Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions.

Autonomy is becoming increasingly important for the robotic exploration of unpredictable environments. One such example is the approach, proximity operation, and surface exploration of small bodies. In this article, we present an overview of an estimation framework to approach and land on small bodies as a key functional capability for an autonomous small-body explorer. We use a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and reach the body, from millions of kilometers down to its surface. We consider a notional spacecraft design that operates across all phases from approach to landing and to maneuvering on the surface of the microgravity body. This SmallSat design makes accommodations to simplify autonomous surface operations. The estimation pipeline combines state-of-the-art techniques with new approaches to estimating the target's unknown properties across all phases. Centroid and light-curve algorithms estimate the body-spacecraft relative trajectory and rotation, respectively, using a priori knowledge of the initial relative orbit. A new shape-from-silhouette algorithm estimates the pole (i.e., rotation axis) and the initial visual hull that seeds subsequent feature tracking as the body gets more resolved in the narrow field-of-view imager. Feature tracking refines the pole orientation and shape of the body for estimating initial gravity to enable safe close approach. A coarse-shape reconstruction algorithm is used to identify initial landable regions whose hazardous nature would subsequently be assessed by dense 3D reconstruction. Slope stability, thermal, occlusion, and terra-mechanical hazards would be assessed on densely reconstructed regions and continually refined prior to landing. We simulated a mission scenario for approaching a hypothetical small body whose motion and shape were unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering the relative body motion and shape solely relying on onboard measurements and estimates with their associated uncertainties and without human input. Current work continues to mature and characterize the algorithms for the last phases of the estimation framework to land on the surface.

An Algorithm for Nonparametric Estimation of a Multivariate Mixing Distribution with Applications to Population Pharmacokinetics.

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing.

Pharmacometric Modeling and Simulation Is Essential to Pediatric Clinical Pharmacology.

Pediatric clinical pharmacology now encompasses a wide range of activities, including drug pharmacokinetic and pharmacodynamic modeling and simulation, also known as pharmacometrics. Pediatric clinical pharmacologists may be physicians but are more likely to be pharmacists or PhD scientists, and pediatric clinical pharmacology today is largely a research specialty rather than a subspecialty for direct patient care. Pharmacometrics, including "top-down" population modeling and "bottom-up" physiologically based pharmacokinetic modeling, has become an indispensable tool for pharmaceutical industry scientists, government regulators, academic researchers, and even a handful of patient-oriented practitioners. This review summarizes the application of pharmacometrics within each of these domains and predicts future trends of further applications across the spectrum of pediatric clinical pharmacology from drug development to patient care.

New Perspectives in Clinical Pharmacokinetics-1: the Importance of Updating the Teaching in Pharmacokinetics that both Clearance and Elimination Rate Constant Approaches Are Mathematically Proven Equally Valid.

The healing professions have only about four main therapeutic tools at their disposal-surgery, drugs, physical therapy, and psychotherapy. For the general profession of internal medicine, drug therapy is its primary tool. Providing an understanding of the state-of-the-art in therapeutic methods, grounded in solid scientific and mathematical rigor, is therefore of the utmost clinical importance for both physicians and clinical pharmacists. This is particularly true where rapidly evolving scientific changes require an up-to-date education upon which students can rely. Unfortunately, relatively little attention has been paid to training clinical pharmacokineticists and physicians to manage drug therapy optimally for patients under their care in their everyday practice. In this paper, we discuss one of these basic deficiencies from the perspective of the longstanding controversy in pharmacokinetic modeling: whether the volume and clearance approach or the volume and rate constant approach is somehow "better". We examine this controversy using the mathematical principle of invariance, which to our knowledge has not been done before. The conclusion of this analysis is that both approaches are rigorously proven mathematically to be equally valid. We also discuss some implications of these equally valid approaches from the framework of mechanistic and non-compartmental models. Ultimately, the conclusion is that the choice of one parameterization over the other is based on preference or usefulness for research or clinical practice, but no longer, because of this analysis, on science.

Prospective Trial on the Use of Trough Concentration versus Area under the Curve To Determine Therapeutic Vancomycin Dosing.

