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PubMed1991

A time-insensitive predictive instrument for acute myocardial infarction mortality: a multicenter study.

This study develops a "time-insensitive" predictive instrument for acute myocardial infarction mortality that would be useful both as a real-time clinical decision aid in the emergency medical setting and also for retrospective assessment and comparison of medical care based on risk-adjusted mortality predictions. This was done using prospectively-collected data on 5,773 patients with chief complaints of chest pain or other symptoms suggesting acute cardiac ischemia who came to six New England hospitals over a 2-year period. In phase one, based upon 4,099 patients, multivariate logistic regression was used to develop the predictive instrument. In phase two, its accuracy and diagnostic performance were tested on an independent sample of 1,387 patients presenting with symptoms compatible with acute cardiac ischemia. Discrimination between patients who lived and those who died was reflected by receiver-operating characteristic (ROC) curve areas of 0.85, 0.80, and 0.76, respectively, for all emergency department study subjects regardless of final diagnosis, subjects who proved to be having acute cardiac ischemia, and subjects who proved to be having acute infarction. Good calibration was shown by the fact that the predicted mortality was found to not vary significantly from actual mortality rates across deciles of predicted probabilities from 0% to 100%. In phase three, based on all 945 study subjects with acute myocardial infarction, each of the six hospitals' actual mortality rates were compared to their rates predicted by the predictive instrument. Actual hospital mortality rates ranged from 9.9% to 19.3%, with one hospital having a significantly higher rate (P = 0.005) and two hospitals both). Predicted mortality rates ranged from 13.4% to 19.4%, with one hospital having a significantly higher predicted rate (P = 0.005) and two hospitals having significantly lower predicted rates (P = 0.04 and P = 0.03). Individual hospitals' differences between actual and predicted mortality ranged from -3.4% to +3.1% (all NS). When grouped by hospital type, the actual mortality rates were 14.9%, 17.3%, and 13.0%, respectively, for urban teaching, smaller city teaching, and rural nonteaching hospitals (all NS). The predicted mortality rates were 16.5%, 17.1%, and 13.6%, respectively, with the rate for rural nonteaching hospitals being significantly lower (P = 0.009). No hospital type had significant differences between their actual and predicted mortality rates (NS). The time-insensitive predictive instrument for acute infarction mortality shows potential for risk-adjusted studies of hospitals mortality for multihospital groups, hospital-to-hospital comparisons, and within-hospital assessment.(ABSTRACT TRUNCATED AT 400 WORDS)

Adult↗
PubMed2006

Validation of a calibrated prediction model for response to growth hormone treatment in an independent cohort.

BACKGROUND: Prediction models, e.g. for prediction of response to growth hormone treatment, need validation in appropriate independent cohorts, comparing predicted and observed outcomes. In a previous validation of a model for predicting the first-year response to growth hormone treatment in children with idiopathic growth hormone deficiency, overfitting was observed. We modified the prediction formula and now report validation of this modified model. PATIENTS AND METHODS: The modified and original prediction models were applied to a group of patients selected from Lilly's GeNeSIS database using the same inclusion and exclusion criteria as for the original model. For both prediction methods, observed first-year height velocity was plotted vs. predicted height velocity in a calibration plot. For a valid prediction, the regression line should correspond to the line of identity (observed outcome is equal to predicted outcome); the regression lines for each prediction model were tested for significant differences from this line of identity. RESULTS: The number of patients fulfilling the criteria was 226. The regression line in the calibration plot of the modified model was not significantly different from the line of identity (p = 0.43), in contrast to the original model (p < 0.001). For the modified model the mean (SD) prediction error was -0.11 (2.05) cm/year and for the original model 0.28 (2.11) cm/year. CONCLUSION: The modified prediction method, obtained after calibration of the original model, performs well in an independent patient sample and gives more accurate predictions than the original model.

Body Height↗
PubMed2006

Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment.

