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PubMed2005

Transition zone cancers undermine the predictive accuracy of Partin table stage predictions.

PURPOSE: The Partin tables represent the most widely used predictor of pathological stage in men with localized prostate cancer (PCa). The accuracy and performance of the tables have been tested across different populations. However, to our knowledge the potential limitations that may stem from differences between transition zone (TZ) and peripheral zone (PZ) prostate cancers has not been explored. We tested the predictive accuracy and performance of the Partin tables according to TZ vs PZ tumor predominance. MATERIALS AND METHODS: Preoperative serum prostate specific antigen, clinical stage and biopsy Gleason sum data on 1,990 patients treated with radical retropubic prostatectomy were used to define the 2001 Partin probabilities of organ confinement and seminal vesicle invasion (SVI). Data on 1,320 patients who underwent staging pelvic lymphadenectomy and radical retropubic prostatectomy were used to define the probabilities of lymph node invasion (LNI) and organ confined disease (OC). ROC area under the curve was used to assess the predictive accuracy of the 2001 Partin tables relative to observed extracapsular extension (ECE), SVI, LNI and OC. Performance characteristics for each prediction were explored graphically with local regression, nonparametric smoothing plots. Results were compared between 222 TZ cancers and 1,768 PZ cancers. RESULTS: The 1,990 radical retropubic prostatectomy specimens demonstrated ECE in 689 cases (34.6%) (TZ in 58 or 27.1% and PZ in 631 or 35.8%) and SVI in 224 (TZ in 13 or 6.1% and PZ in 211 or 11.9%). The 1,320 lymphadenectomy specimens demonstrated LNI in 56 cases (TZ in 2 or 0.9% and PZ in 54 or 4.6%). OC was found in 784 cases (59.4%) (TZ in 95 or 69.9% and PZ in 689 or 58.2%). Predictive accuracy was for ECE 76.4% (TZ 69.0% and PZ 77.2%), 78.0% for SVI (TZ 73.5% and PZ 78.3%), 78.6% for LNI (TZ 44.5% and PZ 79.9%) and 79.4% for OC (TZ 73.8% and PZ 80.0%). CONCLUSIONS: The biological tumor characteristics of TZ PCa differ from those of PZ PCa. These differences appear to undermine the accuracy of pathological stage predictions.

Humans↗

PubMed2005

Predicting pressure ulcers: cases missed using a new clinical prediction rule.

AIM: The aim of this paper is to report a study describing patients with pressure ulcers that were incorrectly classified as 'not at risk' by the prediction rule and comparing them with patients who were correctly classified as 'not at risk'. BACKGROUND: Patients admitted to hospital are at risk of developing pressure ulcers. Although the majority of pressure ulcers can be predicted using a recently developed prediction rule, up to 30% of patients with pressure ulcers may still be misclassified. METHODS: Between January 1999 and June 2000 a prospective cohort study was conducted in two large hospitals in the Netherlands. Patients admitted to neurology, internal, surgical, and elder care wards for more than 5 days were included (n = 1229), and were examined weekly. Information on potential prognostic determinants for pressure ulcers mentioned in the literature was recorded. Outcome was defined as occurrence of a pressure ulcer grade 2 or worse during hospital admission. RESULTS: Patients who developed pressure ulcers experienced more problems with 'friction and shear' and underwent surgery more often and longer. Also, they were more often admitted because of malignant conditions. CONCLUSION: We found no specific characteristics that clearly distinguished patients with pressure ulcers that were incorrectly classified as 'not at risk' by the prediction rule from patients who were correctly classified as 'not at risk'. It appears difficult to improve further on the prediction of pressure ulcers using available clinical information.

Adolescent↗

PubMed2006

Predicting long-term independency in activities of daily living after middle cerebral artery stroke: does information from MRI have added predictive value compared with clinical information?

