Ann M. Sheehy

Glucose control in the intensive care unit.

Objective:Hyperglycemia, be it secondary to diabetes, impaired glucose tolerance, impaired fasting glucose, or stress-induced is common in the critically ill. Hyperglycemia and glucose variability in intensive care unit (ICU) patients has some experts calling for routine administration of intensive insulin therapy to normalize glucose levels in hyperglycemic patients. Others, however, have raised concerns over the optimal glucose level, the accuracy of measurements, the resources required to attain tight glycemic control (TGC), and the impact of TGC across the heterogeneous ICU population in patients with diabetes, previously undiagnosed diabetes or stress-induced hyperglycemia. Increased variability in glucose levels during critical illness and the therapeutic intervention thereof have recently been reported to have a deleterious impact on survival, particularly in nondiabetic hyperglycemic patients. The incidence of hypoglycemia (<40 mg/dL or 2.2 mmol) associated with TGC is reported to be as high as 18.7%, by Van den Berghe in a medical ICU, although application of various approaches and computer-based algorithms may improve this. The impact of hypoglycemia, particularly in patients with septic shock and those with neurologic compromise, warrants further evaluation. This review briefly discusses the epidemiology of hyperglycemia in the acutely ill and glucose metabolism in the critically ill. It comments on present limitations in glucose monitoring, outlines current glucose management approaches in the critically ill, and the transition from the ICU to the intermediate care unit or ward. It closes with comment on future developments in glycemic care of the critically ill. Methods:The awareness of the potential deleterious impact of hyperglycemia was heightened after Van den Berghe et al presented their prospective trial in 2001. Therefore, source data were obtained from PubMed and Cochrane Analysis searches of the medical literature, with emphasis on the time period after 2000. Recent meta-analyses were reviewed, expert editorial opinion collated, and the Web site of the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation Trial investigated. Summary and Conclusions:Hyperglycemia develops commonly in the critically ill and impacts outcome in patients with diabetes but, even more so, in patients with stress-induced hyperglycemia. Despite calls for TGC by various experts and regulatory agencies, supporting data remain somewhat incomplete and conflicting. A recently completed large international study, Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation, should provide information to further guide best practice. This concise review interprets the current state of adult glycemic management guidelines to provide a template for care as new information becomes available.

Undiagnosed hypertension among young adults with regular primary care use.

Objective: Young adults meeting hypertension diagnostic criteria have a lower prevalence of a hypertension diagnosis than middle-aged and older adults. The purpose of this study was to compare the rates of a new hypertension diagnosis for different age groups and identify predictors of delays in the initial diagnosis among young adults who regularly use primary care. Methods: A 4-year retrospective analysis included 14 970 patients, at least 18 years old, who met clinical criteria for an initial hypertension diagnosis in a large, Midwestern, academic practice from 2008 to 2011. Patients with a previous hypertension diagnosis or prior antihypertensive medication prescription were excluded. The probability of diagnosis at specific time points was estimated by Kaplan–Meier analysis. Cox proportional hazard models (hazard ratio; 95% confidence interval) were fit to identify predictors of delays to an initial diagnosis, with a subsequent subset analysis for young adults (18–39 years old). Results: After 4 years, 56% of 18–24-year-olds received a diagnosis compared with 62% (25–31-year-olds), 68% (32–39-year-olds), and more than 70% (≥40-year-olds). After adjustment, 18–31-year-olds had a 33% slower rate of receiving a diagnosis (18–24 years hazard ratio 0.66, 0.53–0.83; 25–31 years hazard ratio 0.68, 0.58–0.79) compared with adults at least 60 years. Other predictors of a slower diagnosis rate among young adults were current tobacco use, white ethnicity, and non-English primary language. Young adults with diabetes, higher blood pressures, or a female provider had a faster diagnosis rate. Conclusion: Provider and patient factors are critical determinants of poor hypertension diagnosis rates among young adults with regular primary care use.

