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At least 973 records · Page 54Linked to original sources
PubMed2006

Development of nonexercise prediction models of maximal oxygen uptake in healthy Japanese young men.

The present study developed nonexercise models for predicting maximal oxygen uptake (VO2max) using skeletal muscle (SM) mass and cardiac dimensions and to investigate the validity of these equations in healthy Japanese young men. Sixty healthy Japanese men were randomly separated into two groups: 40 in the development group and 20 in the validation group. VO2max during treadmill running was measured using an automated breath-by-breath mass spectrometry system. Left ventricular internal dimensions at end-diastole (LVIDD) and at end-systole (LVIDS) were measured using M-mode ultrasound with a 2.5 MHz transducer. Stroke volume (SV) was calculated based on the Pombo rule. SM mass was predicted by B-mode ultrasound muscle thickness. Correlations were observed between VO2max and predicted thigh (r = 0.74, P < 0.001) and lower leg SM mass (r = 0.55, P < 0.001). Furthermore, there were correlations between VO2max and LVIDD (r = 0.74, P < 0.001) and SV (r = 0.72, P < 0.001). Stepwise regression analysis was applied to thigh SM mass and SV for prediction of VO2max in the development group, and these parameters were closely correlated with absolute measured VO2max (R2 = 0.72, P < 0.001) by multiple regression analysis. When the VO2max prediction equations were applied to the validation group, significant correlations were also observed between the measured and predicted VO2max (R2 = 0.83, P < 0.001). These results suggested that nonexercise prediction of VO2max using thigh SM mass and cardiac dimension is a valid method to predict VO2max in young Japanese adults.

Adult↗
PubMed2006

A clinical prognostic prediction of lymph node-negative breast cancer by gene expression profiles.

PURPOSE: To set up a method by use of gene expression data to predict the prognosis of breast cancer patients on the basis of genes as few as possible, but maintaining the accuracy of prediction, we reanalyze the data from van't Veer et al. (Nature 415:530-536, 2002) and van de Vijver et al. (N Engl J Med 347:1999-2009, 2002). METHODS: A three-step method based on re-sampling strategy is employed to select the prognostic genes. And based on these genes, a predictive approach is established. Validation sets are used to testify the predictive power of the prognostic genes. RESULTS: We have discovered 13 genes as the most informative ones to predict the clinical outcomes of breast cancer patients with lymph node-negative. The validation results show the robust performances of these genes. And the results of further analysis illustrate the significant association of the prediction to the time of metastases and overall survival. CONCLUSION: Our predictive approach is useful in prognosis prediction for breast cancer patients with lymph node-negative. The gene markers provide valuable information for the progression of breast cancer and suggest potential target genes for treating the cancer.

Breast Neoplasms↗
PubMed2003

A predictive model for outcome after conservative decompression surgery for lumbar spinal stenosis.

This study was designed to develop predictive models for surgical outcome based on information available prior to lumbar stenosis surgery. Forty patients underwent decompressive laminarthrectomy. Preop and 1-year postop evaluation included Waddell's nonorganic signs, CT scan, Waddell disability index, Oswestry low back pain disability questionnaire, low back outcome score (LBOS), visual analog scale (VAS) for pain intensity, and trunk strength testing. Statistical comparisons of data used adjusted error rates within families of predictors. Mathematical models were developed to predict outcome success using stepwise logistic regression and decision-tree methodologies (chi-squared automatic interaction detection, or CHAID). Successful outcome was defined as improvement in at least three of four criteria: VAS, LBOS, and reductions in claudication and leg pain. Exact logistic regression analysis resulted in a three-predictor model. This model was more accurate in predicting unsuccessful outcome (negative predictive value 75.0%) than in successful outcome (positive predictive value 69.6%). A CHAID model correctly classified 90.1% of successful outcomes (positive predictive value 85.7%, negative predictive value 100%). The use of conservative surgical decompression for lumbar stenosis can be recommended, as it demonstrated a success rate similar to that of more invasive techniques. Given its physiologic and biomechanical advantages, it can be recommended as the surgical method of choice in this indication. Underlying subclinical vascular factors may be involved in the complaints of spinal stenosis patients. Those factors should be investigated more thoroughly, as they may account for some of the failures of surgical relief. The CHAID decision tree appears to be a novel and useful tool for predicting the results of spinal stenosis surgery

Adolescent↗
PubMed2025

Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.

