hypoglycemic parameters.

CONCLUSIONS

The implementation of an inpatient multidisciplinary glucose control management program led to significant reductions in hyperglycemic events. The key elements for this achievement were the development of institutional inpatient glycemic control protocols, establishment of a multidisciplinary team, and continuing educational programs for hospital personnel. Altogether, these actions resulted in improvements in care processes, patient safety, and clinical outcomes of hospitalized patients.

[Assessment of hypoglycemic status among hospitalized elderly patients with type 2 diabetes]

OBJECTIVE

To investigate the hypoglycemic characteristics of hospitalized elderly patients with type 2 diabetes mellitus (T2DM).

METHODS

From January, 2014 to December, 2015, the data of 58 565 blood measurements using a standard blood glucose monitoring system (BGMS) were collected from 1187 cases of patients with type 2 diabetes during hospitalization in the Department of Endocrinology, Guangdong General Hospital (Guangzhou, China). Stratified analyses were conducted by dividing the patients into 3 age groups, namely <45 years group (128 cases), 45-64 years group (594 cases), and ≥65 years group (465 cases). The incidence and time distribution of hypoglycemia in these patients were compared among the 3 age groups.

RESULTS

The risk of hypoglycemia increased with age. Compared with those below 45 years of age, the patients beyond or equal to 65 years had a significantly increased hypoglycemic density (0.95% vs 0.40%, P<0.001), a higher proportion of patients with hypoglycemia (28.17% vs 10.94%, P<0.001), and greater patient-days with hypoglycemia (4.48% vs 1.76%, P<0.001). In the elderly patients, hypoglycemia occurred most frequently before dawn, at which time the hypoglycemic density was 2.66% in patients ≥65 years of age, significantly higher than that in patients below 45 years (1.09%, P<0.05) and between 45 and 64 years (1.90%, P<0.05); the proportion of patients with hypoglycemia was also significantly higher in the elderly patients (14.57%) than in those below 45 years (3.77%, P<0.02) and between 45 and 64 years (9.42%, P<0.02). The proportion of patients with recurrent hypoglycemia (≥2 times) was significantly higher in patients ≥65 years (13.33%) than in younger patients (2.34% in <45 years group and 9.43% in 45-64 years group, P<0.05).

CONCLUSION

The hypoglycemic risk in hospitalized elderly patients with T2DM is significantly higher than that in younger patients, especially before dawn and in terms of recurrent hypoglycemia. Clinicians should develop differential blood glucose monitoring and management strategies for these elderly patients to improve the clinical safety.

How is the weather? Forecasting inpatient glycemic control

Aim

Apply methods of damped trend analysis to forecast inpatient glycemic control.

Method

Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting.

Results

The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries.

Conclusion

Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement.

Glucometrics and Insulinometrics

PURPOSE OF REVIEW

Glucometrics is the systematic analysis of inpatient glucose data and is of key interest as hospitals strive to improve inpatient glycemic control. Insulinometrics is the systematic analysis and reporting of inpatient insulin therapy. This paper reviews some of the questions to be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines.

RECENT FINDINGS

There remains a lack of standardization on how glucometircs should be measured and reported. Before hospitals can commit resources to compiling and extracting data, consensus must be reached on such questions as which measures to report, definitions of glycemic targets, and how data should be obtained. Examples are provided on how insulin administration can be measured and reported. Hospitals should begin assessment of glucometrics and insulinometrics. However, consensus and standardization must first occur to allow for a national benchmarking process.

How Low Can You Go? Reducing Rates of Hypoglycemia in the Non-critical Care Hospital Setting

PURPOSE OF REVIEW

The purpose of this review is to discuss strategies to reduce rates of hypoglycemia in the non-critical care setting.

RECENT FINDINGS

Strategies to reduce hypoglycemia rates should focus on the most common causes of iatrogenic hypoglycemia. Creating a standardized insulin order set with built-in clinical decision support can help reduce rates of hypoglycemia. Coordination of blood glucose monitoring, meal tray delivery, and insulin administration is an important and challenging task. Protocols and processes should be in place to deal with interruptions in nutrition to minimize risk of hypoglycemia. A glucose management page that has all the pertinent information summarized in one page allows for active surveillance and quick identification of patients who may be at risk of hypoglycemia. Finally, education of prescribers, nurses, food and nutrition services, and patients is important so that every member of the healthcare team can work together to prevent hypoglycemia. By implementing strategies to reduce hypoglycemia, we hope to lower rates of adverse events and improve quality of care while also reducing hospital costs. Future research should focus on the impact of an overall reduction in hypoglycemia to determine whether the expected benefits are achieved.