BACKGROUND AND PURPOSE: To investigate whether neuroimaging information has added predictive value compared with clinical information for independency in activities of daily living (ADL) 1 year after stroke. METHODS: Seventy-five first-ever middle cerebral artery stroke survivors were evaluated in logistic regression analyses. Model 1 was derived on the basis of clinical variables; for model 2, neuroimaging variables were added to model 1. Independent variables were stroke severity (National Institutes of Health Stroke Scale), consciousness (Glasgow Coma Scale), urinary continence, demographic variables (age, gender, relationship, educational level), hospital of admission, and clinical instruments: sitting balance (trunk control test), motor functioning (Motricity Index), and ADL (Barthel Index). Neuroimaging variables, determined on conventional MRI scans, included: number of days to scanning, lesion volume, lesion localization (cortex/subcortex), hemisphere, and the presence of white matter lesions. ADL independency was defined as 19 and 20 points on Barthel Index. Differences in accuracy of prediction of ADL independence between models 1 and 2 were analyzed by comparing areas under the curve (AUC) in a receiver operating characteristic analysis. RESULTS: Model 1 contained as significant predictors: age and ADL (AUC 0.84), correctly predicting 77%. In model 2, number of days to scanning, hemisphere, and lesion volume were added to model 1, increasing the AUC from 0.84 to 0.87, accurately predicting 83% of the surviving patients. CONCLUSIONS: Clinical variables in the second week after stroke are good predictors for independency in ADL 1 year after stroke. Neuroimaging variables on conventional MRI scans do not have added value in long-term prediction of ADL.

Activities of Daily Living↗

PubMed1999

The validity of medical history, classic symptoms, and chest radiographs in predicting pulmonary tuberculosis: derivation of a pulmonary tuberculosis prediction model.

STUDY OBJECTIVE: To improve the respiratory isolation policy for patients with suspected pulmonary tuberculosis (TB). DESIGN: Prospective, descriptive, French multicenter study. SETTING: Emergence of nosocomial outbreaks of TB. PATIENTS: All consecutive patients admitted with suspicion of pulmonary TB. MEASUREMENTS AND RESULTS: Medical history, social factors, symptoms, and chest radiograph (CXR) pattern (symptoms and CXR both scored as typical of pulmonary TB, compatible, negative, or atypical) were obtained on admission. Serial morning sputa were collected. Of the 211 patients, 47 (22.3%) had culture-proven pulmonary TB, including 31 (14.7%) with a positive smear. Mean age was 46.2 years; 52 patients were HIV positive (24.6%). The sensitivity of the respiratory isolation policy was 71.4%, specificity was 51.7%, negative predictive value (NPV) was 88.2%, and positive predictive value (PPV) was 26.3%. On univariate analysis, predictive factors of culture-proven pulmonary TB were CXR (p < 0.00001), symptoms (p = 0.0004), age (mean, 40.8 years for TB patients vs 47.5 years for non-TB patients; p = 0.04), absence of HIV infection (89.4% vs 71.3%; p = 0.01), immigrant status (72% vs 55%; p = 0.03), and bacillus Calmette-Guérin status (p = 0.025). On multivariate analysis, CXR pattern (p < 0.00001), HIV infection (p = 0.002), and symptoms (p = 0.009) remained independently predictive. Based on these data, a model was proposed using a receiver operating characteristics curve. In the derivation cohort, the sensitivity and NPV of the model in detecting smear-positive pulmonary TB would have been 100%. The specificity and PPV would have been 48.4% and 25%, respectively. The model performed less well when evaluated on two retrospective groups, but its sensitivity remained above that of the current respiratory isolation policy (91.1% and 82.4% for the retrospective groups vs 71.1% for the current policy). CONCLUSIONS: Improved interpretation of clinical and radiologic data available on patient admission could improve adequacy of respiratory isolation. A prediction model is proposed.

BCG Vaccine↗

PubMed2006

Why does the brain predict sensory consequences of oculomotor commands? Optimal integration of the predicted and the actual sensory feedback.

When the brain initiates a saccade, it uses a copy of the oculomotor commands to predict the visual consequences: for example, if one fixates a reach target, a peripheral saccade will produce an internal estimate of the new retinal location of the target, a process called remapping. In natural settings, the target likely remains visible after the saccade. So why should the brain predict the sensory consequence of the saccade when after its completion, the image of the target remains visible? We hypothesized that in the post-saccadic period, the brain integrates target position information from two sources: one based on remapping and another based on the peripheral view of the target. The integration of information from these two sources could produce a less variable target estimate than is possible from either source alone. Here, we show that reaching toward targets that were initially foveated and remapped had significantly less variance than reaches relying on peripheral target information. Furthermore, in a more natural setting where both sources of information were available simultaneously, variance of the reaches was further reduced as predicted by integration. This integration occurred in a statistically optimal manner, as demonstrated by the change in integration weights when we manipulated the uncertainty of the post-saccadic target estimate by varying exposure time. Therefore, the brain predicts the sensory consequences of motor commands because it integrates its prediction with the actual sensory information to produce an estimate of sensory space that is better than possible from either source alone.

Adult↗

PubMed1997

Predicting endoscopic diagnosis in the dyspeptic patient. The value of predictive score models.

