RESUMEN
BACKGROUND: Predicting an intensive care unit patient's outcome is highly desirable. An end goal is for computational techniques to provide updated, accurate predictions about changing patient condition using a manageable number of physiologic parameters. METHODS: Principal component analysis was used to select input parameters for critical care patient outcome models. Vital signs and laboratory values from each patient's hospital stay along with outcomes ("Discharged" vs. "Deceased") were collected retrospectively at a Level I Trauma-Military Medical Center in the southwest; intensive care unit patients were included if they had been admitted for burn, infection, or hypovolemia during a 5-year period ending October 2007. Principal component analysis was used to determine which of the 24 parameters would serve as inputs in a bayesian network developed for outcome prediction. RESULTS: Data for 581 patients were collected. Pulse pressure, heart rate, temperature, respiratory rate, sodium, and chloride were found to have statistically significant differences between Discharged and Deceased groups for "Hypovolemia" patients. For "Burn" patients, pulse pressure, hemoglobin, hematocrit, and potassium were found to have statistically significant differences. For a "Combined" group, heart rate, temperature, respiratory rate, sodium, and chloride had statistically significant differences. A bayesian network based on these results, developed for the Combined group, achieved an accuracy of 75% when predicting patient outcome. CONCLUSIONS: Outcome prediction for critical care patients is possible. Future work should explore model development using additional temporal data and should include prospective validation. Such technology could serve as the basis of real-time intelligent monitoring systems for critical patients.
Asunto(s)
Teorema de Bayes , Cuidados Críticos/métodos , Enfermedad Crítica/mortalidad , Mortalidad Hospitalaria , Análisis de Componente Principal , Heridas y Lesiones/mortalidad , Adulto , Causas de Muerte , Enfermedad Crítica/terapia , Femenino , Hospitales Militares , Humanos , Unidades de Cuidados Intensivos , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Alta del Paciente/estadística & datos numéricos , Valor Predictivo de las Pruebas , Medición de Riesgo , Análisis de Supervivencia , Centros Traumatológicos , Resultado del Tratamiento , Heridas y Lesiones/diagnóstico , Heridas y Lesiones/terapia , Adulto JovenRESUMEN
Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.
Asunto(s)
Algoritmos , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Imagenología Tridimensional/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Procesamiento de Señales Asistido por Computador , Tomografía de Coherencia Óptica/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
The vast majority of bladder cancers originate within 600 microm of the tissue surface, making optical coherence tomography (OCT) a potentially powerful tool for recognizing cancers that are not easily visible with current techniques. OCT is a new technology, however, and surgeons are not familiar with the resulting images. Technology able to analyze and provide diagnoses based on OCT images would improve the clinical utility of OCT systems. We present an automated algorithm that uses texture analysis to detect bladder cancer from OCT images. Our algorithm was applied to 182 OCT images of bladder tissue, taken from 68 distinct areas and 21 patients, to classify the images as noncancerous, dysplasia, carcinoma in situ (CIS), or papillary lesions, and to determine tumor invasion. The results, when compared with the corresponding pathology, indicate that the algorithm is effective at differentiating cancerous from noncancerous tissue with a sensitivity of 92% and a specificity of 62%. With further research to improve discrimination between cancer types and recognition of false positives, it may be possible to use OCT to guide endoscopic biopsies toward tissue likely to contain cancer and to avoid unnecessary biopsies of normal tissue.