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At least 1,225 records · Page 68Linked to original sources
PubMed2000

The stroke impairment assessment set: its internal consistency and predictive validity.

OBJECTIVE: To study the scale quality and predictive validity of the Stroke Impairment Assessment Set (SIAS) developed for stroke outcome research. DESIGN: Rasch analysis of the SIAS; stepwise multiple regression analysis to predict discharge functional independence measure (FIM) raw scores from demographic data, the SIAS scores, and the admission FIM scores; cross-validation of the prediction rule. SETTING: Tertiary rehabilitation center in Japan. PATIENTS: One hundred ninety stroke inpatients for the study of the scale quality and the predictive validity; a second sample of 116 stroke inpatients for the cross-validation study. MAIN OUTCOME MEASURES: Mean square fit statistics to study the degree of fit to the unidimensional model; logits to express item difficulties; discharge FIM scores for the study of predictive validity. RESULTS: The degree of misfit was acceptable except for the shoulder range of motion (ROM), pain, visuospatial function, and speech items; and the SIAS items could be arranged on a common unidimensional scale. The difficulty patterns were identical at admission and at discharge except for the deep tendon reflexes, ROM, and pain items. They were also similar for the right- and left-sided brain lesion groups except for the speech and visuospatial items. For the prediction of the discharge FIM scores, the independent variables selected were age, the SIAS total scores, and the admission FIM scores; and the adjusted R2 was .64 (p < .0001). Stability of the predictive equation was confirmed in the cross-validation sample (R2 = .68, p < .001). CONCLUSIONS: The unidimensionality of the SIAS was confirmed, and the SIAS total scores proved useful for stroke outcome prediction.

Adult↗
PubMed2003

The accuracy of video imaging prediction in soft tissue outcome after bimaxillary orthognathic surgery.

PURPOSE: The purpose of the present study was to evaluate the accuracy of the outcome in soft tissue prediction through use of a computer imaging system after bimaxillary orthognathic surgery. MATERIALS AND METHODS: The study sample consisted of 30 adults who had undergone orthognathic surgery that included the Wassmund and Köle procedures and optional genioplasty to correct bimaxillary protrusion. All the patients had lateral cephalometric radiographs and profile photographs taken within 6 months before surgery and at least 6 months after surgery. The computer-generated soft tissue image and the actual postsurgical profile were compared. The accuracy of this computer-generated profile image was evaluated. RESULTS: The results indicated that the nasal tip, soft tissue A point, and upper lip presented the least predicted errors in sagittal plane. While the nasal tip presented higher reliability. Lower lip prediction was found to be the least accurate region and it tended to be located anterior to the actual position. In the vertical plane, most of the predictions revealed higher accuracy than those in the sagittal plane. There were no statistically significant differences between the predictions of the groups with and those without genioplasty. CONCLUSIONS: Computer-generated image prediction was suitable for patient education and communication. However, efforts are still needed to improve the accuracy and reliability of the prediction program and to include the consideration of changes in soft tissue tension and muscle strain. The accuracy of this system in soft tissue prediction should be carefully interpreted.

Adult↗
PubMed2003

Characteristics of the best prognostic evidence: an example on prediction of outcome after clomiphene citrate induction of ovulation in normogonadotropic oligoamenorrheic infertility.

The standard first-line treatment for normogonadotropic anovulatory infertile patients [referred to as World Health Organization group 2 (WHO 2)] is ovulation induction using clomiphene citrate (CC) in incremental doses. Twenty to 25% of women show clomiphene-resistant anovulation (CRA), that is, they remain anovulatory even after multiple attempts with increased doses of CC. About 50% of the ovulatory CC patients conceive within six CC-induced cycles. Given the heterogeneous nature of the group, the individual prognosis (i.e., the chance of success) will vary considerably between patients. In the event an individual prognosis of each patient would be available before the start of the treatment, the overall efficiency of ovulation induction could be improved. Prognostic evidence at an individual level should use multiple patient variables, including results from previous treatments (if any). When variables are interdependent, a statistical model can be used to relate individual characteristics with the predicted outcome. Such a model will provide estimates of prognosis for individualized patient profiles, allowing new patients to profit from the experience of the cohort of previous patients used to build the model. This paper discusses the prediction of time to pregnancy following induction of ovulation with CC. This prediction was broken down in two steps, leading to two separate prognostic models. The first model predicts an intermediate outcome, the chance that the patient will be CRA (i.e., no ovulation in response to CC medication); the second model predicts the final outcome (time until pregnancy) in women who do ovulate. The CRA model was based on a prospective cohort study of 201 patients with normogonadotropic oligoamenorrheic infertility, 45 of whom were CRA (22%). It contained four predictor variables all related to the diagnosis of PCOS within the group of WHO 2: Increased free androgen index (FAI; hyperandrogenemia), elevated body mass index (BMI; obesity), greater mean ovarian volume (as an ultrasound feature of polycystic ovaries), and amenorrhea were all predictive for CRA. The second model was based on the non-CRA patients and contained two prognostic variables: increased age and oligomenorrhea were predictive for longer time to pregnancy after first ovulation with CC. Using the example of the prediction of time to pregnancy following induction of ovulation with CC, we present and discuss characteristics of good prognostic evidence for clinical use, focusing on study design, statistical analysis, evaluation, and presentation of results.