Improving Glycemic Control Safely in Non-Critical Care Patients: A Collaborative Systems Approach in Nine Hospitals

BACKGROUND

Practice variations in insulin management and glycemic adverse events led nine Dignity Health hospitals to initiate a collaborative effort to improve hypoglycemia, uncontrolled hyperglycemia, and glycemic control.

METHODS

Non-critical care adult inpatients with ≥4 point-of-care blood glucose (BG) readings in a ≥2-day period were included. Balanced glucometric goals for each hospital were individualized to improve performance by 10%-20% from baseline or achieve top performance derived from Society of Hospital Medicine (SHM) benchmarking studies. Baseline measures (2011) were compared to mature results (postintervention, 2014). Protocols for insulin management and hypoglycemia prevention were piloted at one facility and were then spread to the cohort. Interventions included standardized order sets, education, mentoring from physician experts, feedback of metrics, and measure-vention (coupling measurement of patients "off protocol" with concurrent intervention to correct lapses in care).

RESULTS

The day-weighted mean BG for the cohort improved by 11.4 mg/dL (95% confidence interval [CI]: 11.0-11.8]; all nine sites improved. Eight of the sites reduced severe hyperglycemic days, and the percentage of patient-days with any BG > 299 mg/dL for the total cohort improved from 11.6% to 8.8% (relative risk, 0.76 [95% CI: 0.74-0.78]). The percentage of patient-days with any BG < 70 mg/dL remained unchanged at 3.6%. Eight of the sites either reduced hypoglycemia by 20% or achieved SHM best-quartile rates.

CONCLUSION

Multihospital improvements in glycemic control and severe hyperglycemia without significant increases in hypoglycemia are feasible using portable low-cost toolkits and metrics.

Glycemic control after hospital discharge in insulin-treated type 2 diabetes: a randomized pilot study of daily remote glucose monitoring

OBJECTIVE

Little is known about glycemic control in type 2 diabetes patients treated with insulin in the high-risk period between hospital discharge and follow-up. We sought to assess the impact of remote glucose monitoring on postdischarge glycemic control and insulin titration.

METHODS

We randomly assigned 28 hospitalized type 2 diabetes patients who were discharged home on insulin therapy to routine specialty care (RSC) or RSC with daily remote glucose monitoring (RGM). We compared the primary outcome of mean blood glucose and exploratory outcomes of hypoglycemia/hyperglycemia rates, change in hemoglobin A1c and glycated albumin, and insulin titration frequency between groups.

RESULTS

Mean blood glucose was not significantly different between the treatment arms (144 ± 34 mg/dL in the RSC group and 172 ± 41 mg/dL in the RGM group; not significant), nor were there significant differences in any of the other measures of glycemia during the month after discharge. Hypoglycemia (glucometer reading <60 mg/dL) was common, occurring in 46% of subjects, with no difference between groups. In as-treated analysis, insulin dose adjustments (29% with an increase and 43% with decrease in insulin dose) occurred more frequently in the patients who used RGM (average of 2.8 vs. 1.2 dose adjustments; P = .03).

CONCLUSION

In this pilot trial in insulin-treated type 2 diabetes, RGM did not affect glycemic control after hospital discharge; however, the high rate of hypoglycemia in the postdischarge transition period and the higher frequency of insulin titration in patients who used RGM suggest a safety role for such monitoring in the transition from hospital to home.

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.

