Monocyte Distribution Width: A Novel Indicator of Sepsis-2 and Sepsis-3 in High-Risk Emergency Department Patients.

OBJECTIVES: Most septic patients are initially encountered in the emergency department where sepsis recognition is often delayed, in part due to the lack of effective biomarkers. This study evaluated the diagnostic accuracy of peripheral blood monocyte distribution width alone and in combination with WBC count for early sepsis detection in the emergency department. DESIGN: An Institutional Review Board approved, blinded, observational, prospective cohort study conducted between April 2017 and January 2018. SETTING: Subjects were enrolled from emergency departments at three U.S. academic centers. PATIENTS: Adult patients, 18-89 years, with complete blood count performed upon presentation to the emergency department, and who remained hospitalized for at least 12 hours. A total of 2,212 patients were screened, of whom 2,158 subjects were enrolled and categorized per Sepsis-2 criteria, such as controls (n = 1,088), systemic inflammatory response syndrome (n = 441), infection (n = 244), and sepsis (n = 385), and Sepsis-3 criteria, such as control (n = 1,529), infection (n = 386), and sepsis (n = 243). INTERVENTIONS: The primary outcome determined whether an monocyte distribution width of greater than 20.0 U, alone or in combination with WBC, improves early sepsis detection by Sepsis-2 criteria. Secondary endpoints determined monocyte distribution width performance for Sepsis-3 detection. MEASUREMENTS AND MAIN RESULTS: Monocyte distribution width greater than 20.0 U distinguished sepsis from all other conditions based on either Sepsis-2 criteria (area under the curve, 0.79; 95% CI, 0.76-0.82) or Sepsis-3 criteria (area under the curve, 0.73; 95% CI, 0.69-0.76). The negative predictive values for monocyte distribution width less than or equal to 20 U for Sepsis-2 and Sepsis-3 were 93% and 94%, respectively. Monocyte distribution width greater than 20.0 U combined with an abnormal WBC further improved Sepsis-2 detection (area under the curve, 0.85; 95% CI, 0.83-0.88) and as reflected by likelihood ratio and added value analyses. Normal WBC and monocyte distribution width inferred a six-fold lower sepsis probability. CONCLUSIONS: An monocyte distribution width value of greater than 20.0 U is effective for sepsis detection, based on either Sepsis-2 criteria or Sepsis-3 criteria, during the initial emergency department encounter. In tandem with WBC, monocyte distribution width is further predicted to enhance medical decision making during early sepsis management in the emergency department.

Comparison of the ATS versus EU Mini Wright peak flow meter in normal volunteers.

OBJECTIVES: The American Thoracic Society (ATS) and European Union (EU) have precise and accurate Mini Wright peak flow meters. The purpose of this investigation was to compare both 1) for accuracy using a pneumotachometer, 2) in volunteers to determine whether they are interchangeable, and 3) to spirometrically predicted peak flows. METHODS: Lab testing: A pneumotachometer was connected in series with each peak flow meter and varying flows pushed through both meters for comparison. Human subjects: Nonsmoking adult volunteers did three standing peak flows. The order of peak flow meter used was random. The best of three efforts was used for analysis. The t-test, concordance correlation coefficient (CCC), Deming regression, and Bland-Altman plot were the analytic strategies used to determine agreement. Peak flow results were compared to spirometrically predicted values. RESULTS: Fifty-seven volunteers, average age 37 ± 12 years and mean BMI 24.9 ± 2.5 years, were included. The average peak flows were different at 541 ± 114 and 526 ± 112 L/min for the ATS and EU meters, respectively (p < .01). Both peak flow meter values were significantly different than spirometrically predicted values of 483 ± 86 L/min (p < .01). The CCC was 0.98 (0.97-0.99) and regression revealed a slope and y-intercept consistent with 1 and 0, respectively. The Bland-Altman plot revealed no increase in scatter of values over the range of peak flows versus the difference with a mean bias of 15 ± 15 L/min. Laboratory testing revealed that the ATS and EU peak flow meters read 3.0 ± 2.1% above and -2.0 ± 1.5% below the comparison pneumotachometer, respectively. The pneumotachometer comparison was significantly different for both meters at p < .01, paired t-test. CONCLUSIONS: The ATS peak flow meter reads 2.8% higher than the EU peak flow meter across a range of flows. Both meters have similar accuracy with a different bias compared with a pneumotachometer. Finally, both peak flow meters read slightly and significantly higher than spirometrically derived peak flows. Therefore, the peak flow meters are not interchangeable and both may obtain slightly higher values than those determined using current spirometrically derived prediction equations.

Modeling the effects of storage temperature excursions on shelf life.