With the rapid development of sequencing technology, the application of genomic prediction has become more and more common in breeding schemes of livestocks and crops. Selecting an appropriate statistical model is of central importance to achieve high prediction accuracy. Recently, machine learning models have been expected to upgrade genomic prediction into a new era. However, the perspective still suffers from lack of evidence that machine learning models can generally outperform the traditional ones on empirical data sets. In this study, we compared two machine learning models based on artificial neural network (ANN) and convolutional neural network (CNN) with four traditional models, including genomic best linear unbiased prediction (GBLUP), Bayesian ridge regression (BRR), BayesA and BayesB, using three published data sets for grain yield in wheat. For each model, we considered two variants: modeling and ignoring the genotype-by-environment ([Formula: see text]) interaction. In the comparison, we considered two strategies of cross-validation: predicting genotypes that have not been evaluated in any environment (CV1) and predicting genotypes that have been tested in other environments (CV2). Our results showed that traditional Bayesian models (BayesA, BayesB, and BRR) outperformed GBLUP, ANN and CNN when considering [Formula: see text] interaction. The accuracies of ANN and CNN were higher than traditional models only in CV1 and when [Formula: see text] interaction was ignored. It was also found that the performance of the two machine learning models was significantly affected by the interaction between the CV strategy and the way of treating the [Formula: see text] interaction, while that of the four traditional models was only influenced by whether the [Formula: see text] interaction was considered or not. Thus, machine learning models can be a powerful complementary to the traditional ones and their superiority may depend on the prediction scenario. Among the two machine learning models, we observed that the accuracy of ANN was higher than CNN in most cases, indicating that it is still challenging to adapt complex machine learning models such as CNN to genomic prediction.

ANN↗
PubMed1994

Multivariate prediction of spontaneous repetitive responses in ventricular myocardium exposed in vitro to simulated ischemic conditions.

Guinea-pig ventricular myocardium was partly exposed to normal Tyrode's superfusion and partly to altered conditions (using modified Tyrode's solution) set to simulate acute myocardial ischemia (PO2 80 +/- 10 mmHg; no glucose; pH 7.00 +/- 0.05; K+ 12 mM). Using a double-chamber tissue bath and standard microelectrode technique, the occurrence of spontaneous repetitive responses was investigated during simulated ischemia (occlusion) and after reperfusing the previously ischemic superfused tissue with normal Tyrode's solution (reperfusion). In 62 experiments (42 animals) the effects of: (1) duration of simulated ischemia (1321 +/- 435 s), (2) stimulation rate (1002 +/- 549 ms) and (3) number of successive simulated ischemic periods (occlusions) (1.58 +/- 0.92) on: (1) resting membrane potential, (2) action potential amplitude, (3) duration of 50 and 90% action potentials and (4) maximal upstroke velocity of action potential were studied. All variables were considered as gradients (delta) between normal and ischemic tissue. Both during occlusion and upon reperfusion, spontaneous repetitive responses were coded as single, couplets, salvos (three to nine and > 10) or total spontaneous repetitive responses (coded present when at least one of the above-mentioned types was seen). The incidence of total spontaneous repetitive responses was 31% (19/62) on occlusion and 85% (53/62) upon reperfusion. Cox's models (forced and stepwise) were used to predict multivariately the occurrence of arrhythmic events considered as both total spontaneous repetitive responses and as separate entities. These models were applicable since continuous monitoring of the experiments enabled exact timing of spontaneous repetitive response onset during both occlusion and reperfusion. In predicting reperfusion spontaneous repetitive responses, total spontaneous repetitive responses and blocks observed during the occlusion period were also considered. Total occlusion spontaneous repetitive responses were predicted by: (1) longer delta 50% action potential duration (t = 2.68), (2) shorter delta 90% action potential duration (t = -2.17) and (3) fewer occlusive periods (t = -2.46). Total reperfusion spontaneous repetitive responses were predicted by a longer delta action potential amplitude (t = 2.18). Due to few events during occlusion, prediction of individual arrhythmic entities was not possible. Upon reperfusion single spontaneous repetitive responses were predicted by longer delta maximal upstroke velocity of action potential (t = 2.59) and shorter delta 90% action potential duration (t = -2.55); couplets were predicted by longer delta 50% action potential duration (t = 3.26); longer delta action potential amplitude predicted salvos (> 10) (t = 3.26).(ABSTRACT TRUNCATED AT 400 WORDS)

