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PubMed1975

Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both beta-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140-142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.

Adenylate Kinase↗
PubMed1993

Does prediction of outcome alter patient management?

A patient's prognosis is a key factor for the clinicians involved in management. We set out to determine if provision of computer-based predictions of outcome after severe head injury resulted in measurable changes in patient management. In particular, we wondered whether introduction of the predictive system would alter the relation between severity of injury and "intensity" of management. 1025 patients admitted to four British neurosurgical units between 1986 and 1989 following a severe head injury, and who were either in coma for 6 h or had an operation for acute intracranial haematoma, were studied. Specified aspects of intensive management were recorded and all patients were followed up after six months. The study had three phases: a baseline period of at least one year before the introduction of computer-based outcome prediction, one year when predictions were provided at specified times, and a final six months when prediction was withdrawn. While predictions were being provided, there was an increase in the use of specified aspects of intensive care in patients predicted to have a good outcome, but a 39% reduction in the use of these same aspects of intensive care in patients predicted to have the worst outcome. There was no evidence that the provision of predictions affected overall outcome, length of stay, or the recording of explicit decisions to limit treatment. We have demonstrated that the introduction of a routine prediction service can alter patient management.

Adult↗
PubMed2005

Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures.

In freshwater systems located in agricultural areas, organisms are exposed to a multitude of toxicologically and structurally different pesticides. For regulatory purposes it is of major importance whether the combined hazard of these substances can be predictively assessed from the single substance toxicity. For artificially designed multi-component mixtures, it has been shown that the mixture toxicity can be predicted by concentration addition (CA) in case of similarly acting substances and by independent action (IA), if mixtures are composed of dissimilarly acting substances. This study aimed to analyse whether these concepts may also be used to predictively assess the toxicity of environmentally realistic mixtures. For this purpose a mixture of 25 pesticides, which reflects a realistic exposure scenario in field run-off water, was studied for its effects on the reproduction of the freshwater alga Scenedesmus vacuolatus. The toxicity of the tested mixtures showed a good predictability by CA. This is consistent with the finding that the toxicity was dominated by a group of similarly acting photosystem II inhibitors, although the mixture included substances with diverse and partly unknown mechanisms of action. IA slightly underestimated the actual mixture toxicity. However, the EC(50) values that can be derived from each prediction, according to CA respectively IA, only differed by a factor of 1.3. The finding of such a small difference is partly explainable by the fact that only few components dominate the mixture scenario in terms of so-called toxic units (TUs). This connection is established by developing an equation that allows to calculate the maximum possible ratio between corresponding predictions of effect concentrations by IA and CA for any given ratio of the TUs of mixture components, irrespective of their individual concentration-response functions and independent from their mechanisms of action. To evaluate whether small quantitative differences between EC(50) values predicted by CA and IA are an exception or rather the rule for agricultural exposure scenarios, this calculation was applied on an additional set of 18 pesticide exposure scenarios that were taken from the literature. For these scenarios, EC(50) values predicted by IA can never exceed those predicted by CA by more than a factor of 2.5. The findings of this study support the view that CA provides a precautious but not overprotective approach to the predictive hazard assessment of pesticide mixtures under realistic exposure scenarios, irrespective of the similarity or dissimilarity of their mechanisms of action.

Animals↗
PubMed2004

The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods.

All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence. In most of these methods, prior to prediction, alignment positions showing a gap in the top sequence are deleted, consequently leading to shrinking of the alignment and loss of position-specific information. In this paper we investigate the effect of this removal of information on secondary structure prediction accuracy. To this end, we have designed SymSSP, an algorithm that post-processes the predicted secondary structure of all sequences in a multiple sequence alignment by (i) making use of the alignment's evolutionary information and (ii) re-introducing most of the information that would otherwise be lost. The post-processed information is then given to a new dynamic programming routine that produces an optimally segmented consensus secondary structure for each of the multiple alignment sequences. We have tested our method on the state-of-the-art secondary structure prediction methods PHD, PROFsec, SSPro2 and JNET using the HOMSTRAD database of reference alignments. Our consensus-deriving dynamic programming strategy is consistently better at improving the segmentation quality of the predictions compared to the commonly used majority voting technique. In addition, we have applied several weighting schemes from the literature to our novel consensus-deriving dynamic programming routine. Finally, we have investigated the level of noise introduced by prediction errors into the consensus and show that predictions of edges of helices and strands are half the time wrong for all the four tested prediction methods.

