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PubMed1993

Protein secondary structure prediction using nearest-neighbor methods.

We have studied the use of nearest-neighbor classifiers to predict the secondary structure of proteins. The nearest-neighbor rule states that a test instance is classified according to the classifications of "nearby" training examples from a database of known structures. In the context of secondary structure prediction, the test instances are windows of n consecutive residues, and the label is the secondary structure type (alpha-helix, beta-strand, or coil) of the center position of the window. To define the neighborhood of a test instance, we employed a novel similarity metric based on the local structural environment scoring scheme of Bowie et al. In this manner, we have attempted to exploit the underlying structural similarity between segments of different proteins to aid in the prediction of secondary structure. Furthermore, in addition to using neighborhoods of fixed radius, we explored a modification of the standard nearest-neighbor algorithm that involved defining an "effective radius" for each exemplar by measuring its performance on a training set. Using these ideas, we achieved a peak prediction accuracy of 68%. Finally, we sought to improve the biological utility of secondary structure prediction by identifying the subset of the predictions that are most likely to be correct. Toward this end, we developed a nearest-neighbor estimator that produced not the traditional "one-state" prediction (alpha-helix, beta-strand, or coil) but rather a probability distribution over the three states. It should be emphasized that this scheme estimates true probability values and that the resulting numbers are not pseudo-probability scores generated by simple normalization of the raw output of the predictor. Applying the mutual information statistic, we found that these probability triplets possess 58% more information than the one-state predictions. Furthermore, the probability estimates allow one to assign an a priori confidence level to the prediction at each residue. Using this approach, we found that the top 28% of the predictions were 86% accurate and the top 43% of the predictions were 81% accurate. These results indicate that, notwithstanding the limitations on overall accuracy of secondary structure prediction, a substantial proportion of a protein can be predicted with considerable accuracy.

Algorithms↗

PubMed1991

Predictions of epidemiology and the evaluation of cancer control measures and the setting of policy priorities.

Cancer incidence predictions may be constructed for administrative and scientific purposes. For administrative purposes it is often important that the predictions come true. The resources planned on the basis of the predictions and allocated on the diagnostics, treatment and rehabilitation can then be optimally utilized. However, predictions that do not materialize can also be useful. The effects of intervention or early detection programmes express themselves as failures of predictions that have been made in the absence of such programmes. Predictions of cancer incidence in Finland are used as examples. The prerequisite for the predictions is a well-functioning population-based cancer registry. The predictions were constructed using time trends and differentials in cancer incidence with or without the aetiological or other risk factors. Short-term, 10-15 year predictions with no explicit use of risk factors, have proven successful with most cancers, e.g. those of the colon, rectum, pancreas and urinary organs, and lymphomas. The marked prediction failures have occurred for cancers of the lung and breast. Predictions for these cancers have been improved by taking aetiological or other risk factors explicitly into account. The cancer consequences of the preventive cardiovascular programme in North Karelia have been evaluated using predictions. The effectiveness of screening for cervical cancer at population level was predicted on the basis of estimated parameters of the natural history of the disease.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

PubMed2004

First principles predictions of the structure and function of g-protein-coupled receptors: validation for bovine rhodopsin.

