ADVANCING
YOUR PRACTICE
Guardian
of glucose: An electronic algorithm for glycemic control
By Craig Harris,
RN, BSN, MPH, and Dawn Greene, RN, CCRN, CSC
Hyperglycemia is strongly
correlated with increased mortality and increased cost
per patient in the postcardiac surgery population. Nomograms
used for optimal glycemic control haven't proven reliable.
Our trial of a software-based, logarithmic equation
program demonstrated optimal glycemic control and time
to control in this patient population.
Background
Hyperglycemia has long been recognized as an inhibitor
of the normal immune response and healing process. Noteworthy
studies, namely the Portland Diabetic Project, have
demonstrated that hyperglycemia, not diagnosis of diabetes
mellitus, is the major causal factor for increased mortality
due to complications such as infection and pump failure.1,2
Moreover, the project demonstrates a direct correlation
between increased cardiac-surgery mortality and a 3-day
average capillary blood glucose (CBG) value of more
than 150 mg/dL, and is an independent predictor of increased
length of stay in this population; between 0.45 and
0.65 days depending on the level of hyperglycemia. Other
research following prospective and cohort case studies
involving cardiothoracic surgery patients demonstrated
that diabetes and postoperative hyperglycemia were independently
associated with the development of surgical site infections
(SSIs).3
Hyperglycemia is a significant
issue for any ICU patient, not just the postcardiothoracic
surgery patient. In a study of 1,548 patients of a 56-bed
predominantly surgical ICU, researchers found that metabolic
control was the strongest contributor to patient outcomes.4
Their analysis indicated that the lowered blood glucose
(rather than the insulin dose) was a statistically significant
related factor to reduced mortality.
Although we're aware of
the importance of glycemic control in our postsurgical
patients, national organizations that focus on patient
outcomes are advocating for tighter glucose control.
The Institute for Health Improvement has identified
glycemic control (between 80 mg/dL and 110 mg/dL in
critically ill patients) as an improvement measure.5
The American Hospital Association has specified glucose
control in the postcardiac surgery patient by 6 a.m.
on postoperative day 1 as a Surgical Care Improvement
Project measure.6
Licensed for 800 beds,
North Carolina's Mission Hospitals, offers award-winning
cardiac care. Solucient named Mission's Heart Program
as a Top 100 Heart Hospital for the years 2000 to 2005
and again in 2007. The Society of Thoracic Surgeons
gave the program its highest rating—three stars.
The cardiovascular recovery
unit (CVRU) at Mission Hospitals is the postanesthesia
care unit (PACU) for the cardiovascular OR. Annually,
approximately 1,000 patients recover in this unit. Postoperative
(postoperative day 1 for post-open heart patients) patients
are transferred from the CVRU to the cardiovascular
progressive care unit or to the cardiovascular ICU (CVICU)
if they require ICU care beyond the PACU phase.
We've historically had
difficulty achieving optimal blood glucose control (CBG
measurement of 71–150 mg/dL per the Portland Diabetic
Project) in our postoperative cardiac and vascular surgery
population. Of the average 1,000 patients treated annually
in the CVRU, we were able to achieve optimal control
in only 17%, with an average of 9 hours required to
achieve control using a nomogram-based order set for
continuous insulin infusion titration that's designed
specifically for the ICU patient. We observed serial
CBG measurements of greater than 150 mg/dL in 80% of
our patients and a 3% rate of hypoglycemia (i.e., CBG
measurement less than 70 mg/dL).
Our trial
We considered several software programs to assist us
with glucose control. Collaboratively with nursing,
pharmacy, endocrinology, CV surgery, and information
technology, we chose one particular software-based tool
to trial. Our choice program is a patent-pending, Health
Insurance Portability and Accountability Act (HIPAA)-compliant
program that derives the patient's unique insulin resistance
using a logarithmic equation. The result is continuous
insulin infusion titration recommendations and supplemental
insulin (also known as “sliding scale”)
recommendations unique to the patient.
We implemented a 2-month
trial from March to May 2007. The only criteria for
tool utilization were two or more capillary blood glucose
measurements of more than 150 mg/dL. During this time
period, 95 of 198 CVRU patients met these criteria and
were managed utilizing the tool. Tool use was continued
upon transfer to the CVICU while on continuous insulin
infusion.
We experienced a dramatic
decrease in CVRU control time from 9 to just over 2
hours. Although we hadn't previously collected data
on control time in the CVICU, we observed this to be
3.8 hours (which seemed anecdotally a significant decrease).
We also observed our percentage of CBGs within the optimal
range (71–150 mg/dL) to be 80% in the CVRU and
91.5% in the CVICU—a significant change from our
prior 17%. The incidence of hypoglycemia also decreased
significantly, from an average rate of 3% prior to the
trial to less than 1% during the trial.
Added benefits
In addition to the obvious clinical benefits, simplicity
of use, the ability to customize based on service line/patient
population, and user support are significant benefits.
The tool is simple to use. A basic profile is established
for the patient that includes administration of steroids,
the patient's serum creatinine, and current insulin
infusion rate. Based on this information, the tool recommends
an infusion rate and specifies time duration until next
CBG measurement. Each subsequent CBG is entered into
the software, current insulin infusion rate is verified,
and additional calories (such as dextrose infusions)
are administered, if applicable. The software then recommends
insulin titration and specifies the time increment for
subsequent CBG measurement. The software alarms, notifying
staff that a CBG is due, and the alarm can't be silenced
until the CBG measurement is entered. This prevents
staff from “overlooking” a CBG and, thus,
prevents hyperglycemic/hypoglycemic spikes.