Algorithms↗
PubMed2005

Predicting the stream of consciousness from activity in human visual cortex.

Can the rapid stream of conscious experience be predicted from brain activity alone? Recently, spatial patterns of activity in visual cortex have been successfully used to predict feature-specific stimulus representations for both visible and invisible stimuli. However, because these studies examined only the prediction of static and unchanging perceptual states during extended periods of stimulation, it remains unclear whether activity in early visual cortex can also predict the rapidly and spontaneously changing stream of consciousness. Here, we used binocular rivalry to induce frequent spontaneous and stochastic changes in conscious experience without any corresponding changes in sensory stimulation, while measuring brain activity with fMRI. Using information that was present in the multivariate pattern of responses to stimulus features, we could accurately predict, and therefore track, participants' conscious experience from the fMRI signal alone while it underwent many spontaneous changes. Prediction in primary visual cortex primarily reflected eye-based signals, whereas prediction in higher areas reflected the color of the percept. Furthermore, accurate prediction during binocular rivalry could be established with signals recorded during stable monocular viewing, showing that prediction generalized across viewing conditions and did not require or rely on motor responses. It is therefore possible to predict the dynamically changing time course of subjective experience with only brain activity.

Adult↗
PubMed2005

Equally valid models gave divergent predictions for mortality in acute myocardial infarction patients in a comparison of logistic [corrected] regression models.

OBJECTIVE: Models that predict mortality after acute myocardial infarction (AMI) contain different predictors and are based on different populations. We studied the agreement and validity of predictions for individual patients. STUDY DESIGN AND SETTING: We compared predictions from five predictive logistic regression models for short-term mortality after AMI. Three models were developed previously, and two models were developed in the GUSTO-I data, where all five models were applied (n =40,830, 7.0% 30-day mortality). Agreement was studied with weighted kappa statistics of categorized predictions. Validity was assessed by comparing observed frequencies with predictions (indicating calibration) and by the area under the receiver operating characteristic curve (AUC), indicating discriminative ability. RESULTS: The predictions from the five models varied considerably for individual patients, with low agreement between most (kappa <0.6). Risk predictions from the three previously developed models were on average too high, which could be corrected by re-calibration of the model intercept. The AUC ranged from 0.76-0.78 and increased to 0.78-0.79 with re-estimated regression coefficients that were optimal for the GUSTO-I patients. The two more detailed GUSTO-I based models performed better (AUC approximately 0.82). CONCLUSION: Models with different predictors may have a similar validity while the agreement between predictions for individual patients is poor. The main concerns in the applicability of predictive models for AMI should relate to the selected predictors and average calibration.

Aged↗
PubMed2005

Comparison of the potential acuity meter and pinhole tests in predicting postoperative visual acuity after cataract surgery.

