SEARCH · PubMed Health

Results for “predictability”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources

PubMed1996

Secondary structure prediction and unrefined tertiary structure prediction for cyclin A, B, and D.

We present heuristic-based predictions of the secondary and tertiary structures of cyclins A, B, and D, representatives of the cyclin superfamily. The list of suggested constraints for tertiary structure assembly was left unrefined in order to submit this report before an announced crystal structure for cyclin A becomes available. To predict these constraints, a master sequence alignment over 270 positions of cyclin types A, B, and D was adjusted based on individual secondary structure predictions for each type. We used new heuristics for predicting aromatic residues at protein-protein interfaces and to identify sequentially distinct regions in the protein chain that cluster in the folded structure. The boundaries of two conjectured domains in the cyclin fold were predicted based on experimental data in the literature. The domain that is important for interaction of the cyclins with cyclin-dependent kinases (CDKs) is predicted to contain six helices; the second domain in the consensus model contains both helices and a beta-sheet that is formed by sequentially distant regions in the protein chain. A plausible phosphorylation site is identified. This work represents a blinded test of the method for prediction of secondary and, to a lesser extent, tertiary structure from a set of homologous protein sequences. Evaluation of our predictions will become possible with the publication of the announced crystal structure.

Amino Acid Sequence↗

PubMed1999

Quantitative drug interactions prediction system (Q-DIPS): a computer-based prediction and management support system for drug metabolism interactions.

OBJECTIVE: Drug biotransformation and interactions are a major source of variability in the response to drugs. The superfamily of cytochromes P450 plays a key role in this phenomenon but, because of the complexity of interactions between drugs and isozymes, it becomes more and more difficult for clinicians to master the knowledge required to predict the occurrence of such drug interactions. To predict and help manage the occurrence of cytochrome P450-dependent interactions, we developed an original computer application: Q-DIPS (quantitative drug interactions prediction system). METHODS: A multidisciplinary work team was created, associating clinical pharmacologists, pharmacists and a computer scientist. Major steps of investigation were: (1) the creation of a database to collect qualitative and quantitative data describing substrates, inhibitors and inducers of specific cytochrome P450 isozymes, with quality assessments; (2) the development of multi-access to these data and (3) their incorporation into extrapolation systems allowing the prediction of in vivo drug interactions on the basis of in vitro data. As an example, prediction and validation studies of CYP3A4 inhibition by ketoconazole and fluconazole will be discussed. RESULTS: Q-DIPS gives up-to-date information, in dynamic tables, describing which specific P450 isozymes metabolise a given drug, as well as which drugs may inhibit or induce a given isozyme. To better answer common clinical questions and help to rapidly evaluate the risk of interactions, it is possible to obtain an overview of substances causing interactions with a specific drug or to focus on drugs taken by a patient ("clinical case"). For each question, key references, relevant quantitative data and quality indices are easily accessible. Two modules allowing input with commercial names and the anatomical therapeutic chemical classification were also included. On the basis of enzymatic and pharmacokinetic data generated in vitro or collected in vivo, the extrapolation module integrates quantitative models to predict the impact of a treatment on enzymatic activities. The simplest model predicted a strong but fluctuating inhibition of CYP3A4 by ketoconazole, whereas the impact of fluconazole was lower. Validations with published in vivo data suggested an appropriate prediction of the risk. CONCLUSION: The current Q-DIPS prototype shows promising potential for helping to improve the management of drug interactions involving metabolism. Validation of extrapolation techniques need to be completed, in view of including important factors such as intrahepatocyte drug accumulation, contribution of metabolites to inhibition as well as in vitro non-specific binding to microsomal proteins. The final goal will be to help select the most judicious clinical studies to be performed so as to avoid useless, expensive and unethical investigations in man.

Antifungal Agents↗

PubMed2006

Intraocular lens power in bilateral cataract surgery: whether adjusting for error of predicted refraction in the first eye improves prediction in the second eye.

