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PubMed2005

Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes.

OBJECTIVES: Many possible methods of risk adjustment exist, but there is a dearth of comparative data on their performance. We compared the predictive validity of 2 widely used methods (Diagnostic Cost Groups [DCGs] and Adjusted Clinical Groups [ACGs]) for 2 clinical outcomes using a large national sample of patients. METHODS: We studied all patients who used Veterans Health Administration (VA) medical services in fiscal year (FY) 2001 (n = 3,069,168) and assigned both a DCG and an ACG to each. We used logistic regression analyses to compare predictive ability for death or long-term care (LTC) hospitalization for age/gender models, DCG models, and ACG models. We also assessed the effect of adding age to the DCG and ACG models. RESULTS: Patients in the highest DCG categories, indicating higher severity of illness, were more likely to die or to require LTC hospitalization. Surprisingly, the age/gender model predicted death slightly more accurately than the ACG model (c-statistic of 0.710 versus 0.700, respectively). The addition of age to the ACG model improved the c-statistic to 0.768. The highest c-statistic for prediction of death was obtained with a DCG/age model (0.830). The lowest c-statistics were obtained for age/gender models for LTC hospitalization (c-statistic 0.593). The c-statistic for use of ACGs to predict LTC hospitalization was 0.783, and improved to 0.792 with the addition of age. The c-statistics for use of DCGs and DCG/age to predict LTC hospitalization were 0.885 and 0.890, respectively, indicating the best prediction. CONCLUSIONS: We found that risk adjusters based upon diagnoses predicted an increased likelihood of death or LTC hospitalization, exhibiting good predictive validity. In this comparative analysis using VA data, DCG models were generally superior to ACG models in predicting clinical outcomes, although ACG model performance was enhanced by the addition of age.

Adolescent↗

PubMed2006

In silico prediction of drug solubility in water-ethanol mixtures using Jouyban-Acree model.

PURPOSE: A predictive method was proposed to predict solubility of drugs in water-ethanol mixtures at various temperatures based on the Jouyban-Acree model. The model requires the experimental solubility data of the drug in mono-solvent systems. METHODS: The accuracy of the proposed prediction method was evaluated using collected experimental solubility data from the literature. The proposed method is: log Xm,T = fc log Xc,T + fw log Xw,T + fcfw[724.21/T + 485.17(fc-fw)/T + 194.41(fc-fw)2/T] Where Xm,T, Xc,T and Xw,T are the solute solubility at temperature (T) in mixed solvent and neat cosolvent and water, respectively, fc and fw denote the solute free fraction of cosolvent (ethanol) and water. The average absolute error (AAE) of the experimental and the predicted solubilities was computed as an accuracy criterion and compared with that of a well-established log-linear model. RESULTS: The AAE (+/-SD) of the Jouyban-Acree and log-linear models were 0.19 (+/-0.13) and 0.48 (+/-0.28), respectively. The mean difference of AAEs was statistically significant (p < 0.0005) revealing that the Jouyban-Acree model was provided more accurate predictions. Although the log-linear model was used to predict solubility at a fixed temperature (25 or 23 degrees C), the results also showed that the model could be employed to predict the solubility in solvent mixtures at various temperatures. CONCLUSION: More accurate predictions were provided using the Jouyban-Acree model in comparison with a previously established log-linear model of Yalkowsky. The prediction methods were successfully extended to predict the solubility in water-ethanol mixtures at various temperatures.

Ethanol↗

PubMed1991

Prediction of reversible ischemia after coronary artery bypass grafting by positron emission tomography.