Action Potentials↗
PubMed1994

Human acute toxicity prediction of the first 50 MEIC chemicals by a battery of ecotoxicological tests and physicochemical properties.

Five acute bioassays consisting of three cyst-based tests (with Artemia salina, Streptocephalus proboscideus and Brachionus calyciflorus), the Daphnia magna test and the bacterial luminescence inhibition test (Photobacterium phosphoreum) are used to determine the acute toxicity of the 50 priority chemicals of the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme. These tests and five physiocochemical properties (n-octanol-water partition coefficient, molecular weight, melting point, boiling point and density) are evaluated either singly or in combination to predict human acute toxicity. Acute toxicity in human is expressed both as oral lethal doses (HLD) and as lethal concentrations (HLC) derived from clinical cases. A comparison has also been made between the individual tests and the conventional rodent tests, as well as between rodent tests and the batteries resulting from partial least squares (PLS), with regard to their predictive power for acute toxicity in humans. Results from univariate regression show that the predictive potential of bioassays (both ecotoxicological and rodent tests) is generally superior to that of individual physicochemical properties for HLD. For HLC prediction, however, no consistent trend could be discerned that indicated whether bioassays are better estimators than physicochemical parameters. Generally, the batteries resulting from PLS regression seem to be more predictive than rodent tests or any of the individual tests. Prediction of HLD appears to be dependent on the phylogeny of the test species: cructaceans, for example, appear to be more important components in the test battery than rotifers and bacteria. For HLC prediction, one anostracan and one cladoceran crustacean are considered to be important. When considering both ecotoxicological tests and physicochemical properties, the battery based on the molecular weight and the cladoceran crustacean predicts HLC substantially better than any other combination.

Animal Testing Alternatives↗
PubMed1992

Observations on the predictive value of perfusion lung scans on post-irradiation pulmonary function among 210 patients with bronchogenic carcinoma.

As a component of treatment planning for thoracic irradiation (RT), 210 bronchogenic carcinoma patients seen at the Fox Chase Cancer Center from 1983 to 1990 underwent quantitative perfusion scans, superimposition of their RT treatment fields onto these scans, and pulmonary function testing. These studies were used to prospectively estimate the influence of the planned thoracic irradiation on pulmonary function, as measured by the forced expiratory volume in one second (FEV1). Among the 156 patients with unresected lesions, the mean pre-RT FEV1 was 1.71 +/- 0.67 liters (+/- standard deviation), and the mean percentage of total lung perfusion within the treatment field was 31.0 +/- 12.1%. Mean values for the 54 patients treated post-operatively were 1.79 liters (pre-RT FEV1) and 28.8% (% perfusion within RT field). Using this technique, the prospectively predicted post-RT FEV1 is the product of the pre-RT FEV1 (1% of total lung perfusion within the treatment field). The mean predicted post-treatment FEV1 for the nonoperative patients was 1.15 +/- 0.43 liters and 1.25 +/- 0.41 liters for the postoperative patients. Forty-three nonoperative and 19 postoperative patients had FEV1 determinations following RT, at a mean post-RT interval of 11 months for nonoperative patients and 23 months for post-operative patients. Among nonoperative patients, 53% had no change in post-RT FEV1, 19% improved, while 22% had readings declining toward the predicted value. Only 5% had readings below predicted. Among postoperative patients, 37% had no change or improvement, 37% declined toward the predicted, 10% declined to predicted, and 11% had values worse than predicted. This technique of superimposing RT fields onto lung perfusion scans predicts for a degree of pulmonary impairment which is observed in only a minority of patients (10%) and which is rarely exceeded (6%).