G-protein-coupled receptors (GPCRs) are involved in cell communication processes and with mediating such senses as vision, smell, taste, and pain. They constitute a prominent superfamily of drug targets, but an atomic-level structure is available for only one GPCR, bovine rhodopsin, making it difficult to use structure-based methods to design receptor-specific drugs. We have developed the MembStruk first principles computational method for predicting the three-dimensional structure of GPCRs. In this article we validate the MembStruk procedure by comparing its predictions with the high-resolution crystal structure of bovine rhodopsin. The crystal structure of bovine rhodopsin has the second extracellular (EC-II) loop closed over the transmembrane regions by making a disulfide linkage between Cys-110 and Cys-187, but we speculate that opening this loop may play a role in the activation process of the receptor through the cysteine linkage with helix 3. Consequently we predicted two structures for bovine rhodopsin from the primary sequence (with no input from the crystal structure)-one with the EC-II loop closed as in the crystal structure, and the other with the EC-II loop open. The MembStruk-predicted structure of bovine rhodopsin with the closed EC-II loop deviates from the crystal by 2.84 A coordinate root mean-square (CRMS) in the transmembrane region main-chain atoms. The predicted three-dimensional structures for other GPCRs can be validated only by predicting binding sites and energies for various ligands. For such predictions we developed the HierDock first principles computational method. We validate HierDock by predicting the binding site of 11-cis-retinal in the crystal structure of bovine rhodopsin. Scanning the whole protein without using any prior knowledge of the binding site, we find that the best scoring conformation in rhodopsin is 1.1 A CRMS from the crystal structure for the ligand atoms. This predicted conformation has the carbonyl O only 2.82 A from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 0.62 A CRMS from the crystal structure. We also used HierDock to predict the binding site of 11-cis-retinal in the MembStruk-predicted structure of bovine rhodopsin (closed loop). Scanning the whole protein structure leads to a structure in which the carbonyl O is only 2.85 A from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 2.92 A CRMS from the crystal structure. The good agreement of the ab initio-predicted protein structures and ligand binding site with experiment validates the use of the MembStruk and HierDock first principles' methods. Since these methods are generic and applicable to any GPCR, they should be useful in predicting the structures of other GPCRs and the binding site of ligands to these proteins.

Algorithms↗

PubMed2001

Uncertainties in model-based outcome predictions for treatment planning.

PURPOSE: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. METHODS AND MATERIALS: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ("noise") is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. RESULTS: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment plans can be ranked based on the probability that one plan is superior to another. Thus, reliability of plan ranking could also be assessed. CONCLUSIONS: A comprehensive framework for incorporating uncertainties into treatment-plan-specific outcome predictions is described. Uncertainty histograms for continuous variable endpoint models provide a straightforward method for visual review of the reliability of outcome predictions for each treatment plan.

Humans↗

PubMed1996

Cerebral control of eye movements. II. Timing of anticipatory eye movements, predictive pursuit and phase errors in focal cerebral lesions.

Smooth pursuit eye movements are known to be driven by a mixture of visual feedback and predictive strategies. Prediction in pursuit allows humans to track predictable stimuli with minimal phase lag. But in certain disease states and focal neurological lesions, normal phase relationships are lost and humans track with increased phase errors. Using a sinusoidal pursuit paradigm, we sorted patients into those with large phase errors and those without. Then, working on the premise that large phase errors may have resulted from lack of prediction, we compared predictive and non-predictive ocular pursuit in patients with large phase errors, patients with normal phase errors and control subjects. Subjects sat in darkness and pursued an intermittently illuminated target moving with constant velocity to the right or left. When the movements were in alternate directions and predictable, all the groups possessed the ability to preprogramme appropriate anticipatory eye movements before target onset, and to use this for predictive pursuit. The difference between patients with large phase errors and normal subjects was not an absolute lack or possession of predictive ability but a difference in the timing at which a preprogrammed motor behaviour was initiated or terminated. The timing variability was wide and formed a graded continuum, the control subjects initiating anticipatory pursuit earlier, and the patients with large phase errors initiating much later. In a second experiment, subjects pursued a predictable ramp stimulus presented at various fixed frequencies. We found that in patients where anticipatory pursuit seemed abolished at one frequency of target presentation, changing the frequency of presentation elicited an anticipatory response. Patients adjusted their pursuit latencies to match the temporal demands of target presentation. At target frequencies above 0.8 Hz, there was a significant positive correlation between latencies in ramp pursuit and phase lags in sinusoidal pursuit. None of our patients showed complete loss of prediction irrespective of how large the phase errors were. Even when severe time delays in the system made it impossible for a subject to initiate anticipatory pursuit before target onset, prediction could still be demonstrated by the significant velocity and timing advantage the subject had in the pursuit of a predictable target stimulus or by the technique of unexpectedly blanking the target.

Adolescent↗

PubMed1996

Use of predicted risk of mortality to evaluate the efficacy of anticytokine therapy in sepsis. The rhIL-1ra Phase III Sepsis Syndrome Study Group.

