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PubMed1996

Prediction of the stability of DNA triplexes.

We present rules that allow one to predict the stability of DNA pyrimidine.purine.pyrimidine (Y.R.Y) triple helices on the basis of the sequence. The rules were derived from van't Hoff analysis of 23 oligonucleotide triplexes tested at a variety of pH values. To predict the enthalpy of triplex formation (delta H degrees), a simple nearest-neighbor model was found to be sufficient. However, to accurately predict the free energy of the triplex (delta G degrees), a combination model consisting of five parameters was needed. These parameters were (i) the delta G degrees for helix initiation, (ii) the delta G degrees for adding a T-A.T triple, (iii) the delta G degrees for adding a C(+)-G.C triple, (iv) the penalty for adjacent C bases, and (v) the pH dependence of the C(+)-G.C triple's stability. The fitted parameters are highly consistent with thermodynamic data from the basis set, generally predicting both delta H degrees and delta G degrees to within the experimental error. Examination of the parameters points out several interesting features. The combination model predicts that C(+) -G.C. triples are much more stabilizing than T-A.T triples below pH 7.0 and that the stability of the former increases approximately equal to 1 kcal/mol per pH unit as the pH is decreased. Surprisingly though, the most stable sequence is predicted to be a CT repeat, as adjacent C bases partially cancel the stability of one another. The parameters successfully predict tm values from other laboratories, with some interesting exceptions.

Base Sequence↗

PubMed1996

The validity of predicted body composition in Chinese adults from anthropometry and bioelectrical impedance in comparison with densitometry.

Body composition was measured by densitometry in a group of eighty-one Chinese males and 124 healthy Chinese females, aged 18-67 years. Biceps, triceps, subscapular and suprailiac skinfolds were measured as well as total body bioelectrical impedance at 50 kHz. Mean heights were 1.71 (SD 0.05) and 1.61 (SD 0.06) m, body weights 66.1 (SD 9.1) and 57.2 (SD 9.4) kg and BMI 22.7 (SD 3.0) and 22.0 (SD 3.3) kg/m2 in males and females respectively. Body fat values from body density were 20.1 (SD 6.2) and 29.7 (SD 7.5)% in males and females respectively. Mean predicted body-fat values from skinfolds, bioelectrical impedance and BMI, using prediction formulas developed in Western populations, did not differ or differed only slightly from body-fat values estimated from body density. The SD of the difference for each method was 5% body fat or 3.3 kg fat-free mass (CV 6%), which is approximately equal to the accuracy level of each predictive method. Compared with densitometry the predictive methods overestimated body fat at the lower levels of body fatness, whereas at the higher levels of body fat the predictive methods tended to underestimate body fatness. It is concluded that prediction formulas for estimating body fat from skinfolds, impedance or BMI developed in Western populations are applicable and valid in the adult Chinese population. However, in very lean subjects the predictive methods overestimate body fat compared with values obtained from body density.

Adipose Tissue↗

PubMed2001

Molecular structure-based prediction of human abdominal skin permeability coefficients for several organic compounds.

Mechanistically based predictions of skin permeability coefficients (Kp) derived solely on the basis of molecular structure information of organic compounds have not been reported previously. The objective of the present study was to predict the human abdominal Kp of structurally unrelated organic compounds using a mechanistic equation that takes into account compound-specific and species-specific determinants of Kp and relates the compound-specific determinants directly to molecular structure information. The method consisted of obtaining predictions based on a modified form of the conventional equation used to describe Kp (= P x D/L, where P, D, and L represent the stratum corneum:water partition coefficient, diffusion coefficient, and path length of diffusion). The value of P was predicted from the n-octanol:water partition coefficient (Po:w) computed from molecular structure information as well as the lipid, protein, and water contents in stratum corneum. The value of D was predicted according to the conventional Einstein-Stoke equation using the molar volume (Vmolar) of compounds computed from molecular structure information as well as data on viscosity of the stratum corneum components. Finally, the value of L was estimated in accordance with the characteristics of the diffusion pathways in the stratum corneum (i.e., transcellular and intercellular pathways). The average ratio of predicted Kp to the corresponding mean experimental values obtained from the literature was 1.25 (SD = 0.68, r = .95) for 47 structurally unrelated organic compounds (volatile organics and drugs), which were characterized by the presence of less than 2 hydrogen bonding groups and Vmolar < 231 cm3/mol. The present study is the first initiative that permits prediction of the human Kp of organic compounds by using molecular structure information as the sole chemical-specific input in a mechanistic equation.