BACKGROUND: Score models to predict endoscopic diagnosis in dyspepsia may compensate for the unreliable clinical diagnosis. This study aimed to construct and test score models designed to predict diagnosis in dyspepstic patients managed in primary care. METHODS: Three models to predict organic dyspepsia, major dyspepsia, or peptic ulcer were constructed by regression analysis of clinical data from 1026 consecutive dyspeptic patients referred for endoscopy. The models were tested in 207 patients in primary care, who were potential candidates for endoscopy. Validation experiments were analysed using receiver operating characteristic (ROC) curves. RESULTS: Significant losses of predictive power were found for all models when applied to primary care patients, and no model could be used as a reliable decision support instrument in primary care. CONCLUSIONS: Predictive score models developed in patients referred for endoscopy are not reliable when applied to patients in primary care who are potential candidates for endoscopy. Future models should be constructed and validated in unselected primary care populations.

Decision Support Techniques↗

PubMed1994

[Failure of prediction of results with APACHE II. Analysis of prediction errors of mortality in critical patients].

BACKGROUND: The evaluation of the prognosis of critically ill patients by the APACHE II method is common in intensive care units (ICU). The aim of the present was to analyze the possible factors associated to errors in prediction. METHODS: A prospective study of 564 consecutive admissions in a department of intensive medical care was carried out. Prediction errors were studied by the calculation of the probability of death established after the first 24 hours of admission by means of APACHE II. The factors analyzed in relation to the prediction errors were: the diagnosis or cause of admission to the ICU, the length of the stay in the ICU, the time until possible death, the possible relation of the death with the cause of admission and the treatment given to the patients during the first 24 hours. Statistical analysis was performed with the SPSS software package with significance being determined at p < 0.05. RESULTS: Mortality was of 20.6% (116 cases) with three cut off points being chosen for probability of death (50, 70, and 90%). Accuracy of precision was 83.5%, 82.8% and 80.1%. There were 64 false survivors (mortality lower than 50%, 13.25%-64/483) and 29 false deaths (survival greater than 50%, 35.8%-29/81). Upon analysis of the cause of admission of these patients in whom there were prediction errors it was found that there were no differences among the false survivors and the false deaths. Significant differences were only detected upon comparison of the false survivors with the verified survivors, however these disappeared when the 136 cases admitted due to myocardial infarction were excluded. Neither did the length of stay in the ICU demonstrate any significant difference except among the verified and false deaths in that the stay was longer in the latter. CONCLUSIONS: The factors analyzed did not demonstrate that they may influence or be associated with errors in prediction of the prognosis of patients admitted to an intensive care unit, with these errors probably being due to errors in the system used.

Adolescent↗

PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗

PubMed2000

Predictions of protein segments with the same aminoacid sequence and different secondary structure: a benchmark for predictive methods.

The most stringent test for predictive methods of protein secondary structure is whether identical short sequences that are known to be present with different conformations in different proteins known at atomic resolution can be correctly discriminated. In this study, we show that the prediction efficiency of this type of segments in unrelated proteins reaches an average accuracy per residue ranging from about 72 to 75% (depending on the alignment method used to generate the input sequence profile) only when methods of the third generation are used. A comparison of different methods based on segment statistics (2nd generation methods) and/or including also evolutionary information (3rd generation methods) indicate that the discrimination of the different conformations of identical segments is dependent on the method used for the prediction. Accuracy is similar when methods similarly performing on the secondary structure prediction are tested. When evolutionary information is taken into account as compared to single sequence input, the number of correctly discriminated pairs is increased twofold. The results also highlight the predictive capability of neural networks for identical segments whose conformation differs in different proteins.

Algorithms↗

PubMed1986

Amino acid sequence homology applied to the prediction of protein secondary structures, and joint prediction with existing methods.

The assumption that homologous segments in different proteins may share a similar conformation is applied to the prediction of secondary structures in proteins. Sequences homologous to a target protein are searched, without allowing any gap, and compared against a number of reference proteins of known three-dimensional structure, and then a conformational state (alpha, beta or coil) for each residue of the protein is predicted by looking at the secondary structure of corresponding homologous segments. This prediction is done in a statistical rather than 'deterministic' way, by assigning the most probable conformation state among homologous data to each residue site of a target protein. A test application for 22 sample proteins yields 60% correctness on the average, a better value in comparison with two other existing methods. Joint prediction combining three methods into one is shown to increase the reliability up to 70%, when only the regions identically predicted with the three methods are taken into account. Application of the present method to 10 proteins of unknown structure is demonstrated.

