Biomedical subjects

Lalit Gupta

Publications and source records attributed to Lalit Gupta.

5 recordsLinked to original sources

PubMed2006

Prospective study of quality of life in adults with newly diagnosed high-grade gliomas.

OBJECTIVE: To assess baseline quality of life (QOL) and its prognostic importance for adults with newly diagnosed high-grade gliomas, we analyzed QOL and outcome data prospectively collected in three phase II high-grade glioma protocols. METHODS: At study entry, patients completed five self-administered forms to assess overall QOL (linear analogue scale assessment [LASA] and Functional Assessment of Cancer Therapy-Brain [FACT-Br]); fatigue (Symptom Distress Scale [SDS]); excessive daytime somnolence (Epworth Sleepiness Scale [ESS]); and depression (POMS-SF). Folstein Mini-Mental State Examination (MMSE) and Eastern Cooperative Oncology Group (ECOG) performance scores (PS) were obtained by the health care provider. RESULTS: Baseline QOL data were available for 194 of 220 patients (88%) enrolled in the three protocols. Differences in baseline QOL among the three studies were not statistically significant. One-third of patients had clinically significant fatigue at baseline. Increased fatigue (P = 0.003), excessive daytime somnolence (P = 0.01), and lower overall QOL scores (LASA, P = 0.001; FACT-Br, P = 0.0001) correlated with worse ECOG PS. No relation was found between QOL and corticosteroid or anticonvulsant therapy, extent of resection, tumor grade, or sex. Multivariate analyses found worse ECOG PS (PS 2, P = 0.007) associated with increased fatigue. Worse ECOG PS (PS 2, P = 0.002) was also associated with worse overall QOL (LASA). On multivariate analyses of survival, increased fatigue (P = 0.003) predicted poorer overall survival. CONCLUSIONS: Performance status is related to QOL in patients with newly diagnosed high-grade brain tumors. Increased fatigue is an independent predictor of overall survival. Interventional studies directed at improving QOL, especially fatigue, may have important benefits for these patients.

Adult↗

PubMed2005

Laparoscopic management of gallstone presenting as obstructive gangrenous appendicitis.

We present an unusual case of a 55-year-old man with symptoms of recurrent appendicitis. Laparoscopy revealed a 1.5 cm gallstone impacted at the base of the appendix, leading to gangrenous appendicitis. This patient did not have any features of gallstone ileus. On imaging he had an inflammatory mass in the region of the right iliac fossa with a hyperintense shadow in the cecal area which was reported as an appendicolith. There was no demonstrable cholelithiasis or biliary-enteric fistula. There were dense omental adhesions in the pericholecystic area on laparoscopy. The case was successfully managed by laparoscopic appendectomy with retrieval of the gallstone. No surgery was undertaken for the gallbladder. Diagnosis was confirmed by biochemical analysis of the stone, which contained calcium bilirubinate and cholesterol. A gallstone obstructing the appendicular lumen is a very rare etiology of gangrenous perforation of the appendix peritonitis. This case was successfully managed laparoscopically.

Appendectomy↗

PubMed2005

Multichannel fusion models for the parametric classification of differential brain activity.

This paper introduces parametric multichannel fusion models to exploit the different but complementary brain activity information recorded from multiple channels in order to accurately classify differential brain activity into their respective categories. A parametric weighted decision fusion model and two parametric weighted data fusion models are introduced for the classification of averaged multichannel evoked potentials (EPs). The decision fusion model combines the independent decisions of each channel classifier into a decision fusion vector and a parametric classifier is designed to determine the EP class from the discrete decision fusion vector. The data fusion models include the weighted EP-sum model in which the fusion vector is a linear combination of the multichannel EPs and the EP-concatenation model in which the fusion vector is a vector-concatenation of the multichannel EPs. The discrete Karhunen-Loeve transform (DKLT) is used to select features for each channel classifier and from each data fusion vector. The difficulty in estimating the probability density function (PDF) parameters from a small number of averaged EPs is identified and the class conditional PDFs of the feature vectors of averaged EPs are, therefore, derived in terms of the PDFs of the single-trial EPs. Multivariate parametric classifiers are developed for each fusion strategy and the performances of the different strategies are compared by classifying 14-channel EPs collected from five subjects involved in making explicit match/mismatch comparisons between sequentially presented stimuli. It is shown that the performance improves by incorporating weights in the fusion rules and that the best performance is obtained using multichannel EP concatenation. It is also noted that the fusion strategies introduced are also applicable to other problems involving the classification of multicategory multivariate signals generated from multiple sources.

Action Potentials↗

PubMed2002

Risk-factor fusion for predicting multifactorial diseases.

A generalized classification methodology is developed to predict the presence or absence of a multifactorial disease from a set of risk factors thought to be correlated with the disease. The methodology includes fusion to combine risk factors into a single feature vector, normalization to overcome the problems associated with fusing features which have different formats and ranges, discrete Karhunen-Loeve transform (DKLT)-based transformation to facilitate parametric classifier development, the selection of features with high interclass separations, and the design of parametric classifiers. The validity of the method is demonstrated by applying it to predict the occurrence of gout from 14 risk factors. Cross-validation evaluations on 96 patients, 48 clinically diagnosed to have gout and 48 diagnosed to not have gout, showed that an average classification accuracy of 75.7% can be obtained. Even more promising is that higher classification accuracies can be achieved through the careful selection of the DKLT transformation matrix which in turn involves selecting design sets that are good representatives of the gout and nongout classes. It is concluded that the generalized methodology developed in this paper is quite effective in predicting multifactorial diseases and can, therefore, assist/support a physician in diagnosing a multifactorial disease.

Diagnosis, Differential↗

PubMed2002

Parametric classification of multichannel averaged event-related potentials.

This paper focuses on the systematic development of a parametric approach for classifying averaged event-related potentials (ERPs) recorded from multiple channels. It is shown that the parameters of the averaged ERP ensemble can be estimated directly from the parameters of the single-trial ensemble, thus, making it possible to design a class of parametric classifiers without having to collect a prohibitively large number of single-trial ERPs. An approach based on random sampling without replacement is developed to generate a large number of averaged ERP ensembles in order to evaluate the performance of a classifier. A two-class ERP classification problem is considered and the parameter estimation methods are applied to independently design a Gaussian likelihood ratio classifier for each channel. A fusion rule is formulated to classify an ERP using the classification results from all the channels. Experiments using real and simulated ERPs are designed to show that, through the approach developed, parametric classifiers can be designed and evaluated even when the number of averaged ERPs does not exceed the dimension of the ERP vector. Additionally, it is shown that the performance of a majority rule fusion classifier is consistently superior to the rule that selects a single best channel.

Computer Simulation↗