Abdomen↗

PubMed2005

Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis.

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log kp) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r2=0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r2=0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.

Algorithms↗

PubMed2002

Can severity-of-illness indices for neonatal intensive care predict outcome at 4 years of age?

AIM: To test four neonatal severity-of-illness indices (CRIB, NTISS, SNAP, SNAP-PE) for their ability to predict short- and long-term outcome in very low-birthweight infants receiving neonatal intensive care. METHODS: Data on 240 newborns with birthweights below 1500 g from two Swedish neonatal units were collected. The predictive values of the indices for an adverse outcome in the neonatal period and at 4 y of age were compared with those of gestational age and birthweight. RESULTS: An early adverse outcome (in-hospital death, severe haemorrhagic-ischaemic brain lesion, retinopathy, chronic lung disease) was better predicted with CRIB (area under ROC curve (Az) = 0.87) and SNAP-PE (Az = 0.86), while SNAP-PE was best for predicting late problems (deviations in growth and psychomotor development, neurosensory impairment, difficulties in concentration, and impairment in vision, and hearing) (Az = 0.63). All indices predicted the early outcome better than the outcome at the 4-y follow-up. Severity-of-illness indices can be used as instruments to follow and improve the level of neonatal intensive care, but unfortunately seem to be of little value in long-term follow-up. CONCLUSION: CRIB and SNAP-PE indices are better in predicting hospital mortality than birthweight. None of the systems can predict adverse outcome at 4 y of age.

Child, Preschool↗

PubMed2005

External validation of the biodegradability prediction model CATABOL using data sets of existing and new chemicals under the Japanese Chemical Substances Control Law.

External validation of the biodegradability prediction model CATABOL was conducted using test data of 338 existing chemicals and 1123 new chemicals under the Japanese Chemical Substances Control Law. CATABOL predicts that 1089 chemicals will have a BOD < 60% while 925 (85%) actually have an observed BOD<60%. The percentage of chemicals with an observed BOD value <60% tends to increase as the predicted BOD values decrease. In contrast, 340 chemicals were predicted to have a BOD > or = 60% and 234 (69%) actually had an observed BOD > or = 60%. The prediction of poor biodegradability was more accurate than the predictions of high biodegradability. The features of chemical structures affecting CATABOL predictability were also investigated.

Biodegradation, Environmental↗

PubMedDate not supplied

Validity of generic risk factors and the strain index for predicting nontraumatic distal upper extremity morbidity.

Nine individual generic risk factors, eight combinations of generic risk factors, the presence of any generic risk factor, and the Strain Index were evaluated for 56 jobs by 2 evaluators blinded to morbidity measures. Jobs then were assigned to dichotomous hazard classifications (problem versus safe) according to recommendations from the literature. OSHA 200 logs were used to ascertain historical evidence of distal upper extremity (DUE) morbidity, and jobs were assigned to a dichotomous morbidity classification (positive versus negative) using none versus one or more recorded cases as the criterion. Evidence of association and measures of predictive validity were evaluated by comparing hazard and morbidity classifications using 2 x 2 contingency tables. Five individual generic risk factors, three generic risk factor combinations, and the presence of any generic risk factor were not associated with morbidity classification. The odds ratio estimates among the four individual generic risk factors and the five combinations of generic risk factors associated with DUE morbidity varied from 3.3-36.0. The Strain Index had the largest estimated odds ratio of any exposure factor at 108.3. The exposure methods were grouped according to patterns of predictive validity. With one exception, the individual generic risk factors and their combinations had high sensitivity with low specificity (many false-positives), low sensitivity with high specificity (many false-negatives), or low sensitivity with low specificity. The only generic risk factor that demonstrated reasonable predictive validity was the use of gloves--its sensitivity, specificity, positive predictive value, and negative predictive value were equal to 0.75. The Strain Index performed better than any of the individual or combinations of generic risk factors. Its sensitivity, specificity, positive predictive value, and negative predictive value were all approximately 0.90.

Arm↗

PubMed2002

Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled-release tablets.