Metabolic imaging using positron emission tomography (PET) facilitates the identification of ischemic but viable myocardium. In this study, the predictive value of PET for identifying improvement in regional function after coronary artery bypass grafting (CABG) was assessed. PET perfusion and metabolic imagings using N-13 ammonia and F-18 deoxyglucose (FDG) were performed before and 5-7 weeks after CABG in 25 patients with coronary artery disease. Each of the 5 myocardial segments of the left ventricle was categorized as normal, ischemic and infarcted based on the findings of perfusion and PET metabolic images. Among 58 hypoperfused segments, abnormal perfusion in 17 of 25 ischemic segments was correctly predicted to be reversible (68% prediction accuracy), and that in 25 of 33 infarcted segments were correctly predicted to be irreversible (76% prediction accuracy) (p < 0.001). Similarly, among 53 asynergy segments assessed by radionuclide ventriculography, abnormal wall motion in 21 of 27 asynergy segments was correctly predicted to be reversible (78% prediction accuracy), and that in 21 of 26 PET viable segments was correctly predicted to be irreversible (81% prediction accuracy) (p < 0.001). Thus, preoperative metabolic imaging using PET appears to be useful for predicting responses to CABG.

Adult↗

PubMed1989

Fetal monitoring and predictions by clinicians: observations during a randomized clinical trial in very low birth weight infants.

Predictions about perinatal outcome in very low birth weight infants were studied in a randomized clinical trial of electronic fetal monitoring and periodic auscultation to assess the effect of diagnostic monitoring information on clinicians' ability to predict perinatal outcomes. The only predictions consistently correct before monitoring information was available were those regarding infant survival (88% correct, kappa [kappa] = 0.40, P less than .001 for the electronic fetal monitoring group; 80% correct, kappa = 0.35, P less than .01 for the periodic auscultation group). After monitoring, predictions of 5-minute Apgar scores and arterial cord pH were significantly more accurate, and clinicians' confidence in their predictions increased significantly in both the electronic fetal monitoring and the auscultation groups. Predictions of 5-minute Apgar scores were significantly more accurate in the electronic fetal monitoring group (92% correct, kappa = 0.80) than in the periodic auscultation group (61% correct, kappa = 0.28) (Z difference = 3.04; P less than .01). We conclude that clinicians gain information during intrapartum monitoring that generally leads to improved predictions and increased confidence in predictions. In this study, they made more accurate predictions about 5-minute Apgar scores with electronic fetal monitoring, suggesting that electronic fetal monitoring may provide better information about neonatal well-being than does periodic auscultation. Improved information, as measured by clinical predictions, is probably highly valued by patients and clinicians and may be an important determinant of acceptance of this diagnostic technology.

Apgar Score↗

PubMed1995

[Prediction of postoperative pulmonary function using 99mTc-MAA perfusion lung SPECT].

In order to predict postoperative pulmonary function, 99mTc-MAA perfusion lung SPECT and spirometry were performed preoperatively in 52 patients with resectable primary lung cancer; 44 underwent lobectomy, eight pneumonectomy. Local pulmonary function (called local effective volume) was evaluated according to the degree of radionuclide distribution of each voxel in the SPECT images. The total effective volume was defined as the sum of the local effective volume, and the residual effective volume was the total effective volume excluding loss after operation. Predicted pulmonary function (VC and FEV1.0) was calculated by the following formula: Predicted value = preoperative value x percent of the residual effective volume. Postoperative pulmonary function was predicted in the same patients by means of 99mTc-MAA perfusion lung planar scintigraphy and X-ray CT. The patients were reinvestigated with spirometry at one and four months after surgery, and the values were compared with the predicted values. The correlations between the predicted values using SPECT and measured postoperative pulmonary function were highly significant (VC: r = 0.867, FEV1.0: r = 0.864 one month after operation; VC: r = 0.860, FEV1.0: r = 0.907 4 months after operation). The predicted values calculated using SPECT were accurate compared with the predicted values calculated using planar scintigraphy or X-ray CT. The patients with predicted FEV1.0 of less than 0.8 liter required home oxygen therapy. This method is valuable for the prediction of postoperative pulmonary function before the surgical procedure.

Adult↗

PubMed1998

Use of an artificial neural network to predict length of stay in acute pancreatitis.