Carcinoma, Bronchogenic↗
PubMed1996

Prognostic value of cardiopulmonary exercise testing using percent achieved of predicted peak oxygen uptake for patients with ischemic and dilated cardiomyopathy.

OBJECTIVES: We tested the hypothesis that percent achieved of predicted peak oxygen uptake (predicted VO2max) improves the prognostic accuracy of identifying high risk ambulatory patients with congestive heart failure considered for heart transplantation compared with absolute peak oxygen uptake (VO2max) in 181 patients with ischemic or dilated cardiomyopathy. BACKGROUND: Peak oxygen uptake during exercise has been shown to be a useful prognostic measurement to risk stratify patients with heart failure. The prognostic value of percent predicted VO2max has not been assessed in these patients. METHODS: We retrospectively studied 181 ambulatory patients referred to the Saint Louis University Heart Failure Unit. Clinical, hemodynamic (137 patients) and coronary angiographic (145 patients) data were recorded, and all patients underwent symptom-limited cardiopulmonary exercise. RESULTS: During a mean follow-up period of 12 +/- 6 months, 26 patients died, and 18 were listed as Status 1 priority for heart transplantation. The actuarial 1- and 2-year survival of the 89 patients who achieved < or = 50% predicted VO2max was 74% and 43%, respectively, compared with 98% and 90% in the 92 who achieved > 50% predicted VO2max (p = 0.001). Multivariable analysis selected < or = 50% predicted VO2max as the most significant predictor of cardiac death (p = 0.007) and cardiac death or Status 1 priority (p = 0.0005). CONCLUSIONS: Percent achieved of predicted VO2max provides important information that can be used to risk stratify ambulatory patients with heart failure with ischemic or dilated etiology that exceeds that provided by measurement of VO2max alone. Patients who achieve > 50% predicted VO2max have an excellent short-term prognosis when treated medically, and heart transplantation can be safely deferred.

Cardiomyopathy, Dilated↗
PubMed2006

Self-prediction of hedonic trajectories for repeated use of body products and foods: poor performance, not improved by a full generation of experience.

This study extends earlier work by [Kahneman, D., and Snell, J. (1992). Predicting a changing taste: Do people know what they will like? Journal of Behavioral Decision Making, 5, 187-200.]. suggesting that people are poor at predicting changes in liking. This is an important issue because an absence of this ability would make it difficult for people to optimize their own choices. Twenty undergraduates and 20 of their parents sampled four relatively unfamiliar consumer products, two foods and two body products, for 8 days. On Day 1, participants rated their initial liking and predicted their liking after seven daily uses of the products. Predictions were compared to actual liking on Day 8. Consistent with prior work, participants were poor at predicting their actual hedonic trajectories because they underestimated the degree to which their preferences would change. Contrary to predictions, parents were no better than students at this task, even though they had some 20-39 years more experience in observing their own hedonic trajectories. There is no evidence for any parent-child resemblance in either liking for the products or ability to accurately predict hedonic trajectory, and no evidence for consistency in ability to predict trajectories across the four different products. In general, participants underestimate the degree to which their preferences will change.

Adolescent↗
PubMed2007

Early prediction of preterm birth for singleton, twin, and triplet pregnancies.

OBJECTIVES: To create prediction models of early preterm birth for singletons, twin, and triplet pregnancies. STUDY DESIGN: We used a historical cohort study with the 1996 birth registration data for singletons and the 1995-1997 linked birth/infant death dataset for multiple births of the United States. Preterm birth was defined as gestational age <32 completed weeks. Eligible study subjects were randomly allocated to two groups: one group (80% subjects) for the creation of the prediction models, and the other group (20% subjects) for the validation of the established prediction models. Multivariate logistic regressions were used to establish the prediction models. We further assessed the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the established prediction models with different cut-off values in the validation group. RESULTS: The sensitivity, specificity, PPV, and NPV of the established model were 24.58, 93.54, 5.91, and 98.69%, respectively for singletons, 64.66, 57.04, 16.29, and 92.59%, respectively for twins, and 63.57, 53.58, 42.96, and 72.78%, respectively for triplets. CONCLUSION: The prediction models of early preterm birth for singleton, twin, and triplet pregnancies created by this study could be useful for obstetricians to identify women being at high risk of preterm birth at early gestation.