OBJECTIVES: To investigate a novel anticytokine therapy in patients with sepsis syndrome, and the relationship between a patient's baseline mortality risk and survival benefit. DESIGN: Data from a recent phase III, double-blind, placebo-controlled, multicenter clinical trial with patients randomized to three treatment arms: an intravenous loading dose of recombinant human interleukin-1-receptor antagonist (rhIL-1ra) or placebo, followed by a continuous infusion of rhIL-1ra (1.0 mg/kg/hr, or 2.0 mg/kg/hr), or placebo for 72 hrs. SETTING: Sixty-three investigative centers in eight countries. PATIENTS: The study population consisted of 893 patients: 302 placebo patients; 298 patients treated with 1.0 mg/kg/hr of rhIL-1ra; and 293 patients treated with 2.0 mg/kg/hr of rhIL-1ra. MEASUREMENTS AND MAIN RESULTS: An independent, sepsis-specific, log-normal regression model that predicts the risk of mortality over 28 days was applied to all patients enrolled into the rhIL-1ra sepsis study. The ability of the Predicted Risk of Mortality model to predict 28-day mortality in the placebo patients was determined and the relationship between mortality risk and efficacy of rhIL-1ra was investigated. The trial data were also analyzed using two other risk-assessment models for comparison with Predicted Risk of Mortality. A significant increase in survival time was demonstrated for all patients treated with rhIL-1ra (n = 893, p < .02 Predicted Risk of Mortality log-normal), but patients with a Predicted Risk of Mortality of < 24% derived little benefit. Retrospective examination of time-to-death data demonstrated that rhIL-1ra reduced risk of death in the first 2 days for patients with > or = 24% Predicted Risk of Mortality (n = 580, p < .005 Predicted Risk of Mortality log-normal). This same effect was not present in patients with a Predicted Risk of Mortality of < 24% on entry into the study. The Predicted Risk of Mortality model predicted a 28-day mortality rate of 35% for placebo patients compared with 34% observed and accurately stratified patients along the full range of risks. There was a wide distribution of individual patient risks for 28-day mortality for all patients, as well as within categorical subgroups, such as shock and organ system dysfunction. Two alternate risk models were assessed and the Acute Physiology Score of Acute Physiology and Chronic Health Evaluation III also demonstrated a statistically significant survival benefit for rhIL-1ra (p = .04 Predicted Risk of Mortality log-normal) for all patients treated. CONCLUSIONS: Using an appropriate analytic model, a statistically significant increase in survival time from rhIL-1ra was measured. A direct relationship was found between a patient's Predicted Risk of Mortality at study entry to efficacy of rhIL-1ra. Individual risk or severity assessment may be a useful tool for evaluating the clinical benefit of new therapeutic approaches to sepsis and for monitoring outcomes at the bedside.

APACHE↗

PubMed2004

Test of CAP88-PC's predicted concentrations of tritium in air at Lawrence Livermore National Laboratory.

Based on annual tritium release rates from the five sources of tritium at Lawrence Livermore National Laboratory and the Tritium Research Laboratory at Sandia National Laboratory, the regulatory dispersion and dose model, CAP88-PC, was used to predict tritium concentrations in air at perimeter and offsite air surveillance monitoring locations for 1986 through 2001. These predictions were compared with mean annual measured concentrations, based on biweekly sampling. Deterministic predictions were compared with deterministic observations using predicted-to-observed ratios. In addition, the uncertainty on observations and predictions was assessed: when the uncertainty bounds of the observations overlapped with the uncertainty bounds of the predictions, the predictions were assumed to agree with the observations with high probability. Deterministically, 54% of all predictions were higher than the observations, and 96% fell within a factor of three. Accounting for uncertainty, 75% of all predictions agreed with the observations; 87% of the predictions either matched or exceeded the observations. Predictions equaled or exceeded observations at those sampling locations towards which the wind blows most frequently, except those in the hills. Under-predictions were seen at locations towards which the wind blows infrequently when released tritium was from elevated sources. When a high fraction of tritium was from area (diffuse) sources, predictions matched observations.

Air Pollutants, Radioactive↗

PubMed2002

Can pre-operative computed tomography predict resectability of ovarian carcinoma at primary laparotomy?