Amenorrhea↗
PubMed1985

Clinical prediction rules. Applications and methodological standards.

The objective of clinical prediction rules is to reduce the uncertainty inherent in medical practice by defining how to use clinical findings to make predictions. Clinical prediction rules are derived from systematic clinical observations. They can help physicians identify patients who require diagnostic tests, treatment, or hospitalization. Before adopting a prediction rule, clinicians must evaluate its applicability to their patients. We describe methodological standards that can be used to decide whether a prediction rule is suitable for adoption in a clinician's practice. We applied these standards to 33 reports of prediction rules; 42 per cent of the reports contained an adequate description of the prediction rules, the patients, and the clinical setting. The misclassification rate of the rule was measured in only 34 per cent of reports, and the effects of the rule on patient care were described in only 6 per cent of reports. If the objectives of clinical prediction rules are to be fully achieved, authors and readers need to pay close attention to basic principles of study design.

Diagnostic Errors↗
PubMed2002

A comparison of the Framingham and European Society of Cardiology coronary heart disease risk prediction models in the normative aging study.

BACKGROUND: A number of prediction models have been developed in an attempt to accurately identify patients at increased risk of a first coronary heart disease event. We sought to determine the ten-year incidence of coronary heart disease events in a healthy cohort with measurable risk factors, and to compare these results with the predicted number of events by use of both the Framingham and European Society of Cardiology risk prediction models. METHODS: We compared the predicted and observed number of events in 5 risk categories in 1393 subjects aged 30 to 74 years who were enrolled in the Normative Aging Study. RESULTS: The risk prediction models reliably stratify populations with regards to relative risk of coronary heart disease events and there is reasonable agreement between the 2 models (weighted kappa = 0.46, P <.01). The Framingham model underestimated the absolute risk of coronary heart disease events in the low-risk group, and both risk prediction models overestimated the absolute risk of events in the high- or very-high-risk groups (Framingham c-statistic = 0.60, European Society of Cardiology c-statistic = 0.58). CONCLUSIONS: Despite simplification, the accuracy of the European model was not significantly different from the Framingham model. But the accuracy of absolute risk prediction, particularly at the extremes of risk, is imperfect. Refinement and validation of these risk prediction models is important because they affect the management of individual patients and the allocation of community resources.

Adult↗
PubMedDate not supplied

Prediction of severe coronary artery disease and long-term outcome in patients undergoing vasodilator SPECT.

BACKGROUND: Vasodilator perfusion imaging has not been extensively evaluated for predicting severe coronary artery disease (CAD) or long-term prognosis. METHODS AND RESULTS: The goals of this study were to develop a model to predict left main/3-vessel CAD in patients undergoing vasodilator thallium 201 imaging and coronary angiography (angiographic population) and to test the long-term prognostic value of this model in a separate cohort of patients who were not referred for angiography (prognostic population). In the angiographic population (n = 653) the chi2 value of the clinical model (containing the variables age, sex, and prior myocardial infarction) in the prediction of severe CAD was 32. The addition of 3 vasodilator Tl-201 variables (magnitude of ST-segment depression, summed reversibility score, and increased lung uptake) increased the model chi2 value to 114 (P <.001). Only 9% of predicted low-risk patients versus 57% of predicted high-risk patients had severe CAD. In the prognostic population (n = 521) survival rates free of cardiac death or myocardial infarction at 7 years were 91%, 73%, and 51%, respectively, for patient groups predicted to be at low, intermediate, and high risk of severe CAD (P <.001). CONCLUSIONS: Clinical and vasodilator Tl-201 variables can accurately predict the risk of severe CAD. Stress Tl-201 variables add incremental information to clinical variables. The same model also predicts patient outcome.

