Biomedical subjects

S Kromhout-Schiro

Publications and source records attributed to S Kromhout-Schiro.

8 recordsLinked to original sources

PubMed1998

The end of the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS): ICISS, an International Classification of Diseases, ninth revision-based prediction tool, outperforms both ISS and TRISS as predictors of trauma patient survival, hospital charges, and hospital length of stay.

INTRODUCTION: Since their inception, the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS) have been suggested as measures of the quality of trauma care. In concept, they are designed to accurately assess injury severity and predict expected outcomes. ICISS, an injury severity methodology based on International Classification of Diseases, Ninth Revision, codes, has been demonstrated to be superior to ISS and TRISS. The purpose of the present study was to compare the ability of TRISS to ICISS as predictors of survival and other outcomes of injury (hospital length of stay and hospital charges). It was our hypothesis that ICISS would outperform ISS and TRISS in each of these outcome predictions. METHODS: "Training" data for creation of ICISS predictions were obtained from a state hospital discharge data base. "Test" data were obtained from a state trauma registry. ISS, TRISS, and ICISS were compared as predictors of patient survival. They were also compared as indicators of resource utilization by assessing their ability to predict patient hospital length of stay and hospital charges. Finally, a neural network was trained on the ICISS values and applied to the test data set in an effort to further improve predictive power. The techniques were compared by comparing each patient's outcome as predicted by the model to the actual outcome. RESULTS: Seven thousand seven hundred five patients had complete data available for analysis. The ICISS was far more likely than ISS or TRISS to accurately predict every measure of outcome of injured patients tested, and the neural network further improved predictive power. CONCLUSION: In addition to predicting mortality, quality tools that can accurately predict resource utilization are necessary for effective trauma center quality-improvement programs. ICISS-derived predictions of survival, hospital charges, and hospital length of stay consistently outperformed those of ISS and TRISS. The neural network-augmented ICISS was even better. This and previous studies demonstrate that TRISS is a limited technique in predicting survival resource utilization. Because of the limitations of TRISS, it should be superseded by ICISS.

Adult↗

PubMed1998

The conundrum of the Glasgow Coma Scale in intubated patients: a linear regression prediction of the Glasgow verbal score from the Glasgow eye and motor scores.

BACKGROUND: The Glasgow Coma Scale (GCS), which is the foundation of the Trauma Score, Trauma and Injury Severity Score, and the Acute Physiology and Chronic Health Evaluation scoring systems, requires a verbal response. In some series, up to 50% of injured patients must be excluded from analysis because of lack of a verbal component for the GCS. The present study extends previous work evaluating derivation of the verbal score from the eye and motor components of the GCS. METHODS: Data were obtained from a state trauma registry for 24,565 unintubated patients. The eye and motor scores were used in a previously published regression model to predict the verbal score: Derived Verbal Score = -0.3756 + Motor Score * (0.5713) + Eye Score * (0.4233). The correlation of the actual and derived verbal and GCS scales were assessed. In addition the ability of the actual and derived GCS to predict patient survival in a logistic regression model were analyzed using the PC SAS system for statistical analysis. The predictive power of the actual and the predicted GCS were compared using the area under the receiver operator characteristic curve and Hosmer-Lemeshow goodness-of-fit testing. RESULTS: A total of 24,085 patients were available for analysis. The mean actual verbal score was 4.4 +/- 1.3 versus a predicted verbal score of 4.3 +/- 1.2 (r = 0.90, p = 0.0001). The actual GCS was 13.6 + 3.5 versus a predicted GCS of 13.7 +/- 3.4 (r = 0.97, p = 0.0001). The results of the comparison of the prediction of survival in patients based on the actual GCS and the derived GCS show that the mean actual GCS was 13.5 + 3.5 versus 13.7 + 3.4 in the regression predicted model. The area under the receiver operator characteristic curve for predicting survival of the two values was similar at 0.868 for the actual GCS compared with 0.850 for the predicted GCS. CONCLUSIONS: The previously derived method of calculating the verbal score from the eye and motor scores is an excellent predictor of the actual verbal score. Furthermore, the derived GCS performed better than the actual GCS by several measures. The present study confirms previous work that a very accurate GCS can be derived in the absence of the verbal component.

Glasgow Coma Scale↗

PubMed1998

Illness severity adjustment for outcomes analysis: validation of the ICISS methodology in all 821,455 patients hospitalized in North Carolina in 1996.

