Failure to achieve euglycemia despite aggressive insulin control signals abnormal physiologic response to trauma

Association Between Achieving Inpatient Glycemic Control and Clinical Outcomes in Hospitalized Patients With COVID-19: A Multicenter, Retrospective Hospital-Based Analysis

OBJECTIVE

Diabetes and hyperglycemia are important risk factors for poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19). We hypothesized that achieving glycemic control soon after admission, in both intensive care unit (ICU) and non-ICU settings, could affect outcomes in patients with COVID-19.

RESEARCH DESIGN AND METHODS

We analyzed pooled data from the Glytec national database including 1,544 patients with COVID-19 from 91 hospitals in 12 states. Patients were stratified according to achieved mean glucose category in mg/dL (≤7.77, 7.83-10, 10.1-13.88, and >13.88 mmol/L; ≤140, 141-180, 181-250, and >250 mg/dL) during days 2-3 in non-ICU patients or on day 2 in ICU patients. We conducted a survival analysis to determine the association between glucose category and hospital mortality.

RESULTS

Overall, 18.1% (279/1,544) of patients died in the hospital. In non-ICU patients, severe hyperglycemia (blood glucose [BG] >13.88 mmol/L [250 mg/dL]) on days 2-3 was independently associated with high mortality (adjusted hazard ratio [HR] 7.17; 95% CI 2.62-19.62) compared with patients with BG <7.77 mmol/L (140 mg/dL). This relationship was not significant for admission glucose (HR 1.465; 95% CI 0.683-3.143). In patients admitted directly to the ICU, severe hyperglycemia on admission was associated with increased mortality (adjusted HR 3.14; 95% CI 1.44-6.88). This relationship was not significant on day 2 (HR 1.40; 95% CI 0.53-3.69). Hypoglycemia (BG <70 mg/dL) was also associated with increased mortality (odds ratio 2.2; 95% CI 1.35-3.60).

CONCLUSIONS

Both hyperglycemia and hypoglycemia were associated with poor outcomes in patients with COVID-19. Admission glucose was a strong predictor of death among patients directly admitted to the ICU. Severe hyperglycemia after admission was a strong predictor of death among non-ICU patients.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Insulin resistance increases before ventilator-associated pneumonia in euglycemic trauma patients

BACKGROUND

Hyperglycemia caused by stress-induced insulin resistance is associated with both infection and mortality in critically injured patients. The onset of infection may increase stress-induced insulin resistance, leading to hyperglycemia. Hyperglycemia has been shown to precede the diagnosis of ventilator-associated pneumonia (VAP) in critically injured adults and has been suggested to have potential diagnostic importance. However, glycemic control (GC) protocols in critically ill patients limit the development of hyperglycemia despite increasing insulin resistance. Our computer-assisted GC protocol achieves excellent GC, limiting infection-related hyperglycemia while capturing prospectively all glucose values, insulin infusion rates, and the multiplier (M) used to calculate the insulin rate. We hypothesized that surrogate measures of insulin resistance, the insulin infusion rate and multiplier M, would increase prior to the clinical suspicion of VAP, even in euglycemic critically injured patients.

METHODS

All critically injured patients (2,656) on the computerized glycemic control protocol were included in the analysis and categorized by those developing VAP and those without pneumonia on days 3-10 of their intensive care unit (ICU) stay. Median blood glucose concentration (BG), insulin infusion rate (IDR), and multiplier (M) [Insulin Drip Rate=M*(BG-60)] were determined for VAP patients (n=329) and non-infected ventilated (NIV) patients (n=2,327) on each day of mechanical ventilation. The day of VAP diagnosis according to U.S. Centers for Disease Control and Prevention (CDC) criteria was defined as day zero and VAP patients matched with NIV patients according to ventilator day from -10 to +10. Comparisons were conducted using the Mann-Whitney U test.