PURPOSE: To compare the accuracy of potential acuity meter (PAM) and pinhole (PH) tests in predicting visual acuity after cataract surgery. SETTING: Department of Ophthalmology and Visual Sciences, University of the Philippines, Philippine General Hospital, Manila, and Asian Eye Institute, Makati, Philippines. METHODS: This prospective study comprised 64 eyes with mild to moderate cataract that had uneventful phacoemulsification. The PAM and PH tests were performed to predict postoperative visual acuity. Best corrected visual acuity (BCVA) 4 weeks after surgery was compared with the predicted visual acuity. The number of lines of inaccuracy was calculated by subtracting the BCVA from the predicted visual acuity. The variables analyzed were type of predictive test and preoperative BCVA. The eyes were divided according to preoperative BCVA as follows: Group 1, 20/20 to 20/50; Group 2, 20/60 to 20/100; Group 3, 20/200 or worse. RESULTS: The PH predicted visual acuity was correct in 5% of eyes and the PAM predicted acuity, in 17%. The PH predicted acuity was accurate within 1, 2, and 3 lines of BCVA in 23%, 40%, and 54% of eyes, respectively, and the PAM predicted acuity, in 64%, 81%, and 92% of eyes, respectively. The mean number of lines of inaccuracy was significantly less with the PH than with the PAM (3.47 lines +/- 2.42 [SD] and 1.60 +/- 1.55 lines, respectively) (P=.0005). The mean lines of inaccuracy in Group 1 were 2.49 +/- 1.52 for the PH and 1.14 +/- 0.99 for the PAM (P=.027); in Group 2, 3.17 +/- 1.99 PH and 1.65 +/- 1.80 PAM (P=.642); and in Group 3, 6.58 +/- 3.03 PH and 2.67 +/- 2.10 PAM (P=.240). CONCLUSIONS: The PAM was more accurate than the PH in predicting visual acuity after cataract surgery. The accuracy of both tests decreased in patients with poorer preoperative visual acuity.

Aged↗
PubMed2006

External validation of the Glasgow Aneurysm Score to predict outcome in elective open abdominal aortic aneurysm repair.

OBJECTIVES: Selecting patients based on their risk profiles could improve the outcome after elective surgery of an abdominal aortic aneurysm (AAA). The Glasgow Aneurysm Score (GAS) is a scoring system developed to determine such risk profiles. In other settings, the GAS has proved to have a predictive value for the postoperative outcome. The aim of this study was to investigate whether the GAS was also valid for the patients in our hospital and to examine risk factors with a possible predictive value for postoperative mortality and morbidity. METHODS: We performed a retrospective cohort study in a university hospital. The medical records of 229 patients who underwent open elective repair for an AAA in the period 1994 to 2003 were retrospectively analyzed to assess the GAS and to determine which of the examined risk factors had a predictive value for the prognosis. RESULTS: Five patients (2.2%) died after surgery and 30 (13.1%) had a major complication. The GAS was predictive for postoperative death (P = .021; sensitivity, 1.00; 95% confidence interval [CI], 0.52 to 1.00; specificity, 0.67; 95% CI, 0.61 to 0.73) and also for major morbidity (P = .029; sensitivity, 0.63; 95% CI, 0.46 to 0.78; specificity, 0.70; 95% CI, 0.64 to 0.76). The positive predictive value (mortality, 0.06; morbidity, 0.24) and the positive likelihood ratio (mortality, 3.07; morbidity, 2.14) were low, however. The best cutoff value for the GAS was determined at 77. All the deceased patients (100%) and 63.3% of those who had a major complication had a risk score of >or=77. Of all examined risk factors, suprarenal clamping during surgery was predictive of in-hospital mortality (8.3%, P = .017). For major morbidity, three risk factors, all of which are components of the GAS, were predictive: age (P = .046), cardiac disease (P = .032), and renal disease (P = .041). CONCLUSIONS: The Glasgow Aneurysm Score has a predictive value for outcome after open elective AAA repair. Because of its relatively low positive predictive value for death and major morbidity, the GAS is of limited value in clinical decision-making for the individual high-risk patient. In some particular cases, however, the GAS can be a useful tool, especially for low-risk patients because it has good negative predictive value for this group. Suprarenal clamping was found to be a risk factor for postoperative death.

Adult↗
PubMed2006

Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties.