Length of stay (LOS) predictions in acute pancreatitis could be used to stratify patients with severe acute pancreatitis, make treatment and resource allocation decisions, and for quality assurance. Artificial neural networks have been used to predict LOS in other conditions but not acute pancreatitis. The hypothesis of this study was that a neural network could predict LOS in patients with acute pancreatitis. The medical records of 195 patients admitted with acute pancreatitis were reviewed. A backpropagation neural network was developed to predict LOS >7 days. The network was trained on 156 randomly selected cases and tested on the remaining 39 cases. The neural network had the highest sensitivity (75%) for predicting LOS >7 days. Ranson criteria had the highest specificity (94%) for making this prediction. All methods incorrectly predicted LOS in two patients with severe acute pancreatitis who died early in their hospital course. An artificial neural network can predict LOS >7 days. The network and traditional prognostic indices were least accurate for predicting LOS in patients with severe acute pancreatitis who died early in their hospital course. The neural network has the advantage of making this prediction using admission data.

APACHE↗

PubMed1980

Predicting the outcome of psychotherapy. findings of the Penn Psychotherapy Project.

Our study of predictability of outcomes of psychotherapy used predictions of two kinds: (1) direct predictions by patients, therapists, and clinical observers; and (2) predictive measures derived from the same sources. Seventy-three nonpsychotic patients were treated in psychoanalytically oriented psychotherapy (mean, 44 sessions). Two thirds of the therapists were residents in psychiatry; one third were more experienced. The two main composite outcome measures, measured at termination, were Raw Gain (residualized) and Rated Benefits, which intercorrelated at .76. Most patients improved and showed a considerable range of benefits. The clinical observers' direct predictions of Rated Benefits were highest (.27, P less than 905). The success of the predictive measures were generally insignificant, and the best of them were in the .2 to .3 range meaning that only 5% to 10% of the outcome variance was predicted. The Prognostic Index Interview variables did the best (eg, emotional freedom composite, .30; a crossvalidation for 30 patients was .39 (P less than .05). Neither the therapist measures nor the early psychotherapy session measures predicted significantly. Reanalysis of the similar Chicago Counseling Center study, in our terms, showed a similar low level of prediction success, eg, adequacy of functioning, marital status match, and length of treatment predicted significantly in both studies.

Adolescent↗

PubMed1997

Imaging for improved prediction of myelotoxicity after radioimmunotherapy.

BACKGROUND: The severity of myelotoxicity after radioimmunotherapy has been predicted from body and blood radiation doses to marrow. However, marrow radiation can be increased substantially if the marrow or skeleton contains the malignancy targeted by the radiolabeled monoclonal antibodies. A study of 29 patients treated with iodine-131 (131I)-Lym-1 showed that radiation doses to marrow from body and blood had little correlation with myelotoxicity. The purpose of the present study was to assess the significance of marrow targeting and other factors for prediction of myelotoxicity. METHODS: Injected radioactivity and nontargeted radiation doses to marrow were compared with peripheral blood cell counts after the first therapy dose of 131I-Lym-1 in 16 heavily pretreated patients with non-Hodgkin's lymphoma (NHL). Bone marrow biopsy, targeted marrow radiation doses, marrow image uptake scores, age, Karnofsky performance score (KPS), previous chemotherapy, and tumor burden were also compared with blood counts. RESULTS: Myelotoxicity was not predicted well by injected radioactivity, total body radiation, or body and blood radiation doses contributed to marrow (P > 0.1). Biopsy-proven bone marrow lymphoma also failed to predict myelotoxicity (P > 0.1). Thrombocytopenia and leukopenia were predicted well by targeted radiation dose to marrow (P < 0.05) obtained by 131I imaging. Similarly, marrow image scores predicted decreases in platelets and white blood cells (WBCs; P < 0.05). Prediction of myelotoxicity using marrow radiation dose methods was slightly improved when serum lactic dehydrogenase (LDH), age, KPS, and prior chemotherapy were included in the analysis (P < or = 0.01). CONCLUSIONS: Prediction of myelotoxicity was improved in this group of patients by assessment of the targeting component of marrow radiation and was better predicted and obtained more easily by semiquantitative marrow image scores. Further improvement in prediction was slight when other factors were considered.