RESULTS

Baseline characteristics between VAP and NIV groups did not differ. Measures of insulin resistance increased from the time of injury in both groups. Patients with VAP had significantly greater change in both measures of insulin resistance, IDR and M, in the 48 hours preceding the diagnosis of VAP. These changes occurred despite the fact that the computer-assisted GC protocol achieved lower glucose values in VAP patients for the majority of study days.

CONCLUSIONS

Measures of insulin resistance increase in the two days prior to the clinical suspicion of VAP for critically injured patients on the GC protocol. These changes occur despite the protocol maintaining euglycemia. This data suggests that markers of insulin resistance may provide clinically useful information in the early diagnosis of VAP.

Insulin Resistance in Critically Injured Adults: Contribution of Pneumonia, Diabetes, Nutrition, and Acuity

PURPOSE

Changes in insulin resistance (IR) cause stress-induced hyperglycemia after trauma, but the numerous factors involved in IR have not been delineated clearly. We hypothesized that a statistical model could help determine the relative contribution of different clinical co-variates to IR in critically injured patients.

PATIENTS AND METHODS

We retrospectively studied 726 critically injured patients managed with a computer-assisted glycemic protocol at an academic level I trauma center (639 ventilated controls without pneumonia (VWP) and 87 patients with ventilator-associated pneumonia (VAP). Linear regression using age, gender, body mass index (BMI), diabetes mellitus, pneumonia, and glycemic provision was used to estimate M, a marker of IR that incorporates both the serum blood glucose concentration (BG) and insulin dose.

RESULTS

Increasing M (p<0.001) was associated with age (1.62%; 95% confidence interval [CI] 1.27%-1.97% per decade), male gender (9.78%; 95% CI 8.28%-12.6%), BMI (4.32% [95% CI 4.02%-4.62%] per 5 points), diabetes mellitus (21.2%; 95% CI 19.2%-23.2%), pneumonia (10.9%; 95% CI 9.31%-12.6%), and glycemic provision (27.3% [95% CI 6.6%-28.1%] per 100 g of glucose). Total parenteral nutrition was associated with a decrease in M of 10.3%; 95% CI 8.52%-12.1%; p<0.001.

CONCLUSIONS

Clinical factors can be used to construct a model of IR. Prospective validation might enable early detection and treatment of infection or other conditions associated with increased IR.

Undiagnosed medical comorbidities in the uninsured: a significant predictor of mortality following trauma

BACKGROUND

Lack of health care insurance has been correlated with increased mortality after trauma. Medical comorbidities significantly affect trauma outcomes. Access to health care and thereby being diagnosed with a pretrauma comorbidity is highly dependent on insurance status. The objective of this study was to determine whether rates of diagnosed or undiagnosed preexisting comorbidities significantly contribute to disparities in mortality rates observed between insured and uninsured trauma patients.

METHODS

Review of trauma patients admitted to a Level I trauma center during a 5-year period. Data extracted from the registry included age, sex, Injury Severity Score (ISS), comorbidities, mortality, and insurance status. Multivariate logistic regression analysis was performed using age, sex, and insurance status to predict comorbidities and age, sex, ISS, and insurance status to predict mortality.

RESULTS

Insured patients were older (54 years vs. 38, p < 0.001) and more likely female (41.3% vs. 22.5%, p < 0.001). When adjusting for age and sex, insured patients were more likely to have a pretrauma diagnosis of coronary artery disease (odds ratio [OR], 2.09; 95% confidence interval [CI], 1.54-2.83), diabetes mellitus (OR, 2.09; 95% CI, 1.61-2.72), hypertension (OR, 1.97; 95% CI, 1.65-2.35), asthma/emphysema (OR, 1.64; 95% CI, 1.32-2.04), neurologic problems (OR, 1.79; 95% CI, 1.31-2.44), and gastroesophageal reflux disease (OR, 2.03; 95% CI, 1.33-3.11), compared with patients without insurance. In the analysis to predict mortality, having insurance was protective (OR, 0.57; 95% CI, 0.45-0.71). Among patients with no diagnosed comorbidities, insured patients had the lowest mortality risk (OR, 0.5; 95% CI, 0.38-0.67). When analyzing only patients with diagnosed comorbidities, insurance status had no impact on mortality risk (OR, 0.81; 95% CI, 0.53-1.22).

