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PubMed2006

Improving prediction of ischemic cardiovascular disease in the general population using apolipoprotein B: the Copenhagen City Heart Study.

BACKGROUND: Apolipoprotein B (apoB) levels predict fatal myocardial infarction. Whether apoB also predicts nonfatal ischemic cardiovascular events is unclear. We tested the following hypotheses: apoB predicts ischemic cardiovascular events, and apoB is a better predictor of ischemic cardiovascular events than low-density lipoprotein cholesterol. METHODS AND RESULTS: We studied 9231 asymptomatic women and men from the Danish general population followed prospectively for 8 years and observed the following incident events: ischemic heart disease 591, myocardial infarction 278, ischemic cerebrovascular disease 313, ischemic stroke 229, and any ischemic cardiovascular event 807. Women with apoB in the upper versus the lower tertile had hazard ratios for ischemic heart disease of 1.8 (1.2 to 2.5), for myocardial infarction 2.6 (1.4 to 4.7), and for any ischemic cardiovascular event 1.8 (1.3 to 2.3), and men had hazard ratios for ischemic heart disease of 1.9 (1.5 to 2.6), for myocardial infarction 2.4 (1.5 to 3.6), and for any ischemic cardiovascular event 1.6 (1.3 to 2.1). Women had similar hazard ratios for ischemic cerebrovascular disease and ischemic stroke. ApoB had a higher predictive ability than low-density lipoprotein cholesterol in the prediction of ischemic heart disease, myocardial infarction, any ischemic cardiovascular event, and any nonfatal ischemic cardiovascular event in both genders (P=0.03 to <0.001). Finally, in smokers older than 60 years with systolic blood pressure >160 mm Hg, apoB contributed 11% in women and 15% in men to the increase in absolute 10-year risk from the lower to the upper apoB tertile. CONCLUSION: ApoB predicts ischemic cardiovascular events in both genders, and is better than low-density lipoprotein cholesterol in this respect. We suggest that prediction of future ischemic cardiovascular events could be improved by measuring apoB.

Adult↗
PubMed2004

Improved prediction of fatal myocardial infarction using the ankle brachial index in addition to conventional risk factors: the Edinburgh Artery Study.

BACKGROUND: Prediction of major cardiovascular and cerebrovascular events using conventional risk factor models is limited. Noninvasive measures of subclinical atherosclerosis such as the ankle brachial index (ABI) could improve risk prediction and provide more focused primary prevention strategies. We wished to determine the added value of a low ABI in the prediction of long-term risk of cardiovascular and cerebrovascular events and death. METHODS AND RESULTS: In 1988, 1592 men and women 55 to 74 years of age were randomly selected from the age-sex registers of 11 general practices in Edinburgh, Scotland, and followed up over a period of 12 years for incident events. After adjustment for age and sex, an ABI < or =0.9 was predictive of an increased risk of fatal myocardial infarction (MI), cardiovascular death, all-cause death, combined fatal and nonfatal MI, and total cardiovascular events. After further adjustment for prevalent cardiovascular disease, diabetes, and conventional risk factors, a low ABI was independently predictive of the risk of fatal MI. Addition of the ABI significantly (P< or =0.01) increased the predictive value of the model for fatal MI compared with a model containing risk factors alone. Comparison of areas under receiver operator characteristic curves confirmed that a model including the ABI discriminated marginally better than one without. CONCLUSIONS: Addition of the ABI significantly improved prediction of fatal MI over and above that of conventional risk factors. We recommend that the ABI be incorporated into routine cardiovascular screening and that the potential of its inclusion into cardiovascular scoring systems (with a view to improving their accuracy) now be examined.

Aged↗
PubMed1997

Electrocardiographic prediction of hyperinflation in children.