Excursions from storage condition requirements may affect product performance and stability. The effects of temperature excursion on stability depend on the amount of time that a product is subjected to these conditions, temperature level, and activation energy. Both time at elevated temperature and the temperature level can be directly measured, while activation energy needs to be estimated from the accelerated stability tests. Coulter Clenz reagent degradation information is used to demonstrate the effects of temperature excursions. The stability of the product is affected by any excursion, but Coulter Clenz will not lose all of its stability for excursion of up to 30 days at 35 degrees C and 20 days at 40 degrees C. Temperature excursion for up to 20 days at 40 degrees C will reduce the stability of a product that has activation energy in the range of 26-30kcalmol(-1) approximately by 5-7 months. Products with lower activation energy will have a significantly lower reduction in stability. The effects of excursions on shelf life performance are less severe when lower level of risk is implemented to establish the claimed shelf life. The proposed model can effectively predict temperature excursion if used within the scope of a product performance and its characteristics.

Uncertainty of measurement and error in stability studies.

It is required that shelf life be determined based on the lower limit of the confidence interval of the estimate from the stability tests. Simulations indicate that a 1-year prediction of shelf life will have approximately 1 month of error. However, this is product specific and is related to the uncertainty of measurement and experimental design. Factors associated with product and experimental design, such as degradation rate, number of time points, implementing a full versus a reduced design, etc., can significantly affect the error of shelf life. Uncertainty in measurement is positively correlated to the amount of error through the manufacturing lot-to-lot variability, precision of the analytical method and calibrator. Experimental design can control random variability and actually can reduce error by increasing number of lots and replicates in stability tests. The decision on the number of lots and replicates will be a balancing act between the uncertainty of the measurement, design and other practical considerations.

Estimating degradation in real time and accelerated stability tests with random lot-to-lot variation: a simulation study.

The effect of different lot-to-lot variability levels on the prediction of stability are studied based on two statistical models for estimating degradation in real time and accelerated stability tests. Lot-to-lot variability is considered as random in both models, and is attributed to two sources-variability at time zero, and variability of degradation rate. Real-time stability tests are modeled as a function of time while accelerated stability tests as a function of time and temperatures. Several data sets were simulated, and a maximum likelihood approach was used for estimation. The 95% confidence intervals for the degradation rate depend on the amount of lot-to-lot variability. When lot-to-lot degradation rate variability is relatively large (CV > or = 8%) the estimated confidence intervals do not represent the trend for individual lots. In such cases it is recommended to analyze each lot individually.

In-use stability modeling.

Experimental design and modeling of in-use stability testing are presented in this paper. In-use open container degradation is considered in terms of time open container or/and the number of instances that the same container is used. Degradation is estimated based on two models, the fixed and the general model. The fixed model estimates in-use degradation for those fixed time points of closed container where the in-used experimental data is collected. The general model estimates in-use degradation for any time point of closed container using the estimated relationship between closed container time and the degradation rate of open container. Data for in-use open container stability does not have to be collected at a closed container time of interest to estimate in-use degradation at this time point as long as this point is within the range of the experiment. Stability of the product in terms of drift from the initial time to the time of interest is calculated as the sum of closed and in-use open containers drifts.

Total lung capacity: single breath methane dilution versus plethysmography in normals.

OBJECTIVE AND BACKGROUND: Methane is an inert tracer gas used to obtain TLC estimates during single breath diffusion capacity (DL(CO)) measurements. The aim of this study was to assess the accuracy of methane dilution TLC in normal subjects undergoing single breath diffusion capacity measurements using plethysmography as the gold standard comparison method. METHODS: Fifty non-smoking adults underwent lung function testing. Total lung volume was obtained by both plethysmography and methane dilution during a single breath DL(CO) measurement. Deming regression and the concordance correlation coefficient, r(ccc), were used to determine agreement between methods for TLC. Bias was the mean difference between methods and limits of agreement were the mean difference between methods +/- 1.96 (SD). All values are mean +/- SD unless otherwise stated. RESULTS: Plethysmography and methane dilution TLC values were not significantly different. The r(ccc) was 0.87 (95% confidence interval (CI) 0.78-0.92). Deming regression revealed a slope of 0.93 (P = 0.17, H(o): beta = 1.0; 95% CI 0.84-1.03) and a y-intercept of 0.20 (P = 0.39, H(o): alpha = 0; 95% CI -0.27-0.70). The bias was 0.11 L favouring plethysmography. Limits of agreement varied as 0.11 +/- 0.92 L. CONCLUSIONS: There is statistical agreement between methods suggesting the average TLC by methane could substitute for plethysmography in normals at the population level. At the individual level, a normal methane dilution value indicates a normal TLC whereas values below the normal range should be validated using plethysmography.

Estimation of bias between 2 analytical methods at clinically important ranges.