Glycemic control in inpatients with diabetes following august changeover of trainee doctors in England

The first Wednesday of August is the day of changeover of trainee doctors in England. It is widely perceived that inexperience and nonfamiliarity with the new hospital systems and policies in these first few weeks lead to increased medical errors, mismanagement, and mortality. The aim of this study was to analyze the impact of the August changeover of trainee doctors on inpatient glycemic control in a single English hospital. This is currently unknown in England. Overall, 16,870 patient-day capillary glucose reading measures in 2730 inpatients with diabetes were analyzed for 4 weeks before and after the changeover period for the years 2012, 2013, and 2014. Only inpatients hospitalized for longer than 1 day were included. Contrary to expectations, inpatient glycemic control did not worsen in the first 4 weeks after changeover compared to the preceding 4 weeks before changeover in the 3-year period. This may be due to forethought and planning by the deanery foundation school and the inpatient diabetes team in this hospital.

A workable model for the management of hyperglycemia in non-critically ill patients in an Asian population

OBJECTIVE

The clinical efficacy of applying a western model for managing hyperglycemia in hospitalized patients in Asia has not been studied.

METHODS

For this observational case-control study, we divided six medical wards into two groups, an intervention group and a control group. The intervention group, consisting three medical wards on the same floor, received care under a computer-assisted consulting model in which special care was automatically indicated for patients who had two successive high glucose measurements in 1 day. The control group, consisting of another three medical wards distributed on different floors, received regular care. Outcome measures were baseline and post-intervention patient-day weighted mean glucose, percentage of patient-day weighted glucose ≥180 mg/dL, proportion of glucose level 100-180 mg/dL, and prevalence of inpatient hyperglycemia (>180 mg/dL) and hypoglycemia (individual measurement <70 mg/dL and patient-day with any measurement <70 mg/dL).

RESULTS

At baseline, the patient-day weighted mean glucose level was 181.6 mg/dL. All parameters were comparable between the intervention and control groups with the exception of prevalence of hypoglycemia, which was found to be higher in the intervention group. After intervention, patient-day weighted mean glucose levels for intervention and control groups were 169.9 mg/dL and 176.7 mg/dL, respectively (p < 0.001). The intervention group had a reduction in hypoglycemia and the control group an increase.

CONCLUSION

This computer-assisted consulting model was found to be potentially very workable for the management of inpatient hyperglycemia in hospitals with high patient volumes in Asia.

DAILY INPATIENT GLYCEMIC SURVEY (DINGS): A PROCESS TO REMOTELY IDENTIFY AND ASSIST IN THE MANAGEMENT OF HOSPITALIZED PATIENTS WITH DIABETES AND HYPERGLYCEMIA

OBJECTIVE

Hyperglycemia, hypoglycemia, and glycemic variability have been associated with increased morbidity, mortality, and overall costs of care in hospitalized patients. At the Stratton VA Medical Center in Albany, New York, a process aimed to improve inpatient glycemic control by remotely assisting primary care teams in the management of hyperglycemia and diabetes was designed.

METHODS

An electronic query comprised of hospitalized patients with glucose values <70 mg/dL or >350 mg/dL is generated daily. Electronic medical records (EMRs) are individually reviewed by diabetes specialist providers, and management recommendations are sent to primary care teams when applicable. Glucose data was retrospectively examined before and after the establishment of the daily inpatient glycemic survey (DINGS) process, and rates of hyperglycemia and hypoglycemia were compared.

RESULTS

Patient-day mean glucose slightly but significantly decreased from 177.6 ± 64.4 to 173.2 ± 59.4 mg/dL (P<.001). The percentage of patient-days with any value >350 mg/dL also decreased from 9.69 to 7.36% (P<.001), while the percentage of patient-days with mean glucose values in the range of 90 to 180 mg/dL increased from 58.1 to 61.4% (P<.001). Glycemic variability, assessed by the SD of glucose, significantly decreased from 53.9 to 49.8 mg/dL (P<.001). Moreover, rates of hypoglycemia (<70 mg/dL) decreased significantly by 41% (P<.001).

CONCLUSION

Quality metrics of inpatient glycemic control improved significantly after the establishment of the DINGS process within our facility. Prospective controlled studies are needed to confirm a causal association.