Peptide binding to MHC is critical for antigen recognition by T-cells. To facilitate vaccine design, computational methods have been developed for predicting MHC-binding peptides, which achieve impressive prediction accuracies of 70-90% for binders and 40-80% for non-binders. These methods have been developed for peptides of fixed lengths, for a limited number of alleles, trained from small number of non-binders, and in some cases based straightforwardly on sequence. These limit prediction coverage and accuracy particularly for non-binders. It is desirable to explore methods that predict binders of flexible lengths from sequence-derived physicochemical properties and trained from diverse sets of non-binders. This work explores support vector machines (SVM) as such a method for developing prediction systems of 18 MHC class I and 12 class II alleles by using 4208-3252 binders and 234,333-168,793 non-binders, and evaluated by an independent set of 545-476 binders and 110,564-84,430 non-binders. Binder accuracies are 86-99% for 25 and 70-80% for 5 alleles, non-binder accuracies are 96-99% for 30 alleles. Binder accuracies are comparable and non-binder accuracies substantially improved against other results. Our method correctly predicts 73.3% of the 15 newly-published epitopes in the last 4 months of 2005. Of the 251 recently-published HLA-A*0201 non-epitopes predicted as binders by other methods, 63 are predicted as binders by our method. Screening of HIV-1 genome shows that, compared to other methods, a comparable percentage (75-100%) of its known epitopes is correctly predicted, while a lower percentage (0.01-5% for 24 and 5-8% for 6 alleles) of its constituent peptides are predicted as binders. Our software can be accessed at .

Alleles↗
PubMed2006

Prediction of lung tumour position based on spirometry and on abdominal displacement: accuracy and reproducibility.

BACKGROUND AND PURPOSE: A simulation investigating the accuracy and reproducibility of a tumour motion prediction model over clinical time frames is presented. The model is formed from surrogate and tumour motion measurements, and used to predict the future position of the tumour from surrogate measurements alone. PATIENTS AND METHODS: Data were acquired from five non-small cell lung cancer patients, on 3 days. Measurements of respiratory volume by spirometry and abdominal displacement by a real-time position tracking system were acquired simultaneously with X-ray fluoroscopy measurements of superior-inferior tumour displacement. A model of tumour motion was established and used to predict future tumour position, based on surrogate input data. The calculated position was compared against true tumour motion as seen on fluoroscopy. Three different imaging strategies, pre-treatment, pre-fraction and intrafractional imaging, were employed in establishing the fitting parameters of the prediction model. The impact of each imaging strategy upon accuracy and reproducibility was quantified. RESULTS: When establishing the predictive model using pre-treatment imaging, four of five patients exhibited poor interfractional reproducibility for either surrogate in subsequent sessions. Simulating the formulation of the predictive model prior to each fraction resulted in improved interfractional reproducibility. The accuracy of the prediction model was only improved in one of five patients when intrafractional imaging was used. CONCLUSIONS: Employing a prediction model established from measurements acquired at planning resulted in localization errors. Pre-fractional imaging improved the accuracy and reproducibility of the prediction model. Intrafractional imaging was of less value, suggesting that the accuracy limit of a surrogate-based prediction model is reached with once-daily imaging.

Abdomen↗
PubMed2006

Use of computer-assisted prediction of toxic effects of chemical substances.

The current revision of the European policy for the evaluation of chemicals (REACH) has lead to a controversy with regard to the need of additional animal safety testing. To avoid increases in animal testing but also to save time and resources, alternative in silico or in vitro tests for the assessment of toxic effects of chemicals are advocated. The draft of the original document issued in 29th October 2003 by the European Commission foresees the use of alternative methods but does not give further specification on which methods should be used. Computer-assisted prediction models, so-called predictive tools, besides in vitro models, will likely play an essential role in the proposed repertoire of "alternative methods". The current discussion has urged the Advisory Committee of the German Toxicology Society to present its position on the use of predictive tools in toxicology. Acceptable prediction models already exist for those toxicological endpoints which are based on well-understood mechanism, such as mutagenicity and skin sensitization, whereas mechanistically more complex endpoints such as acute, chronic or organ toxicities currently cannot be satisfactorily predicted. A potential strategy to assess such complex toxicities will lie in their dissection into models for the different steps or pathways leading to the final endpoint. Integration of these models should result in a higher predictivity. Despite these limitations, computer-assisted prediction tools already today play a complementary role for the assessment of chemicals for which no data is available or for which toxicological testing is impractical due to the lack of availability of sufficient compounds for testing. Furthermore, predictive tools offer support in the screening and the subsequent prioritization of compound for further toxicological testing, as expected within the scope of the European REACH program. This program will also lead to the collection of high-quality data which will broaden the database for further (Q)SAR approaches and will in turn increase the predictivity of predictive tools.

