Manuel Varela

Differences in complexity of glycemic profile in survivors and nonsurvivors in an intensive care unit: A pilot study*

Objective:To investigate glycemic dynamics and its relation with mortality in critically ill patients. We searched for differences in complexity of the glycemic profile between survivors and nonsurvivors in patients admitted to a multidisciplinary intensive care unit. Design:Prospective, observational study, convenience sample. Settings:Multidisciplinary intensive care unit of a teaching hospital in Madrid, Spain. Patients:A convenience sample of 42 patients, aged 29 to 86 yrs, admitted to an intensive care unit with an Acute Physiology and Chronic Health Evaluation II score of ≥14 and with an anticipated intensive care unit stay of >72 hrs. Interventions:A continuous glucose monitoring system was used to measure subcutaneous interstitial fluid glucose levels every 5 mins for 48 hrs during the first days of intensive care unit stay. A 24-hr period (n = 288 measurements) was used as time series for complexity analysis of the glycemic profile. Measurements:Complexity of the glycemic profile was evaluated by means of detrended fluctuation analysis. Other conventional measurements of variability (range, sd, and Mean Amplitude of Glycemic Excursions) were also calculated. Main Results:Ten patients died during their intensive care unit stay. Glycemic profile was significantly more complex (lower detrended fluctuation analysis) in survivors (mean detrended fluctuation analysis, 1.49; 95% confidence interval, 1.44–1.53) than in nonsurvivors (1.60; 95% confidence interval, 1.52–1.68). This difference persisted after accounting for the presence of diabetes. In a logistic regression model, the odds ratio for death was 2.18 for every 0.1 change in detrended fluctuation analysis.Age, gender, Simplified Acute Physiologic Score 3 or Acute Physiologic and Chronic Health Evaluation II scores failed to explain differences in survivorship. Conventional variability measurements did not differ between survivors and nonsurvivors. Conclusions:Complexity of the glycemic profile of critically ill patients varies significantly between survivors and nonsurvivors. Loss of complexity in glycemia time series, evaluated by detrended fluctuation analysis, is associated with higher mortality.

The route to diabetes: Loss of complexity in the glycemic profile from health through the metabolic syndrome to type 2 diabetes.

Aims: In many physiologic systems, the evolution from health to disease correlates with a loss of complexity in the system’s output. We analyze the difference in complexity of the glycemic profile in healthy volunteers (H), patients with the metabolic syndrome (MS), and patients with type 2 diabetes mellitus (DM). Methods: We measured interstitial fluid glucose every 5 minutes for 3 days in 10 H, 10 MS, and 10 DM. Complexity of the glycemic profile was evaluated by means of detrended fluctuation analysis (DFA). Mean amplitude of glycemic excursions (MAGE) was also calculated. Results: Glucose profile was more complex (lower DFA) in healthy subjects than in patients with MS or DM (mean DFA [SD]: H: 1.25 (0.10), MS: 1.39 (0.07), DM: 1.42 (0.10). ANOVA: F2,27 = 9.94, p = 0.001). DM had also a less complex profile than MS, but this difference was not statistically significant. There was an inverse relation between complexity (lower DFA) and the number of MS defining criteria (rho = 0.55, p = 0.002) and between complexity and MAGE (r = 0.68, p < 0.0001). Conclusions: There is a progressive loss of complexity in the glycemic profile from health, through the metabolic syndrome to type 2 diabetes mellitus. This loss of complexity precedes hyperglycemia and correlates with other markers of disease progression. Complexity analysis may be a useful tool to track the evolution from health to type 2 diabetes. Furthermore, it may provide a way to measure glycemic control in real-life situations and has some distinct advantages over other conventional variability metrics.

Clinical implications of temperature curve complexity in critically ill patients.