We hypothesized that dosing vancomycin to achieve trough concentrations of >15 mg/liter overdoses many adults compared to area under the concentration-time curve (AUC)-guided dosing. We conducted a 3-year, prospective study of vancomycin dosing, plasma concentrations, and outcomes. In year 1, nonstudy clinicians targeted trough concentrations of 10 to 20 mg/liter (infection dependent) and controlled dosing. In years 2 and 3, the study team controlled vancomycin dosing with BestDose Bayesian software to achieve a daily, steady-state AUC/MIC ratio of ≥400, with a maximum AUC value of 800 mg · h/liter, regardless of trough concentration. For Bayesian estimation of AUCs, we used trough samples in years 1 and 2 and optimally timed samples in year 3. We enrolled 252 adults who were ≥18 years old with ≥1 available vancomycin concentration. Only 19% of all trough concentrations were therapeutic versus 70% of AUCs (P < 0.0001). After enrollment, median trough concentrations by year were 14.4, 9.7, and 10.9 mg/liter (P = 0.005), with 36%, 7%, and 6% over 15 mg/liter (P < 0.0001). Bayesian AUC-guided dosing in years 2 and 3 was associated with fewer additional blood samples per subject (3.6, 2.0, and 2.4; P = 0.003), shorter therapy durations (8.2, 5.4, and 4.7 days; P = 0.03), and reduced nephrotoxicity (8%, 0%, and 2%; P = 0.01). The median inpatient stay was 20 days among nephrotoxic patients versus 6 days (P = 0.002). There was no difference in efficacy by year, with 42% of patients having microbiologically proven infections. Compared to trough concentration targets, AUC-guided, Bayesian estimation-assisted vancomycin dosing was associated with decreased nephrotoxicity, reduced per-patient blood sampling, and shorter length of therapy, without compromising efficacy. These benefits have the potential for substantial cost savings. (This study has been registered at ClinicalTrials.gov under registration no. NCT01932034.).

Experiment design for nonparametric models based on minimizing Bayes Risk: application to voriconazole¹.

An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a NP model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the NP model. Specifically, the problem of identifying an individual from a NP prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient's behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (multiple-model optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications.

Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software.

BACKGROUND: Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden. METHODS: The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building. RESULTS: Mean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (≤12 kg, 67% in the target range) and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (-2.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was -9.2% (-16.7% to -1.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (-0.3% to 4.2%, P = 0.08). CONCLUSIONS: BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.

Describing Assay Precision-Reciprocal of Variance Is Correct, Not CV Percent: Its Use Should Significantly Improve Laboratory Performance.

BACKGROUND: Describing assay error as percent coefficient of variation (CV%) fails as measurements approach zero. Results are censored if below some arbitrarily chosen lower limit of quantification (LLOQ). CV% gives incorrect weighting to data obtained by therapeutic drug monitoring, with incorrect parameter values in the resulting pharmacokinetic models, and incorrect dosage regimens for patient care. METHODS: CV% was compared with the reciprocal of the variance (1/var) of each assay measurement. This method has not been considered by the laboratory community. A simple description of assay standard deviation (SD) as a polynomial function of the assay measurement over its working range was developed, the reciprocal of the assay variance determined, and its results compared with CV%. RESULTS: CV% does not provide correct weighting of measured serum concentrations as required for optimal therapeutic drug monitoring. It does not permit optimally individualized models of the behavior of a drug in a patient, resulting in incorrect dosage regimens. The assay error polynomial described here, using 1/var, provides correct weighting of such data, all the way down to and including zero. There is no need to censor low results, and no need to set any arbitrary LLOQ. CONCLUSIONS: Reciprocal of variance is the correct measure of assay precision and should replace CV%. The information is easily stored as an assay error polynomial. The laboratory can serve the medical community better. There is no longer any need for LLOQ, a significant improvement. Regulatory agencies should implement this more informed policy.

Achieving target voriconazole concentrations more accurately in children and adolescents.