OBJECTIVE: To assess the ability of computed tomography in predicting whether suspected ovarian cancer could be fully excised at primary laparotomy. DESIGN: Retrospective analysis of patient notes and pre-operative computed tomography scans. Setting A UK NHS cancer centre. POPULATION: Seventy-seven women who underwent laparotomy for an ovarian tumour and who had had a pre-operative computed tomography scan. METHODS: Women who had a computed tomography scan before laparotomy for an ovarian tumour were identified. Analysis was undertaken to determine the accuracy of computed tomography in predicting malignancy, stage and residual disease. The computed tomography parameters significantly associated with residual disease were determined by a chi2 analysis. These parameters, in addition to age and CA125, were used to generate a predictive model. This model was further refined by stepwise logistic regression and a clinical scoring index was generated. MAIN OUTCOME MEASURES: To identify those computed tomography parameters significantly associated with residual disease and to use these with CA125 and age to generate a useful clinical scoring index to predict residual disease in suspected ovarian cancer. RESULTS: Seventy-seven women underwent a laparotomy for an ovarian tumour and had a pre-operative computed tomography scan. Fifty-one of these women had malignant disease and twenty-five of these women had residual disease remaining. The sensitivity of computed tomography in predicting malignancy was 90% with a specificity of 85% and the overall accuracy of computed tomography for predicting stage of disease was 73% (37/51). The overall sensitivity of computed tomography in predicting residual disease was 88%, the specificity was 92% and the positive predictive value was 85%. The parameters on computed tomography that were significantly (P < 0.05) associated with residual disease were ascites, omental cake, mesenteric disease, paracolic gutter deposits, diaphragmatic deposits and pleural effusion. The predictive model generated was more accurate than computed tomography alone (sensitivity 88%, specificity 98%, positive predictive value 95%). Using stepwise logistic regression enabled the predictive model to be simplified to include mesenteric disease, omental cake, age and CA125 without any change in sensitivity or specificity and this model was used to generate a scoring index. CONCLUSION: This study shows that prediction of resectability by computed tomography is excellent and is further improved by the generation of a predictive model, which can be used to generate a simple scoring index. This scoring system now needs to be tested prospectively to ensure that its performance remains as good in an independent sample population.

Adult↗

PubMed1995

Lung scanning and exercise testing for the prediction of postoperative performance in lung resection candidates at increased risk for complications.

OBJECTIVE: To analyze the value of preoperative lung scanning and exercise testing for the prediction of postoperative complications and of the short- as well as long-term performance in lung resection candidates at increased risk for complications. DESIGN: Prospective clinical trial. SETTING: Clinical pulmonary function laboratory in a university teaching hospital. PATIENTS: Twenty-five (mean age, 63 years; 17 men) of 84 consecutive lung resection candidates were considered at increased risk for postoperative complications due to impaired pulmonary function (FEV1 < 2 L or diffusion of carbon monoxide [DCO] < 50% predicted, or FEV1 and DCO < or = 80% predicted combined with New York Heart Association dyspnea index > or = 2). INTERVENTIONS: Candidates underwent radionuclide ventilation/perfusion scans and exercise testing to predict postoperative (= ppo) values for FEV1, DCO, and maximal O2 uptake (VO2max). They all underwent thoracotomy for neoplastic lesions; 7 had pneumonectomies, 18 lobectomies. Six patients had postoperative complications (within 30 days), of whom three died. Three and 6 months postoperatively, pulmonary function tests and VO2max were repeated. MEASUREMENTS AND RESULTS: In the 22 survivors, the observed values were then compared with the predicted values. At 3 months, there were excellent correlations (absolute/predicted values): for FEV1 r = 0.78 and 0.81; for DCO, r = 0.77 and 0.74; and for VO2max, r = 0.71 and 0.83. The means of FEV1 and VO2max did not differ from the predicted values, whereas the predicted DCO was lower than the observed value (mL/min/mm Hg: 15.1 vs 17.9; percent predicted: 59.6 vs 70.9) (p < 0.05). At 6 months, correlations remained very good for FEV1 (r = 0.81 and 0.84) and for DCO (r = 0.76 and 0.74), but had decreased for VO2max to 0.56 and 0.65, respectively. All means were higher than predicted (p < 0.05) owing to recovery in the lobectomy group. Patients with postoperative complications (group B) had a lower preoperative VO2max in percent predicted (62.8 +/- 7.5% vs 84.6 +/- 19.7%) (p < 0.01) and also a lower VO2max-ppo (10.6 +/- 3.6 vs 14.8 +/- 3.5 mL/kg/min and 44.3 +/- 13.5 vs 68.0 +/- 20.7% predicted) (p < 0.05) than patients without complications (group A). A VO2max-ppo < 10 mL/kg/min was associated with a 100% mortality. Although FEV1-ppo and DCO-ppo were lower in group B, the difference did not reach significance. CONCLUSIONS: Radionuclide-based calculations of postoperative VO2max are predictive of operative morbidity and mortality: a VO2max-ppo of < 10 mL/kg/min may indicate inoperability. Further, short-term postoperative performance is accurately predicted by FEV1-ppo and VO2max-ppo, but long-term function is underestimated after lobectomy.