Improvement in inpatient glycemic care: pathways to quality

The management of inpatient hyperglycemia is a focus of quality improvement projects across many hospital systems while remaining a point of controversy among clinicians. The association of inpatient hyperglycemia with suboptimal hospital outcomes is accepted by clinical care teams; however, the clear benefits of targeting hyperglycemia as a mechanism to improve hospital outcomes remain contentious. Glycemic management is also frequently confused with efforts aimed at intensive glucose control, further adding to the confusion. Nonetheless, several regulatory agencies assign quality rankings based on attaining specified glycemic targets for selected groups of patients (Surgical Care Improvement Project (SCIP) measures). The current paper reviews the data supporting the benefits associated with inpatient glycemic control projects, the components of a successful glycemic control intervention, and utilization of the electronic medical record in implementing an inpatient glycemic control project.

Association of inpatient and outpatient glucose management with inpatient mortality among patients with and without diabetes at a major academic medical center

BACKGROUND

Hospitalized patients with diabetes have experienced a disproportionate reduction in mortality over the past decade.

OBJECTIVE

To examine whether this differential decrease affected all patients with diabetes, and to identify explanatory factors.

DESIGN

Serial, cross-sectional observational study.

SETTING

Academic medical center.

PATIENTS

All adult, nonobstetric patients with an inpatient discharge between January 1, 2000 and December 31, 2010.

MEASUREMENT

We assessed in-hospital mortality; inpatient glycemic control (percentage of hospital days with glucose below 70, above 299, and between 70 and 179 mg/dL, and standard deviation of glucose measurements), and outpatient glycemic control (hemoglobin A1c).

RESULTS

We analyzed 322,938 admissions, including 76,758 (23.8%) with diabetes. Among 54,645 intensive care unit (ICU) admissions, there was a 7.8% relative reduction in the odds of mortality in each successive year for patients with diabetes, adjusted for age, race, payer, length of stay, discharge diagnosis, comorbidities, and service (odds ratio [OR]: 0.923, 95% confidence interval [CI]: 0.906-0.940). This was significantly greater than the 2.6% yearly reduction for those without diabetes (OR: 0.974, 95% CI: 0.963-0.985; P < 0.001 for interaction). In contrast, the greater decrease in mortality among non-ICU patients with diabetes did not reach significance. Results were similar among medical and surgical patients. Among ICU patients with diabetes, the significant decline in mortality persisted after adjustment for inpatient and outpatient glucose control (OR: 0.953, 95% CI: 0.914-0.994).

CONCLUSIONS

Patients with diabetes in the ICU have experienced a disproportionate reduction in mortality that is not explained by glucose control. Potential explanations include improved cardiovascular risk management or advances in therapies for diseases commonly affecting patients with diabetes.

Novel approach to inpatient glucometric monitoring and variability in a community hospital setting

Hyperglycemia and glucose variability in the hospital environment are associated with higher rates of complications, longer lengths of stay, and mortality. Standardized metrics are needed to assess the efficacy and safety of glucose management interventions. Glucometric data were collected from 2024 inpatients in a San Diego hospital between 2009 and 2011. As a complementary measure of glucose control, individual patient excursion rates were calculated using counts of distinct excursions from normal to critical glucose ranges >180 or <70 mg/dL. Prediction models for excursion rates were devised, based on patient demographic and clinical characteristics. Patients were predominantly male (51.2%), Caucasian (86.0%), and elderly (median age 72 years). Obesity was prevalent: 32% were overweight and 33% were obese. Median length of hospitalization was 5.0 days (range, 0.8-139.4 days). Unadjusted rate of excursions >180 mg/dL was 0.456 per 24 hours. The proportion of zero excursions decreased as severity of illness decreased, but was unrelated to age. Excursion rates were slightly smaller for major and extreme severity of illness compared to mild or moderate illness severity. Excursion rates did not vary in a monotone fashion with age, although the general pattern reflected a reduction in excursion rates from the first age quartile (19 to 59) through the last age quartile (83 to 100). Using the Akaike information criterion, zero-inflated negative binomial models were identified as appropriate for analyzing glucose excursion rates. Systematic approaches to glucose reporting and management in the hospital environment offer "windows of opportunity" to improve diabetes care.