Animal Testing Alternatives↗
PubMed2005

Is depth perception of stereo plaids predicted by intersection of constraints, vector average or second-order feature?

Stereo plaid stimuli were created to investigate whether depth perception is determined by an intersection of constraints (IOC) or vector average (VA) operation on the Fourier components, or by the second-order (non-Fourier) feature in a pattern. We first created stereo plaid stimuli where IOC predicted vertical disparity, VA predicted positive diagonal disparity and the second-order feature predicted negative diagonal disparity. In a depth discrimination task, observers indicated whether they perceived the pattern as 'near' or 'far' relative to a zero-disparity aperture. Observers' perception was consistent with the disparity predicted by VA, indicating its dominance over IOC and the second-order feature in this condition. Additional stimuli in which VA predicted vertical disparity were created to investigate whether VA would dominate perception when it was a less reliable cue. In this case, observers' performance was consistent with disparity predicted by IOC or the second-order feature, not VA. Finally, in order to determine whether the second-order feature contributes to depth perception, stimuli were created where IOC and VA predicted positive horizontal disparity while the second-order feature predicted negative horizontal disparity. When the component gratings were oriented near horizontal (+/-83 degrees from vertical), depth perception corresponded to that predicted by the second-order feature. However, as the components moved away from horizontal (+/-75 degrees and +/-65 degrees from vertical), depth perception was increasingly likely to be predicted by an IOC or VA operation. These experiments suggest that the visual system does not rely exclusively on a single method for computing pattern disparity. Instead, it favours the most reliable method for a given condition.

Contrast Sensitivity↗
PubMed1999

Improved secondary structure predictions for a nicotinic receptor subunit: incorporation of solvent accessibility and experimental data into a two-dimensional representation.

Abstract A refined prediction of the nicotinic acetylcholine receptor (nAChR) subunits' secondary structure was computed with third-generation algorithms. The four selected programs, PHD, Predator, DSC, and NNSSP, based on different prediction approaches, were applied to each sequence of an alignment of nAChR and 5-HT3 receptor subunits, as well as a larger alignment with related subunit sequences from glycine and GABA receptors. A consensus prediction was computed for the nAChR subunits through a "winner takes all" method. By integrating the probabilities obtained with PHD, DSC, and NNSSP, this prediction was filtered in order to eliminate the singletons and to more precisely establish the structure limits (only 4% of the residues were modified). The final consensus secondary structure includes nine alpha-helices (24.2% of the residues, with an average length of 13.9 residues) and 17 beta-strands (22.5% of the residues, with an average length of 6.6 residues). The large extracellular domain is predicted to be mainly composed of beta-strands, with only two helices at the amino-terminal end. The transmembrane segments are predicted to be in a mixed alpha/beta topology (with a predominance of alpha-helices), with no known equivalent in the current protein database. The cytoplasmic domain is predicted to consist of two well-conserved amphipathic helices joined together by an unfolded stretch of variable length and sequence. In general, the segments predicted to occur in a periodic structure correspond to the more conserved regions, as defined by an analysis of sequence conservation per position performed on 152 superfamily members. The solvent accessibility of each residue was predicted from the multiple alignments with PHDacc. Each segment with more than three exposed residues was assumed to be external to the core protein. Overall, these data constitute an envelope of structural constraints. In a subsequent step, experimental data relative to the extracellular portion of the complete receptor were incorporated into the model. This led to a proposed two-dimensional representation of the secondary structure in which the peptide chain of the extracellular domain winds alternatively between the two interfaces of the subunit. Although this representation is not a tertiary structure and does not lead to predictions of specific beta-beta interaction, it should provide a basic framework for further mutagenesis investigations and for fold recognition (threading) searches.