Adult↗
PubMed2004

Impact of repeated antral follicle counts on the prediction of poor ovarian response in women undergoing in vitro fertilization.

OBJECTIVE: To study the value of a single antral follicle count and the additional value of repeated counts in different cycles for the prediction of poor ovarian response in IVF. DESIGN: Prospective. SETTING: Tertiary fertility center. PATIENT(S): One hundred twenty women undergoing their first IVF cycle. INTERVENTION(S): Measurement of the number of antral follicles on cycle day 3 in two spontaneous cycles. Ovarian response. RESULT(S): A single antral follicle count is clearly predictive of poor ovarian response and there is good agreement between repeated measurements in subsequent cycles (area under the receiver operating characteristic curve [ROC(AUC)]; cycle 1: 0.87, cycle 2: 0.85). In a logistic regression analysis, information obtained after the second cycle contributed significantly to the prediction of poor response by the antral follicle count of the first cycle. The predictive accuracy of the highest of two counts (ROC(AUC) 0.89) was slightly better than that of each single count. The predictive model with the highest count yielded slightly higher values of specificity and positive predictive value. Sensitivity, negative predictive value, and error rates were slightly lower. CONCLUSION(S): A single antral follicle count is a good predictor of poor ovarian response in IVF. Although the impact of a second antral follicle count on ovarian response predictions in IVF is statistically significant, clinical relevance is very limited. Repeating an antral follicle count in a subsequent cycle is not recommended.

Adult↗
PubMed2006

How well do paramedics predict admission to the hospital? A prospective study.

A study was designed to determine whether paramedics accurately predict which patients will require admission to the hospital, and in those requiring admission, whether they will need a ward bed or intensive care unit (ICU) monitoring. This prospective, cross-sectional study of consecutive Emergency Medical Service (EMS) transport patients was conducted at an urban city hospital. Paramedics were asked to predict if the patient they were transporting would require admission to the hospital, and if so, whether that patient would be admitted to a ward bed or require an ICU bed. Predictions were compared to actual patient disposition. During the study period, 1349 patients were transported to our hospital. Questionnaires were submitted in 985 cases (73%) and complete data were available for 952 (97%) of these patients. Paramedics predicted 202 (22%) patients would be admitted to the hospital, of whom 124 (61%) would go the ward and 78 (39%) would require intensive care. The actual overall admission rate was 21%, although the sensitivity of predicting any admission was 62% with a positive prediction value (PPV) of 59%. Further, the paramedics were able to predict admission to intensive care with a sensitivity of 68% and PPV of 50%. It is concluded that paramedics have very limited ability to predict whether transported patients require admission and the level of required care. In our EMS system, the prehospital diversion policies should not be based solely on paramedic determination.

Allied Health Personnel↗
PubMed2006

Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties.

BACKGROUND: Computational methods have been developed for predicting allergen proteins from sequence segments that show identity, homology, or motif match to a known allergen. These methods achieve good prediction accuracies, but are less effective for novel proteins with no similarity to any known allergen. METHODS: This work tests the feasibility of using a statistical learning method, support vector machines, as such a method. The prediction system is trained and tested by using 1005 allergen proteins from the Allergome database and 22,469 non-allergen proteins from 7871 Pfam families. RESULTS: Testing results by an independent set of 229 allergen and 6717 non-allergen proteins from 7871 Pfam families show that 93.0% and 99.9% of these are correctly predicted, which are comparable to the best results of other methods. Of the 18 novel allergen proteins non-homologous to any other proteins in the Swissprot database, 88.9% is correctly predicted. A further screening of 168,128 proteins in the Swissprot database finds that 2.9% of the proteins are predicted as allergen proteins, which is consistent with the estimated numbers from motif-based methods. CONCLUSIONS: Our study suggests that SVM is a potentially useful method for predicting allergen proteins and it has certain capability for predicting novel allergen proteins. Our software can be accessed at .

