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Aim. To explore the potential usefulness of the mean, standard deviation (SD), and coefficient of variation (CV = SD/mean) of oximeter oxygen saturations in the clinical care of preterm babies. Methods. This was an exploratory investigation involving 31 preterm babies at 36 weeks postmenstrual age. All babies were healthy, but two were considered to be clinically unstable and required greater attention. Each baby's oxygen saturations were recorded using an oximeter and summarized by the mean, SD, and CV. The potential usefulness of each measure was assessed by its ability to distinguish the two unstable babies from the others. This was achieved using box plots and hierarchical clustering together with the Calinski-Harabasz (CH) index to quantify clustering performance (higher CH index indicates stronger clustering outcome). Results. The box plots flagged both unstable babies as outliers and none of the other babies. Successful clustering of the stable and unstable babies was achieved using the CV (CH = 72.8) and SD (CH = 63.3) but not with the mean. Conclusion. Taking the box plots and clustering results together, it seems plausible that variability might be more effective than mean level for detecting instability in oxygen saturation in preterm babies and that the combination of variability and level through the CV might be even better.

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Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (S(I)) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity S(I) values were calculated from glycemic control data of 36 patients with sepsis. The hourly S(I) is compared to the hourly sepsis score (ss) for these patients (ss=0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss ≥ 2) are created for both S(I) and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S(I) cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining S(I), temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.

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Hyperglycemia is a common metabolic problem in premature, low-birth-weight infants. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition in intensive care. A dynamic model capturing the fundamental dynamics of the glucose regulatory system provides a measure of insulin sensitivity (S(I)). Forecasting the most probable future S(I) can significantly enhance real-time glucose control by providing a clinically validated/proven level of confidence on the outcome of an intervention, and thus, increased safety against hypoglycemia. A 2-D kernel model of S(I) is fitted to 3567 h of identified, time-varying S(I) from retrospective clinical data of 25 neonatal patients with birth gestational age 23 to 28.9 weeks. Conditional probability estimates are used to determine S(I) probability intervals. A lag-2 stochastic model and adjustments of the variance estimator are used to explore the bias-variance tradeoff in the hour-to-hour variation of S(I). The model captured 62.6% and 93.4% of in-sample S(I) predictions within the (25th-75th) and (5th-95th) probability forecast intervals. This overconservative result is also present on the cross-validation cohorts and in the lag-2 model. Adjustments to the variance estimator found a reduction to 10%-50% of the original value provided optimal coverage with 54.7% and 90.9% in the (25th-75th) and (5th-95th) intervals. A stochastic model of S(I) provided conservative forecasts, which can add a layer of safety to real-time control. Adjusting the variance estimator provides a more accurate, cohort-specific stochastic model of S(I) dynamics in the neonate.

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BACKGROUND: There is an urgent need for a simple and accurate measure of insulin sensitivity to diagnose insulin resistance in the general population and quantify changes due to clinical intervention. A new physiological control model of glucose and insulin metabolism is validated with the euglycemic-hyperinsulinemic clamp during steady and transient states. METHODS: The data consist of n = 60 (15 lean, 15 overweight, 15 obese, and 15 morbidly obese) euglycemic-hyperinsulinemic clamp trials performed on normoglycemic insulin-resistant individuals. The glucose and insulin model is fitted using an integral-based method. Correlations between clamp-derived insulin sensitivity index (ISI) and the model's insulin sensitivity parameter (SI) are obtained during steady and transient states. Results are compared with log-homeostasis model assessment (HOMA), a widely used fasting surrogate for insulin sensitivity. RESULTS: Correlation between model-based insulin sensitivity, SI, and ISIG (ISI normalized by steady-state glucose) is r = 0.99 (n = 60) at steady state and r = 0.97 at transient state, respectively. Correlations did not significantly change across subgroups, with narrow 95% confidence intervals. Log-HOMA correlations are r=-0.72 to SI and r=-0.71 to ISIG for the overall population but are significantly lower in the subgroups, with wide 95% confidence intervals. CONCLUSIONS: The model-based insulin sensitivity parameter, SI, highly correlates to ISIG in all subgroups, even when only considering a transient state. The high correlation of SI offers the potential for a short, simple yet highly correlated, model-based assessment of insulin sensitivity that is not currently available.

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In physiological system modelling for control or decision support, model validation is a critical element. A nonparametric approach for assessing the validity of deterministic dynamic models against empirical data is developed, based on kernel regression and kernel density estimation, yielding visual graphical assessment tools as well as numerical metrics of compatibility between the model and the data. Nonparametric regression has been suggested for assessing a parametric statistical model by constructing a confidence band for the proposed model and then checking whether the nonparametric regression curve lies within the band. However, for deterministic models, there is no confidence band that can be constructed. A reversal of roles is therefore suggested--construct a probability band for the nonparametric regression curve and check whether the proposed model lies within the band. This approach extends the utility of nonparametric regression for model assessment to deterministic models. Weighted kernel density estimation is incorporated to derive a density profile for the regression curve, creating a local graphical validation tool. In addition, the density profile is used to define and compute two numerical measures--average normalized density (AND) and relative average normalized density (RAND), representing global statistical validity measures. These tools are demonstrated using a biomedical system model for agitation-sedation and sedation management control.

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