An approach for estimating bias between 2 analytical methods at different clinical ranges is introduced in this article. The approach models replicated data obtained from the reference and the test method in terms of repeatability and trueness bias. The latter can be partitioned into constant and proportional bias. The approach is based on maximum likelihood estimation and can accommodate normal as well as Poisson and binomial distributions that apply to hematology applications and/or other laboratory methods that count particles per unit of volume and/or time. A full spectrum of statistical inference in the form of confidence intervals for each estimate as well as any statistical hypothesis testing is provided. At the same time these estimates can be practically interpreted and related to any clinical important range or decision point. We recommend this approach as an alternative to the National Committee for Clinical Laboratory Standards (NCCLS) EP9-A2 approach in cases where the application of the NCCLS standard is not appropriate.

Establishing tolerance levels for customer complaints.

Customer complaints data are usually expressed as counts for a period of time and are governed by a Poisson process. This process is stationary when the number of complaints is constant, while a change in these numbers would indicate a potential change in the product performance. In this paper we describe an approach for establishing the maximum tolerance level for the number of complaints received within a month. Tolerance level is based on a relatively stable period of time when the Poisson process is stationary. A change-point analysis is performed to the complaints data that exhibit large changes to partition the relatively stable period from the problematic period. Examples that illustrate this approach are provided.

Bias estimation in method comparison studies.

A test and a reference analytical method are usually compared for agreement based on paired data obtained from several independent subjects. Bias between two methods can be classified as constant and proportional. In this article, we provide an approach for maximum likelihood estimation of total bias between two methods and partitioning it into constant and proportional bias for each subject. Normal, binomial, or Poisson distribution are the conditional distributions of the response variable that we have considered here, whereas subjects are considered to be random sample from a normally distributed population. Real data on blood cell counts and hemoglobin are used for demonstration. The estimate of biases can be used to test different statistical hypotheses and/or for graphical interpretation of the agreement. The partitioning of total biases in terms of constant and proportional gives an insight on the sources of disagreement between two methods and helps designers and manufacturer's define a remedial strategy.

Linearity evaluation of analytical methods that count particles.

Linearity evaluation of an analytical method is important for both manufacturers of diagnostic devices and laboratory users. Some of the statistical assumptions for estimation and testing in linear regression are violated in analytical methods that count particles per unit of volume or/and time, leading to potential erroneous evaluation of linearity. The objective of this paper is to provide an approach for evaluating linearity in these cases. The number of counts for each concentration level has a Poisson probability distribution that is linear, second-, or higher-order polynomial function of the concentration. Maximum likelihood approach is used to estimate the parameters of the models. Deviance of a particular model and the likelihood ratio test are used to test for linearity. An evaluation of linearity of an analytical method in multiple experiments is also described. No particular changes to the standard testing protocols and data collections are necessary. There are several statistical software packages that can perform the calculations. Formulas and SAS codes presented in this article can also assist in estimation and statistical testing.

Accelerated stability model for predicting shelf-life.

Second- and higher-order degradation reactions sometimes cannot be approximated with linear or exponential relationships and need to be appropriately modeled. Events above the COULTER HmX Analyzer white blood cell (WBC) counting threshold were recorded for the HmX PAK reagent system stored at five elevated temperatures. An accelerated stability model for a second-degree polynomial degradation pattern was used. The shelf-life of the reagent, along with 95% lower bound confidence intervals, is predicted using the same pattern of degradation as well as the Arrhenius approximation. Experiments indicated that the degradation of the HmX PAK reagent occurred in two phases, the lag phase and the degradation phase, in all tested temperatures. The phase durations are temperature-dependent, and the Arrhenius approximation is appropriate (P=0.639). The degradation of the reagent during the lag phase was experimentally undetectable. Changes of the reagent were nonsignificant for a predicted period of 164 days at 25 degrees C. The rate of degradation increased significantly later on during the degradation phase. The lower bound of the 95% confidence interval of this prediction indicated that it would take at least 326 days before the HmX PAK reagent would have any performance issue related to aging at storage temperature.

Determining shelf life by comparing degradations at elevated temperatures.

The degradation of a biological product follows a specific pattern that depends on the kinetics of the chemical reaction. For most biological and pharmaceutical products, accelerated stability tests are preferred to establish shelf life. We describe a new approach for accelerated stability tests, for situations in which the Arrhenius equation is not appropriate. This approach consists of estimating stability at elevated temperatures and comparing these results with the stability estimates for a similar product with a known shelf life. In this article, "Test" refers to Immuno-Trol Low Cells, and "Control" (the product with a known shelf life) refers to Immuno-Trol Cells. The degradation rates and stabilities at elevated temperatures of three antigens of the Test are estimated and compared to their respective Control values. Most of the degradation occurs at the beginning of the experimental period and then slows down until it levels off to form a plateau at the minimum level. Both Control and Test showed similar degradation patterns at three elevated temperatures, indicating that they both have the same mechanism of degradation. Thus, it is expected that they will degrade similarly at storage temperature and have nonsignificantly different shelf lives. The approach for accelerated stability testing discussed here is applicable to situations in which the Arrhenius equation is not appropriate, and the chemical properties of both the Test and Control products are similar.