Adenosine↗
PubMed2005

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests.

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of alpha and alpha+beta proteins [60-70 residues with C(alpha) rms deviation (rmsd) <6 A]. However, for alpha+beta proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two alpha and three alpha+beta, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue alpha+beta protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-A C(alpha) rmsd. So far this protein is the largest alpha+beta protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly alpha-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 A rmsd, except the 32 C-terminal residues, most of which form a beta-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.

Amino Acid Sequence↗
PubMed2003

Coupled prediction of protein secondary and tertiary structure.

The strong coupling between secondary and tertiary structure formation in protein folding is neglected in most structure prediction methods. In this work we investigate the extent to which nonlocal interactions in predicted tertiary structures can be used to improve secondary structure prediction. The architecture of a neural network for secondary structure prediction that utilizes multiple sequence alignments was extended to accept low-resolution nonlocal tertiary structure information as an additional input. By using this modified network, together with tertiary structure information from native structures, the Q3-prediction accuracy is increased by 7-10% on average and by up to 35% in individual cases for independent test data. By using tertiary structure information from models generated with the ROSETTA de novo tertiary structure prediction method, the Q3-prediction accuracy is improved by 4-5% on average for small and medium-sized single-domain proteins. Analysis of proteins with particularly large improvements in secondary structure prediction using tertiary structure information provides insight into the feedback from tertiary to secondary structure.

Computer Simulation↗
PubMed2001

On the impossibility of predicting the behavior of rational agents.

A foundational assumption in economics is that people are rational: they choose optimal plans of action given their predictions about future states of the world. In games of strategy this means that each player's strategy should be optimal given his or her prediction of the opponents' strategies. We demonstrate that there is an inherent tension between rationality and prediction when players are uncertain about their opponents' payoff functions. Specifically, there are games in which it is impossible for perfectly rational players to learn to predict the future behavior of their opponents (even approximately) no matter what learning rule they use. The reason is that in trying to predict the next-period behavior of an opponent, a rational player must take an action this period that the opponent can observe. This observation may cause the opponent to alter his next-period behavior, thus invalidating the first player's prediction. The resulting feedback loop has the property that, a positive fraction of the time, the predicted probability of some action next period differs substantially from the actual probability with which the action is going to occur. We conclude that there are strategic situations in which it is impossible in principle for perfectly rational agents to learn to predict the future behavior of other perfectly rational agents based solely on their observed actions.

Forecasting↗
PubMed1989

Effects of feather wear and temperature on prediction of food intake and residual food consumption.

Heat production, which accounts for 0.6 of gross energy intake, is insufficiently represented in predictions of food intake. Especially when heat production is elevated (for example by lower temperature or poor feathering) the classical predictions based on body weight, body-weight change and egg mass are inadequate. Heat production was reliably estimated as [35.5-environmental temperature (degree C)] x [Defeathering (=%IBPW) + 21]. Including this term (PHP: predicted heat production) in equations predicting food intake significantly increased accuracy of prediction, especially under suboptimal conditions. Within the range of body weights tested (from 1.6 kg in brown layers to 2.8 kg in dwarf broiler breeders), body weight as an independent variable contributed little to the prediction of food intake; especially within strains its effect was better included in the intercept. Significantly reduced absolute values of residual food consumption were obtained over a wide range of conditions by using predictions of food intake based on body-weight change, egg mass, predicted heat production (PHP) and an intercept, instead of body weight, body-weight change, egg mass and an intercept.

Animals↗
PubMed1992

Comparison of equations for predicting the metabolisable energy intake of laying pullets.

1. The accuracy of equations to predict metabolisable energy intake of laying hens was compared using a random sample of the data set of Marsden and Morris (1987). 2. The equation of Pesti et al. (1992) was found to be significantly better at predicting metabolisable energy intake than the equations of Byerly (1941), Emmans (1974), Byerly et al. (1980), and the National Research Council (1984) when equation residual mean square errors were tested. 3. The equation of Pesti et al. (1992) had the highest coefficient of determination (R2), the smallest average residual, and smallest mean square error. The NRC equation predicted the average metabolisable energy intake best, indicating that over- and under-predictions offset each other. 4. The equations of Emmans (1974) and Pesti et al. (1992) were essentially without bias across temperature zones: less than 20, greater than = 20 less than 25, greater than = 25 less than 30, and greater than = 30 degrees C. The equation of Byerly (1941) over-predicted below 25 and above 30 degrees C, but under-predicted between 25 and 30 degrees C. The equation of Byerly et al. (1980) under-predicted below 30 degrees C while the NRC (1984) equation under-predicted above 20 degrees C.