Objective:In certain physiologic systems, disease is associated with a loss of complexity in system’s output. We test the hypothesis that, in critically ill patients, there is an inverse relation between the complexity of the temperature curve and the clinical status. We also consider whether complexity analysis of the temperature curve may have prognostic value. Design:Prospective, observational study. Setting:Intensive care unit of a general hospital in Madrid, Spain. Patients:Twenty-four successive patients admitted in the intensive care unit with multiple organ failure. Interventions:Skin temperature was measured every 10 mins from inclusion in the study until discharge or death (median length of stay 18.8 days, interquartile range 86). Measurements:From the temperature time series, hourly approximate entropy measurements were obtained. Clinical status was evaluated using the Sequential Organ Failure Assessment (SOFA) score. Main Results:A significant inverse relationship between approximate entropy and the attributed SOFA score was observed in 89% of the patients considered. Both mean and minimum approximate entropy were significantly lower in patients who died than in patients who survived (mean approximate entropy, 0.47 vs. 0.61; minimum approximate entropy, 0.24 vs. 0.40; in both cases p < .001). To evaluate the prognostic value of both mean and minimum approximate entropy, we fitted logistic regression models against survival. An increase in 0.1 units in minimum or mean approximate entropy increased 15.4- and 18.5-fold the odds of surviving, respectively. Conclusions:The clinical status of patients suffering multiple organ failure is inversely correlated to the complexity of the temperature curve expressed as approximate entropy. Reduced complexity has dismal prognostic implications. Its assessment is noninvasive and inexpensive and allows for real-time continuous monitoring of clinical status.

Cystatin C is associated with the metabolic syndrome and other cardiovascular risk factors in a hypertensive population.

Serum cystatin C has been associated with cardiovascular disease. We investigated whether cystatin C concentration is associated with the metabolic syndrome and with other cardiovascular risk factors in a hypertensive population. In this cross-sectional study, we prospectively included 611 essential hypertensive patients during a 12-month period. Cystatin C concentration was measured by nephelometry. The metabolic syndrome was present in 46% of the patients. Cystatin C was significantly higher in patients with the metabolic syndrome (0.94 +/- 0.27 mg/L) than in those without (0.87 +/- 0.23 mg/L) (P < .0001). Pearson partial correlation analysis showed a significant correlation between cystatin C and body mass index (r = 0.240; P = .001); waist circumference (r = 0.173; P = .012); microalbuminuria (r = 0.273; P < .0001); triglycerides (r = 0.138; P = .047); C-reactive protein (r = 0.190; P = .006); uric acid (r = 0.284; P < .0001); age (r = 0.409; P < .0001); and glomerular filtration rate (GFR) (r = -0.638; P < .0001). Multivariate analysis showed that GFR (B = -0.0061; 95% confidence interval [CI], -0.0073 to -0.0049; P < .0001), age (B = 0.0023; 95% CI, 0.0005-0.0041; P = .009), microalbuminuria (B = 0.0005; 95% CI, 0.0002-0.0007; P < .0001), uric acid (B = 0.0252; 95% CI, 0.0085-0.0418; P = .003), body mass index (B = 0.0051, 95% CI, 0.0012-0.0089; P = .011), and C-reactive protein (B = 0.0048; 95% CI, 0.0015-0.0082; P = .005) were independent determinants of cystatin C concentration. Measuring cystatin C concentration in hypertensive patients may be useful for evaluating their cardiovascular risk profile.

Glucose series complexity in hypertensive patients

Nonlinear methods have been applied to the analysis of biological signals. Complexity analysis of glucose time series may be a useful tool for the study of the initial phases of glucoregulatory dysfunction. This observational, cross-sectional study was performed in patients with essential hypertension. Glucose complexity was measured with detrended fluctuation analysis (DFA), and glucose variability was measured by the mean amplitudes of glycemic excursion (MAGE). We included 91 patients with a mean age of 59 ± 10 years. We found significant correlations for the number of metabolic syndrome (MS)-defining criteria with DFA (r = 0.233, P = .026) and MAGE (r = 0.396, P < .0001). DFA differed significantly between patients who complied with MS and those who did not (1.44 vs. 1.39, P = .018). The MAGE (f = 5.3, P = .006), diastolic blood pressures (f = 4.1, P = .018), and homeostasis model assessment indices (f = 4.2, P = .018) differed between the DFA tertiles. Multivariate analysis revealed that the only independent determinants of the DFA values were MAGE (β coefficient = 0.002, 95% confidence interval: 0.001-0.004, P = .001) and abdominal circumference (β coefficient = 0.002, 95% confidence interval: 0.000015-0.004, P = .048). In our population, DFA was associated with MS and a number of MS criteria. Complexity analysis seemed to be capable of detecting differences in variables that are arguably related to the risk of the development of type 2 diabetes.

Glucose time series complexity as a predictor of type 2 diabetes

Complexity analysis of glucose profile may provide valuable information about the gluco‐regulatory system. We hypothesized that a complexity metric (detrended fluctuation analysis, DFA) may have a prognostic value for the development of type 2 diabetes in patients at risk.