Despite the documented benefit of voriconazole therapeutic drug monitoring, nonlinear pharmacokinetics make the timing of steady-state trough sampling and appropriate dose adjustments unpredictable by conventional methods. We developed a nonparametric population model with data from 141 previously richly sampled children and adults. We then used it in our multiple-model Bayesian adaptive control algorithm to predict measured concentrations and doses in a separate cohort of 33 pediatric patients aged 8 months to 17 years who were receiving voriconazole and enrolled in a pharmacokinetic study. Using all available samples to estimate the individual Bayesian posterior parameter values, the median percent prediction bias relative to a measured target trough concentration in the patients was 1.1% (interquartile range, -17.1 to 10%). Compared to the actual dose that resulted in the target concentration, the percent bias of the predicted dose was -0.7% (interquartile range, -7 to 20%). Using only trough concentrations to generate the Bayesian posterior parameter values, the target bias was 6.4% (interquartile range, -1.4 to 14.7%; P = 0.16 versus the full posterior parameter value) and the dose bias was -6.7% (interquartile range, -18.7 to 2.4%; P = 0.15). Use of a sample collected at an optimal time of 4 h after a dose, in addition to the trough concentration, resulted in a nonsignificantly improved target bias of 3.8% (interquartile range, -13.1 to 18%; P = 0.32) and a dose bias of -3.5% (interquartile range, -18 to 14%; P = 0.33). With the nonparametric population model and trough concentrations, our control algorithm can accurately manage voriconazole therapy in children independently of steady-state conditions, and it is generalizable to any drug with a nonparametric pharmacokinetic model. (This study has been registered at ClinicalTrials.gov under registration no. NCT01976078.).

A two-compartment population pharmacokinetic-pharmacodynamic model of digoxin in adults, with implications for dosage.

A population pharmacokinetic/pharmacodynamic model of digoxin in adult subjects was originally developed by Reuning et al in 1973. They clearly described the 2-compartment behavior of digoxin, the lack of correlation of effect with serum concentrations, and the close correlation of the observed inotropic effect of digoxin with the calculated amount of drug present in the peripheral nonserum compartment. Their model seemed most attractive for clinical use. However, to make it more applicable for maximally precise dosage, its model parameter values (means and SD's) were converted into discrete model parameter distributions using a computer program developed especially for this purpose using the method of maximum entropy. In this way, the parameter distributions became discrete rather than continuous, suitable for use in developing maximally precise digoxin dosage regimens, individualized to an adult patient's age, gender, body weight, and renal function, to achieve desired specific target goals either in the central (serum) compartment or in the peripheral (effect) compartment using the method of multiple model dosage design. Some illustrative clinical applications of this model are presented and discussed. This model with a peripheral compartment reflecting clinical effect has contributed significantly to an improved understanding of the clinical behavior of digoxin in patients than is possible with models having only a single compartment, and to the improved management of digoxin therapy for more than 20 years.

Two general methods for population pharmacokinetic modeling: non-parametric adaptive grid and non-parametric Bayesian.

Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approaches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org.

IMPLICIT DUAL CONTROL BASED ON PARTICLE FILTERING AND FORWARD DYNAMIC PROGRAMMING.

This paper develops a sampling-based approach to implicit dual control. Implicit dual control methods synthesize stochastic control policies by systematically approximating the stochastic dynamic programming equations of Bellman, in contrast to explicit dual control methods that artificially induce probing into the control law by modifying the cost function to include a term that rewards learning. The proposed implicit dual control approach is novel in that it combines a particle filter with a policy-iteration method for forward dynamic programming. The integration of the two methods provides a complete sampling-based approach to the problem. Implementation of the approach is simplified by making use of a specific architecture denoted as an H-block. Practical suggestions are given for reducing computational loads within the H-block for real-time applications. As an example, the method is applied to the control of a stochastic pendulum model having unknown mass, length, initial position and velocity, and unknown sign of its dc gain. Simulation results indicate that active controllers based on the described method can systematically improve closed-loop performance with respect to other more common stochastic control approaches.

Noninvasive hemodynamic monitoring for combat casualties.

The aims of this study were to develop and to test a noninvasive hemodynamic monitoring system that could be applied to combat casualties to supplement conventional vital signs, to use an advanced information system to predict outcomes, and to evaluate the relative effectiveness of various therapies with instant feedback information during acute emergency conditions. In a university-run inner city public hospital, we evaluated 1,000 consecutively monitored trauma patients in the initial resuscitation period, beginning shortly after admission to the emergency department. In addition to conventional vital signs, we used noninvasive monitoring devices (cardiac index by bioimpedance with blood pressure and heart rate to measure cardiac function, arterial hemoglobin oxygen saturation by pulse oximetry to reflect changes in pulmonary function, and tissue oxygenation by transcutaneous oxygen tension indexed to fractional inspired oxygen concentration and carbon dioxide tension to evaluate tissue perfusion). The cardiac index, mean arterial pressure, pulse oximetry (arterial hemoglobin oxygen saturation), and transcutaneous oxygen tension/fractional inspired oxygen concentration were significantly higher in survivors, whereas the heart rate and carbon dioxide tension were higher in nonsurvivors. The calculated survival probability was a useful outcome predictor that also served as a measure of severity of illness. The rate of misclassification of survival probability was 13.5% in the series as a whole but only 6% for patients without severe head injuries and brain death. Application of noninvasive hemodynamic monitoring to acute emergency trauma patients in the emergency department is feasible, safe, and inexpensive and provides accurate hemodynamic patterns in continuous, on-line, real-time, graphical displays of the status of cardiac, pulmonary, and tissue perfusion functions. Combined with an information system, this approach provided an early outcome predictor and evaluated, with an objective individualized method, the relative efficacy of alternative therapies for specific patients.