Hypoglycemia in the hospital: systems-based approach to recognition, treatment, and prevention

Hypoglycemia causes immediate adverse reactions and is associated with unfavorable clinical outcomes and increased health care costs. It is also one of the barriers to optimization of inpatient glycemic control. Prioritizing quality improvement efforts to address hypoglycemia in hospitalized patients with diabetes is of critical importance. Acute illness, hospital routine, and gaps in quality care predispose patients to hypoglycemia. Many of these factors can be minimized when approached from a systems-based perspective. This requires creation of a multidisciplinary team to develop strategies to prevent hypoglycemic events by targeting many factors, such as systemic analysis of blood glucometrics, policies and protocols, coordination of nutrition and insulin administration, transitions of care, staff and patient education, and communication. This article reviews recommendations of the American Diabetes Association, the Endocrine Society, and the Society of Hospital Medicine, and highlights our institution's approach in each of these areas. Despite a multitude of challenges, we believe that it is feasible to improve the safety and quality of inpatient diabetes care and avoid hypoglycemia without requiring significant additional hospital resources. Physician leaders play a major role in guiding this process and encouraging participation of interdisciplinary members of the hospital team.

Addressing Inpatient Glycaemic Control with an Inpatient Glucometry Alert System

Background. Poor inpatient glycaemic control has a prevalence exceeding 30% and results in increased length of stay and higher rates of hospital complications and inpatient mortality. The aim of this study was to improve inpatient glycaemic control by developing an alert system to process point-of-care blood glucose (POC-BG) results. Methods. Microsoft Excel Macros were developed for the processing of daily glucometry data downloaded from the Cobas IT database. Alerts were generated according to ward location for any value less than 4 mmol/L (hypoglycaemia) or greater than 15 mmol/L (moderate-severe hyperglycaemia). The Diabetes Team provided a weekday consult service for patients flagged on the daily reports. This system was implemented for a 60-day period. Results. There was a statistically significant 20% reduction in the percentage of hyperglycaemic patient-day weighted values >15 mmol/L compared to the preimplementation period without a significant change in the percentage of hypoglycaemic values. The time-to-next-reading after a dysglycaemic POC-BG result was reduced by 14% and the time-to-normalization of a dysglycaemic result was reduced from 10.2 hours to 8.4 hours. Conclusion. The alert system reduced the percentage of hyperglycaemic patient-day weighted glucose values and the time-to-normalization of blood glucose.

Feasibility, efficacy, and safety of a simple insulin infusion protocol in a large volume cardiac surgery unit in India

AIM

Inpatient hyperglycemia management is essential, but difficult to achieve especially in a large volume cardiac surgery setup, thus necessitating use of nurse-led insulin protocols. A rapid flux of nurses dealing with a huge workload has been a cause for traditionally not using nurse-led protocols in most Indian institutes. The challenges we faced were to have a simple protocol for the nurses to accept it without compromising on glycemic control. Therefore, this observational study was planned to measure the efficacy and safety of the insulin infusion protocol in cardiac surgery patients.

MATERIALS AND METHODS

Insulin protocol was implemented, using seven fixed columns of infusion with the nurse making decisions to initiate and titrate doses based on simple rules. Blood glucose (BG) data captured from blood gas analyzers (glucometrics) in the intervention group (i.e., after protocol implementation) were compared to control group (i.e., before the protocol implementation).

RESULTS

The mean BG for the first 48 h was lower in the intervention group as compared to control group, without an increase in the episodes of hypoglycemia. The nurses found the protocol easy to understand, less time-consuming and there was no protocol deviation over 8 months after implementation.

CONCLUSION

A small change in the process, allowing nurses to titrate insulin doses based on some rules and having seven fixed columns of insulin infusion rates, improved glycemic control and efficiency.

Impact of a hypoglycemia reduction bundle and a systems approach to inpatient glycemic management

OBJECTIVE

Uncontrolled hyperglycemia and iatrogenic hypoglycemia represent common and frequently preventable quality and safety issues. We sought to demonstrate the effectiveness of a hypoglycemia reduction bundle, proactive surveillance of glycemic outliers, and an interdisciplinary data-driven approach to glycemic management.

METHODS

POPULATION

all hospitalized adult non-intensive care unit (non-ICU) patients with hyperglycemia and/or a diagnosis of diabetes admitted to our 550-bed academic center across 5 calendar years (CYs).