The mean frontal P wave axis in an electrocardiogram (ECG), which reflects the atrial orientation in the thorax, is altered by the relationship between atria and the diaphragm and, therefore, by hyperinflation. To examine this relationship, 102 children (ages 6-18) with asthma were prospectively studied. Lung volumes were estimated by plethysmography and a standard ECG obtained before and after bronchodilator. The mean thoracic gas volume (TGV) was 120.7 +/- 2.1% of predicted and the mean P axis was 54.9 +/- 1.5 degrees. Sixty-two subjects (61%) had a "vertical" P axis (> or = 60 degrees). Of 27 subjects with moderate or severe hyperinflation (TGV > or = 130% predicted), 23 (85%) had a vertical P axis. As a measure of significant hyperinflation, a vertical P axis had a sensitivity of 85%, specificity of 49%, positive predictive value of 38% and a negative predictive value of 90%. After nebulized albuterol, the mean TGV decreased to 96.4 +/- 1.3% predicted and the mean P axis decreased by 7.1 +/-1.6 degrees. Sixty-two of 76 subjects (82%) with > or = 15% decrease in TGV also had a decrease in P axis, and 62/67 subjects (93%) with a decrease in P axis also had > or = 15% decrease in TGV. The sensitivity was 82%, specificity 81%, and positive predictive value 93% for a decrease in P axis as a measure of decrease in TGV. A vertical P axis combined with a decrease in P axis after bronchodilator is highly sensitive and predictive for hyperinflation in children.

Adolescent↗
PubMed1990

Single-breath carbon monoxide diffusing capacity prediction equations from a Mediterranean population.

Because of unanswered questions about prediction equations for the single-breath carbon monoxide diffusing capacity (DLCO) and as part of a larger collaborative project, standardized DLCO measurements were carried out in a selected sample of 361 healthy nonsmoking volunteers (194 men and 167 women) living in the Barcelona metropolitan area (Spain). Except for the test FIO2 (0.18), the study essentially followed the American Thoracic Society (ATS) and European Community for Coal and Steel (ECCS) recommendations for standardizing the methodology of measuring DLCO. Prediction equations for ages 20 through 70 were calculated separately for both sexes. Simple linear equations using age, height, and body weight as independent variables predicted the DLCO indices (DLCO, VA, and DL/VA) as well as more complex equations. In addition, a complete analysis of the residuals (predicted measured values) showed that the assumptions of the multiple regression analysis (independence, homoscedasticity and Gaussian distribution of residuals) were fulfilled using simple linear equations. Correction for the instrumental and anatomic dead spaces decreased the DLCO an average of 4.7%. The standard error of estimates was lower than those reported from other series in the literature. The predicted values from this study were lower than those reported by some investigators and were in reasonable agreement with other studies. A portion but not all of the differences could be explained on the basis of recognized differences in testing methodology. The results of this study may be of value to clinical laboratories seeking predictive equations for DLCO most appropriate for their testing methodology and patient population, and may assist in the resolution of some controversies regarding differences among predictive equations for DLCO.

Adolescent↗
PubMedDate not supplied

The prediction of human skin responses by using the combined in vitro fluorescein leakage/Alamar Blue (resazurin) assay.

A range of cosmetics formulations with human patch-test data were supplied in a coded form, for the examination of the use of a combined in vitro permeability barrier assay and cell viability assay to generate, and then test, a prediction model for assessing potential human skin patch-test results. The target cells employed were of the Madin Darby canine kidney cell line, which establish tight junctions and adherens junctions able to restrict the permeability of sodium fluorescein across the barrier of the confluent cell layer. The prediction model for interpretation of the in vitro assay results included initial effects and the recovery profile over 72 hours. A set of the hand-wash, surfactant-based formulations were tested to generate the prediction model, and then six others were evaluated. The model system was then also evaluated with powder laundry detergents and hand moisturisers: their effects were predicted by the in vitro test system. The model was under-predictive for two of the ten hand-wash products. It was over-predictive for the moisturisers, (two out of six) and eight out of ten laundry powders. However, the in vivo human patch test data were variable, and 19 of the 26 predictions were correct or within 0.5 on the 0-4.0 scale used for the in vivo scores, i.e. within the same variable range reported for the repeat-test hand-wash in vivo data.

Animal Testing Alternatives↗
PubMed2001

Isolated demyelinating syndromes: comparison of CSF oligoclonal bands and different MR imaging criteria to predict conversion to CDMS.