Animals↗
PubMed2000

Prediction and validation of fat-free mass in the lower limbs of young adult male Rugby Union players using dual-energy X-ray absorptiometry as the criterion measure.

The aim was to derive and cross-validate prediction equations to estimate fat-free mass (FFM) in the lower limbs of young adult male Rugby Union players. Thirty players of mean +/- SD age of 21.1 +/- 2.1 years were recruited. Bone mineral mass, fat mass and lean tissue mass were measured with a Hologic QDR 1000/W whole-body scanner. Anthropometry included circumferences, segmental leg lengths and skinfold thicknesses. Players were divided randomly into prediction (n = 15) and cross-validation (n = 15) samples. Regression equations were derived from the prediction sample and validated on the cross-validation sample. Seven equations were formulated to predict leg FFM. The two equations providing the lowest standard errors of estimate were leg length with circumferences at the knee (0.7262) and calf (0.7382); the multiple correlation was 0.83 in both instances. Cross-validation statistics found no significant differences (p > 0.05) between measured (12.4 +/-1.5 kg) and predicted leg FFM (12.1-12.4 kg). The smallest mean difference was -0.05 kg, the largest 0.26 kg; these were equivalent to -0.4 and 2.1% of the measured leg FFM respectively. Correlations between measured and predicted leg FFM were reasonably high (0.79-0.90, p < 0.001). The ratio limits of agreement confirmed that there was little bias between measured and predicted leg FFM (1.00-1.02) and a good level of agreement (1.12-1.16). Because prediction equations tend to be age, gender and population specific, unless validated for other athletic groups, the present equations should be applied to male Rugby Union players with characteristics similar to those described.

Absorptiometry, Photon↗
PubMed1998

Need for job adjustment in pregnancy. Early prediction based on work history.

OBJECTIVE: To examine whether a woman's need for job adjustment in pregnancy can be predicted by a short interview on working conditions at the first prenatal visit. DESIGN: Midwives included a semi-structured work history during the interview of unselected first prenatal visits. Their early prediction about the woman's need for job adjustment was compared with the woman's own later report of such need and the need expressed as a risk score for preterm birth based on the woman's self-reported working conditions. Data on both were collected by a questionnaire presented to the woman at about the 36th week of pregnancy. SETTING: Seven maternity centres in Oslo, Norway, April 1993-March 1994. SUBJECTS: 160 pregnant women in paid work. MAIN OUTCOME MEASURES: The proportion of predictions of presence (positive predictive value) or absence (negative predictive value) of need that was confirmed by the woman's later report, or the risk score. RESULTS: The positive predictive value was 86% and the negative predictive value 50% with the woman's later report as reference, and 56 and 79%, respectively, with the risk score for preterm birth as reference. CONCLUSION: The work history allows early prediction of need for job adjustment in pregnancy.

Adult↗
PubMedDate not supplied

Prediction of body composition in elderly men over 75 years of age.

A comprehensive number of body composition predictions (involving weight, height, skinfold thicknesses, bioelectrical impedance and near-infrared interactance-NIRI) were evaluated against total body water (TBW from isotope dilution), in 23 randomly selected men over 75 years old, and dual-energy X-ray absorptiometry (DXA), in 15 volunteers from this group. Comparisons were made between anthropometric and impedance methods for estimating limb muscle mass (obtained using DXA). Bias and 95% limits of agreement between measured TBW and DXA estimates were -2.1 kg and 3.1 kg, respectively (for fat, 5.4% and 6.1% body weight). Agreement between TBW predictions and reference measurements was remarkably variable, irrespective of whether TBW was predicted from TBW-specific equations or indirectly from estimates of fat or fat-free mass: for predictions using anthropometry, bias ranged from -4.7 kg to 1.6 kg and 95% limits of agreement from bias +/- 3.8 kg to +/- 5.0 kg; using impedance, bias was -8.8 kg to 3.2 kg and 95% limits of agreement were bias +/- 3.6 kg to +/- 7.8 kg; corresponding values for NIRI were -5.3 kg and +/- 5.4 kg. Although some non-age-specific equations appeared valid, age-specific equations generally predicted TBW better. Limb muscle mass (DXA) was predicted better using the segmental impedance method, from indices of limb muscle area (r = 0.76; SEE = 1.9 kg) and volume (r = 0.86; SEE = 1.6 kg), than by anthropometry alone (r = 0.61 and 0.71; SEE = 2.3 kg and 2.1 kg, respectively). In conclusion, some body composition predictions are unacceptable (at least for TBW) in older men, and care is recommended when selecting from these methods or equations. Also, the segmental impedance method is as good as, if not better than, anthropometry alone in predicting limb muscle mass (DXA) in older men.