Allergens↗
PubMed2006

Machine learning approaches for estimation of prediction interval for the model output.

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

Algorithms↗
PubMed2004

Preoperative neural network using combined magnetic resonance imaging variables, prostate-specific antigen, and gleason score for predicting prostate cancer biochemical recurrence after radical prostatectomy.

OBJECTIVES: To develop and test an artificial neural network (ANN) for predicting biochemical recurrence based on the combined use of pelvic coil magnetic resonance imaging (pMRI), prostate-specific antigen (PSA) measurement, and biopsy Gleason score, after radical prostatectomy and to investigate whether it is more accurate than logistic regression analysis (LRA) in men with clinically localized prostate cancer. METHODS: We evaluated 191 consecutive men who had undergone retropubic radical prostatectomy for clinically localized prostate cancer. None of the men had lymph node metastasis as determined by adequate follow-up and pathologic criteria. The preoperative predictive variables included clinical TNM stage, serum PSA level, biopsy Gleason score, and pMRI findings. The predicted result was biochemical failure (PSA level of 0.1 ng/mL or greater). The patient data were randomly split into four cross-validation sets and used to develop and validate the LRA and ANN models. The predictive ability of the ANN was compared with that of LRA, Han tables, and the Kattan nomogram using area under the receiver operating characteristic curve (AUROC) analysis. RESULTS: Of the 191 patients, 57 (30%) developed disease progression at a median follow-up of 64 months (mean 61, range 2 to 86). Using all the input variables, the AUROC of the ANN was significantly greater (P <0.05) than the AUROC of LRA, Han tables, or the Kattan nomogram for the prediction of PSA recurrence 5 years after radical prostatectomy (0.897 +/- 0.063 versus 0.785 +/- 0.060, 0.733 +/- 0.061, and 0.737 +/- 0.071, respectively). Removing the pMRI findings from the previous models, the AUROC of the ANN decreased statistically significantly (P <0.05) and was comparable to the AUROC of conventional predictive tools (P >0.05). CONCLUSIONS: Using the pMRI findings, the ANN was superior to LRA, predictive tables, and nomograms to predict biochemical recurrence accurately. Confirmatory studies are warranted.

Adult↗
PubMed1998

Development and validation of an echocardiographic model for predicting progression of discrete subaortic stenosis in children.

The clinical course of discrete subaortic stenosis (DSS) varies considerably between patients. This study was performed to identify echocardiographic characteristics of DSS that distinguish progressive from nonprogressive disease. The study included 100 patients from 2 institutions and was performed in 2 stages. In phase I, a prediction model was developed based on multivariate analysis of morphometric and Doppler variables obtained from the initial echocardiogram in 52 children with DSS from Texas Children's Hospital. In phase II, the performance characteristics of the prediction model were tested in 48 patients with DSS followed at Children's Hospital in Boston. Patients were divided into 3 outcome groups: nonprogressive, progressive, and intermediate progression. In phase I, multivariate analysis identified 3 independent predictors of progressive disease: indexed aortic valve to subaortic membrane distance, anterior mitral leaflet involvement, and initial Doppler gradient. The logistic regression equation--Probability = [1 + e-(-322+0.334X1+4.06X2-0.708X3)](-1), where X = initial gradient in mm Hg; X2 = absence (0) or presence (1) of mitral leaflet involvement; and X3 = indexed distance between aortic valve and subaortic membrane in mm/body surface area0.5 were used to predict progression. When the prediction model was applied to phase II study patients, none of the patients with nonprogressive DSS had a prediction value > 0.29 and none of the patients with progressive DSS had a prediction value < 0.58. Thus, a prediction value > 0.55 yielded a 100% sensitivity and 100% specificity for distinguishing progressive from nonprogressive DSS. Patients with intermediate progression were indistinguishable from progressive DSS but were clearly separable from nonprogressing patients. We conclude that progressive subaortic obstruction in children with DSS can be predicted from morphologic, morphometric, and Doppler echocardiographic analysis of left ventricular outflow.