AIM OF THE STUDY: To evaluate and compare the capacity of oligoclonal bands (OB) and three sets of MR imaging criteria to predict the conversion of clinically isolated syndromes (CIS) to clinically definite multiple sclerosis (CDMS). PATIENTS AND METHODS: One hundred and twelve patients with CIS were prospectively studied with MR imaging and determination of OB. Based on the clinical follow-up (conversion or not conversion to CDMS), we calculated the sensitivity, specificity accuracy, positive and negative predictive value of the OB, and MR imaging criteria proposed by Paty et al, Fazekas et al and Barkhof et al. RESULTS: CDMS developed in 26 (23.2%) patients after a mean follow-up of 31 months (range 12-62). OB were positive in 70 (62.5%) patients and were associated with a higher risk of developing CDMS. OB showed a sensitivity of 81%, specificity of 43%, accuracy of 52%, positive predictive value (PPV) of 30% and negative predictive value (NPV) of 88%. Paty and Fazekas criteria showed the same results with a sensitivity of 77%, specificity of 51%, accuracy of 57%, positive predictive value of 32% and negative predictive value of 88%. Barkhof criteria showed a sensitivity of 65%, specificity of 70%, accuracy of 69%, PPV of 40% and NPV of 87%. The greatest accuracy was achieved when patients with positive OB and three or four Barkhof's criteria were selected. CONCLUSIONS: We observed a high prevalence of OB in CIS. OB and MR imaging (Paty's and Fazekas' criteria) have high sensitivity. Barkhof's criteria have a higher specificity. Both OB and MR imaging criteria have a high negative predictive value.

Adolescent↗
PubMed2002

Prediction of MHC class I binding peptides, using SVMHC.

BACKGROUND: T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested. RESULTS: Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/. CONCLUSIONS: Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences.

Animals↗
PubMed2004

Information assessment on predicting protein-protein interactions.

BACKGROUND: Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. RESULTS: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. CONCLUSIONS: In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.

Artificial Intelligence↗
PubMed2005

Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines.

BACKGROUND: Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria. RESULTS: We have developed a system for predicting the subcellular localization of proteins for Gram-negative bacteria based on amino acid subalphabets and a combination of multiple support vector machines. The recall of the extracellular site and overall recall of our predictor reach 86.0% and 89.8%, respectively, in 5-fold cross-validation. To the best of our knowledge, these are the most accurate results for predicting subcellular localization in Gram-negative bacteria. CONCLUSION: Clustering 20 amino acids into a few groups by the proposed greedy algorithm provides a new way to extract features from protein sequences to cover more adjacent amino acids and hence reduce the dimensionality of the input vector of protein features. It was observed that a good amino acid grouping leads to an increase in prediction performance. Furthermore, a proper choice of a subset of complementary support vector machines constructed by different features of proteins maximizes the prediction accuracy.

Algorithms↗
PubMed2005

Evaluating eukaryotic secreted protein prediction.

BACKGROUND: Improvements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated. RESULTS: Prediction accuracies were evaluated using 372 unbiased, eukaryotic, SwissProt protein sequences. TargetP, SignalP 3.0 maximum S-score and SignalP 3.0 D-score were the most accurate single scores (90-91% accurate). The combination of a positive TargetP prediction, SignalP 2.0 maximum Y-score, and SignalP 3.0 maximum S-score increased accuracy by six percent. CONCLUSION: Single predictive scores could be highly accurate, but almost all accuracies were slightly less than those reported by program authors. Predictive accuracy could be substantially improved by combining scores from multiple methods into a single composite prediction.

Animals↗
PubMed2005

A method of precise mRNA/DNA homology-based gene structure prediction.

BACKGROUND: Accurate and automatic gene finding and structural prediction is a common problem in bioinformatics, and applications need to be capable of handling non-canonical splice sites, micro-exons and partial gene structure predictions that span across several genomic clones. RESULTS: We present a mRNA/DNA homology based gene structure prediction tool, GIGOgene. We use a new affine gap penalty splice-enhanced global alignment algorithm running in linear memory for a high quality annotation of splice sites. Our tool includes a novel algorithm to assemble partial gene structure predictions using interval graphs. GIGOgene exhibited a sensitivity of 99.08% and a specificity of 99.98% on the Genie learning set, and demonstrated a higher quality of gene structural prediction when compared to Sim4, est2genome, Spidey, Galahad and BLAT, including when genes contained micro-exons and non-canonical splice sites. GIGOgene showed an acceptable loss of prediction quality when confronted with a noisy Genie learning set simulating ESTs. CONCLUSION: GIGOgene shows a higher quality of gene structure prediction for mRNA/DNA spliced alignment when compared to other available tools.