Absorptiometry, Photon↗
PubMed1990

Secondary structure of the human membrane-associated folate binding protein using a joint prediction approach.

The secondary structures of the human membrane-associated folate binding protein (FBP) and bovine soluble FBP are assessed by a joint prediction approach that combines neural network models, information theory, homology modeling and the Chou-Fasman methods. Two new profile maps are used to characterize the non-regular secondary structure and to assist in assigning buried and exposed parts of secondary structure: (i) the loop potential profile and (ii) the long range contact profile. Approximately half of human FBP is predicted to form regular secondary structure (alpha-helices-35% or beta-sheets - 12%, excluding the transmembrane helices) and the rest is predicted to form coil, turns or loops. The bovine milk soluble FBP is predicted to have a similar secondary structure as expected because of the high degree of homology between the FBP's. Discriminant analysis predicts two transmembrane segments for the human FBP sequence, one at the amino terminus (a leader sequence) and the other at the carboxy terminus. These predicted transmembrane domains are absent in the bovine milk soluble FBP, further supporting these predictions. The present set of secondary structural predictions for human FBP is obtained by 'consensus' to aid in modeling the super-secondary structure of the protein.

Amino Acid Sequence↗
PubMedDate not supplied

Using neuroimaging to predict treatment response in mood and anxiety disorders.

BACKGROUND: Functional neuroimaging has begun to show promise as a clinical tool in the prediction of treatment response in mood and anxiety disorders. Given the variance in patient responses to psychiatric treatments, the use of such predictive tools could be tremendously valuable, especially in situations where treatments carry substantial risks or costs. METHODS: A literature search was conducted in December 2004 to identify published neuroimaging treatment prediction papers. "Neuroimaging," "treatment," and "depression or anxiety" were used as keywords. Studies of treatment prediction were complemented by studies of treatment effects to provide context. RESULTS: Fifteen original published papers were identified as investigations of treatment prediction in mood and anxiety disorders. These studies have predominantly been conducted in patients with major depression (MDD) and obsessive-compulsive disorder (OCD). We review this literature and provide a discussion of design considerations in psychiatric neuroimaging studies of treatment response prediction. CONCLUSIONS: The neuroimaging literature pertaining to treatment response prediction is largely limited to studies of MDD and OCD. While these initial reports are preliminary, the findings reviewed suggest that treatment outcome may be predicted by patterns of pre-treatment brain activity in psychiatric patients. However, the actual clinical utility of such tests remains to be shown.

Affect↗
PubMed2000

Prediction of programmed-temperature retention values of naphthas by artificial neural networks.

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.

Chemical Industry↗
PubMed1995

Validation of a bacteremia prediction model.

OBJECTIVE: To validate a previously published model for predicting bacteremia in hospitalized patients. DESIGN: Application of a published bacteremia prediction model to a prospective validation cohort of patients and comparison of its predictability to that found in the derivation cohort. SETTING: Urban, university-affiliated, 550-bed public hospital. PATIENTS: The validation cohort consisted of 342 patients with 559 blood culture episodes between October 14, 1992, and December 5, 1992. Each blood culture episode was scored based on the presence or absence of seven predictors of bacteremia and the findings compared with published results (derivation cohort). INTERVENTIONS: None. RESULTS: Application of the bacteremia prediction model to the validation cohort identified episodes with a low risk (3%) and a high risk (17%) for true bacteremia, similar to the findings in the derivation cohort (1% and 16%, respectively). Comparison of the predictions of the model in the two cohorts by receiver operator characteristic curve analysis revealed that the overall predictability of the model in the validation cohort was not as good as in the derivation cohort. CONCLUSIONS: Although the bacteremia prediction model did not perform as well overall in the validation cohort, the model still was able to clearly define two extreme groups: those with a low risk and those with a high risk for true bacteremia. This predictive capability may aid physicians in prescribing empiric antimicrobial therapy and also may be useful to hospital epidemiologists in assessing quality of care.

Adolescent↗