Aged↗

PubMed1978

Our surprising inability to predict the outcomes of psychological treatments--with special reference to treatments for drug abuse.

Our ability to predict the outcome of psychological treatments, particularly for drug dependence, is examined by (1) new data on a VA sample, (2) a review of studies predicting the outcome of drug abuse treatment, and (3) a review of predicting the outcome of psychotherapy for other types of patients. For (1), the direct predictions of 13 staff members' ratings of the outcome of treatment correlated .27 with the outcome. Although staff predictions improved when grosser predictions were examined, the results are disappointing given the level of discrimination required and the modest levels of prediction attained. For (2), prediction success with drug abuse patients in other studies were also disappointing and the bases for predictions often did not hold up on crossvalidation. For (3), direct predictions in the Penn Psychotherapy Study were similar to the VA sample in level of success. Other studies produced insignificant or similarly low levels of prediction success. In our discussion we suggest that rather than relying mainly upon pretreatment status for prediction, two promising areas should be examined in future studies: (1) the patient's early response to the treatment and to the therapist, (2) the patient's environment posttreatment that should be altered to consolidate and perpetuate the gains made during treatment. In conclusion, it is true that we are surprisingly unable to predict the outcomes of psychological treatments beyond a very modest level.

Adult↗

PubMed2002

Transmembrane topology prediction methods: a re-assessment and improvement by a consensus method using a dataset of experimentally-characterized transmembrane topologies.

We selected 10 transmembrane (TM) prediction methods (KKD, TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0 and HMMTOP 2.0) and re-assessed its prediction performance using a reliable dataset with 122 entries of experimentally-characterized TM topologies. Then, we improved prediction performance by a consensus prediction method. Prediction performance during re-assessment and consensus prediction were based on four attributes: (i) the number of transmembrane segments (TMSs), (ii) the number of TMSs plus TMS-position, (iii) N-tail location and (iv) TM topology. We noted that hidden Markov model-based methods dominate over other methods by individual prediction performance for all four attributes. In addition, all top-performing methods generally were model-based. Among prokaryotic sequences, HMMTOP 2.0 solely topped among other methods with prediction accuracies ranging from 64% to 86% across all attributes. However, among eukaryotic sequences, prediction performance for all the attributes was relatively poor compared with prokaryotic ones. On the other hand, our results showed that our proposed consensus prediction method significantly improved prediction performance by, at least, an additional nine percentage points particularly among prokaryotic sequences for the number of TMS (84%), number of TMS and position (80%), and TM topology attributes (74%). Although our consensus prediction method improved also the prediction performance among eukaryotic sequences, the obtained accuracies for all attributes were relatively lower than that obtained by prokaryotic counterparts particularly for TM topology.

Cell Membrane↗

PubMed1989

Clinical experience and predicting survival in coronary disease.

To study the accuracy with which long-term prognosis can be predicted in patients with coronary artery disease, prognostic predictions obtained from a large, diverse sample of practicing cardiologists were compared with predictions from a multivariable statistical model. Test samples of 10 patients each were selected from a large series of medically treated patients with significant coronary disease. Using detailed clinical summaries, 49 cardiologists each predicted the probability of 3-year survival and infarction-free survival for 10 patients. Cox regression models, developed using patients who were not in the test samples, were also used to predict corresponding outcome probabilities for each test patient. Overall, the model estimates of prognosis were significantly better than the doctors' predictions. The rank correlation of model predictions with 3-year survival was 0.60, compared with 0.52 for the physicians. Model predictions added significant prognostic information to the doctors' predictions, whereas the converse was not true. Where predictions were made by multiple doctors, the inter-physician variability was substantial. Neither practice characteristics nor extent of clinical experience significantly affected the physicians' predictive accuracy. In coronary artery disease, statistical models developed from carefully collected data can provide prognostic predictions that are more accurate than predictions of experienced clinicians made from detailed case summaries.