Algorithms↗
PubMed2004

Role of evolutionary information in prediction of aromatic-backbone NH interactions in proteins.

In this study, an attempt has been made to develop a neural network-based method for predicting segments in proteins containing aromatic-backbone NH (Ar-NH) interactions using multiple sequence alignment. We have analyzed 3121 segments seven residues long containing Ar-NH interactions, extracted from 2298 non-redundant protein structures where no two proteins have more than 25% sequence identity. Two consecutive feed-forward neural networks with a single hidden layer have been trained with standard back-propagation as learning algorithm. The performance of the method improves from 0.12 to 0.15 in terms of Matthews correlation coefficient (MCC) value when evolutionary information (multiple alignment obtained from PSI-BLAST) is used as input instead of a single sequence. The performance of the method further improves from MCC 0.15 to 0.20 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields an overall prediction accuracy of 70.1% and an MCC of 0.20 when tested by five-fold cross-validation. Overall the performance is 15.2% higher than the random prediction. The method consists of two neural networks: (i) a sequence-to-structure network which predicts the aromatic residues involved in Ar-NH interaction from multiple alignment of protein sequences and (ii) a structure-to structure network where the input consists of the output obtained from the first network and predicted secondary structure. Further, the actual position of the donor residue within the 'potential' predicted fragment has been predicted using a separate sequence-to-structure neural network. Based on the present study, a server Ar_NHPred has been developed which predicts Ar-NH interaction in a given amino acid sequence. The web server Ar_NHPred is available at and (mirror site).

Amino Acids, Aromatic↗
PubMed1995

Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival.

OBJECTIVE: To develop a mathematical model that predicts sperm cryodamage based on the kinematic characteristics of seminal sperm as detected by computer-aided sperm analysis (CASA). DESIGN: Computer-aided sperm analysis was performed on donor semen before and after freezing. An iterative multivariate statistical analysis technique was developed to identify sperm subpopulations and to select the best variables for modeling. Stepwise, multivariate regression was performed on the selected subpopulations to predict the post-thaw percentage of motile sperm from prefreeze kinematic values. SETTING: Andrology laboratories, IVF laboratories, and sperm cryobanks. PARTICIPANTS: Semen donors in an academic research environment. MAIN OUTCOME MEASURES: Identification of predictive kinematic variables; number of sperm subpopulations per sample; number of kinematic variables per subpopulation; prediction error for subpopulation membership; and an equation for prediction of post-thaw percentage of motile sperm from prefreeze CASA variables. RESULTS: The number of subpopulations for each specimen was predicted by 3 to 5 kinematic variables. Straight-line velocity (VSL) and linearity were the most commonly predictive primary variables, whereas curvilinear velocity and amplitude of lateral head displacement were the most commonly predictive secondary variables. The best linear model predicted the post-thaw percentage of motile sperm from the difference in VSL between the subpopulation with the highest value and the subpopulation with the lowest value in each prefreeze specimen. CONCLUSIONS: A small number of consistent kinematic variables accurately described physiologic subpopulations of sperm in prefreeze and post-thaw specimens from different men. An equation based on the characteristics of these subpopulations predicts the post-thaw percentage of motile sperm (i.e., sperm recovery) from simple prefreeze kinematic variables. This equation could improve specimen screening by eliminating the requirements for freezing and thawing in order to identify a specimen's vulnerability to cryodamage.

Cell Survival↗
PubMed1997

Does extracorporeal membrane oxygenation benefit neonates with congenital diaphragmatic hernia? Application of a predictive equation.