The purpose of this study was to evaluate four commercially available artificial neural network (ANN) software programs: NeuroShell2 v3.0, BrainMaker v3.7, CAD/Chem v5.0, and NeuralWorks Professional II/Plus for prediction of in vitro dissolution-time profiles of controlled-release tablets containing a model sympathomimetic drug. Seven independent formulation variables and three other tablet variables (moisture content of granules, granule particle size, and tablet hardness), for 22 tablet formulations, were used as the ANN model input. In vitro dissolution time-profiles at 10 different sampling times were used as the output. The models' optimum architectures were determined for each ANN software by varying the number of hidden layers and number of nodes in hidden layer(s). The ANN developed from the four software programs were validated by predicting the in vitro dissolution time-profiles of each of the 19 formulations, which were excluded from the training process. Although the same data set was used, the optimum ANN architectures generated from the four software programs were different. Using the four optimum ANN models, the plots of predicted vs. observed percentage of drug dissolved gave slopes ranging from 0.95 to 1.01 and r2 values ranging from 0.95 to 0.99 for all 190 dissolution data points for the 19 training formulations. The difference factors (f1) and similarity factors (f2) between the ANN predicted and the observed in vitro dissolution profiles were also used to compare the predictions for the four software programs. It was concluded that the four programs provided reasonable predictions of in vitro dissolution profiles for the data set employed in this study, with NeuralShell2 showing the best overall prediction.

Delayed-Action Preparations↗

PubMed2005

A graphical user interface for diabetes management that integrates glucose prediction and decision support.

BACKGROUND: The promise of the Diabetes Control and Complications Trial (DCCT) has yet to be realized in clinical practice. Notwithstanding intensive education and intensified therapy, there is a distinct lack of a suitable alternative to the intensive decision support that was also provided in the DCCT. Recently, a novel glucose predicting engine has been developed and validated. Use of its predictions in decision support in respect to medication dosing, diet, exercise, and stress promises to empower patients to achieve better diabetes control while reducing hypoglycemia and preventing body weight gain. A graphical user interface (GUI) suitable for these purposes is here described. METHODS: The kernel of the GUI is a registry database located on a server accessible to both patients and their providers. The patient-GUI includes the resources of the glucose predicting engine and user-friendly, intuitive means to enter body weight and all home-monitored blood glucose levels. In response, means to modify medication dosages (dosing decision support) and modify planned diet and physical activity (lifestyle decision support) are afforded the user. Each action is animated so that the patient can visually see the impact of his or her changes on predicted glucose outcomes and the pending risks of hypoglycemia. RESULTS: A staged sequence of screens supports the self-management tasks, including selection of the current meal period, the entry of data, and documentation. The GUI returns current medications and presents up-down buttons for adjusting dosages, for changing carbohydrates, for changing exercise, and for predicting the effects of stress. For each adjustment, the impact on medications or predicted glycemia outcomes is animated. CONCLUSIONS: A new GUI that incorporates a novel glucose predicting engine is intended for all insulin-treated patients with diabetes. It may help patients and their providers to realize better glycemic control and thereby achieve the promise of the DCCT.

Blood Glucose↗

PubMed2004

Prediction of resting energy expenditure in a large population of obese children.

BACKGROUND: Recommendations for energy intake in obese children rely on accurate methods for measuring energy expenditure that cannot be assessed systematically. OBJECTIVE: The aim was to establish and validate new equations for predicting resting energy expenditure (REE), specifically in obese children. DESIGN: REE (indirect calorimetry) and body composition (bioelectrical impedance analysis) were measured in 752 obese subjects aged 3-18 y. The first cohort (n=471) was used to establish predictive equations, the second (and independent) cohort (n=211) was used to validate these equations, and the third cohort, a follow-up group of children who lost weight (n=70), was used to examine predictive REE in the postobese period. REE values predicted with the use of various published equations and the new established equation were compared with measured REE by using the Bland-Altman method and Student's t tests. RESULTS: In cohort 1, significant determinants of the new prediction equations were fat-free mass in boys (model R2=0.79) and age and fat-free mass in girls (model R2=0.76). External validation conducted by using the Bland-Altman method and Student's t tests, in cohort 2, showed no significant difference between measured REE and predicted REE with the new equation. When already published equations were applied, systematical bias appeared with all published equations except for that of the World Health Organization. In cohort 3, the children who lost weight, almost all equations significantly underestimated REE. CONCLUSIONS: These new predictive equations allow clinicians to estimate REE in an obese pediatric population with sufficient and acceptable accuracy. This estimation may be a strong basis for energy recommendations in childhood obesity.