Bone Marrow↗

PubMed2001

Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs.

Tissue:plasma (P(t:p)) partition coefficients (PCs) are important parameters describing tissue distribution of drugs. The ultimate goal in early drug discovery is to develop and validate in silico methods for predicting a priori the P(t:p) for each new drug candidate. In this context, tissue composition-based equations have recently been developed and validated for predicting a priori the non-adipose and adipose P(t:p) for neutral organic solvents and pollutants. For ionizable drugs that bind to different degrees to common plasma proteins, only their non-adipose P(t:p) values have been predicted with these equations. The only compound-dependent input parameters for these equations are the lipophilicity parameter, such as olive oil-water PC (K(vo:w)) or n-octanol-water PC (P(o:w)), and/or unbound fraction in plasma (fu(p)) determined under in vitro conditions. Tissue composition-based equations could potentially also be used to predict adipose tissue-plasma PCs (P(at:p)) for ionized drugs. The main objective of the present study was to modify these equations for predicting in vivo P(at:p) (white fat) for 14 structurally unrelated ionized drugs that bind substantially to plasma macromolecules in rats, rabbits, or humans. The second objective was to verify whether K(vo:w) or P(o:w) provides more accurate predictions of in vivo P(at:p) (i.e., to verify whether olive oil or n-octanol is the better surrogate for lipids in adipose tissue). The second objective was supported by comparing in vitro data on P(at:p) with those on olive oil-plasma PC (K(vo:p)) for five drugs. Furthermore, in vivo P(at:p) was not only predicted from K(vo:w) and P(o:w) of the non-ionized species, but also from K*(vo:w) and P*(o:w), taking into account the ionized species in addition. The P(at:p) predicted from K*(vo:w), P*(o:w), and P(o:w) differ from the in vivo P(at:p) by an average factor of 1.17 (SD = 0.44, r = 0.95), 15.0 (SD = 15.7, r = 0.59), and 40.7 (SD = 57.2, r = 0.33), respectively. The in vitro values of K(vo:p) differ from those of P(at:p) by an average factor of 0.86 (SD = 0.16, r = 0.99, n = 5). The results demonstrate that (i) the equation using only data on fu(p) as input and olive oil as lipophilicity surrogate is able to provide accurate predictions of in vivo P(at:p), and (ii) olive oil is a better surrogate of the adipose tissue lipids than n-octanol. The present study is an innovative method for predicting in vivo fat partitioning of drugs in mammals.

Adipose Tissue↗

PubMed1994

Protein structural similarities predicted by a sequence-structure compatibility method.

A method for protein structure prediction has been developed, which evaluates the compatibility of an amino acid sequence with known 3-dimensional structures and identifies the most likely structure. The method was applied to a large number of sequences in a database, and the structures of the following proteins were predicted: (1) shikimate kinase (SKase), (2) the hydrophilic subunit of mannose permease (IIABMan), (3) rat tyrosine aminotransferase (Tyr AT), and (4) threonine dehydratase (TDH). The functional and evolutionary implications of the predictions are discussed. (1) The structural similarity between SKase and adenylate kinase was predicted. Alignment of their sequences reveals that the ATP-binding type A sequence motif and 2 ATP-binding arginine residues are conserved. The prediction suggests a similarity in their functional mechanisms as well as an evolutionary relationship. (2) The structural similarity between IIABMan and galactose/glucose-binding protein (GGBP) was predicted. The IIA and IIB domains are aligned with the N- and C-terminal domains of GGBP, respectively. The 2 phosphorylated residues, His 10 and His 175, of IIABMan are threaded onto loops located in the substrate-binding cleft of GGBP. The prediction accounts for the phosphoryl transfer from His 10 to His 175, and to the sugar substrate. (3) The structural similarity between rat Tyr AT and Escherichia coli aspartate AT was predicted, as well as (4) the structural similarity between TDH and the tryptophan synthase beta subunit. Predictions (3) and (4) support the previous predictions based on observations of the functional similarities between the proteins.