Coronary Disease↗

PubMed1997

The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data.

We describe a comprehensive retrospective analysis in which the abilities of several methods by which human pharmacokinetic parameters are predicted from preclinical pharmacokinetic data and/or in vitro metabolism data were assessed. The prediction methods examined included both methods from the scientific literature as well as some described in this report for the first time. Four methods were examined for their ability to predict human volume of distribution. Three were highly predictive, yielding, on average, predictions that were within 60% to 90% of actual values. Twelve methods were assessed for their utility in predicting clearance. The most successful allometric scaling method yielded clearance predictions that were, on average, within 80% of actual values. The best methods in which in vitro metabolism data from human liver microsomes were scaled to in vivo clearance values yielded predicted clearance values that were, on average, within 70% to 80% of actual values. Human t1/2 was predicted by combining predictions of human volume of distribution and clearance. The best t1/2 prediction methods successfully assigned compounds to appropriate dosing regimen categories (e.g., once daily, twice daily and so forth) 70% to 80% of the time. In addition, correlations between human t1/2 and t1/2 values from preclinical species were also generally successful (72-87%) when used to predict human dosing regimens. In summary, this retrospective analysis has identified several approaches by which human pharmacokinetic data can be predicted from preclinical data. Such approaches should find utility in the drug discovery and development processes in the identification and selection of compounds that will possess appropriate pharmacokinetic characteristics in humans for progression to clinical trials.

Animals↗

PubMedDate not supplied

Predictive value of amniotic fluid index for oligohydramnios in patients with prolonged pregnancies.

The objective of this study was to evaluate the predictive values of the amniotic fluid index for measures of perinatal morbidity and for clinical observations consistent with oligohydramnios. We evaluated positive and negative predictive value of the amniotic fluid index for measures of perinatal morbidity and for clinical observations consistent with oligohydramnios at various cutoff values for amniotic fluid index in a cohort of 449 consecutive postdates patients who had a clinician's observation of amniotic fluid quantity and quality recorded at the time of rupture of membranes. Newborn morbidity was a rare event. Clinical observations consistent with oligohydramnios had significant positive and negative predictive values for some measures of newborn morbidity. The last amniotic fluid index performed during antepartum testing had 95% confidence intervals for relative risks for these measures of newborn morbidity that included unity and therefore were not significant. At a cutoff value of 5.0 cm, the positive predictive value of the amniotic fluid index for clinical observations consistent with oligohydramnios was 50%; the negative predictive value was 85%, with a prevalence of clinical observations consistent with oligohydramnios of 19%. The presence of fetal heart rate decelerations did not significantly improve the positive predictive value of the amniotic fluid index. Higher positive predictive values were obtained at cutoff values of 4 cm and 3 cm with minimal loss in negative predictive value. The amniotic fluid index did not possess significant predictive value for measures of newborn morbidity. Clinical observations consistent with oligohydramnios at the time of rupture of membranes did have predictive value for some of these measures and thus probably are a reflection of the actual amount of fluid present inside the uterus prior to rupture of membranes. The amniotic fluid index is only a fair predictor of clinical observations consistent with oligohydramnios. Thus, a positive test correctly predicted these observations 50% of the time, with 50% false-positive results. Undertaking delivery in the 50% of patients without clinical observations consistent with oligohydramnios may lead to a higher cesarean section rate since these patients do not require induction and are subject to the risk of a failed induction of labor. A negative test correctly predicted observations consistent with normal fluid 85% of the time, with a false-negative rate of 15%. Thus, a negative test was no guarantee that observations consistent with oligohydramnios, and thus newborn morbidity, would not subsequently appear. Frequent testing with multiple modalities and induction of labor when the Bishop score is favorable remain sensible options. Induction of labor in postdates patients with a low amniotic fluid index needs to be evaluated in a yet-to-be-performed prospective randomized control trial before a low amniotic fluid index is assumed to be the sole indicator for induction of labor. More stringent cutoff values for amniotic fluid index may be justified.

Amniocentesis↗

PubMed2000

A model for predicting surgical outcome in patients with advanced ovarian carcinoma using computed tomography.