INTERVENTIONS

hypoglycemia reduction bundle targeting most common remediable contributors to iatrogenic hypoglycemia; clinical decision support in standardized order sets and glucose management pages; measure-vention (daily measurement of glycemic outliers with concurrent intervention by the inpatient diabetes team); educational programs.

MEASURES AND ANALYSIS

Pearson chi-square value with relative risks (RRs) and 95% confidence intervals (CIs) were calculated to compare glycemic control, hypoglycemia, and hypoglycemia management parameters across the baseline time period (TP1, CY 2009-2010), transitional (TP2, CY 2011-2012), and mature postintervention phase (TP3, CY 2013). Hypoglycemia defined as blood glucose <70 mg/dL, severe hypoglycemia as <40 mg/dL, and severe hyperglycemia >299 mg/dL.

RESULTS

A total of 22,990 non-ICU patients, representing 94,900 patient-days of observation were included over the 5-year study. The RR TP3:TP1 for glycemic excursions was reduced significantly: hypoglycemic stay, 0.71 (95% CI, 0.65 to 0.79); severe hypoglycemic stay, 0.44 (95% CI, 0.34 to 0.58); recurrent hypoglycemic day during stay, 0.78 (95% CI, 0.64 to 0.94); severe hypoglycemic day, 0.48 (95% CI, 0.37 to 0.62); severe hyperglycemic day (>299 mg/dL), 0.76 (95% CI, 0.73 to 0.80).

CONCLUSION

Hyperglycemia and hypoglycemia event rates were both improved, with the most marked effect on severe hypoglycemic events. Most of these interventions should be portable to other hospitals.

Point-of-care blood glucose testing for diabetes care in hospitalized patients: an evidence-based review

Glycemic control in hospitalized patients with diabetes requires accurate near-patient glucose monitoring systems. In the past decade, point-of-care blood glucose monitoring devices have become the mainstay of near-patient glucose monitoring in hospitals across the world. In this article, we focus on its history, accuracy, clinical use, and cost-effectiveness. Point-of-care devices have evolved from 1.2 kg instruments with no informatics to handheld lightweight portable devices with advanced connectivity features. Their accuracy however remains a subject of debate, and new standards for their approval have now been issued by both the International Organization for Standardization and the Clinical and Laboratory Standards Institute. While their cost-effectiveness remains to be proved, their clinical value for managing inpatients with diabetes remains unchallenged. This evidence-based review provides an overall view of its use in the hospital setting.

Prevention of inpatient hypoglycemia with a real-time informatics alert

BACKGROUND

Severe hypoglycemia (SH), defined as a blood glucose (BG) <40 mg/dL, is associated with an increased risk of adverse clinical outcomes in inpatients.

OBJECTIVE

To determine whether a predictive informatics hypoglycemia risk-alert supported by trained nurse responders would reduce the incidence of SH in our hospital.

DESIGN

A 5-month prospective cohort intervention study.

SETTING

Acute care medical floors in a tertiary care academic hospital in St. Louis, Missouri.

PATIENTS

From 655 inpatients on designated medical floors with a BG of <90 mg/dL, 390 were identified as high risk for hypoglycemia by the alert system.

MEASUREMENTS

The primary outcome was the incidence of SH occurring in high-risk intervention versus high-risk control patients. Secondary outcomes included: number of episodes of SH in all study patients, incidence of BG < 60 mg/dL and severe hyperglycemia with a BG >299 mg/dL, length of stay, transfer to a higher level of care, the frequency that high-risk patient's orders were changed in response to the alert-intervention process, and mortality.

RESULTS

The alert process, when augmented by nurse-physician collaboration, resulted in a significant decrease by 68% in the rate of SH in alerted high-risk patients versus nonalerted high-risk patients (3.1% vs 9.7%, P = 0.012). Rates of hyperglycemia were similar on intervention and control floors at 28% each. There was no difference in mortality, length of stay, or patients requiring transfer to a higher level of care.

CONCLUSION

A real-time predictive informatics-generated alert, when supported by trained nurse responders, significantly reduced inpatient SH.