Amino Acid Sequence↗

PubMed1992

Protein structure prediction from predicted residue properties utilizing a digital encoding algorithm.

Although many disparate methods have been applied to the problem, the accuracy of protein structural prediction still remains disappointingly low, averaging about 65% correct secondary structure assignment. A novel predictive method is presented here, which attempts to address some of the shortfalls inherent in representing a protein as a simple text-like sequence of amino acids, by deriving pattern-matching data from the predicted physical properties of a protein chain rather than from the sequence itself. A unique binary encoding algorithm is used to enable the property profiles to be correlated with known secondary structure, and hence to predict secondary structures for proteins with unknown structures. By treating the sequence in this manner, predictive accuracies averaging over 75% have been achieved.

Algorithms↗

PubMed2000

Building mental health professionals' decisional models into tests of predictive validity: the accuracy of contextualized predictions of violence.

To safely manage potentially violent patients in the community, mental health professionals (MHPs) must assess when and under what conditions a patient may be involved in a violent act. This study applies a more ecologically sensitive approach than past research by building the conditions that MHPs believe make patient violence more likely into tests of their predictive validity. In specific, the accuracy of MHPs' predictions that patients were more likely to become violent when they consumed alcohol was assessed based on a sample of 714 patients. The results indicate that MHPs do not discriminate well between patients who are likely to become violent during periods in which they drink from those who are not. MHPs' predictions appear more descriptive of the drinking behavior of a high-risk group than predictive of alcohol-related violent incidents. Thus, even when their apparent decisional processes are considered in tests of accuracy, MHPs' predictions of violence are only moderately more accurate than chance. This paper analyzes the implications of these findings for risk assessment practice and for conducting further clinically relevant research.

Adolescent↗

PubMed2001

Predicting evolutionary potential. I. Predicting the evolution of a lactose-PTS system in Escherichia coli.

Genomes contain not only information for current biological functions, but also information for potential novel functions that may allow the host to adapt to new environments. The field of experimental evolution studies that potential by selecting for novel functions and deducing the means by which the function evolved, but until now it has not attempted to predict the outcomes of such experiments. Here I present a model system that is being developed specifically to examine the issue of what kind of information is most useful in predicting how novel functions will evolve. The system is the evolution of a Lac-PTS transport system and a phospho-beta-galactosidase hydrolase system as a novel pathway for metabolism of lactose in Escherichia coli. Two kinds of information, sequence-based phylogenetic inference and biochemical activity, are considered as predictors of which E. coli genes will evolve the required new functions. Both biochemical data and phylogenetic inference predict that the cryptic celABC genes, which currently specify a PTS-beta-glucoside transport system, are most likely to evolve into a PTS-lactose transport system. Phylogenetic inference predicts that the bglA gene, which currently specifies a phospho-beta-glucosidase, is most likely to evolve into a phospho-beta-galactosidase. In contrast, biochemical data predict that the cryptic bglB gene, which also currently specifies a phospho-beta-glucosidase, is most likely to evolve into a phospho-beta-galactosidase.

Base Sequence↗

PubMed2000

The 2000 Olympic Games of protein structure prediction; fully automated programs are being evaluated vis-à-vis human teams in the protein structure prediction experiment CAFASP2.

In this commentary, we describe two new protein structure prediction experiments being run in parallel with the CASP experiment, which together may be regarded as the 2000 Olympic Games of structure prediction. The first new experiment is CAFASP, the Critical Assessment of Fully Automated Structure Prediction. In CAFASP, the participants are fully automated programs or Internet servers, and here the automated results of the programs are evaluated, without any human intervention. The second new experiment, named LiveBench, follows the CAFASP ideology in that it is aimed towards the evaluation of automatic servers only, while it runs on a large set of prediction targets and in a continuous fashion. Researchers will be watching the 2000 protein structure prediction Olympic Games, to be held in December, in order to learn about the advances in the classical 'human-plus-machine' CASP category, the fully automated CAFASP category, and the comparison between the two.

Amino Acid Sequence↗

PubMed2004

Predicting osteoarthritic knee rehabilitation outcome by using a prediction model developed by data mining techniques.