CONCLUSION

Undiagnosed preexisting comorbidities play a crucial role in determining outcomes following trauma. Diagnosis of medical comorbidities may be a marker of access to health care and may be associated with treatment, which may explain the gap in mortality rates between insured and uninsured trauma patients.

LEVEL OF EVIDENCE

Prognostic/epidemiologic study, level III.

Elderly patients may benefit from tight glucose control

BACKGROUND

While minimizing hyperglycemia in critically injured patients improves outcomes, it is debatable whether postinjury glucose control should aim for conventional glucose control levels (≤180 mg/dL) or tight glucose control levels (81-108 mg/dL). We queried our 17-year prospective database of patients at risk for postinjury multiple organ failure to examine the association between glucose levels and adverse outcomes.

METHODS

Acutely injured patients admitted to a Level I trauma center intensive care unit from 1992 to 2008 who were more than 15 years of age, had Injury Severity Scores >15, and who survived >48 hours were eligible for the study. Multiple logistic regression was used to determine the independent association of glucose control with adverse outcomes (death, ventilator-free days, intensive care unit-free days, and major infections), adjusted for Injury Severity Score, age, and red blood cell transfusion in the first 12 hours.

RESULTS

Overall, 2,231 patients were eligible, of whom 153 (6.9%) died. The mean age was 37.8 ± 0.4 years, and the median Injury Severity Score was 27 (interquartile range, 21-35). The majority (77%) of these patients maintained mean glucose within conventional glucose control levels and only 10% achieved mean glucose levels within tight glucose control levels. Nonsurvivors required greater doses of insulin to control glucose levels and had greater mean insulin to glucose ratios (t test; P = .025). After adjusting for confounders, mean glucose remained significantly associated with the studied adverse outcomes. Age significantly modified all these associations with older patients seeming to benefit more from tight glucose control levels than their younger counterparts.

CONCLUSION

Age is an effect modifier of the association between glucose levels and adverse outcomes. Future studies including larger samples of elderly trauma patients are needed to determine the ideal levels for glucose control in this growing population.

The place for glycemic control in the surgical patient

BACKGROUND

Hyperglycemia is common in surgical patients and is associated with adverse outcomes. Conflicting data exist regarding the best method and the value of glycemic control in various patient populations. The contributions to hyperglycemia and the components of its control are complex and overlapping and likely contribute to the documented variation in outcomes. We provide an overview of the physiologic contributors to hyperglycemia and its control, review the differences in the major randomized trial results, and summarize the data regarding glycemic control in surgical patients.

METHODS

Major reviews of the pathophysiology of hyperglycemia in surgical patients, large randomized trials in critically ill and peri-operative populations, and meta-analyses were reviewed. Summations are provided for the critically ill population and for the peri-operative group.

RESULTS

A substantial physiologic rationale exists for the control of hyperglycemia in surgical patients during critical illness and in the peri-operative period. Randomized, controlled studies are limited predominately to critically ill populations. The data support controlling hyperglycemia to a serum glucose concentration <200 mg/dL, but the absolute target range remains controversial and studied inadequately. The data indicate the benefit of tight glycemic control using insulin to achieve a target of 80-110 mg/dL (intensive insulin therapy [IIT]) vs. a liberal target of 180-200 mg/dL in critically ill surgical patients, although hypoglycemia is more common with IIT. Inadequate studies are available in the peri-operative period to draw conclusions about non-critically ill surgical patients, but the weight of the data suggests control to < 200 mg/dL likely is beneficial.

CONCLUSIONS

Surgical patients benefit from maintaining serum glucose concentrations <200 mg/dL. Intensive insulin therapy (80-110 mg/dL), which appears beneficial in critically ill surgical patients but requires frequent measurement of glucose to avoid hypoglycemia. Further studies are needed to determine the appropriate target range and the influence of nutritional provision and other factors on outcome.