Aortic Valve Stenosis↗
PubMed2000

Comparison of electrocardiographic-gated technetium-99m sestamibi single-photon emission computed tomographic imaging and rest-redistribution thallium-201 in the prediction of myocardial viability.

Although the combined assessment of perfusion and function using rest electrocardiographic (ECG)-gated technetium-99m (Tc-99m) sestamibi single-photon emission computed tomographic (SPECT) imaging has been shown to improve sensitivity and accuracy over perfusion alone in the prediction of myocardial viability, no data are available comparing this technique with rest-redistribution thallium-201. Thirty patients with coronary artery disease and left ventricular dysfunction (ejection fraction < or = 40%) underwent rest-redistribution thallium-201 and rest ECG-gated Tc-99m sestamibi SPECT imaging before revascularization and rest ECG-gated Tc-99m sestamibi SPECT imaging at 1 or 6 weeks after revascularization. All thallium-201 and Tc-99m sestamibi images were interpreted by a consensus agreement of 3 experienced readers without knowledge of patient identity or time of imaging with Tc-99m sestamibi (before or after revascularization) using a 17-segment model. Concordance between techniques for the prediction of viability was 89% (kappa 0.556 +/- 0.109). With rest-redistribution thallium-201, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy were 95%, 59%, 88%, 78%, and 86%, respectively. With rest ECG-gated Tc-99m sestamibi SPECT imaging, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy were 96%, 55%, 87%, 80%, and 86%, respectively (p = NS vs rest-redistribution thallium-201). Although both techniques are comparable for detecting viable myocardium, rest ECG-gated Tc-99m sestamibi SPECT imaging allows direct assessment of both myocardial perfusion and ventricular function, which may be clinically useful in patients who require assessment of myocardial viability.

Coronary Disease↗
PubMed2001

Predictive validity of the overvalued ideas scale: outcome in obsessive-compulsive and body dysmorphic disorders.

Overvalued ideas have been theoretically implicated in treatment failure for obsessive-compulsive disorder (OCD). Until recently, there have not been valid assessments for determining severity of overvalued ideas. One recent scale, the Overvalued Ideas Scale (OVIS; Neziroglu, McKay, Yaryura-Tobias, Stevens & Todaro, 1999, Behaviour Research and Therapy, 37, 881-902) has been found to validly measure overvalued ideas. However, its predictive utility has not been determined. Two studies were conducted to examine the extent to which the OVIS predicts treatment response. Study 1 examined the response to behavioral therapy in a group of participants diagnosed with OCD. Residual gain scores showed a significant correlation between treatment outcome for compulsions and pretreatment OVIS scores (28.1% variance accounted). Pretreatment OVIS scores were not significantly correlated with residual gains in obsessions (1.7% variance accounted). The predictive utility of the OVIS was superior to a single item assessment of overvalued ideas available on the Yale-Brown Obsessive Scale in predicting outcome for compulsions. For this item, the variance accounted for compulsions was 6.3% and for obsessions was 3.9%. Study 2 examined the response to behavioral therapy in a group of participants diagnosed with body dysmorphic disorder (BDD), a condition ostensibly linked to OCD and presumed to present with higher levels of overvalued ideas. Residual gains scores showed a significant relationship between obsessions and OVIS (accounting for 34.8% of the variance), but not for compulsions (10.2% variance accounted). As in Study 1, the predictive utility of the OVIS was superior to the single item assessment (with 0.2% variance accounted for compulsions, 2.4% variance accounted for obsessions). Taken together, the studies reported here show that this OVIS is predictive of treatment outcome, and the predictive value depends on which symptoms are used to assess outcome. Further, the scale is more effective in predicting outcome than a widely used single item assessment.

Adolescent↗