Adolescent↗

PubMed2005

A cellular-level approach to predicting resting energy expenditure across the adult years.

BACKGROUND: We previously derived a whole-body resting energy expenditure (REE) prediction model by using organ and tissue mass measured by magnetic resonance imaging combined with assumed stable, specific resting metabolic rates of individual organs and tissues. Although the model predicted REE well in young persons, it overpredicted REE by approximately 11% in elderly adults. This overprediction may occur because of a decline in the fraction of organs and tissues as cell mass with aging. OBJECTIVE: The aim of the present study was to develop a cellular-level REE prediction model that would be applicable across the adult age span. Specifically, we tested the hypothesis that REE can be predicted from a combination of organ and tissue mass, the specific resting metabolic rates of individual organs and tissues, and the cellular fraction of fat-free mass. DESIGN: Fifty-four healthy subjects aged 23-88 y had REE, organ and tissue mass, body cell mass, and fat-free mass measured by indirect calorimetry, magnetic resonance imaging, whole-body (40)K counting, and dual-energy X-ray absoptiometry, respectively. RESULTS: REE predicted by the cellular-level model was highly correlated with measured REE (r = 0.92, P < 0.001). The mean difference between measured REE (x+/- SD: 1487 +/- 294 kcal/d) and predicted REE (1501 +/- 300 kcal/d) for the whole group was not significant, and the difference between predicted and measured REE was not associated with age (r = 0.009, NS). CONCLUSION: The present approach establishes an REE-body composition link with the use of a model at the cellular level. The combination of 2 aging-related factors (ie, decline in both the mass and the cellular fraction of organs and tissues) may account for the lower REE observed in elderly adults.

Absorptiometry, Photon↗

PubMed2006

Optimized predictions of absolute and relative amounts of body fat from weight, height, other anthropometric predictors, and age 1.

BACKGROUND: Body mass index (BMI) is the dominating weight-for-height index, but its validity as a body fat (BF) index has not been properly examined. OBJECTIVES: Our aims were to establish and validate optimal weight-for-height indexes for predicting absolute and relative (percentage) amounts of BF, to examine whether other commonly available anthropometric variables or age could add to the predictive power, and to explore the upper limit for percentage BF. DESIGN: One thousand one hundred twelve randomly selected subjects, and an additional 149 obese subjects, were included in the study. The subjects were randomly allocated to either a primary study group or a validation group. BF was measured with dual-energy X-ray absorptiometry. The relations between weight/heightx (W/Hx) and BF (absolute or percentage) were examined for values of the exponent x that ranged from 0.0 to 3.0. The predictive power of equations that were based on optimal weight-for-height indexes was compared with equations based on weight, height, other anthropometric variables, and age. RESULTS: Absolute BF was optimally and linearly predicted by W/H1, whereas the percentage BF was optimally and nonlinearly predicted by W/H2. The percentage BF asymptotically approached 52% in women and 56% in men. The percentage BF increased only marginally from BMI (in kg/m2) values of >35 in women and >60 in men. Predictions of absolute BF were associated with smaller errors (8.5% for men and 5.7% for women) than were predictions of percentage BF (8.7% for men and 7.9% for women). The addition of other anthropometric measurements for both men and women, and the addition of age for women only, in the regression analyses moderately reduced these errors. CONCLUSION: Our data suggest that W/H may be a more optimal weight-for-height index than is BMI, particularly at high body weights.

Absorptiometry, Photon↗

PubMed1993

Advanced ovarian cancer. Neural network analysis to predict treatment outcome.

BACKGROUND: Quantitative methods for the analysis of prognostic information are important in order to use this knowledge optimally. The neural network is a new quantitative method where the fundamental building blocks are units which can be likened to neurons, and weighted connections which can be likened to synapses. The more the hidden units, the more complex the patterns that can be learnt. MATERIALS AND METHODS: Data from two Dutch studies in ovarian cancer were used to compare the previously reported survival rates predicted by the Cox's prognostic index with the prediction obtained by a neural network. RESULTS: Both the Cox's analysis and the neural network agreed on residual tumour size, stage, and performance status as being important for survival. The neural network identified additional predictive factors such as place of diagnosis and age. As the Cox's prognostic index has not been tested to predict survival on an independent data set a comparison with the results obtained in the neural network test set could not be performed. CONCLUSIONS: Neural networks perform at least as well as Cox's method for the prediction of survival, and prognostic factors can easily be identified. The analysis not only revealed the predictive power of some characteristics, but also the non-predictive power of the others.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed2026

Prediction of bacterial protein-compound interactions with only positive samples.