Artificial neural networks (ANN) have been applied to assist in clinical decision-making and prediction. While we consider possible effective treatments for patients with osteoarthritic knee such as Transcutaneous Electrical Nerve Stimulation (TENS), exercise, and TENS with exercise respectively, we have to select a treatment protocol for patients such that they would gain the best improvements according to their clinical conditions. To facilitate this functionality with the existing patient assessment, we hope to apply the ANN programming techniques to develop a computerized prediction system. A preliminary validation was performed to test the validity of the newly developed prediction protocol on knee rehabilitation. We input the key clinical attributes of 62 patients who have undergone the three above-mentioned knee treatments to the protocol. The expected pain improvement of each patient as predicted by the protocol was obtained. Spearman rank-order correlation was used to identify whether there was a significant correlation between the rankings of the observed and expected pain improvement. We found that the Spearman's rho was 0.424, which is statistically significant at p < 0.001. From this preliminary analysis, we are confident that this newly developed prediction protocol will be useful when deciding which treatment regime best suits a patient.

Combined Modality Therapy↗

PubMedDate not supplied

Predicting danger: the nature, consequences, and neural mechanisms of predictive fear learning.

The ability to detect and learn about the predictive relations existing between events in the world is essential for adaptive behavior. It allows us to use past events to predict the future and to adjust our behavior accordingly. Pavlovian fear conditioning allows anticipation of sources of danger in the environment. It guides attention away from poorer predictors toward better predictors of danger and elicits defensive behavior appropriate to these threats. This article reviews the differences between learning about predictive relations and learning about contiguous relations in Pavlovian fear conditioning. It then describes behavioral approaches to the study of these differences and to the examination of subtle variations in the nature and consequences of predictive learning. Finally, it reviews recent data from rodent and human studies that have begun to identify the neural mechanisms for direct and indirect predictive fear learning.

Adaptation, Psychological↗

PubMed2002

A predictive fatigue model--II: Predicting the effect of resting times on fatigue.

We have recently developed a force- and fatigue-model system that accurately predicted the effect of stimulation frequency on muscle fatigue. The data used to test the model were produced by stimulation trains with resting times of 500 ms. Because the resting times between stimulation trains affect muscle fatigue, this study tested the model's ability to predict the effect of resting times on fatigue. In addition, because this study included different subjects than those used to develop the model, the validity of the model could be tested. Data were collected from human quadriceps femoris muscles using fatigue protocols that included resting times of 500, 750, or 1000 ms. Our results showed that the model predicted fatigue as being a decreasing function of resting time, which was consistent with experimental data. Reliability tests between the experimental data and predictions showed interclass correlation coefficients of 0.97, 0.95, and 0.81 for the initial, final, and percentage decline in peak forces, respectively, suggesting strong agreement between the experimental data and the predictions by the model. The success of our current force- and fatigue-model system helps to validate the model and suggests its potential use in identifying the optimal activation pattern during clinical application of functional electrical stimulation.

Algorithms↗

PubMed2000

Modifiable risk factors predict functional decline among older women: a prospectively validated clinical prediction tool. The Study of Osteoporotic Fractures Research Group.

OBJECTIVE: To identify modifiable predictors of functional decline among community-residing older women and to derive and validate a clinical prediction tool for functional decline based only on modifiable predictors. DESIGN: A prospective cohort study. SETTING: Four geographic areas of the United States. PARTICIPANTS: Community-residing women older than age 65 recruited from population-based listings between 1986 and 1988 (n = 6632). MEASUREMENTS: Modifiable predictors were considered to be those that a clinician seeing an older patient for the first time could reasonably expect to change over a 4-year period: benzodiazepine use, depression, low exercise level, low social functioning, body-mass index, poor visual acuity, low bone mineral density, slow gait, and weak grip. Known predictors of functional decline unlikely to be amenable to intervention included age, education, medical comorbidity, cognitive function, smoking history, and presence of previous spine fracture. All variables were measured at baseline; only modifiable predictors were candidates for the prediction tool. Functional decline was defined as loss of ability over the 4-year interval to perform one or more of five vigorous or eight basic daily activities. RESULTS: Slow gait, short-acting benzodiazepine use, depression, low exercise level, and obesity were significant modifiable predictors of functional decline in both vigorous and basic activities. Weak grip predicted functional decline in vigorous activities, whereas long-acting benzodiazepine use and poor visual acuity predicted functional decline in basic activities. A prediction rule based on these eight modifiable predictors classified women in the derivation set into three risk groups for decline in vigorous activities (12%, 25%, and 39% risk) and two risk groups for decline in basic activities (2% and 10% risk). In the validation set, the probabilities of functional decline were nearly identical. CONCLUSIONS: A substantial portion of the variation of functional decline can be attributed to risk factors amenable to intervention over the short term. Using eight modifiable predictors that can be identified in a single office visit, clinicians can identify older women at risk for functional decline.

Activities of Daily Living↗