BACKGROUND: This study analyzes the predictions of a number of promoter predictors on the ENCODE regions of the human genome as part of the ENCODE Genome Annotation Assessment Project (EGASP). The systems analyzed operate on various principles and we assessed the effectiveness of different conceptual strategies used to correlate produced promoter predictions with the manually annotated 5' gene ends. RESULTS: The predictions were assessed relative to the manual HAVANA annotation of the 5' gene ends. These 5' gene ends were used as the estimated reference transcription start sites. With the maximum allowed distance for predictions of 1,000 nucleotides from the reference transcription start sites, the sensitivity of predictors was in the range 32% to 56%, while the positive predictive value was in the range 79% to 93%. The average distance mismatch of predictions from the reference transcription start sites was in the range 259 to 305 nucleotides. At the same time, using transcription start site estimates from DBTSS and H-Invitational databases as promoter predictions, we obtained a sensitivity of 58%, a positive predictive value of 92%, and an average distance from the annotated transcription start sites of 117 nucleotides. In this experiment, the best performing promoter predictors were those that combined promoter prediction with gene prediction. The main reason for this is the reduced promoter search space that resulted in smaller numbers of false positive predictions. CONCLUSION: The main finding, now supported by comprehensive data, is that the accuracy of human promoter predictors for high-throughput annotation purposes can be significantly improved if promoter prediction is combined with gene prediction. Based on the lessons learned in this experiment, we propose a framework for the preparation of the next similar promoter prediction assessment.

Computational Biology↗
PubMed2000

Accuracy of physical and occupational therapists' early predictions of recovery after severe middle cerebral artery stroke.

INTRODUCTION: The ability of physical therapists (PTs) and occupational therapists (OTs) to predict level of outcome accurately was investigated prospectively in 91 severely disabled stroke patients with a first-ever middle cerebral artery (MCA) stroke. METHODS: Within the second and fifth week after stroke onset, 364 predictions were made by 59 PTs and 47 OTs about walking ability, dexterity, activities of daily living (ADL), need for additional care in ADL, time required to achieve independent walking ability and maximal level of ADL, and destination of discharge at six months after stroke. The functional recovery patterns of stroke patients were assessed by an independent observer. The accuracy of the therapists' predictions was compared with that of derived prediction models. In addition, the influence of characteristics of patients and therapists on the accuracy of the predictions was investigated. RESULTS: Compared to observed outcomes at six months after stroke, therapists' lowest accuracies of prediction were found for the moment at which maximal ADL score was achieved (rs = 0.07; p = NS), and highest accuracy was for level of dexterity of the hemiplegic arm (rs = 0.78; p <0.01). Therapists' predictions of functional outcome at six months tended to be too pessimistic. No significant differences were observed for dexterity and walking ability when the predictions by PTs and OTs were compared with those of regression models, whereas significant differences were found for the accuracies of OTs' and PTs' first prediction of destination of discharge and second predictions of outcome in ADL and need for additional care in ADL. No significant differences were found between the accuracy of PTs' and OTs' predictions, and their ability to predict functional outcome was not significantly influenced by the characteristics of patient and therapists. CONCLUSIONS: At two and five weeks after stroke, OTs and PTs can accurately predict level of walking ability and dexterity at six months. The prediction of time required for achieving maximal level of recovery, destination of discharge, outcome of ADL as well as need for additional care in ADL leaves room for improvement.

Activities of Daily Living↗
PubMed2006

A novel strategy for physiologically based predictions of human pharmacokinetics.