The overall survival of neonates with congenital diaphragmatic hernia (CDH) remains poor despite the advent of extracorporeal membrane oxygenation (ECMO). Attempts at accurately predicting survival have been largely unsuccessful. The purpose of this study was twofold: (1) to identify independent predictors of survival from a cohort of CDH neonates treated at the authors' institution when ECMO was not available and combine them to form a predictive equation, and (2) to apply the equation prospectively in a cohort of CDH neonates, treated at the same institution when ECMO was available, to determine whether ECMO improves outcome. From the clinical data of 62 CDH neonates treated at the authors' center by the same team of university neonatologists and pediatric surgeons between 1983 and 1993 (before ECMO availability), 15 preoperative and seven operative variables were selected as potential independent predictors. When subjected to multivariate, stepwise logistic regression analysis, four variables were identified as statistically significant (P < .05), independent predictors of survival: (1) ventilatory index (VI), (2) best preoperative PaCO2, (3) birth weight (BW), and (4) Apgar score at 5 minutes. When combined via logistic regression analysis, the following predictive equation was formulated: P (probability of survival to discharge) = [1 + e(x)]-1 where x = 4.9 - 0.68 (Apgar) - 0.0032 (BW) + 0.0063 (VI) + 0.063 (PaCO2). Applying a standard cut-off rate of survival at less than 20%, the equation yielded a sensitivity of 94% and a specificity of 82% in identifying the correct outcome of patients treated with conventional ventilatory management. The overall survival rate was 66%. Since the availability of ECMO at the center, 32 CDH neonates were treated using the same conventional ventilatory treatment and surgical repair by the same university staff. The overall survival rate was 69%. The predictive equation was applied prospectively to all neonates to determine predicted outcome, but was not used to decide the treatment method. Eighteen neonates received conventional therapy alone; 16 of 18 survived (89%). Fifteen of the 16 patients who survived had their outcomes predicted correctly (94%). Fourteen neonates did not respond to conventional therapy and required ECMO; 6 of 14 survived (43%). Six of the eight patients predicted to survive, lived (75%). All six patients predicted to die, died despite the addition of ECMO therapy (100%). The mean hospital cost, per ECMO patient who died, was $277,264.75 +/- $59,500.71 (SE). An odds ratio analysis, using the four independent predictors to standardize for degree of illness, was performed to assess the risk associated with adding ECMO therapy. The result was 1.25 (P = 0.75). Although the cohort was not large enough to eliminate significant beta error, the data strongly suggested no advantage of ECMO. At this center, absolute survival rates for neonates with CDH have not been significantly altered since ECMO has become available (66% v 69%). The authors conclude that the predictive equation remains an accurate measurement of survival at their center even when ECMO is used as a salvage therapy. The method of creating a predictive equation may be applied at any institution to determine the potential outcome of CDH neonates and assess the effect of ECMO, or other salvage therapies, on survival rates.

Decision Support Techniques↗
PubMed1993

Predictability of recurrent and progressive disease in individual patients with primary superficial bladder cancer.

The ultimate goal of prognostic assessment is optimization of individual counseling. Often, however, studies on prognostic factors focus on discriminating between high risk and low risk subgroups without considering the relevance of 1 or more factors for predicting disease outcome in individual patients. We quantified the accuracy of prediction of future recurrences and disease progression in individual patients with primary superficial bladder cancer. The study cohort consisted of 1,674 patients who were followed prospectively between 1983 and 1991 in the Netherlands. By analyzing half of the patients with proportional hazards regression, we computed relative risks of recurrence and progression. A prognostic index score based on these relative risks was then applied to the other half of the patients to determine whether group outcome could be predicted accurately. To assess the accuracy of prediction in individuals we used a method similar to the construction of receiver operating characteristic curves in diagnostic test assessment. The 3-year risk of first recurrence was 55% (95% confidence interval 51 to 59%). The 3-year risk of first progressive disease was 10% (95% confidence interval 8 to 12%). For the risk of first recurrence, tumor stage, tumor extent and multicentricity had statistically significant prognostic ability. Prognostic factors for the risk of disease progression were tumor stage, grade, multicentricity and the result of random biopsies from cystoscopically normal-appearing urothelium. For patients with a prognostic index score that suggested a low risk for recurrent and progressive disease the predicted 3-year risk of first recurrence was still 44% but the predicted 3-year risk of progression was only 3%. For patients with a prognostic index score that suggested a high risk the predicted risks were 74% and 22%, respectively. These predicted risks appeared to be fairly accurate when applied to the other half of our case series. However, in any 2 patients chosen at random the chance that the patient with the worst predicted prognosis would have a shorter recurrence-free and progression-free followup was calculated to be only 58% and 67%, respectively. Although the available prognostic factors in superficial bladder cancer may be useful to identify high risk and low risk subgroups, predictability in individuals is highly inaccurate. More relevant prognostic factors are needed to decrease current overtreatment and undertreatment rates, and to improve the followup policy.