Amino Acid Sequence↗

PubMed1995

Comparison of and limits of accuracy for statistical analyses of vibrational and electronic circular dichroism spectra in terms of correlations to and predictions of protein secondary structure.

This work provides a systematic comparison of vibrational CD (VCD) and electronic CD (ECD) methods for spectral prediction of secondary structure. The VCD and ECD data are simplified to a small set of spectral parameters using the principal component method of factor analysis (PC/FA). Regression fits of these parameters are made to the X-ray-determined fractional components (FC) of secondary structure. Predictive capability is determined by computing structures for proteins sequentially left out of the regression. All possible combinations of PC/FA spectral parameters (coefficients) were used to form a full set of restricted multiple regressions with the FC values, both independently for each spectral data set as well as for the two VCD sets and all the data grouped together. The complete search over all possible combinations of spectral parameters for different types of spectral data is a new feature of this study, and the focus on prediction is the strength of this approach. The PC/FA method was found to be stable in detail to expansion of the training set. Coupling amide II to amide I' parameters reduced the standard deviations of the VCD regression relationships, and combining VCD and ECD data led to the best fits. Prediction results had a minimum error when dependent on relatively few spectral coefficients. Such a limited dependence on spectral variation is the key finding of this work, which has ramifications for previous studies as well as suggests future directions for spectral analysis of structure. The best ECD prediction for helix and sheet uses only one parameter, the coefficient of the first subspectrum. With VCD, the best predictions sample coefficients of both the amide I' and II bands, but error is optimized using only a few coefficients. In this respect, ECD is more accurate than VCD for alpha-helix, and the combined VCD (amide I' + II) predicts the beta-sheet component better than does ECD. Combining VCD and ECD data sets yields exceptionally good predictions by utilizing the strengths of each. However, the residual error, its distribution, and, most importantly, the lack of dependence of the method on many of the significant components derived from the spectra leads to the conclusion that the heterogeneity of protein structure is a fundamental limitation to the use of such spectral analysis methods. The underutilization of these data for prediction of secondary structure suggests spectral data could predict a more detailed descriptor.

Circular Dichroism↗

PubMed2003

Real value prediction of solvent accessibility from amino acid sequence.

The solvent accessibility of amino acid residues has been predicted in the past by classifying them into exposure states with varying thresholds. This classification provides a wide range of values for the accessible surface area (ASA) within which a residue may fall. Thus far, no attempt has been made to predict real values of ASA from the sequence information without a priori classification into exposure states. Here, we present a new method with which to predict real value ASAs for residues, based on neighborhood information. Our real value prediction neural network could estimate the ASA for four different nonhomologous, nonredundant data sets of varying size, with 18.0-19.5% mean absolute error, defined as per residue absolute difference between the predicted and experimental values of relative ASA. Correlation between the predicted and experimental values ranged from 0.47 to 0.50. It was observed that the ASA of a residue could be predicted within a 23.7% mean absolute error, even when no information about its neighbors is included. Prediction of real values answers the issue of arbitrary choice of ASA state thresholds, and carries more information than category prediction. Prediction error for each residue type strongly correlates with the variability in its experimental ASA values.

Amino Acid Sequence↗

PubMed1997

Predicting the conformational class of short and medium size loops connecting regular secondary structures: application to comparative modelling.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using comparative procedures. Although a loop can sometimes be modelled from a homologue, very often it must be selected from outside the family. The loop prediction procedure, SLoop, attempts to identify the conformational class of the loop rather than to select a specific loop from a set of fragments extracted from known structures or generated ab initio. Templates are constructed for each of the 161 loop conformational classes that have been identified from the clustering of the structures of some 2024 loops of one to eight residues in length. A class template describes both sequence preferences and relative disposition of bounding secondary structures. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible. The procedure is tested on an unrelated non-redundant set of 1785 loops under stringent and lax evaluation schemes. Optimal sequence score cut-offs are identified such that the prediction rate is equal to the percentage of loops assigned to acceptable classes. Under the stringent evaluation, at the optimal sequence score cut-off, a conformation is predicted for 50% of loops of which 47% are correct, while under the lax evaluation a conformation is predicted for 63% of loops of which 54% are correct. Sequence score is shown to be a good indicator of the probability of a prediction being correct. Loop length also has a strong affect on prediction outcomes. Considering only loops of two to five residues in length, under the stringent evaluation 62% of loops are predicted with 52% of these predictions being correct while under the lax evaluation predictions are provided for 75% of loops of which 57% are correct.