PURPOSE: To assess whether the retrospectively calculated intraocular lens (IOL) position value in the first eye reduces the error of predicted refraction in the second. SETTING: Prince of Wales Hospital, Sydney, Australia. METHODS: One hundred twenty-one consecutive patients who had bilateral cataract surgery with the same IOL (SI-30NB, Advanced Medical Optics) were identified. The case-derived A-constant in the first eye was calculated from the postoperative refraction. This value was used to calculate the adjusted error of predicted refraction in the second eye and compared against the unadjusted error in that eye (calculated using manufacturer's A-constant). RESULTS: Axial length (r = 0.97), corneal power (r = 0.97), and IOL power (r = 0.90) were strongly correlated between eyes with no statistically significant mean interocular difference. Although there was no significant interocular difference in the mean error of predicted refraction (SRK/T), there was only a moderate correlation between eyes (r = 0.40). Using the axial length vergence formula, the mean adjusted error of predicted refraction in the second eye (-0.66 diopter [D]) was significantly larger than the mean unadjusted error (-0.47 D) (P = .029). The standard deviation of the adjusted error of predicted refraction (SRK/T) in the second eye (0.85 D) was greater than the standard deviation of the unadjusted error (0.79). Similarly, the adjusted mean absolute error of predicted refraction (0.65 D) was greater than the unadjusted error (0.63 D). CONCLUSION: Adjusting the IOL power in the second eye by the amount of overprediction or underprediction in the first eye did not improve prediction accuracy because the error of predicted refraction varied independently between the 2 eyes of an individual.

Cataract Extraction↗

PubMed2000

Pain after uterine artery embolization for leiomyomata: can its severity be predicted and does severity predict outcome?

PURPOSE: To determine whether the severity of postprocedure pain associated with uterine artery embolization (UAE) for leiomyomata can be predicted and if its severity can predict outcome. MATERIALS AND METHODS: Eighty-one patients underwent UAE and had postprocedure pain managed with use of patient-controlled analgesia (PCA) in the form of an intravenous morphine pump. Baseline uterine and dominant fibroid volumes were calculated for each patient. Attempted doses, doses given, total morphine dose, and maximum numerical rating scale (NRS) score during postprocedure hospitalization were recorded. At 3 months postprocedure, repeat imaging was used to determine uterine and dominant fibroid volume reduction. Each patient also completed a questionnaire assessing change in menstrual bleeding, pelvic pain and pressure symptoms, and satisfaction with symptomatic outcome. Simple regression analysis was used to determine if baseline volumes predicted postprocedure pain and if the pain-related variables could be used to predict outcome. RESULTS: Neither baseline uterine volume nor dominant fibroid volume predicted the severity of postprocedure pain. Similarly, none of the pain-related variables predicted uterine or fibroid volume reduction, symptomatic improvement, or satisfaction with outcome. CONCLUSIONS: Postprocedural pain cannot be predicted based on baseline uterine or fibroid volume and the severity of pain experienced cannot be used to predict outcome.

Adult↗

PubMed1996

Predicted alpha-helix/beta-sheet secondary structures for the zinc-binding motifs of human papillomavirus E7 and E6 proteins by consensus prediction averaging and spectroscopic studies of E7.

The E7 and E6 proteins are the main oncoproteins of human papillomavirus types 16 and 18 (HPV-16 and HPV-18), and possess unknown protein structures. E7 interacts with the cellular tumour-suppressor protein pRB and contains a zinc-binding site with two Cys-Xaa2-Cys motifs spaced 29 or 30 residues apart. E6 interacts with another cellular tumour-suppressor protein p53 and contains two zinc-binding sites, each with two Cys-Xaa2-Cys motifs at a similar spacing of 29 or 30 residues. By using the GOR I/III, Chou-Fasman, SAPIENS and PHD methods, the effectiveness of consensus secondary structure predictions on zinc-finger proteins was first tested with sequences for 160 transcription factors and 72 nuclear hormone receptors. These contain Cys2His2 and Cys2Cys2 zinc-binding regions respectively, and possess known atomic structures. Despite the zinc- and DNA-binding properties of these protein folds, the major alpha-helix structures in both zinc-binding regions were correctly identified. Thus validated, the use of these prediction methods with 47 E7 sequences indicated four well-defined alpha-helix (alpha) and beta-sheet (beta) secondary structure elements in the order beta beta alpha beta in the zinc-binding region of E7 at its C-terminus. The prediction was tested by Fourier transform infrared spectroscopy of recombinant HPV-16 E7 in H2O and 2H2O buffers. Quantitative integration showed that E7 contained similar amounts of alpha-helix and beta-sheet structures, in good agreement with the averaged prediction of alpha-helix and beta-sheet structures in E7 and also with previous circular dichroism studies. Protein fold recognition analyses predicted that the structure of the zinc-binding region in E7 was similar to a beta beta alpha beta motif found in the structure of Protein G. This is consistent with the E7 structure predictions, despite the low sequence similarities with E7. This predicted motif is able to position four Cys residues in proximity to a zinc atom. A model for the zinc-binding motif of E7 was constructed by combining the Protein G coordinates with those for the zinc-binding site in transcription factor TFIIS. Similar analyses for the two zinc-binding motifs in E6 showed that they have different alpha/beta secondary structures from that in E7. When compared with 12 other zinc-binding proteins, these results show that E7 and E6 are predicted to possess novel types of zinc-binding structure.