Algorithms↗
PubMed2004

Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing.

BACKGROUND: Alternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts. METHODS: We predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types. RESULTS: Our strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms. CONCLUSIONS: We propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts.

Alternative Splicing↗
PubMed2004

Prediction of Saccharomyces cerevisiae replication origins.

BACKGROUND: Autonomously replicating sequences (ARSs) function as replication origins in Saccharomyces cerevisiae. ARSs contain the 17 bp ARS consensus sequence (ACS), which binds the origin recognition complex. The yeast genome contains more than 10,000 ACS matches, but there are only a few hundred origins, and little flanking sequence similarity has been found. Thus, identification of origins by sequence alone has not been possible. RESULTS: We developed an algorithm, Oriscan, to predict yeast origins using similarity to 26 characterized origins. Oriscan used 268 bp of sequence, including the T-rich ACS and a 3' A-rich region. The predictions identified the exact location of the ACS. A total of 84 of the top 100 Oriscan predictions, and 56% of the top 350, matched known ARSs or replication protein binding sites. The true accuracy was even higher because we tested 25 discrepancies, and 15 were in fact ARSs. Thus, 94% of the top 100 predictions and an estimated 70% of the top 350 were correct. We compared the predictions to corresponding sequences in related Saccharomyces species and found that the ACSs of experimentally supported predictions show significant conservation. CONCLUSIONS: The high accuracy of the predictions indicates that we have defined near-sufficient conditions for ARS activity, the A-rich region is a recognizable feature of ARS elements with a probable role in replication initiation, and nucleotide sequence is a reliable predictor of yeast origins. Oriscan detected most origins in the genome, demonstrating previously unrecognized generality in yeast replication origins and significant discriminatory power in the algorithm.

Algorithms↗
PubMed2000

Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

BACKGROUND: Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. AIMS: To review the current status of violence risk prediction research. METHOD: Literature search (Medline). Key words: violence, risk prediction, mental disorder. RESULTS: Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. CONCLUSIONS: Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

Clinical Protocols↗
PubMed2000

Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in prostate cancer.

PURPOSE: Several studies have defined risk groups for predicting the outcome after external-beam radiotherapy of localized prostate cancer. However, most models formed patient risk groups, and none of these models considers radiation dose as a predictor variable. The purpose of this study was to develop a nomogram to improve the accuracy of predicting outcome after three-dimensional conformal radiotherapy. MATERIALS AND METHODS: This study was a retrospective, nonrandomized analysis of patients treated at the Memorial Sloan-Kettering Cancer Center between 1988 and 1998. Clinical parameters of the 1,042 patients included stage, biopsy Gleason score, pretreatment serum prostate-specific antigen (PSA) level, whether neoadjuvant androgen deprivation therapy was administered, and the radiation dose delivered. Biochemical (PSA) treatment failure was scored when three consecutive rises of serum PSA occurred. A nomogram, which predicts the probability of remaining free from biochemical recurrence for 5 years, was validated internally on this data set using a bootstrapping method and externally using a cohort of patients treated at the Cleveland Clinic, Cleveland, OH. RESULTS: When predicting outcomes for patients in the validation data set from the Cleveland Clinic, the nomogram had a Somers' D rank correlation between predicted and observed failure times of 0.52. Predictions from this nomogram were more accurate (P<.0001) than the best of seven published risk stratification systems, which achieved a Somers' D coefficient of 0.47. CONCLUSION: The development process illustrated here produced a nomogram that seems to predict more accurately than other available systems and may be useful for treatment selection by both physicians and patients.

Aged↗
PubMed2001

When is a tumor marker ready for prime time? A case study of c-erbB-2 as a predictive factor in breast cancer.