Analysis of Guidelines for Screening Diabetes Mellitus in an Ambulatory Population

OBJECTIVES To compare the case-finding ability of current national guidelines for screening diabetes mellitus and characterize factors that affect testing practices in an ambulatory population. PATIENTS AND METHODS In this retrospective analysis, we reviewed a database of 46,991 nondiabetic patients aged 20 years and older who were seen at a large Midwestern academic physician practice from January 1, 2005, through December 31, 2007. Patients were included in the sample if they were currently being treated by the physician group according to Wisconsin Collaborative for Healthcare Quality criteria. Pregnant patients, diabetic patients, and patients who died during the study years were excluded. The prevalence of patients who met the American Diabetes Association (ADA) and/or US Preventive Services Task Force (USPSTF) criteria for diabetes screening, percentage of these patients screened, and number of new diabetes diagnoses per guideline were evaluated. Screening rates were assessed by number of high-risk factors, primary care specialty, and insurance status. RESULTS A total of 33,823 (72.0%) of 46,991 patients met either the ADA or the USPSTF screening criteria, and 28,842 (85.3%) of the eligible patients were tested. More patients met the ADA criteria than the 2008 USPSTF criteria (30,790 [65.5%] vs 12,054 [25.6%]), and the 2008 USPSTF guidelines resulted in 460 fewer diagnoses of diabetes (33.1%). By single high-risk factor, prediabetes (15.8%) and polycystic ovarian syndrome (12.6%) produced the highest rates of diagnosis. The number of ADA high-risk factors predicted diabetes, with 6 (23%) of 26 patients with 6 risk factors diagnosed as having diabetes. Uninsured patients were tested significantly less often than insured patients (54.9% vs 85.4%). CONCLUSION Compared with the ADA recommendations, the new USPSTF guidelines result in a lower number of patients eligible for screening and decrease case finding significantly. The number and type of risk factors predict diabetes, and lack of health insurance decreases testing.

An Overview of Preoperative Glucose Evaluation, Management, and Perioperative Impact

Perioperative hyperglycemia is a common phenomenon affecting patients both with and without a known prior history of diabetes. Despite an exponential rise in publications and studies of inpatient hyperglycemia over the last decade, many questions still exist as to what defines optimal care of these patients. Initial enthusiasm for tight glycemic control has waned as the unanticipated reality of hypoglycemia and mortality has been realized in some prospective studies. The recent dramatic modification of national practice guidelines to endorse more modest inpatient glycemic targets highlights the dynamic nature of current knowledge as the next decade approaches. This review discusses perioperative hyperglycemia and the categories of patients affected by it. It reviews current recommendations for ambulatory diabetes screening and its importance in preoperative patient care. Finally, it concludes with a review of current practice guidelines, as well as a discussion of future direction and goals for inpatient perioperative glycemic control.

Critical illness-induced dysglycaemia: diabetes and beyond

Type 2 diabetes has reached epidemic proportions in many parts of the world. The disease is projected to continue to increase and double within the foreseeable future. Dysglycaemia develops in the form of hyperglycaemia, hypoglycaemia and marked glucose variability in critically ill adults whether they are known to have premorbid diabetes or not. Patients with such glucose dysregulation have increased morbidity and mortality. Whether this is secondary to cause and effect from dysglycaemia or is just related to critical illness remains under intense investigation. Identification of intensive care unit (ICU) patients with unrecognised diabetes remains a challenge. Further, there are few data regarding the development of type 2 diabetes in survivors after hospital discharge. This commentary introduces the concept of critical illness-induced dysglycaemia as an umbrella term that includes the spectrum of abnormal glucose homeostasis in the ICU. We outline the need for further studies in the area of glucose regulation and for follow-up of the natural history of abnormal glucose control during ICU admission and beyond.

Back to Wilson and Jungner: 10 Good Reasons to Screen for Type 2 Diabetes Mellitus