Amino Acid Sequence↗

PubMed1995

SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments.

Recently a new method called the self-optimized prediction method (SOPM) has been described to improve the success rate in the prediction of the secondary structure of proteins. In this paper we report improvements brought about by predicting all the sequences of a set of aligned proteins belonging to the same family. This improved SOPM method (SOPMA) correctly predicts 69.5% of amino acids for a three-state description of the secondary structure (alpha-helix, beta-sheet and coil) in a whole database containing 126 chains of non-homologous (less than 25% identity) proteins. Joint prediction with SOPMA and a neural networks method (PHD) correctly predicts 82.2% of residues for 74% of co-predicted amino acids. Predictions are available by Email to deleage@ibcp.fr or on a Web page (http:@www.ibcp.fr/predict.html).

Databases, Factual↗

PubMed2000

Prediction of rates of inbreeding in populations selected on best linear unbiased prediction of breeding value.

Predictions for the rate of inbreeding (DeltaF) in populations with discrete generations undergoing selection on best linear unbiased prediction (BLUP) of breeding value were developed. Predictions were based on the concept of long-term genetic contributions using a recently established relationship between expected contributions and rates of inbreeding and a known procedure for predicting expected contributions. Expected contributions of individuals were predicted using a linear model, u(i)(()(x)()) = alpha + betas(i), where s(i) denotes the selective advantage as a deviation from the contemporaries, which was the sum of the breeding values of the individual and the breeding values of its mates. The accuracy of predictions was evaluated for a wide range of population and genetic parameters. Accurate predictions were obtained for populations of 5-20 sires. For 20-80 sires, systematic underprediction of on average 11% was found, which was shown to be related to the goodness of fit of the linear model. Using simulation, it was shown that a quadratic model would give accurate predictions for those schemes. Furthermore, it was shown that, contrary to random selection, DeltaF less than halved when the number of parents was doubled and that in specific cases DeltaF may increase with the number of dams.

Animal Husbandry↗

PubMed2006

Pregnancy is predictable: a large-scale prospective external validation of the prediction of spontaneous pregnancy in subfertile couples.

BACKGROUND: Prediction models for spontaneous pregnancy may be useful tools to select subfertile couples that have good fertility prospects and should therefore be counselled for expectant management. We assessed the accuracy of a recently published prediction model for spontaneous pregnancy in a large prospective validation study. METHODS: In 38 centres, we studied a consecutive cohort of subfertile couples, referred for an infertility work-up. Patients had a regular menstrual cycle, patent tubes and a total motile sperm count (TMC) >3 x 10(6). After the infertility work-up had been completed, we used a prediction model to calculate the chance of a spontaneous ongoing pregnancy (www.freya.nl/probability.php). The primary end-point was time until the occurrence of a spontaneous ongoing pregnancy within 1 year. The performance of the pregnancy prediction model was assessed with calibration, which is the comparison of predicted and observed ongoing pregnancy rates for groups of patients and discrimination. RESULTS: We included 3021 couples of whom 543 (18%) had a spontaneous ongoing pregnancy, 57 (2%) a non-successful pregnancy, 1316 (44%) started treatment, 825 (27%) neither started treatment nor became pregnant and 280 (9%) were lost to follow-up. Calibration of the prediction model was almost perfect. In the 977 couples (32%) with a calculated probability between 30 and 40%, the observed cumulative pregnancy rate at 12 months was 30%, and in 611 couples (20%) with a probability of >or=40%, this was 46%. The discriminative capacity was similar to the one in which the model was developed (c-statistic 0.59). CONCLUSIONS: As the chance of a spontaneous ongoing pregnancy among subfertile couples can be accurately calculated, this prediction model can be used as an essential tool for clinical decision-making and in counselling patients. The use of the prediction model may help to prevent unnecessary treatment.