BACKGROUND: The major aim of this study was to develop a strategy for predicting human pharmacokinetics using physiologically based pharmacokinetic (PBPK) modelling. This was compared with allometry (of plasma concentration-time profiles using the Dedrick approach), in order to determine the best approaches and strategies for the prediction of human pharmacokinetics. METHODS: PBPK and Dedrick predictions were made for 19 F. Hoffmann-La Roche compounds. A strategy for the prediction of human pharmacokinetics using PBPK modelling was proposed in this study. Predicted values (pharmacokinetic parameters, plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. RESULTS: By following the proposed strategy for PBPK, a prediction would have been made prospectively for approximately 70% of the compounds. The prediction accuracy for these compounds in terms of the percentage of compounds with an average-fold error of <2-fold was 83%, 50%, 75%, 67%, 92% and 100% for apparent oral clearance (CL/F), apparent volume of distribution during terminal phase after oral administration (V(z)/F), terminal elimination half-life (t(1/2)), peak plasma concentration (C(max)), area under the plasma concentration-time curve (AUC) and time to reach C(max) (t(max)), respectively. For the other 30% compounds, unacceptable prediction accuracy was obtained in animals; therefore, a prospective prediction of human pharmacokinetics would not have been made using PBPK. For these compounds, prediction accuracy was also poor using the Dedrick approach. In the majority of cases, PBPK gave more accurate predictions of pharmacokinetic parameters and plasma concentration-time profiles than the Dedrick approach. CONCLUSIONS: Based on the dataset evaluated in this study, PBPK gave reasonable predictions of human pharmacokinetics using preclinical data and is the recommended approach in the majority of cases. In addition, PBPK modelling is a useful tool to gain insights into the properties of a compound. Thus, PBPK can guide experimental efforts to obtain the relevant information necessary to understand the compound's properties before entry into human, ultimately resulting in a higher level of prediction accuracy.

Animals↗
PubMed1993

The efficacy of predicting dystocia in yearling beef heifers: I. Using ratios of pelvic area to birth weight or pelvic area to heifer weight.

Three methods for predicting difficult births were tested on 4,140 yearling heifers measured before breeding and(or) at pregnancy check approximately 6 mo later. These heifers were from 115 beef herds in Alberta and British Columbia. The overall incidence of dystocia for normal presentations was 26.5%, which included 17.2% easy assists, 7.7% hard pulls, and 1.6% Caesarean sections. In Method 1, heifers were predicted as difficult (hard pull and Caesarean section) or easy (unassisted and easy pull) calvers by dividing their pelvic area (PA) by previously calculated PA to calf birth weight (PA/BWT) ratios. The ratio used depended on heifer weight and age. Method 1 predicted 63.7% to be difficult calvers. Of these only 10.4% were actually difficult calvers. The accuracy of this method was 40.0% and was not a useful on-farm method for predicting difficult births in first-calf, 2-yr-old heifers. In Method 2, 3,278 heifers measured before breeding and 1,125 heifers measured at pregnancy check were predicted as difficult or easy calvers by dividing their PA by 4.19 at prebreeding or 5.51 at pregnancy check. These values were PA/calf BWT ratios previously determined to be threshold levels. The accuracy of Method 2 was 78.5% and culling by this method would have reduced difficult birth rate by 9.6%. However, of the 738 heifers (16.8%) predicted to be difficult calvers, 86.0% actually calved easily. Heifers predicted to be easy calvers by Method 2 were heavier (P < .001), had a larger PA (P < .001), had more PA per kilogram of BW (P < .001), and had heavier (1.0 kg) calves at birth (P = .05) than heifers predicted to be difficult calvers. In Method 3, 3,269 heifers measured before breeding and 1,087 heifers measured at pregnancy check were predicted as difficult or easy calvers by dividing their PA by their BW. Heifers having ratios that were among the lowest 16% of the herd were predicted to be difficult calvers, and the rest were predicted to be easy calvers. The accuracy of Method 3 was 79.4% and culling by this method would have reduced difficult birth rate by 9.5%. However, of the 677 (15.5%) heifers predicted to be difficult calvers, 85.7% actually calved easily. Heifers predicted to be easy calvers by Method 3 were lighter (P < .001), had a larger PA (P < .001), had more PA per kilogram of BW (P < .001), and had lighter (1.2 kg) calves at birth (P = .04) than heifers predicted to be difficult calvers.(ABSTRACT TRUNCATED AT 400 WORDS)

Age Factors↗
PubMedDate not supplied

Prediction of motor status 3 and 6 months post severe traumatic brain injury: a preliminary study.