Procalcitonin and C-reactive protein levels as diagnostic tools in febrile patients admitted to a General Internal Medicine ward.

OBJECTIVE We study the extent to which procalcitonin (Pro-CT) and/or C-reactive protein (CRP) may be helpful in the early triage of febrile patients admitted to a general internal medicine ward. METHODS This is a prospective, observational study on 62 admitted patients in whom a temperature >38°C had been observed the day before inclusion. RESULTS Neither Pro-CT nor CRP was able to discriminate infectious (or bacterial) diseases from the other etiologies as a group, with an area under the ROC curve of 0.63 (95% CI 0.47-0.79, p=0.15) for Pro-CT and 0.61, (95CI 0.44-0.78, p=0.23) for CRP. Sensitivity and specificity for Pro-CT varied between 0.59 and 0.67 for a cut-off point of 0.2 ng/mL and 0.03 and 1 for a cut-off point of 10.0 ng/mL. However, in subgroup analysis, Pro-CT was able to discriminate between infectious and inflammatory diseases (Welch two sample t-test t=2.39, df=44.3, p=0.021).

Glucose series complexity at the threshold of diabetes.

One of the earliest signs of dysfunction in a complex system is the simplification of its output. A well‐accepted method to measure this phenomenon is detrended fluctuation analysis (DFA). Herein, we evaluated the usefulness of DFA at the threshold of type 2 diabetes mellitus (T2DM).

Glucose series complexity at the threshold of diabetes 糖尿病阈值的血糖序列的复杂性

One of the earliest signs of dysfunction in a complex system is the simplification of its output. A well‐accepted method to measure this phenomenon is detrended fluctuation analysis (DFA). Herein, we evaluated the usefulness of DFA at the threshold of type 2 diabetes mellitus (T2DM).

NONALCOHOLIC STEATOHEPATITIS IN A HYPERTENSIVE POPULATION

Objective: Nonalcoholic steatohepatitis (NASH) is strongly associated with overweight or obesity, the Metabolic Syndrome (MS) and type 2 diabetes mellitus (DM2). Our objective was to analyse their relationship with essential hypertension, a condition frequently linked to these pathologies. Design and method: Prospective, cross-sectional study conducted in a Hypertension Unit. We defined NASH as the presence of ultrasound hepatic steatosis with the increase in AST > 1.5 times high-reference laboratory values, in the absence of other causes of hepatopathy and with an alcohol intake less of 30 g/day (males) and 15 g/day (females). Results: We included a total of 2251 patients (51.3% males), with an average age of 56 years and a BMI of 30. 57% had MS and 11.5% DM2. 91 patients (4%) presented NASH criteria (4.9% males and 3.1% females, p = 0.032). Patients with NASH had higher abdominal circumference (106 vs. 100 cm, p < 0.0001), uric acid (6.4 vs. 5.8 mg/dl, p = 0.002), triglycerides (162 vs. 128 mg/dl, p = 0.005), basal glycaemia (116 vs. 107 mg/ dl, p = 0.026), HbA1c (6.5% vs. 6.1%, p = 0.015), basal insulin (18.4 vs. 13 mUI/ml, p = 0.025), DBP (83 vs. 80 mmHg, p = 0.036), ferritin (365 vs. 157 mg/dl, p < 0.0001), prevalence of MS (78% vs. 56%, p < 0.0001) and DM2 (20% vs.11%, p = 0.017). NASH also correlated with the number of MS factors (r = 0.045, p = 0.035). In the multivariate analysis, the variables independently associated with NASH were the abdominal circumference (Exp.(B) = 1034, 95%CI: 1001–1.068, p = 0.042), uric acid (Exp.(B) = 1.263, 95%CI: 1.005–1.588, p = 0.045), ferritin (Exp.(B) = 1.003, 95%CI: 1.002–1.005, p < 0.0001), the presence of MS (Exp.(B) = 4.358, 95%CI: 1.001–19.529, p = 0.005) and DM2 (Exp.(B) = 2.399, 95%CI: 1.040–5.537, p = 0.04). Triglycerides, basal glycaemia, HbA1c, basal insulin and DBP resulted excluded in the final model (model R2: 0.49). Conclusions: In our patients NASH was independently associated with the presence of DM2 and MS and with several of the defining or related components of MS. Thus NASH can represent the hepatic correlation of MS in essential hypertension.