Adult↗

PubMed1992

University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk etiologic models.

The purpose of this analysis is to compare three different statistical models for predicting children likely to be at risk of developing dental caries over a 3-yr period. Data are based on 4117 children who participated in the University of North Carolina Caries Risk Assessment Study, a longitudinal study conducted in the Aiken, South Carolina, and Portland, Maine areas. The three models differed with respect to either the types of variables included or the definition of disease outcome. The two "Prediction" models included both risk factor variables thought to cause dental caries and indicator variables that are associated with dental caries, but are not thought to be causal for the disease. The "Etiologic" model included only etiologic factors as variables. A dichotomous outcome measure--none or any 3-yr increment, was used in the "Any Risk Etiologic model" and the "Any Risk Prediction Model". Another outcome, based on a gradient measure of disease, was used in the "High Risk Prediction Model". The variables that are significant in these models vary across grades and sites, but are more consistent among the Etiologic model than the Predictor models. However, among the three sets of models, the Any Risk Prediction Models have the highest sensitivity and positive predictive values, whereas the High Risk Prediction Models have the highest specificity and negative predictive values. Considerations in determining model preference are discussed.

Child↗

PubMed2002

Pitfalls in predicting resting energy requirements in critically ill children: a comparison of predictive methods to indirect calorimetry.

BACKGROUND: Critical illness in children is thought to have profound effects on nutritional status. It is essential to avoid complications associated with inadequate nutrition support and delivery of excess energy. OBJECTIVE: To compare the results of several commonly used methods for predicting energy requirements in a group of critically ill children indirect calorimetry was used to measure energy expenditure in these children. DESIGN: Resting energy expenditures estimated by different prediction methods for energy were compared with measurements of actual resting energy expenditure obtained by indirect calorimetry in 52 children admitted to a pediatric intensive care unit. Agreement between each predictive method and indirect calorimetry was evaluated by Bland-Altman limits of agreement and by whether the methods met the predetermined criterion for accuracy of within 10% of the measured value. RESULTS: None of the equations predicted individual values accurately. Each of the predictive equations gave a wide and variable scatter of predicted values around the median. The recommended dietary allowance for energy was the least accurate and differed significantly even from the other predictive methods, overestimating energy expenditure in 50 of 52 patients. None of the remaining methods stood out as being more precise. CONCLUSIONS: Predictive methods commonly used to estimate energy expenditure in critically ill children are very imprecise and may lead to overprovision or underprovision of nutrition support. Resting energy expenditure should be measured by indirect calorimetry whenever possible.

Journal Article↗

PubMed2000

Prediction of rates of inbreeding in populations selected on best linear unbiased prediction of breeding value

Journal Article↗

PubMed1995

Are we all of one mind? Clinicians' and patients' opinions regarding the development of a service protocol for predictive testing for Huntington disease. Canadian Collaborative Study for Predictive Testing for Huntington Disease.

There are currently different research programs in place to assess the effects of predictive testing for a few late-onset disorders, including Huntington disease (HD) and familial cancers. Prior to providing predictive testing as a service, we sought the views of both the patients and the clinicians as to the importance and value of different items in a research protocol for HD. We mailed questionnaires to 41 clinicians and 351 at-risk patients who had participated in the research protocol, to solicit their opinions on the relative importance of various components of the HD predictive testing research protocol. Completed questionnaires were received from 256 patients (73%) and 33 clinicians (80%). Most participants (96%) were satisfied with the program, and < 3% of persons receiving a modification of risk felt that predictive testing had impaired their quality of life. While there was consensus on the importance of most components of the protocol, significantly more clinicians than patients (97% vs. 72%; P = 0.02) felt it was essential to keep written material about HD as part of a service protocol. More patients than clinicians (83% vs. 27%) considered it essential to have 24-hr contact numbers following disclosure of test results (P < 0.0001). Patients also felt more strongly about the importance of counseling about technical aspects of predictive testing (84% vs. 77%; P < 0.02), and about having a support person attend counselling sessions with the patient (62% vs. 48%; P = 0.04). Nearly 25% of participants indicated that they would not want their general practitioner routinely involved in the predictive testing program. These findings have influenced the development of our service protocol, and they underscore the importance of involving both providers and consumers of predictive testing in the development of a service protocol for genetic testing.