Outcome prediction by a mathematical model based on noninvasive hemodynamic monitoring.

BACKGROUND: The aims are to apply a mathematical search and display model based on noninvasive hemodynamic monitoring, to predict outcome early in a consecutively monitored series of 661 severely injured patients. METHODS: A prospective observational study by a previously designed protocol in a Level I trauma service in a university-run inner city public hospital was conducted. The survival probabilities were calculated at the initial resuscitation on admission and at subsequent intervals during their hospitalization beginning shortly after admission to the emergency department. Cardiac function was evaluated by cardiac output (CI), heart rate (HR), and mean arterial blood pressure (MAP), pulmonary function by pulse oximetry (SapO2), and tissue perfusion function by transcutaneous oxygen indexed to FiO2, (PtcO2/FiO2), and carbon dioxide (PtcCO2) tension. RESULTS: The survival probability (SP) averaged 89 +/- 0.4% for survivors and 75.7 +/- 1.6% (p < 0.001) for nonsurvivors in the first 24-hour period of resuscitation. The CI, MAP, SapO2, PtcO2, and PtcO2/FiO2 were significantly higher in survivors than in nonsurvivors in initial resuscitation, whereas HR and PtcCO2 were higher in nonsurvivors. CONCLUSIONS: During the initial resuscitation period, misclassifications were 102 of 661 or 15%. The SP provided early objective criteria to evaluate hospital outcome and to track changes throughout the hospital course based on a large database of patients with similar clinical-hemodynamic states.

Stochastic model for outcome prediction in acute illness.

The aims were to apply a stochastic model to predict outcome early in acute emergencies and to evaluate the effectiveness of various therapies in a consecutively monitored series of severely injured patients with noninvasive hemodynamic monitoring. The survival probabilities were calculated beginning shortly after admission to the emergency department (ED) and at subsequent intervals during their hospitalization. Cardiac function was evaluated by cardiac output (CI), heart rate (HR), and mean arterial blood pressure (MAP), pulmonary function by pulse oximetry (SapO(2)), and tissue perfusion function by transcutaneous oxygen indexed to FiO(2),(PtcO(2)/FiO(2)), and carbon dioxide (PtcCO(2)) tension. The survival probability (SP) of survivors averaged 81.5+/-1.1% (SEM) and for nonsurvivors 57.7+/-2.3% (p<0.001) in the first 24-hour period of resuscitation and subsequent management. The CI, SapO(2),PtcO(2)/FiO(2) and MAP were significantly higher in survivors than in nonsurvivors during the initial resuscitation, while HR and PtcCO(2) tensions were higher in the nonsurvivors. Predictions made during the initial resuscitation period in the first 24-hours after admission were compared with the actual outcome at hospital discharge, which were usually several weeks later; misclassifications were 9.6% (16/167). The therapeutic decision support system objectively evaluated the responses of alternative therapies based on responses of patients with similar clinical-hemodynamic states.

Continuous noninvasive monitoring of cardiac performance and tissue perfusion in pediatric trauma patients.