Adult↗
PubMed2002

Method and timing of tumor volume measurement for outcome prediction in cervical cancer using magnetic resonance imaging.

PURPOSE: Recently, imaging-based tumor volume before, during, and after radiation therapy (RT) has been shown to predict tumor response in cervical cancer. However, the effectiveness of different methods and timing of imaging-based tumor size assessment have not been investigated. The purpose of this study was to compare the predictive value for treatment outcome derived from simple diameter-based ellipsoid tumor volume measurement using orthogonal diameters (with ellipsoid computation) with that derived from more complex contour tracing/region-of-interest (ROI) analysis 3D tumor volumetry. METHODS AND MATERIALS: Serial magnetic resonance imaging (MRI) examinations were prospectively performed in 60 patients with advanced cervical cancer (Stages IB2-IVB/recurrent) at the start of RT, during early RT (20-25 Gy), mid-RT (45-50 Gy), and at follow-up (1-2 months after RT completion). ROI-based volumetry was derived by tracing the entire tumor region in each MR slice on the computer work station. For the diameter-based surrogate "ellipsoid volume," the three orthogonal diameters (d1, d2, d3) were measured on film hard copies to calculate volume as an ellipsoid (d1 x d2 x d3 x pi/6). Serial tumor volumes and regression rates determined by each method were correlated with local control, disease-free and overall survival, and the results were compared between the two measuring methods. Median post-therapy follow-up was 4.9 years (range, 2.0-8.2 years). RESULTS: The best method and time point of tumor size measurement for the prediction of outcome was the tumor regression rate in the mid-therapy MRI examination (at 45-50 Gy) using 3D ROI volumetry. For the pre-RT measurement both the diameter-based method and ROI volumetry provided similar predictive accuracy, particularly for patients with small (<40 cm3) and large (> or =100 cm3) pre-RT tumor size. However, the pre-RT tumor size measured by either method had much less predictive value for the intermediate-size (40-99 cm3) tumors, which accounted for the majority of patients (55%). Tumor regression rate (fast vs. slow) obtained during mid-RT (45-50 Gy), which could only be appreciated by 3D ROI volumetry, had the best outcome prediction rate for local control (84% vs. 22%, p < 0.0001) and disease-free survival (63% vs. 20%, p = 0.0005). Within the difficult to classify intermediate pre-RT size group, slow ROI-based regression rate predicted all treatment failures (local control rate: 0% vs. 91%, p < 0.0001; disease-free survival: 0% vs. 73%, p < 0.0001). Mid-RT regression rate based on simple diameter measurement did not predict outcome. The early-RT and post-RT measurements were least useful with either measuring method. CONCLUSION: Our preliminary data suggest that for the prediction of treatment outcome in cervical cancer, initial tumor volume can be estimated by simple diameter-based measurement obtained from film hard copies. When initial tumor volume is in the intermediate size range, ROI volumetry and an additional MRI during RT are needed to quantitatively analyze tumor regression rate for the prediction of treatment outcome.

Adenocarcinoma↗