BACKGROUND: A reliable model for predicting the outcome of primary cytoreductive surgery may be a useful tool in the clinical management of patients with advanced epithelial ovarian carcinoma. METHODS: Forty-one women with a preoperative computed tomographic (CT) scan of the abdomen and pelvis and a histologic diagnosis of Stage III or IV epithelial ovarian carcinoma following primary surgery performed by one of nine gynecologic oncologists were identified from tumor registry databases. All CT scans were analyzed retrospectively using a panel of 25 radiographic features without knowledge of the operative findings. Patient demographics, surgical findings and outcome, Gynecologic Oncology Group performance status, and pre-operative serum CA125 values were collected from patient medical records. Residual disease measuring < or = 1 cm in maximal diameter was considered an optimal surgical result. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each radiographic feature and clinical characteristic. Based on statistical probability of each factor predicting cytoreductive outcome, 13 radiographic features, in addition to performance status, were selected for inclusion in the final model. Each parameter was assigned a numeric value based on the strength of statistical association, and a total Predictive Index score was tabulated for each patient. Receiver operating characteristic (ROC) curve analysis was used to assess the ability of the model to predict surgical outcome. Statistical significance was evaluated using the Fisher exact test. RESULTS: Twenty of 41 patients (48.8%) underwent optimal cytoreduction to </= 1 cm residual disease. CT features of peritoneal thickening, peritoneal implants (>/= 2 cm), bowel mesentery involvement (>/= 2 cm), suprarenal paraaortic lymph nodes (>/= 1 cm), omental extension (spleen, stomach, or lesser sac), and pelvic sidewall involvement and/or hydroureter were most strongly associated with surgical outcome. Using the Predictive Index scores, a receiver operating characteristic curve was generated with an area under the curve = 0. 969 +/- 0.023. In the final model, a Predictive Index score >/= 4 had the highest overall accuracy at 92.7% and identified patients undergoing suboptimal surgery with a sensitivity of 100% (21/21). The specificity, or ability to identify patients undergoing optimal surgery, was 85.0% (17/20). The PPV of a Predictive Index score >/= 4 was 87.5% (21/24), and the NPV was 100%. The ability of this model to correctly predict surgical outcome was statistically significant (P < 0.001). CONCLUSIONS: In this model, a Predictive Index score >/= 4 demonstrated high sensitivity, specificity, PPV, and NPV, and was highly accurate in identifying patients with advanced epithelial ovarian carcinoma unlikely to undergo optimal primary cytoreductive surgery. The Predictive Index model may have clinical utility in guiding the management of patients with ovarian carcinoma.

Adult↗

PubMedDate not supplied

An analysis of interleukin-8, interleukin-6 and C-reactive protein serum concentrations to predict fever, gram-negative bacteremia and complicated infection in neutropenic cancer patients.

A prospective study was performed to assess the potential value of interleukin (IL)-8, IL-6, and C-reactive protein (CRP) serum levels to predict fever, gram-negative bacteremia and complicated infection in neutropenic patients with cancer. Serum samples were obtained three times a week during 208 neutropenic episodes following cytotoxic chemotherapy. Fever of any cause developed during 104 out of 191 evaluable episodes. Serum levels of neither cytokine nor CRP were predictive of fever within more than 24 h before its onset. Unlike CRP, both IL-6 and IL-8 serum levels were significantly different between microbiologically documented infections and unexplained fevers. The highest values of IL-6 and IL-8 were observed in episodes of gram-negative bacteremia. Using receiver-operating-characteristic curves, the analysis of cytokine levels measured around the onset of fever indicated that IL-8 is potentially useful for predicting gram-negative bacteremia, with a high negative predictive value of > 90% and a moderate positive predictive value of 50% (sensitivity, 70%; specificity, 91%). In patients with persistent fever, predictions of further clinical complications, defined as prolonged fever of more than 7 days' duration, pneumonia, shock and/or death due to infection, were best predicted by IL-6. With an IL-6 cutoff value of 250 pg/ml in samples obtained 8 to 32 h after onset of fever, the positive predictive value was 92%, the negative predictive value 91% (sensitivity, 85%; specificity, 95%). The positive predictive value of IL-6 in samples obtained another 24 h later from patients still febrile remained > 90%, but the negative predictive value dropped to 47%. In any of the analyses, the predictive values of CRP levels were poor and inferior to either cytokine. These findings may have clinical value in identifying subgroups of patients requiring different therapeutic approaches.