The US Preventive Services Task Force (USPSTF) operates under the Agency for Healthcare Research and Quality, one of 11 divisions of the US Department of Health and Human Services. A panel of preventive medicine and health care experts, the USPSTF reviews existing literature and evidence to make recommendations regarding preventive care measures.1 Primary care practitioners rely on such guidelines to make medical decisions, and insurance companies and government agencies use this information to determine payment for services rendered and for credentialing of institutions. Recently, the USPSTF updated its guidelines on screening adults for type 2 diabetes mellitus2,3 (Table 1).4 This report was based on a literature review of the existing data in both MEDLINE and the Cochrane Library databases. Members of the USPSTF concluded that screening for diabetes in asymptomatic individuals with hypertension (blood pressure >135/80 mm Hg) was merited. Unfortunately, they did not recommend or highlight the importance of screening other at-risk populations who would be screened under the American Diabetes Association (ADA) guidelines, citing lack of direct or indirect evidence supporting population screening.2 However, the authors acknowledge that direct evidence, such as a randomized trial that compared treated vs untreated persons in whom screening detected diabetes, will not be available because “withholding treatment from persons with known diabetes is unethical….” The ADA also concluded that a study of this type is unlikely to occur and developed guidelines based on existing data and expert opinion that recommend screening for diabetes mellitus in a much wider population because of the epidemic in the United States.4 TABLE 1. ADA and USPSTF Criteria to Screen for Diabetes Mellitusa In addition, the criteria used to develop these new USPSTF recommendations have been a subject of interest. Although USPSTF states its guidelines are based on explicit criteria,1 experts have questioned its more restrictive screening criteria used to develop its diabetes screening guidelines compared with that used for another chronic disease, obesity.5 This query has merit considering the impact USPSTF guidelines have on population health. Clearly, screening for disease must meet certain criteria to be medically and financially acceptable. Perhaps the most recognized criteria were determined by Wilson and Jungner6 in 1968. These principles (Table 2), which the World Health Organization follows, define the basis of preventive medicine 40 years later and are largely considered the standards by which screening tests are judged and determined. TABLE 2. Wilson and Jungner Criteria for Disease Screening (Adopted by the World Health Organization) Because of the potential far-reaching implications of the new USPSTF guidelines, it is important to examine the merits of diabetes screening using the 10 criteria of Wilson and Jungner since national screening recommendations profoundly affect health care delivery and outcomes.

Minority Status and Diabetes Screening in an Ambulatory Population

OBJECTIVE Ethnicity has been identified as a risk factor not only for having type 2 diabetes but for increased morbidity and mortality with the disease. Current American Diabetes Association (ADA) guidelines advocate screening high-risk minorities for diabetes. This study investigates the effect of minority status on diabetes screening practices in an ambulatory, insured population presenting for yearly health care. RESEARCH DESIGN AND METHODS This is a retrospective population–based study of patients in a large, Midwestern, academic group practice. Included patients were insured, had ≥1 primary care visit yearly from 2003 to 2007, and did not have diabetes but met ADA criteria for screening. Odds ratios (ORs), 95% confidence intervals (CI), and predicted probabilities were calculated to determine the relationship between screening with fasting glucose, glucose tolerance test, or hemoglobin A1c and patient and visit characteristics. RESULTS Of the 15,557 eligible patients, 607 (4%) were of high-risk ethnicity, 61% were female, and 86% were ≥45 years of age. Of the eight high-risk factors studied, after adjustment, ethnicity was the only factor not associated with higher diabetes screening (OR = 0.90 [95% CI 0.76–1.08]) despite more primary care visits in this group. In overweight patients <45 years, where screening eligibility is based on having an additional risk factor, high-risk ethnicity (OR 1.01 [0.70–1.44]) was not associated with increased screening frequency. CONCLUSIONS In an insured population presenting for routine care, high-risk minority status did not independently lead to diabetes screening as recommended by ADA guidelines. Factors other than insurance or access to care appear to affect minority-preventive care.

Observation and inpatient status: Clinical impact of the 2‐midnight rule

BACKGROUND In response to growing concern over frequency and duration of observation encounters, the Centers for Medicare and Medicaid Services enacted a rules change on October 1, 2013, classifying most hospital encounters of <2 midnights as observation, and those ≥2 midnights as inpatient. However, limited data exist to predict the impact of the new rule. OBJECTIVE To answer the following: (1) Will the rule reduce observation encounter frequency? (2) Are short-stay (<2 midnights) inpatient encounters often misclassified observation encounters? (3) Do 2 midnights separate distinct clinical populations, making this rule logical? (4) Do nonclinical factors such as time of day of admission impact classification under the rule? DESIGN, SETTING AND PATIENTS Retrospective descriptive study of all observation and inpatient encounters initiated between January 1, 2012 and February 28, 2013 at a Midwestern academic medical center. MEASUREMENTS Demographics, insurance type, and characteristics of hospitalization were abstracted for each encounter. RESULTS Of 36,193 encounters, 4,769 (13.2%) were observation. Applying the new rules predicted a net loss of 14.9% inpatient stays; for Medicare only, a loss of 7.4%. Less than 2-midnight inpatient and observation stays were different, sharing only 1 of 5 top International Classification of Diseases, 9th Revision (ICD-9) codes, but for encounters classified as observation, 4 of 5 top ICD-9 codes were the same across the length of stay. Observation encounters starting before 8:00 am less commonly spanned 2 midnights (13.6%) than later encounters (31.2%). CONCLUSIONS The 2-midnight rule adds new challenges to observation and inpatient policy. These findings suggest a need for rules modification.