The prediction of outcome following severe traumatic brain injury has received considerable attention in recent years. Previous prediction studies have focused on a long-term follow-up or prediction period. The reported outcome measures generally adopted a global approach (e.g. independent living) in terms of the prediction of physical function. The objective of the present study was to construct clinically useful predictive equations of motor system status, as represented by selected postural reactions (indicators of central nervous system function). Specifically, these equations would serve to predict the recovery of equilibrium and protective reactions both at 3 and 6 months post-injury, respectively. A stepwise multiple logistic regression analysis was performed, where nine predictive variables were considered using a multivariate approach. The results indicate that coma duration followed by age contribute significantly to the predictive capability of the models at both 3 and 6 months post-injury. Specifically, at 3 months, the predictive variables 'coma duration' and 'age' enabled an 84.62% correct prediction rate, whereas, at 6 months, 'coma duration' and 'age' enabled a 79.49% correct prediction rate. In addition, the exact probabilities (for given sample ages and coma durations) and associated 95% confidence intervals were calculated based on the predictive models obtained. The theoretical framework underlying these predictive models can form the basis for further studies. Furthermore, these preliminary predictive models have potential implications for early treatment planning and patient management.

Adult↗
PubMed2006

Prediction of dry matter intake throughout lactation in a dynamic model of dairy cow performance.

In the dynamic modeling of dairy cow performance over a full lactation, the difference between net energy intake and net energy used for maintenance, growth, and output in milk accumulates in body reserves. A simple dynamic model of net energy balance was constructed to select, out of some common dry matter intake (DMI) prediction equations, the one that resulted in a minimum cumulative bias in body energy deposition. Dry matter intake was predicted using the Cornell Net Carbohydrate and Protein System, Agricultural Research Council, or National Research Council (NRC) DMI equations from body weight (BW) and predicted fat-corrected milk yield. The instantaneous BW of cows at progressive weeks of lactation was simulated as the numerical integral of the BW change obtained from the predicted net energy balance. Predicted DMI and BW from each DMI equation, using either of 2 equations to describe maintenance energy expenditures, were compared statistically against observed data from 21 herd average published full lactation data sets. All DMI equations underpredicted BW and DMI, but the NRC DMI equation resulted in the minimum cumulative error in predicted BW and DMI. As a general solution to prevent predicted BW from deviating substantially over time from the observed BW, a lipostatic feedback mechanism was integrated into the NRC DMI equation as a 2-parameter linear function of the relative size of simulated body reserves and week of lactation. Residual sum of squares was reduced on average by 52% for BW predictions and by 41% for DMI predictions by inclusion of the negative feedback with parameters taken from the average of all 21 least squares fits. Similarly, root mean square prediction error (%) was reduced by 30% on average for BW predictions and by 23% for DMI predictions. Inclusion of a feedback of energy reserves onto predicted DMI, simulating lipostatic regulation of BW, solved the problem of final BW deviation within a dynamic model and improved its DMI prediction to a satisfactory level.

Adipose Tissue↗
PubMed1997

Comparison of the APACHE III, APACHE II and Glasgow Coma Scale in acute head injury for prediction of mortality and functional outcome.