Adolescent↗

PubMed2003

Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model.

BACKGROUND: Models based on artificial neural networks (ANN) are useful in predicting outcome of various disorders. There is currently no useful predictive model for risk assessment in acute lower-gastrointestinal haemorrhage. We investigated whether ANN models using information available during triage could predict clinical outcome in patients with this disorder. METHODS: ANN and multiple-logistic-regression (MLR) models were constructed from non-endoscopic data of patients admitted with acute lower-gastrointestinal haemorrhage. The performance of ANN in classifying patients into high-risk and low-risk groups was compared with that of another validated scoring system (BLEED), with the outcome variables recurrent bleeding, death, and therapeutic interventions for control of haemorrhage. The ANN models were trained with data from patients admitted to the primary institution during the first 12 months (n=120) and then internally validated with data from patients admitted to the same institution during the next 6 months (n=70). The ANN models were then externally validated and direct comparison made with MLR in patients admitted to an independent institution in another US state (n=142). FINDINGS: Clinical features were similar for training and validation groups. The predictive accuracy of ANN was significantly better than that of BLEED (predictive accuracy in internal validation group for death 87% vs 21%; for recurrent bleeding 89% vs 41%; and for intervention 96% vs 46%) and similar to MLR. During external validation, ANN performed well in predicting death (97%), recurrent bleeding (93%), and need for intervention (94%), and it was superior to MLR (70%, 73%, and 70%, respectively). INTERPRETATION: ANN can accurately predict the outcome for patients presenting with acute lower-gastrointestinal haemorrhage and may be generally useful for the risk stratification of these patients.

Acute Disease↗

PubMed2005

Simple clinical variables predict liver histology in hepatitis C: prospective validation of a clinical prediction model.

OBJECTIVE: A recent single-center multivariate analysis of hepatitis C (HCV) patients showed that having any two criteria: 1) ferritin > or =200 microg/l and 2) spider nevi and/or albumin < or = 35 g/l predicted grade 2 or greater histological inflammation; the presence of any two of the following criteria: spider nevi, platelets < or =150 x 109/l, palpable splenomegaly and/or albumin < or =35 g/l predicted stage 2 or greater histological fibrosis. Absence of predictors also predicted a lack of inflammation and fibrosis. Our aim was prospectively to validate this clinical prediction model using an independent multicenter sample. MATERIAL AND METHODS: Eighty-one patients with previously untreated active chronic HCV underwent physical examination, laboratory investigation, and liver biopsy. Biopsies were read, in blinded fashion, by a single pathologist, using a modified Hytiroglou (1995) scale. The clinical scoring system was correlated with histology; likelihood ratios (LRs), Fisher's exact p-values, and receiver operating characteristics (ROCs) were calculated. RESULTS: Data recording was complete in 77 and 38 patients regarding fibrotic stage and inflammatory grade, respectively. For fibrosis, 3/3 patients with any three criteria (LR 17, positive predictive value (PPV) 100%), 4/5 patients with any two criteria (LR 5.1), and 15/47 with no criteria (LR 0.6, negative predictive value (NPV) 68%) had stage 2 or greater fibrosis on biopsy (p=0.01). For inflammation, 5/5 patients with both criteria (LR 15, PPV 100%), and 8/19 patients with no criteria (LR 0.5, NPV 58%) had moderate-severe inflammation on liver biopsy (p=0.036). When missing variables were assumed to be normal, recalculated LRs were almost identical. An alanine aminotransferase (ALAT) level <60 U/l may increase the NPVs. CONCLUSIONS: This independent multicenter data set has validated our published model which uses simple clinical variables accurately and significantly to predict hepatic fibrosis and inflammation in HCV patients.

Adult↗

PubMedDate not supplied

Predicting pressure ulcer risk: a multisite study of the predictive validity of the Braden Scale.