PURPOSE: The aim of this study was to assess the accuracy of a continuous survival probability prediction using noninvasive measures of cardiac performance and tissue perfusion in severely injured pediatric patients. METHODS: Review of all patients entered into a prospective noninvasive monitoring protocol. Cardiac index (CI) was measured using a thoracic bioimpedance device and tissue perfusion was assessed by transcutaneous carbon dioxide (Ptcco(2)) tension and oxygen tension indexed to the fraction of inspired oxygen (Ptco(2)/Fio(2)). Survival probability (SP) was continuously calculated using a stochastic analysis program. RESULTS: There were 45 patients with a total of 953 data sets. The mean age was 11 years (range, 1-16 years) with a mean Injury Severity Score of 24 (+/-16). There was no difference between survivors (n = 32) and nonsurvivors (n = 13) at study entry for heart rate, blood pressure, CI, or pulse oximetry (all P > .05). However, survivors demonstrated higher Ptcco(2) (45 vs 35), higher Ptco(2)/Fio(2) (236 vs 156), and higher predicted SP (89% vs 62%) compared with nonsurvivors at study entry and throughout the monitoring period (all P < .01). For the entire data set, the strongest independent predictors of survival were Ptco(2)/Fio(2) and SP. The area under the receiver operating characteristic curve for mortality prediction was 0.83 for SP and 0.71 for Ptco(2)/Fio(2), compared with 0.6 for heart rate, 0.51 for blood pressure, and 0.53 for CI. Similar hemodynamic patterns were observed for all injury patterns with the exception of those with severe brain injury. CONCLUSIONS: Thoracic bioimpedance and transcutaneous monitoring give critical real-time hemodynamic and tissue perfusion data that can provide early identification of pathologic flow patterns and accurately predict survival.

A stochastic control program to predict outcome and to support therapeutic decisions: a preliminary report.

BACKGROUND AND OBJECTIVES: Early noninvasive hemodynamic monitoring with an outcome predictor and a therapeutic decision support system may be useful to identify and correct hemodynamic deficiencies in emergency patients. The first aim was to apply a stochastic (probability) search and display model to predict outcome as early as possible. The second aim was to explore the usefulness of a therapeutic decision support system to evaluate the relative effectiveness of various therapies. METHODS: A stochastic control and display program based on noninvasive hemodynamic monitoring was applied in 100 consecutive critically ill patients admitted to the emergency department of an inner city public hospital. The program continuously displayed the noninvasive hemodynamic data and the patient's predicted survival probability (SP) that was based on the patient's diagnosis, covariates, and hemodynamic data. The accuracy of the SP at the initial resuscitation on admission to the emergency department (ED) was evaluated by the actual outcome at hospital discharge. The therapeutic decision support program evaluated the relative effectiveness of various therapies on based on their hemodynamic and SP responses and outcome of patients with similar clinical-hemodynamic states. RESULTS: The cardiac index, mean arterial pressure, arterial saturation, transcutaneous oxygen and carbon dioxide tensions were appreciably higher in survivors than in nonsurvivors in the initial resuscitation. Heart rate was higher in the nonsurvivors. The calculated Survival Probability (SP) of survivors averaged 81 +/- 1.4% in the first 24-hour observation period. It was 58 +/- 2.2% for nonsurvivors during this period. Misclassifications were 10/100 or 10%.

Outcome prediction in chest injury by a mathematical search and display program.

OBJECTIVE: This study applies a stochastic or probability search and display model to prospectively predict outcome and to evaluate therapeutic effects in a consecutively monitored series of 396 patients with severe thoracic and thoracoabdominal injuries. STUDY DESIGN: Prospective observational study of outcome prediction using noninvasive hemodynamic monitoring by previously designed protocols and tested against actual outcome at hospital discharge in a level 1 trauma service of a university-run, inner-city public hospital. METHODS: Cardiac index (CI), heart rate (HR), mean arterial pressure (MAP), arterial oxygen saturation measured by pulse oximetry (Sp(O2)), transcutaneous oxygen tension (PtC(O2)), and transcutaneous carbon dioxide tension (Ptc(CO2)) were monitored beginning shortly after admission to the emergency department. The stochastic search and display model with a decision support program based on noninvasive hemodynamic monitoring was applied to 396 severely ill patients with major thoracic and thoracoabdominal trauma. The survival probability (SP) was calculated during initial resuscitation continuously until patients were stabilized, and compared with the actual outcome when the patient was discharged from the hospital usually a week or more later. RESULTS: The CI, Sp(O2), Ptc(O2), and MAP were appreciably higher in survivors than in nonsurvivors. HR and Ptc(CO2) were higher in the nonsurvivors. The calculated SP in the first 24-h observation period of survivors of chest wounds averaged 83 +/- 18% (+/- SD) and 62 +/- 19% for nonsurvivors. Misclassifications were 9.6%. The relative effects of alternative therapies were evaluated before and after therapy, using hemodynamic monitoring and SP as criteria. CONCLUSIONS: Noninvasive hemodynamic monitoring with an information system provided a feasible approach to early outcome predictions and therapeutic decision support.