MOTIVATION: Prediction of Compound-Protein Interactions (CPI) in bacteria is crucial to advance various pharmaceutical and chemical engineering fields, including biocatalysis, drug discovery, and industrial processing. However, current CPI models cannot be applied for bacterial CPI prediction due to the lack of curated negative interaction samples. RESULTS: We propose a novel Positive-Unlabeled (PU) learning framework, named BIN-PU, to address this limitation. BIN-PU generates pseudo positive and negative labels from known positive interaction data, enabling effective training of deep learning models for CPI prediction. We also propose a weighted positive loss function that weights to truly positive samples. We have validated BIN-PU coupled with multiple CPI backbone models, comparing the performance with the existing PU models using bacterial cytochrome P450 (CYP) data. Extensive experiments demonstrate the superiority of BIN-PU over the benchmark models in predicting CPIs with only truly positive samples. Furthermore, we have validated BIN-PU on additional bacterial proteins obtained from literature review, human CYP datasets, and uncurated data for its reproducibility. We have also validated the CPI prediction for the uncurated CYP data with biological and biophysical experiments. BIN-PU represents a significant advancement in CPI prediction for bacterial proteins, opening new possibilities for improving predictive models in related biological interaction tasks. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/datax-lab/CYP.

Bacterial Proteins↗

PubMed2026

CROP: a feature-independent context-aware method for CRISPR-Cas9 frameshift prediction.

MOTIVATION: The CRISPR-Cas9 complex has revolutionized genome-editing technologies. By designing a 20 nt-long guide RNA, a Cas9 nuclease can be guided to cleave almost any genomic target site (followed by NGG). The cleavage induces double-stranded DNA breaks, which are then repaired by cellular pathways. Accurate CRISPR-Cas9 repair-outcome prediction is essential for designing guide RNAs with desired genomic effects, such as gene knockout. A central challenge is quantifying the rate of frameshifts, i.e. repair-outcomes that lead to a change in the local length that is not a multiple of three. Previous methods for frameshift-rate prediction were trained on only a few experimental or cellular contexts, mostly relied on manually defined microhomology features, and were limited by sparse features and class labels. RESULTS: We developed CROP, a feature-independent context-aware repair-outcome prediction method. By aggregating specific repair outcomes as Δlength classes, CROP overcomes class sparsity. We designed CROP to work with variable input sequence lengths and output classes to utilize multiple datasets simultaneously. We benchmarked CROP against state-of-the-art repair-outcome prediction methods over 18 datasets, which we curated and standardized from various studies. Across all datasets, CROP outperformed all competing methods in frameshift-rate prediction. We performed cross-experiment and cross-cellular frameshift-rate predictions to investigate the generalizability of repair mechanisms. Finally, we show that CROP learned microhomology principles from raw sequences without explicit feature engineering, establishing an end-to-end architecture for CRISPR-Cas9 repair-outcome prediction that learns from multiple datasets. AVAILABILITY AND IMPLEMENTATION: CROP is available at https://github.com/OrensteinLab/CROP.

CRISPR-Cas Systems↗

PubMed2003

Prediction of delayed renal allograft function using an artificial neural network.

BACKGROUND: Delayed graft function (DGF) is one of the most important complications in the post-transplant period, having an adverse effect on both the immediate and long-term graft survival. In this study, an artificial neural network was used to predict the occurrence of DGF and compared with traditional logistical regression models for prediction of DGF. METHODS: A total of 304 cadaveric renal transplants performed at the Jewish Hospital, Louisville were included in the study. Covariate analysis by artificial neural networks and traditional logistical regression were done to predict the occurrence of DGF. RESULTS: The incidence of DGF in this study was 38%. Logistic regression analysis was more sensitive to prediction of no DGF (91 vs 70%), while the neural network was more sensitive to prediction of yes for DGF (56 vs 37%). Overall prediction accuracy for both logistic regression and the neural network was 64 and 63%, respectively. Logistic regression was 36.5% sensitive and 90.7% specific. The neural network was 63.5% sensitive and 64.8% specific. The only covariate with a P < 0.001 was the transplant of a white donor kidney to a black recipient. Cox proportional hazard regression was used to test for the negative effect of DGF on long-term graft survival. One year graft survival in patients without DGF was 92 +/- 2% vs 81 +/- 3% in patients with DGF. The 5-year graft survival was not affected by DGF in this study. CONCLUSION: Artificial neural networks may be used for prediction of DGF in cadaveric renal transplants. This method is more sensitive but less specific than logistic regression methods.