BACKGROUND: Previous work has demonstrated that the International Classification of Diseases 9th Revision (ICD-9) Based Illness Severity Score (ICISS) methodology developed by Rutledge and Osler can perform well in this role as a severity adjustment tool in trauma patients. The purpose of the present study was to extend this previous work to determine the ability of ICISS to predict outcomes in all types of hospitalized patients. METHODS: The ICISS methodology was used to derive predictions of survival, length of hospital stay, and hospital charges in the entire study population. RESULTS: A total of 821,455 hospitalized patients in North Carolina in 1996 had complete data available for analysis. The overall hospital mortality rate was 2.9%. ICISS was an accurate predictor of hospital survival in all hospitalized patients (accuracy 95.9%, sensitivity 97.2%, and specificity 52.7%.) The area of the receiver operator characteristic curve was 0.93. By adding to the model, the area under the receiver operator characteristic curve increased to 0.95. ICISS also explained a large amount of the variance in hospital stay and charges (R2 = 0.38 and 0.56, respectively, P < .0001). CONCLUSIONS: This study extends previous work suggesting that ICISS may be an important improvement over other presently available severity adjustment models. If these findings are confirmed in comparison with other predictive tools, ICISS may find an important place in assessing illness severity.

Hospital Charges↗

PubMed1997

Automated planning target volume generation: an evaluation pitting a computer-based tool against human experts.

PURPOSE: Software tools are seeing increased use in three-dimensional treatment planning. However, the development of these tools frequently omits careful evaluation before placing them in clinical use. This study demonstrates the application of a rigorous evaluation methodology using blinded peer review to an automated software tool that produces ICRU-50 planning target volumes (PTVs). METHODS AND MATERIALS: Seven physicians from three different institutions involved in three-dimensional treatment planning participated in the evaluation. Four physicians drew partial PTVs on nine test cases, consisting of four nasopharynx and five lung primaries. Using the same information provided to the human experts, the computer tool generated PTVs for comparison. The remaining three physicians, designated evaluators, individually reviewed the PTVs for acceptability. To exclude bias, the evaluators were blinded to the source (human or computer) of the PTVs they reviewed. Their scorings of the PTVs were statistically examined to determine if the computer tool performed as well as the human experts. RESULTS: The computer tool was as successful as the human experts in generating PTVs. Failures were primarily attributable to insufficient margins around the clinical target volume and to encroachment upon critical structures. In a qualitative analysis, the human and computer experts displayed similar types and distributions of errors. CONCLUSIONS: Rigorous evaluation of computer-based radiotherapy tools requires comparison to current practice and can reveal areas for improvement before the tool enters clinical practice.

Expert Systems↗

PubMed1995

Three dimensional planning target volumes: a model and a software tool.

PURPOSE: Three dimensional (3D) target volumes are an essential component of conformal therapy because the goal is to shape the treatment volume to the target volume. The planning target volume (PTV) is defined by ICRU 50 as the clinical target volume (CTV) plus a margin to ensure that the CTV receives the prescribed dose. The margin must include all interfractional and intrafractional treatment variations. This paper describes a software tool that automatically generates 3D PTVs from CTVs for lung cancers and immobile head and neck cancers. METHODS AND MATERIALS: Values for the interfractional and intrafractional treatment variations were determined by a literature review and by targeted interviews with physicians. The software tool is written in Common LISP and conforms to the specifications for shareable software of the Radiotherapy Treatment Planning Tools Collaborative Working Group. RESULTS: The tool is a rule-based expert system in which the inputs are the CTV contours, critical structure contours, and qualitative information about the specific patient. The output is PTV contours, which are a cylindrical expansion of the CTV. A model for creating PTVs from CTVs is embedded in the tool. The interfractional variation of setup uncertainty and the intrafractional variations of movement of the CTV (e.g., respiration) and patient motion are included in the model. Measured data for the component variations is consistent with modeling the components as independent samples from 3D Gaussian distributions. The components are combined using multivariate normal statistics to yield the cylindrical expansion factors. Rules are used to represent the values of the components for certain patient conditions (e.g., setup uncertainty for a head and neck patient immobilized in a mask). The tool uses a rule interpreter to combine qualitative information about a specific patient with rules representing the value of the components and to enter the appropriate component values for that patient into the cylindrical expansion formula. CONCLUSION: The portable software tool allows the rapid, consistent, and automatic generation of 3D PTVs from CTVs.

Head and Neck Neoplasms↗

PubMed1994

Portable software tools for 3D radiation therapy planning.