Training for use of the
tool is minimal. Since we utilize an electronic medical
record, staff members were accustomed to working with
electronic patient-care documentation. The manufacturer
provided on-site training. The tool provides administrative
reports that simplify data analysis for performance
improvement and labor studies. The software provides
for customization of insulin management allowing different
service lines to tailor their control and insulin regimens
based upon their unique patient population.
Disadvantages
Since the software customizes control to the individual
patient, frequent CBGs (every hour or more) are often
required until control is achieved. This, along with
the insulin titrations associated with the frequent
CBG measurements, significantly increases initial nursing
time.
Compatibility with existing
electronic medical record systems and application delivery
systems can be an issue. There's no interface between
the tool and our electronic medical record and application
delivery system. This requires that nursing staff members
manually enter values into the record and document recommendations
using a printed label (which the software generates).
The lack of interface with our application delivery
system restricted software deployment in each patient
room unless manually installed. Therefore, we deployed
the software on key computers throughout the unit.
Posttrial validation
Following the trial, we resumed use of our standard,
nomogram-based insulin order sets. We recollected CBG
data to validate our results and found a return to our
pretrial baseline. Optimal blood glucose control was
achieved in only 14% of cases (which was down even from
our prior rate of 17%), along with a 4% rate of hypoglycemia,
an 84% rate of hyperglycemia, and increased control
time in our CVRU patients.
Setting a standard
Our trial data provided overwhelming evidence that the
tool improves glycemic control and decreases control
time in this patient population. In November 2007, we
implemented the tool as our standard for continuous
insulin infusion in the CVRU and CVICU.
Our hospital uses an acuity-based
software system for recommended staffing based on projected
workload. Using the indicators within this system, we're
able to account for the increased workload required
to achieve glycemic control utilizing the tool. This
allows us to plan for and justify the additional nursing
time required.
The manufacturer has been
able to provide interface with our application delivery
system since implementation. Additionally, our Information
Technology department is working closely with the manufacturer's
engineers to develop an interface with our electronic
medical record. In the interim, we continue to enter
CBG values manually and utilize the software-generated
label for documentation of recommendations.
We've also implemented
a “softer” lancet, which presents less discomfort
to patients from increased fingersticks, and we've minimized
using the central-line for blood sampling for CBGs as
to not increase the potential central-line-associated
bloodstream infections from frequent line interruptions.
Return on investment
Within our program, we're investigating the tool as
a solution in transitioning patient from N.P.O. or continuous
enteral nutrition status (and continuous insulin infusion)
to intermittent oral intake (and correction dose insulin).
The tool is designed primarily for use with steady carbohydrate
intake. Although the tool can derive a basal subcutaneous
insulin regimen, including prandial and correction dosing,
for patients who are eating, we utilized this only in
a small group of patients. Utilization of this regimen
requires daily evaluation of insulin requirements as
the patient's resistance changes.
Because our trial focused
specifically on postoperative cardiovascular surgery
patients, specific efficacy with other ICU patient populations
needs further study. We're also unable to assess the
tool's value in the step-down area.
Hyperglycemia is becoming
increasingly recognized as a significant factor in increased
mortality particularly in the postcardiac surgery patient.
Our experience with standard insulin nomograms and empirical
attempts to customize glycemic control hasn't been successful.
Several software-based glycemic control programs exist.
During our trial with the software-based tool, we observed
a 67% improvement in control time, an 84% decrease in
hypoglycemia incidence, an 80% increase in CBGs within
the optimal range, and an 84% decrease in hyperglycemic
values when comparing prior values to the tool values.
The projected cost savings from decreased length of
stay and mortality (such as prevention of SSIs) further
supports the cost and labor expense associated with
the software and increased testing.
References
1. Zerr KJ, Furnary AP, Grunkemeier GL, et al. Glucose
control lowers the risk of wound infection in diabetics
after open-heart operations. Ann Thorac Surg.
1997; 63(2): 356–361.
2. Furnary A, YingXing Wu, Bookin S. Effect of hyperglycemia
and continuous intravenous insulin infusions on outcomes
of cardiac surgical procedures: the Portland diabetic
project. http://www.providence.org/resources/oregon/images/portlandprotocol/pdfs/EndocrPract04.pdf.
3. Latham R, Lancaster AD, Covington JF, et al. The
association of diabetes and glucose control with surgical-site
infections among cardiothoracic surgery patients. Infect
Control Hosp Epidemiol. 2001; 22(10): 607–612.
4. Van den Berghe G, Wouters PJ, Bouillon R, et al.
Outcome benefit of intensive insulin therapy in the
critically ill: insulin dose versus glycemic control.
Crit Care Med. 2003;31(2):359–366.
5. Institute for Healthcare Improvement: Implement Effective
Glucose Control. http://www.ihi.org/IHI/Topics/CriticalCare/IntensiveCare/Changes/ImplementEffectiveGlucoseControl.htm.
6. AHA: www.aha.org.
Source: Nursing2009
Critical Care. March 2009.
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