Adolescent↗

PubMed2006

Pharmacokinetics of low-dose nedaplatin and validation of AUC prediction in patients with non-small-cell lung carcinoma.

PURPOSE: The aim of this study was to determine the pharmacokinetics of low-dose nedaplatin combined with paclitaxel and radiation therapy in patients having non-small-cell lung carcinoma and establish the optimal dosage regimen for low-dose nedaplatin. We also evaluated predictive accuracy of reported formulas to estimate the area under the plasma concentration-time curve (AUC) of low-dose nedaplatin. PATIENTS AND METHODS: A total of 19 patients were administered a constant intravenous infusion of 20 mg/m(2) body surface area (BSA) nedaplatin for an hour, and blood samples were collected at 1, 2, 3, 4, 6, 8, and 19 h after the administration. Plasma concentrations of unbound platinum were measured, and the actual value of platinum AUC (actual AUC) was calculated based on these data. The predicted value of platinum AUC (predicted AUC) was determined by three predictive methods reported in previous studies, consisting of Bayesian method, limited sampling strategies with plasma concentration at a single time point, and simple formula method (SFM) without measured plasma concentration. Three error indices, mean prediction error (ME, measure of bias), mean absolute error (MAE, measure of accuracy), and root mean squared prediction error (RMSE, measure of precision), were obtained from the difference between the actual and the predicted AUC, to compare the accuracy between the three predictive methods. RESULTS: The AUC showed more than threefold inter-patient variation, and there was a favorable correlation between nedaplatin clearance and creatinine clearance (Ccr) (r = 0.832, P < 0.01). In three error indices, MAE and RMSE showed significant difference between the three AUC predictive methods, and the method of SFM had the most favorable results, in which %ME, %MAE, and %RMSE were 5.5, 10.7, and 15.4, respectively. CONCLUSIONS: The dosage regimen of low-dose nedaplatin should be established based on Ccr rather than on BSA. Since prediction accuracy of SFM, which did not require measured plasma concentration, was most favorable among the three methods evaluated in this study, SFM could be the most practical method to predict AUC of low-dose nedaplatin in a clinical situation judging from its high accuracy in predicting AUC without measured plasma concentration.

Aged↗

PubMed2005

Combined physico-chemical and water transfer modelling to predict bacterial growth during food processes.

The quality and safety of food products depend on the microorganisms, the food characteristics and the process. The prediction of conditions that prevent growth in complex situations due to the characteristics of the process and of the food cannot be obtained by predictive models of bacterial growth only. Thus, a combined modelling approach was developed by integrating three models, which were selected in a first step: (1) a bacterial model that predicts the bacterial growth from the physico-chemical properties of the media; (2) a water transfer model that predicts the effects of the drying process variables on the medium characteristics; and (3) a thermodynamic model that predicts the water activity aw and the pH of the media from its composition. A second step consisted in separately validating each selected model in which all of the physical, chemical or biological parameters appearing in the equations were previously measured. The third step combined the three knowledge models. The global model was validated on the basis of experimental results concerning the growth of Listeria innocua on the surface of a gelatine gel, the surface of which was submitted to a drying process (changes in relative humidity and air velocity). It was shown that bacterial growth models had to be modified: a specific model was set up to predict the maximum growth rate and another for the lag. Additionally, growth models set up in broth could not be applied in gelatine, leading to the development of a specific growth model on a solid surface. The thermodynamic model accurately predicted the pH and aw of bacterial broth in which high concentrations of solutes were added, and those of the solid media, the gelatine. The water transfer model was applied on gelatine data to predict the evolution of its surface aw during the drying process. The three models-bacterial, water transfer and thermodynamic, separately validated-were combined according to an integrated modelling strategy. The water transfer model coupled with the thermodynamic model predicted the aw on the gel surface. The predicted surface aw explained why growth inhibition was observed. Indeed, growth stopped at a predicted surface aw <0.94, corresponding to L. innocua minimum aw during the drying process. The global model satisfactorily predicted L. innocua growth on the surface of the gel. This study proves the validity of the approach and shows that the combination of the water transfer and thermodynamic models compensates for the lack of aw measurement techniques.

Adaptation, Physiological↗