Perioperative glucose control: what is enough?

TYPE 2 diabetes mellitus, impaired fasting glucose/impaired glucose tolerance, and stress-induced hyperglycemia (SIH) are ubiquitous in the adult population and represent major public health concerns. Almost 10% of adult Americans have type 2 diabetes mellitus, an additional 20–25% have impaired glucose tolerance/impaired fasting glucose, and an unknown number develop SIH. Upwards of one third of affected patients are unaware of the presence of dysglycemia and its systemic effects. Projections predict a continued, dramatic increase in the incidence and prevalence of type 2 diabetes over the next several decades, with its deleterious impact on quality of life and life expectancy. In this issue of ANESTHESIOLOGY, Drs. Lipshutz and Gropper address the impact of dysglycemia on perioperative management. Patients with diabetes require acute and critical care, procedural interventions, and hospitalizations more commonly than those with normal glucose tolerance. When patients with diabetes require hospitalization or undergo certain procedures, they sustain greater morbidity and mortality. Studies from this decade have shown that a minimalist approach to glucose control in selected perioperative and critically ill patient populations is unwarranted, and improved glucose control leads to less morbidity and better outcomes, particularly in those with SIH. Key questions remain unanswered. How tight should glycemic control be? Are all hyperglycemic patients at equal risk for morbid and lethal events at a given degree of dysglycemia? What is the incidence and degree of morbidity when tight glycemic control (TGC) is universally applied? Identification of the dysglycemic patient and application of reliable glucose monitoring and glucose management techniques to a proper endpoint are crucial to achieving adequate perioperative glucose control. Identification of new-onset glucose intolerance in the perioperative patient should be followed by appropriate referral to the patient’s primary care provider for ambulatory unstressed diabetes testing. Drs. Lipshutz and Gropper emphasize that the current data reporting the benefits in reducing morbidity and mortality in intensive care unit patients using intensive insulin therapy to provide TGC be interpreted with care in light of risks reported when this approach is applied universally. They comment on the potential differences in glucose control and outcome related to type 1 versus type 2 diabetes or SIH, the effect of glucose variability during the course of intense monitoring and therapy, and the current risk-benefit data on TGC in various populations. They caution about extrapolating intensive care unit studies directly to the perioperative patient. We would go a step further and caution against a sudden call for intraoperative normalization of blood glucose (80–110 mg/dL; 4.4–6.1 mmol). Additional data should be obtained before implementing rigid perioperative standards of glucose management while tying reimbursement for care of the hyperglycemic perioperative patient to potentially unsubstantiated goals. This thorough review briefly comments on the importance of glucose monitoring, quality control of bedside glucose measurements versus laboratory techniques, and attempts at developing continuous and closed loop systems to control glucose. The reliability of glucose measurements is important to remember when controlling glucose levels during the dynamic perioperative period. Practical pitfalls in glucose monitoring secondary to sample site and source, technique of monitoring, impact of concurrent pathophysiologic states and interfering substances such as nonglucose sugars, and various medications are now recognized. The source of glucose monitoring, point-of-care device, blood gas analyzer, or central laboratory evaluation may explain some of the conflicting results reported when intensive insulin therapy and TGC protocols are instituted. Point-of-care glucose monitoring using finger-stick capillary blood, the most common approach to perioperative evaluation, is based on application of ambulatory technology using photoreflectometry or electrochemical reaction. The Food and Drug Administration mandates a 20% agreement between the pointof-care device and laboratory gold standard.§ Differences between laboratory and point-of-care–derived values are particularly important in intensive care unit patients who are anemic, hypothermic, or hypoperfused. Potentially critical disagreements between the central laboratory value and point-of-care measurement may lead to inappropriate insulin management. Certain operative patients, particularly those in shock or actively hemorrhaging, are likely to be affected. Multidisciplinary teams should develop glucose control protocols, set reasonable goals for control, monitor the effectiveness of controlling glucose, and recognize This Editorial View accompanies the following article: Lipshutz AKM, Gropper MA: Perioperative glycemic control: An evidence-based review. ANESTHESIOLOGY 2009; 110:408–21.