PURPOSE: Produce a collection of software tools (computer programs) that support three-dimensional (3D) radiation therapy planning. The tools are not a complete 3D planning system. Instead, they work with any 3D planning system that meets certain minimal specifications. The tools assist in deriving anatomic data from images, generating target volume contours, evaluating treatment plans, and verifying accurate treatment delivery. The tools are portable: they can run without source code changes in any computing environment that provides a library of functions and data definitions called the Foundation. The Foundation couples the portable tools to the (usually nonportable) file system and dose calculation associated with a particular 3D planning system. METHODS AND MATERIALS: Tools were written at three different (geographically separated) institutions. Software developers from all three sites specified the Foundation. The programmers' interface to the Foundation is portable, but a Foundation implementation need not be portable. Each group implemented a Foundation adapted to the (different) 3D planning system used at their site. RESULTS: All tools run at all three sites without source code changes. Each Foundation was implemented in a few person-months of programming effort. The program text and documentation for the tools have been placed in the public domain. CONCLUSIONS: It is practical and economical to produce portable radiotherapy treatment planning tools. Providers of 3D planning programs should offer Foundations for their systems, so they can be used with tools. Researchers considering new computer programs should write them as tools, so they can work with any 3D planning system.

Costs and Cost Analysis↗

PubMed1993

Relationship of perfusion to edema in the 9L gliosarcoma.

The relationship between tumor perfusion and edema was analyzed, with edema characterized as tumor wet/dry weight ratio. Perfusion of subcutaneous 9L gliosarcoma was measured by injection of 133Xe in saline into the tumor core, followed by gamma camera imaging of 133Xe washout kinetics. A significant inverse correlation was found between edema and tumor perfusion (p < 0.0002), suggesting that edema can limit tumor perfusion, perhaps through a mechanism of increased interstitial fluid pressure. The perfusion rate of highly edematous tumors was reduced to less than 10% of the perfusion rate of less edematous tumors (p < 0.001). It was also found that tumor edema increased significantly with increasing tumor volume (p < 0.001), which could account for the finding that perfusion declined significantly with increasing tumor volume (p < 0.02). These findings are potentially important because it is possible to quantify tumor edema in vivo, with millimeter resolution, using 1H magnetic resonance imaging (MRI). Thus MRI may provide a non-invasive technique for characterizing tumor perfusion or tumor drug delivery.

Animals↗

PubMedDate not supplied

The role of neural networks in improving the accuracy of MR spectroscopy for the diagnosis of head and neck squamous cell carcinoma.

BACKGROUND AND PURPOSE: MR Spectroscopy (MRS) has the unique ability to analyze tissue at the molecular level noninvasively. The purpose of this study was to determine if peak heights revealed by proton MRS ((1)H-MRS) signals showed that neural networks (NN) provided better accuracy than linear discriminant analysis (LDA) in differentiating head and neck squamous cell carcinoma (SCCA) from muscle METHODS: In vitro 11-T (1)H-MR spectra were obtained on SCCA tissue samples (n = 16) and muscle (n = 12). The peak heights at seven metabolite resonances were measured: olefinic acids at 5.3 ppm, inositol at 3.5 ppm, taurine at 3.4 ppm, choline (Cho) at 3.2 ppm, creatine (Cr) at 3.0 ppm, sialic acid at 2.2 ppm, and methyl at 0.9 ppm. Using leave-one-out experimental design and receiver operating characteristic curve analysis, the ability of NN and LDA classifiers to distinguish SCCA from muscle were compared (given equal weighting of false-negative and false-positive errors). These classifiers were also compared with an existing method that forms a diagnosis by using LDA of the Cho/Cr peak area ratio. RESULTS: NN classifiers, which were identified using height data, achieved better sensitivity and specificity rates in distinguishing SCAA from muscle than did LDA using height or area data. Sensitivity/specificity for the NN analysis of the seven metabolite peak heights were 87.5 % and 83.3%, respectively, for a one-hidden-node network and 81.2% and 91.7%, respectively, for a two-hidden-node network. Additional nodes did not improve accuracy. The sensitivity and specificity were 81.2% and 50%, respectively, for LDA of the seven peak heights, and 68% and 83%, respectively, for LDA of the Cho/Cr peak area ratio. CONCLUSION: NN classifiers with peak height data were superior to LDA of the peak heights and LDA of the Cho/Cr peak area ratio for differentiating SCCA from normal muscle. These results show neural network analysis can improve the diagnostic accuracy of (1)H-MRS in differentiating muscle from malignant tissue. Further studies are necessary to confirm our initial findings.

Carcinoma, Squamous Cell↗