OBJECTIVES: This study examines the efficacy of the predicting power for hospital mortality and functional outcome of three different scoring systems for head injury in a neurosurgical intensive care unit (NICU). DESIGN: On the day of admission, data were collected from each patient to compute the Acute Physiology, Age, and Chronic Health Evaluation (APACHE) II and III, and Glasgow Coma Scale (GCS) scores. Hospital mortality was defined as the deaths of patients before discharge from hospital. Early mortality was defined as death before the 14th day after admission. Late mortality was defined as death after the 15th day from admission. Functional outcome was evaluated by Index of Independence in Activities of Daily Living (Index of ADL). SETTING: An 8-bed NICU in a 1270-bed medical center in Taichung Veterans General Hospital. PATIENTS AND PARTICIPANTS: Two hundred non-selected patients with acute head injury were included in our study in a consecutive period of 2 years. Patients less than 14 years old were not included. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Sensitivity, specificity and correct prediction outcome were measured by the chi-square method in three scoring systems. The Youden index was also obtained. The best cut-off point in each scoring system was determined by the Youden index. The difference in Youden index was calculated by Z score. A difference was also considered if the probability value was less than 0.05. The area under Receiver Operating Characteristic (ROC) curve was computed. Then the area under ROC of each scoring system was compared by Z score. There was statistical significance if p was less than 0.05. For prediction of hospital mortality, the best cut-off points are 55 for APACHE III, 17 for APACHE II and 5 for GCS. The correct prediction outcome is 82.4% in APACHE III, 78.4% in APACHE II and 81.9% in the GCS. The Youden index has best cut-off points at 0.68 for APACHE III 0.59 for APACHE II, and 0.56 for GCS. The area under Receiver Operating Characteristic (ROC) curve is 0.90 in the APACHE III, 0.84 in the APACHE II and 0.86 in the GVS. There are no statistical differences among APACHE III and II, and GCS in terms of correct prediction outcome, Youden Index and the area under the ROC curve. Other physiological variables excluding GCS in APACHE III and II (AP III-GCS, AP II-GCS) have less statistical value in the determination of mortality for acute head injury. For the prediction of late mortality, APACHE III and II yield significantly better results in the area under the ROC curve, correct prediction and Youden index than those of GCS. Other physiological variables (AP III-GCS and AP II-GCS) play an important role in the prediction of late mortality in APACHE scores. For prediction of the functional outcome of surviving patients with acute head injury, the APACHE III yields the best results of correct prediction outcome, Youden index and the area under the ROC curve. CONCLUSION: The APACHE III and II may not replace the role of GCS in cases of acute head injury for hospital or early mortality assessment. But for prediction of the late mortality, the APACHE III and II have better accuracy than GCS. Other physiological variables excluding GCS in the APACHE system play a crucial contribution for late mortality. GCS is simple, less time-consuming and economical for patients with acute head injury for the prediction of hospital and early mortality. The APACHE III provides better prediction for severe morbidity than GCS and APACHE II. Therefore, the APACHE III provides a good assessment not only for hospital and late mortality, but also for functional outcome.

APACHE↗
PubMed2000

Laparoscopic cholecystectomy for acute cholecystitis: can the need for conversion and the probability of complications be predicted? A prospective study.

BACKGROUND: Laparoscopic cholecystectomy (LC) in acute cholecystitis is associated with a relatively high rate of conversion to an open procedure as well as a high rate of complications. The aim of this study was to analyze prospectively whether the need to convert and the probability of complications is predictable. METHODS: A total of 215 patients undergoing LC for acute cholecystitis were studied prospectively by analyzing the data accumulated in the process of investigation and treatment. Factors associated with conversion and complications were assessed to determine their predictive power. RESULTS: Conversion was indicated in 44 patients (20.5%), and complications occurred in 36 patients (17%). Male gender and age >60 years were associated with conversion, but these factors had no sensitivity and no positive predictive value. The same factors, together with a disease duration of >96 h, a nonpalpable gallbladder, a white blood count (WBC) of >18,000/cc(3), and advanced cholecystitis, predicted conversion with a sensitivity of 74%, a specificity of 86%, a positive predictive value of approximately 40%, and a negative predictive value of 96%. However, these data became available only when LC was underway. Male gender and a temperature of >38 degrees C were associated with complications, but these factors had no sensitivity and no positive predictive value. Progression along the stages of admission and therapy did not add predictive factors or improve the predictive characteristics. Male gender, abdominal scar, bilirubin >1 mg%, advanced cholecystitis, and conversion to open cholecystectomy were associated with infectious complications. Their sensitivity and positive predictive value remained 0 despite progression along the stages of admission and therapy. CONCLUSION: Although certain preoperative factors are associated with the need to convert a LC for acute cholecystitis, they have limited predictive power. Factors with higher predictive power are obtained only during LC. The need to convert can only be established during an attempt at LC. Preoperative and operative factors associated with total and infectious complications have no predictive power.