The Role of Copy-and-Paste in the Hospital Electronic Health Record

Before electronic health records: If you did not document it, you did not do it. After electronic health records: You documented it, but did you do it? After a slow start, hospitals in the United States have rapidly adopted electronic health records, as encouraged by the Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH).1 By May 2013, more than 3800 hospitals, or about 80% of the hospitals that were eligible, had received incentive payments from the Centers for Medicare & Medicaid Services (CMS) related to the adoption, implementation, upgrading, or “meaningful use” of these records.2 Yet the application of electronic health records can be a double-edged sword. Their use can increase efficiency, facilitate information sharing, standardize hospital processes, and improve patient care1,3,4 But their use can also have unintended consequences and be subject to abuse, such as when data are duplicated or templates and checkboxes are used to generate standardized text without a good medical reason. The duplication of data in the electronic health record from one location to another is known as “cloning”5 or “copy-and-paste”3,6 and may more generally refer to multiple features, including autopopulate and templates and checkboxes that generate standardized text. Copy-and-paste is related to, yet differs from, “overdocumentation,”3,6 the practice “of inserting false or irrelevant documentation to create the appearance of support for billing higher level services,”3,6 as well as “upcoding,”5 the assignment of an inaccurate billing code to a medical procedure, treatment, or visit to inflate reimbursement. In September 2012, federal officials warned about “the misuse of electronic health records to bill for services never provided,”5 and that law enforcement agencies “will take action where warranted.”5 Two recent reports from the Office of Inspector General of the Department of Health and Human Services (OIG) analyzed how electronic health record technology can make it easier to commit fraud and found deficiencies in the implementation of recommended safeguards.3,6 The office recommended that the CMS develop a “comprehensive plan to address fraud vulnerabilities”3 and provide guidance to hospitals on the use of copy-and-paste.3 The OIG also recommended that CMS instruct its auditors to detect fraud and that audit logs that detect duplicated text be operationalized and used by CMS contractors to assist in fraud detection.3,6 Although the federal government has focused on hospitals, the misuse of copy-and-paste in office-based physician practices would raise similar issues. Does the Use of Copy-and-Paste Equal Fraud? Clearly, technology makes it easier to commit fraud when physicians use tools such as copy-and-paste or templates inappropriately. The use of these features may also contribute to poor quality in clinical notes. For instance, a social history copied and pasted into an admission note may indicate that a patient who is a candidate for a liver transplant is still consuming alcohol, when in fact the patient has been sober for months. A physician using templates and prefilled checkboxes may carelessly document a complete physical examination by default when he or she only conducted a more limited evaluation. With the erroneous use of copy-and-paste, the physician’s assessment and plan may document a decision to start treatment with antibiotics “today” for several days in a row, before the mistake is recognized and corrected. Yet these same features of electronic health records can be efficient and clinically useful when used properly. Although traditional handwritten notes may often have been more concise and exclusively served a clinical need, the purposes of a physicians’ notes have been broadened by their use for billing, to fulfill regulatory requirements, such as compliance with federal standards for the meaningful use of certified electronic health records technology,4 and to collect data for use in standardized measures of quality. For example, a core measure of meaningful use is a problem list of current and active diagnoses that all physicians update and use. Unless the problem list changes, it should be identical in each note that refers to it. Time spent in “counseling and coordination of care” may appear in a template to remind physicians to document the time spent with the patient, not to upcode but to support payment for actual care provided. A template or checklist for the care of a patient with myocardial infarction may help the physician to remember to prescribe a β-blocker or to offer smoking cessation counseling. And if a successful cholecystectomy happens in exactly the same way for 3 consecutive patients, the accompanying identical documentation of the surgical procedures should be welcomed. The federal government uses a range of federal laws, including the False Claims Act, in detecting and prosecuting health care fraud.7 When copy-and-paste is used, fraud is a concern when the documentation is known to have been duplicated or created prior to the episode of care for which reimbursement is claimed. Yet it is too easy, and often mistaken, to equate a physician’s routine use of copy-and-paste with fraud. Data replication is a feature of electronic health records; facts beyond the mere use of duplicated text are required to establish that a note may be fraudulent. Any process by which care is documented could be fraudulent. However, no process VIEWPOINT