PURPOSE: c-erbB-2 (HER-2, c-neu) might play a role as a predictive factor in breast cancer. However, the clinical utility of the marker in this disease is still not established. We conducted a critical analysis of the literature, in which we reviewed the factors that contribute to the lack of acceptance of c-erbB-2 for clinical use and attempted to determine the predictive role of c-erbB-2 for response to specific therapies. METHODS: We conducted a MEDLINE literature search using the keywords c-erbB-2, HER2, neu, and breast cancer, reviewed the references included in each publication, and reviewed abstracts that have been reported in the 1997-2000 proceedings to the American Association of Cancer Research and American Society for Clinical Oncology annual meetings. RESULTS: The preclinical and clinical data reported to date suggest that amplification or overexpression of c-erbB-2 is a weak to moderate negative pure prognostic factor. c-erbB-2 seems to be a weak to moderate negative predictive factor for response to endocrine therapy. The marker is also a moderate negative predictive factor for response to alkylating agents and a moderate positive predictive factor for response to anthracyclines. The data regarding response to taxanes or radiotherapy are not sufficient to make recommendations regarding treatment decision making. Finally, c-erbB-2 is a strong predictive factor for response to trastuzumab. CONCLUSION: We conclude that, in the adjuvant setting, c-erbB-2 status should not be used to determine whether a woman should receive adjuvant systemic therapy (weak prognostic factor). In addition, c-erbB-2 status should not be used to determine whether a patient should receive endocrine therapy. When adjuvant chemotherapy is recommended, anthracycline-based therapy should be the preferred regimen for c-erbB-2-positive patients. However, when anthracyclines are contraindicated, alkylating agent-based therapy should not be withheld. To determine the true predictive role and strength of the marker for response to each therapy, prospective randomized clinical trials or formal meta-analyses are required.

Antibiotics, Antineoplastic↗
PubMed1998

Sensitivity and specificity of asymmetric recall on WADA test to predict outcome after temporal lobectomy.

Some reports suggest that the intracarotid amobarbital test (IAT) is useful for predicting good seizure outcome after temporal lobectomy. The sensitivity, specificity, and predictive value of the IAT in this condition has not been previously studied. We designed this study to establish the value of memory recall asymmetry on the IAT as a predictor of outcome after temporal lobectomy. We studied memory recall on the IAT in 108 consecutive patients with intractable epilepsy who underwent presurgical evaluation for temporal lobectomy and had at least 1 year follow up after surgery. At a level of 30% asymmetry of recall, specificity for favorable outcome (Engel Class I and II) was 100% (95% confidence interval [CI], 85 to 100), sensitivity 51% (95% CI, 40 to 62), positive predictive value 100% (95% CI, 92 to 100), and negative predictive value 34% (95% CI, 23 to 47). At the same level of asymmetry, specificity for seizure-free outcome (Engel Class I) was 88% (95% CI, 68-95), sensitivity 37% (95% CI, 40 to 64), positive predictive value 87% (95% CI, 71 to 96), and negative predictive value 38% (95% CI, 27 to 50). Asymmetric recall on the IAT is highly specific but not very sensitive in predicting outcome after temporal lobectomy.

Adult↗
PubMed2004

The intravenous ketamine test: a predictive response tool for oral dextromethorphan treatment in neuropathic pain.

IV infusion tests performed to predict subsequent response to oral analgesics are an increasingly popular method used to enhance medical care and conserve resources. Because no infusion test is completely accurate, the potential benefits of these tests must be weighed against the frustration and waste in resources encountered with false-positive results, and the failure to use a potentially beneficial treatment with false-negative results. In recent years, drugs that act antagonistically at N-methyl-d-aspartate receptors have been shown to be valuable adjuncts in the treatment of pain. To determine the predictive value of small-dose (0.1 mg/kg) IV ketamine on an oral dextromethorphan (DX) treatment regimen, we analyzed the analgesic response to these drugs in 25 patients at 2 tertiary care military treatment facilities, institutions at which DX is not readily accessible. When >/=50% response for both drugs was used as the outcome measure for success, the positive predictive value of the ketamine test was 64%, the negative predictive value 73%, and the observed agreement 68%. However, when >/=67% relief with ketamine was used as an outcome measure (as determined by a receiver operating characteristic curve), the positive predictive value was 90%, the negative predictive value 80%, and the observed agreement increased to 84%. Based on these results, we conclude that an IV ketamine test may be useful in predicting response to oral DX. More research is needed to determine the ideal candidates for such a test, and the optimal dose and cutoff value for the response to ketamine.

Administration, Oral↗