Using the glucometrics website to benchmark ICU glucose control before and after the NICE-SUGAR study

Prior to 2009, intensive glycemic control was the standard in main intensive care units (ICUs). Glucose targets have been recalibrated after publication of the NICE-SUGAR study in that year, followed by updated guidelines that endorsed more moderated control. We sought to determine if the prevalence of hyperglycemia in US ICUs had increased after the NICE-SUGAR study's results were reported. We used data from hospitals submitted to the Yale Glucometrics™ website to assess mean blood glucose values, percentage of blood glucose within various ranges, and the prevalence of hypo- and hyperglycemic excursions, based on the patient-day method, comparing the pre- to post-NICE-SUGAR time period. Among more than a total of 2 million blood glucose determinations, comprising 408 790 patient-days, median patient-day blood glucose decreased from 144 mg/dL to 141 mg/dL (P < .001) in the pre- versus post-NICE-SUGAR time period. The percentage of patient days with a mean blood glucose of 110-179 mg/dl increased from 58.3 to 63.6%. The percentage of patient-days with either hypoglycemia (<70 mg/dl) or severe hyperglycemia (≥300 mg/dl) decreased during this time. Our results suggest that glycemic control in US ICUs has improved when comparing time periods before versus after publication of the NICE-SUGAR study. We found no evidence that fewer hypoglycemic events were achieved at the expense of more hyperglycemia.

Best Practices for Interdisciplinary Care Management by Hospital Glycemic Teams: Results of a Society of Hospital Medicine Survey Among 19 U.S. Hospitals

Objective. The Society for Hospital Medicine (SHM) conducted a survey of U.S. hospital systems to determine how nonphysician providers (NPPs) are utilized in interdisciplinary glucose management teams. Methods. An online survey grouped 50 questions into broad categories related to team functions. Queries addressed strategies that had proven successful, as well as challenges encountered. Fifty surveys were electronically distributed with an invitation to respond. A subset of seven respondents identified as having active glycemic committees that met at least every other month also participated in an in-depth telephone interview conducted by an SHM Glycemic Advisory Panel physician and NPP to obtain further details. The survey and interviews were conducted from May to July 2012. Results. Nineteen hospital/hospital system teams completed the survey (38% response rate). Most of the teams (52%) had existed for 1-5 years and served 90-100% of noncritical care, medical critical care, and surgical units. All of the glycemic control teams were supported by the use of protocols for insulin infusion, basal-bolus subcutaneous insulin orders, and hypoglycemia management. However, > 20% did not have protocols for discontinuation of oral hypoglycemic agents on admission or for transition from intravenous to subcutaneous insulin infusion. About 30% lacked protocols assessing A1C during the admission or providing guidance for insulin pump management. One-third reported that glycemic triggers led to preauthorized consultation or assumption of care for hyperglycemia. Institutional knowledge assessment programs were common for nurses (85%); intermediate for pharmacists, nutritionists, residents, and students (40-45%); and uncommon for fellows (25%) and attending physicians (20%). Many institutions were not monitoring appropriate use of insulin, oral agents, or insulin protocol utilization. Although the majority of teams had a process in place for post-discharge referrals and specific written instructions were provided, only one-fourth were supported with written protocols to standardize medication, education, equipment, and follow-up instructions. Conclusion. Inpatient glycemic control teams with NPPs often function in environments without a full set of measurement, education, standardization, transition, and order tools. Executive hospital leaders, community partners, and the glycemic control teams themselves need to address these deficiencies to optimize team effectiveness.

Medanta insulin protocols in patients undergoing cardiac surgery

Hyperglycemia is common in patients undergoing cardiac surgery and is associated with poor outcomes. This is a review of the perioperative insulin protocol being used at Medanta, the Medicity, which has a large volume cardiac surgery setup. Preoperatively, patients are usually continued on their preoperative outpatient medications. Intravenous insulin infusion is intiated postoperatively and titrated using a column method with a choice of 7 scales. Insulin dose is calculated as a factor of blood glucose and patient's estimated insulin sensitivity. A comparison of this protocol is presented with other commonly used protocols. Since arterial blood gas analysis is done every 4 hours for first two days after cardiac surgery, automatic data collection from blood gas analyzer to a central database enables collection of glucose data and generating glucometrics. Data auditing has helped in improving performance through protocol modification.