Computer Simulation↗

PubMed2003

Secreted protein prediction system combining CJ-SPHMM, TMHMM, and PSORT.

To increase the coverage of secreted protein prediction, we describe a combination strategy. Instead of using a single method, we combine Hidden Markov Model (HMM)-based methods CJ-SPHMM and TMHMM with PSORT in secreted protein prediction. CJ-SPHMM is an HMM-based signal peptide prediction method, while TMHMM is an HMM-based transmembrane (TM) protein prediction algorithm. With CJ-SPHMM and TMHMM, proteins with predicted signal peptide and without predicted TM regions are taken as putative secreted proteins. This HMM-based approach predicts secreted protein with Ac (Accuracy) at 0.82 and Cc (Correlation coefficient) at 0.75, which are similar to PSORT with Ac at 0.82 and Cc at 0.76. When we further complement the HMM-based method, i.e., CJ-SPHMM + TMHMM with PSORT in secreted protein prediction, the Ac value is increased to 0.86 and the Cc value is increased to 0.81. Taking this combination strategy to search putative secreted proteins from the International Protein Index (IPI) maintained at the European Bioinformatics Institute (EBI), we constructed a putative human secretome with 5235 proteins. The prediction system described here can also be applied to predicting secreted proteins from other vertebrate proteomes.

Computational Biology↗

PubMed2006

Numbats and aardwolves--how low is low? A re-affirmation of the need for statistical rigour in evaluating regression predictions.

Many comparative physiological studies aim to determine if a particular species differs from a prediction based on a linear allometric regression for other species. However, the judgment as to whether the species in question conforms to this allometric relationship is often not based on any formal statistical analysis. An appropriate statistical method is to compare the new species' value with the 95% confidence limits for predicting an additional datum from the relationship for the other species. We examine the basal metabolic rate (BMR) of the termitivorous numbat (Myrmecobius fasciatus) and aardwolf (Proteles cristatus) to demonstrate the use of the 95% prediction limits to determine statistically if they have a lower-than-expected BMR compared to related species. The numbat's BMR was 83.6% of expected from mass, but fell inside the 95% prediction limits for a further datum; a BMR < 72.5% of predicted was required to fall below the one-tail 95% prediction limits. The aardwolf had a BMR that was only 74.2% of predicted from the allometric equation, but it also fell well within the 95% prediction limits; a BMR of only 41.8% of predicted was necessary to fall below the one-tail 95% prediction limits. We conclude that a formal statistical approach is essential, although it is difficult to demonstrate that a single species statistically differs from a regression relationship for other species.

Animals↗

PubMed2000

Reference values and prediction equations for FVC and FEV(1) in the Greek elderly.

Spirometry prediction equations obtained from middle-age adults, when extrapolated for the elderly, may lead to inaccurate interpretations. The purpose of this study was to determine prediction equations for forced vital capacity (FVC) and forced expiratory volume (FEV(1)) in the Greek elderly population. Spirometry prediction equations for normal FVC and FEV(1) have been derived from tests on 71 healthy persons (38 men, 33 women) aged older than 60 years (range, 65-85 years), nonsmokers, white race, urban population using techniques and equipment that meet American Thoracic Society recommendations. Regression analysis using age, height, and weight as independent variables was used to provide prediction equations and values for both sexes. The FVC age coefficient in this healthy group was about 47.19 mL/y for elderly men and 34.27 mL/y for elderly women, and the FEV(1) age coefficient was about 52.8 mL/y for elderly men and 46.4 mL/y for elderly women. Values from this study predicted equations were compared with those from some of the most commonly used sources of spirometry predicted equations. The FVC and FEV(1) predicted values were found to be of less mean square error than that of other compared studies. Higher correlation is between FVC and FEV(1) predicted values by the present model and FVC and FEV(1) observed values in both sexes. The higher correlation between FVC and FEV(1) predicted and observed from this study allows the use of our model for predicting in a rather reliable way the FVC and FEV(1) for elderly Greek individuals.