BACKGROUND: There have been no studies that have tested the Braden Scale for predictive validity and established cutoff points for assessing risk specific to different settings. OBJECTIVES: To evaluate the predictive validity of the Braden Scale in a variety of settings (tertiary care hospitals, Veterans Administration Medical Centers [VAMCs], and skilled nursing facilities [SNFs]). To determine the critical cutoff point for classifying risk in these settings and whether this cutoff point differs between settings. To determine the optimal timing for assessing risk across settings. METHOD: Randomly selected subjects (N= 843) older than 19 years of age from a variety of care settings who did not have pressure ulcers on admission were included. Subjects were 63% men, 79% Caucasian, and had a mean age of 63 (+/-16) years. Subjects were assessed for pressure ulcers using the Braden Scale every 48 to 72 hours for 1 to 4 weeks. The Braden Scale score and skin assessment were independently rated, and the data collectors were blind to the findings of the other measures. RESULTS: One hundred eight of 843 (12.8%) subjects developed pressure ulcers. The incidence was 8.5%, 7.4%, and 23.9% in tertiary care hospitals, VAMCs, and SNFs, respectively. Subjects who developed pressure ulcers were older and more likely to be female than those who did not develop ulcers. Braden Scale scores were significantly (p = .0001) lower in those who developed ulcers than in those who did not develop ulcers. Overall, the critical cutoff score for predicting risk was 18. Risk assessment on admission is highly predictive of pressure ulcer development in all settings but not as predictive as the assessment completed 48 to 72 hours after admission. CONCLUSIONS: Risk assessment on admission is important for timely planning of preventive strategies. Ongoing assessment in SNFs and VAMCs improves prediction and permits fine-tuning of the risk-based prevention protocols. In tertiary care the most accurate prediction occurs at 48 to 72 hours after admission and at this time the care plan can be refined.

Adult↗

PubMed2003

Saccades exhibit abrupt transition between reactive and predictive; predictive saccade sequences have long-term correlations.

To compensate for neural delays, organisms require predictive motor control. We investigated the transition between reaction and prediction in saccades (rapid eye movements) to periodically paced targets. Tracking at low frequencies (0.2-0.3 Hz) is reactive (eyes lag target) and at high frequencies (0.9-1.0 Hz) is predictive (eyes anticipate target); there is an abrupt rather than smooth transition between the two modes (a "phase transition," as found in bistable physical systems). These behaviors represent stable modes of the oculomotor control system, with attendant rapid switching between the neural pathways underlying the different modes. Furthermore, predictive saccades exhibit long-term correlations (slow decay of the autocorrelation function, manifest as a 1/f alpha spectrum). This indicates that predictive trials are not independent. The findings have implications for the understanding of predictive motor control: predictive performance during a given trial is influenced by a feedback process that takes into account the latency of previous trials.

Humans↗

PubMed2006

An assessment of the ratio of height to thyromental distance compared to thyromental distance as a predictive test for prediction of difficult tracheal intubation in Thai patients.

BACKGROUND AND RATIONALE: Preoperative evaluation is important in the detection of patients at risk for difficult tracheal intubation. Thyromental distance (TMD) is often used for these purposes, but its value as an indicator for difficult intubation is questionable, as it varies with patient size and body proportions. The purpose of the present study was to evaluate and compare the accuracies of the ratio of patient's height to TMD (ratio of height to TMD = RHTMD) and TMD alone in the prediction of difficult tracheal intubation in Thai patients. MATERIAL AND METHODS: The authors collected data on 382 consecutive patients scheduled to receive general anesthesia requiring endotracheal intubation for elective surgery. Thyromental distance and RHTMD were evaluated preoperatively. Difficult intubation was defined in the present study by Cormack and Lehane grade 3 or 4. The optimal predictive value was chosen using a receiver operating characteristic (ROC) curve. The areas under the ROC curves (AUC) of TMD and RHTMD were compared to determine the performance of the different predictive tests used. The sensitivity, specificity, and positive and negative predictive values of each of the predictive tests were calculated according to standard formulae. RESULTS: Difficult intubation occurred in 42 patients (10.9 %). The predictive advantage of RHTMD has a similar specificity with improved sensitivity in comparison with TMD. The AUC of RHTMD was significantly greater than the AUC of TMD (p = 0.00). The authors concluded that RHTMD had better accuracy in predicting difficult intubation than TMD.

Body Height↗

PubMed2006

Thermodynamic modeling of activity coefficient and prediction of solubility: Part 1. Predictive models.

A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.

Butanones↗