Adult↗

PubMed1997

Prediction of improvement of ventricular function after revascularization. 18F-fluorodeoxyglucose single-photon emission computed tomography vs low-dose dobutamine echocardiography.

AIMS: To compare assessment of myocardial flow and glucose metabolism by single-photon emission computed tomography (SPECT) with low-dose dobutamine echocardiography in predicting improvement in regional and global left ventricular function after coronary artery bypass grafting. METHODS AND RESULTS: Thirty patients with regional wall motion abnormalities (mean ejection fraction 32 +/- 19%) were studied with low-dose dobutamine echocardiography (5 and 10 micrograms. kg-1 min-1) and thallium-201/ 18F-fluorodeoxyglucose(FDG) SPECT prior to surgery. For comparative analysis, a 13-segment model was used. Postoperative improvement was predicted if the echocardiogram showed that wall motion abnormalities were reversible during the dobutamine infusion and there was normal perfusion or relatively increased FDG uptake in perfusion defects (mismatch) in dyssynergic segments on SPECT. After surgery, ventricular function was reassessed. An echocardiogram was taken at the 3 month follow-up with the patient at rest. Regional wall motion had improved in 62/168 (37%) revascularized segments. In predicting functional outcome, low-dose dobutamine echocardiography reached a sensitivity of 89% and a specificity of 82%, with a positive predictive value of 74% and a negative predictive value of 93%, whereas for thallium-201/FDG SPECT these values were 84%, 86%, 78% and 90%, respectively. In patients with more than two viable segments on either technique, the wall motion score index, a surrogate of global ventricular function, improved significantly. CONCLUSION: For the optimal prediction of functional outcome, combined assessment of flow and FDG imaging is needed. Both thallium-201/FDG SPECT and low-dose dobutamine echocardiography appear comparable and similarly accurate in predicting improvement of left ventricular function after surgical revascularization.

Analysis of Variance↗

PubMed1997

Selection of dichotomy limits for multifactorial prediction of arrhythmic events and mortality in survivors of acute myocardial infarction.

AIMS: To evaluate the predictive value and optimum dichotomy limits for different combinations of prognostic indicators for the prediction of arrhythmic events and cardiac mortality in post-infarction patients. BACKGROUND: Studies of new interventions based on risk stratification after myocardial infarction have often used a single variable as a predictor of risk. However, whether the dichotomy limits of these single variables, derived from univariate analyses, should be altered when such variables are combined for the prediction of risk after myocardial infarction has not been examined. METHODS: Left ventricular ejection fraction, signal-averaged electrocardiography, heart rate variability index, mean heart rate and ventricular extrasystole frequency were recorded pre-discharge in 439 survivors of their first myocardial infarction. Arrhythmic events and cardiac mortality were recorded during 1 year (range 1-6 years) follow-up. RESULTS: During follow-up for at least 1 year, there were 25 cardiac deaths and 23 arrhythmic events. Different optimum dichotomy limits were obtained for the prediction of cardiac mortality vs arrhythmic events, for different combinations of variables, for different selected levels of sensitivity and for different numbers of variables abnormal before identification of those at risk. The dichotomy limit of the heart rate variability index for the prediction of events appeared to be the least affected by the inclusion of other variables. For example, when predicting arrhythmic events using combinations of left ventricular ejection fraction and/or heart rate variability, the optimum dichotomy limits when each variable was used alone was 32% and 18 units respectively; 43% and 18 units when either left ventricular ejection fraction or heart rate variability are required to be abnormal, and 52% and 19 units when both are required to be abnormal before identification of those at risk of arrhythmic events. CONCLUSIONS: Dichotomy limits derived from univariate analyses do not optimally predict events when used in the multivariate setting. Risk stratification can be improved by using several variables in combination and is further improved by using dichotomy limits of these variables which are different from those used in or derived from univariate analyses.

Aged↗