Aged↗

PubMed2004

Encoding microbial metabolic logic: predicting biodegradation.

Prediction of microbial metabolism is important for annotating genome sequences and for understanding the fate of chemicals in the environment. A metabolic pathway prediction system (PPS) has been developed that is freely available on the world wide web (http://umbbd.ahc.umn.edu/predict/), recognizes the organic functional groups found in a compound, and predicts transformations based on metabolic rules. These rules are designed largely by examining reactions catalogued in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) and are generalized based on metabolic logic. The predictive accuracy of the PPS was tested: (1) using a 113-member set of compounds found in the database, (2) against a set of compounds whose metabolism was predicted by human experts, and (3) for consistency with experimental microbial growth studies. First, the system correctly predicted known metabolism for 111 of the 113 compounds containing C and H, O, N, S, P and/or halides that initiate existing pathways in the database, and also correctly predicted 410 of the 569 known pathway branches for these compounds. Second, computer predictions were compared to predictions by human experts for biodegradation of six compounds whose metabolism was not described in the literature. Third, the system predicted reactions liberating ammonia from three organonitrogen compounds, consistent with laboratory experiments showing that each compound served as the sole nitrogen source supporting microbial growth. The rule-based nature of the PPS makes it transparent, expandable, and adaptable.

Bacteria↗

PubMed1991

A prospective evaluation of elevated serum theophylline concentrations to determine if high concentrations are predictable.

PURPOSE: To evaluate prospectively whether serum theophylline concentrations of 25 mg/L and greater were predictable (and presumably preventable) by use of basic pharmacokinetic calculations. DESIGN: Prospective study. PATIENTS: Fifty-five patients with a serum theophylline concentration of at least 25.0 mg/L were evaluated initially and if subsequent elevated theophylline concentrations occurred. INTERVENTIONS: The predicted steady-state serum theophylline concentration was calculated from the dosage rate divided by the predicted clearance to determine how many elevated concentrations (greater than 20 mg/L) were predictable. Predicted clearances were 0.04 L/kg/hour for normal subjects less than 70 years of age and 0.02 L/kg/hour for patients with congestive heart failure, chronic obstructive pulmonary disease, or liver disease. Estimated clearances were determined and compared with predicted clearances. If patients did not have steady-state concentrations, additional calculations were made. MAIN RESULTS: From 6,368 consecutive theophylline determinations, 69 (1.08%) samples from 55 patients were 25 mg/L or higher. Predictably high concentrations occurred in 23 of 33 (69.7%) fully evaluable cases. These concentrations occurred because of a failure to consider decreased elimination clearance from congestive heart failure, chronic obstructive pulmonary disease, or hepatic disease. Five fatalities occurred, and in two cases, theophylline appeared to contribute to the patient's death. Three other patients experienced syncope. The predicted elimination clearance of theophylline of 0.02 L/kg/hour was too high in eight patients over 70 years old with cardiac or pulmonary disease. Nursing and pharmacy oversights were identified as three patients were given two theophylline products simultaneously. CONCLUSIONS: Most elevated theophylline concentrations are predictable (and preventable) by basic pharmacokinetic calculations. Patients experiencing elevated theophylline concentrations often had comorbid conditions and were greater than 60 years of age. The dosage rate of theophylline (mg/hour) can be estimated from predicted clearance (L/kg/hour) times desired steady-state serum concentration (mg/L).

Adult↗