Adolescent↗
PubMed2005

Can biological tests assist prediction of suicide in mood disorders?

Predicting suicide is difficult due to its low base-rate and the limited specificity of clinical predictors. Prospective biological studies suggest that dysfunctions in the serotonergic system and hypothalamic-pituitary-adrenal axis have some predictive power for completed suicide in mood disorders. A prediction model that incorporates biological testing to increase specificity and sensitivity of prediction of suicide is of potential clinical value. Meta-analyses of prospective biological studies of suicide and cerebrospinal fluid 5-hydroxyindoleacetic acid (CSF 5-HIAA) and suicide and the dexamethasone suppression test (DST) in mood disorders using the penalized quasi-likelihood (PQL) and bootstrap method yield odds ratios for prediction of suicide of 4.48 and 4.65 respectively. Two combinatory prediction models, the first requiring positive results on more than one test, and the second requiring a positive result on either one of two tests, were tested to assess their sensitivity, specificity, and predictive power using biological data from published and unpublished studies. The prediction model that requires both DST and CSF 5-HIAA tests to be positive results in 37.5% sensitivity, 88% specificity, and has a positive predictive value of 23%. The prediction model that requires either DST or CSF 5-HIAA tests to be positive results in 87.5% sensitivity, 28% specificity, and has a positive predictive value of 10%. Thus, models attempting to predict a lethal outcome that is uncommon perform very differently making model choice of major importance. Further work on refining biological predictors and integration with clinical predictors is needed to optimize a model to predict suicide in the clinic.

Biomarkers↗
PubMed2003

Comparative gene prediction in human and mouse.

The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.

Animals↗
PubMed1995

Predicting postoperative pulmonary function in patients undergoing lung resection.

OBJECTIVE: Our aim was to determine the effect of lung resection on spirometric lung function and to evaluate the accuracy of simple calculation in predicting postoperative pulmonary function in patients undergoing lung resection. DESIGN: We reviewed preoperative and postoperative pulmonary function test results on patients who were followed in the multidisciplinary lung cancer clinic between July 1991 and March 1994 and who underwent lung resection. The predicted postoperative FEV1 and FVC were calculated based on the number of segments resected and were compared with the actual postoperative FEV1 and FVC. SETTING: This study was conducted at a university, tertiary referral hospital. PATIENTS: All patients were evaluated at a multidisciplinary lung cancer clinic and underwent lung resection by one surgeon (L.A.L.). MEASUREMENTS AND MAIN RESULTS: Sixty patients undergoing 62 pulmonary resections were reviewed. The predicted postoperative FEV1 and FVC were calculated using the following formula: predicted postoperative FEV1 (or FVC) = preoperative FEV1 (or FVC) x (1-(S x 0.0526)); where S = number of segments resected. The actual postoperative FEV1 and FVC correlated well with the predicted postoperative FEV1 and FVC for patients undergoing lobectomy (r = 0.867 and r = 0.832, respectively); however, the predicted postoperative FEV1 consistently underestimated the actual postoperative FEV1 by approximately 250 mL. For patients undergoing pneumonectomy, the actual postoperative FEV1 and FVC did not correlate as well with the predicted postoperative FEV1 and FVC (r = 0.677 and r = 0.741, respectively). Although there was considerable variability, the predicted postoperative FEV1 consistently underestimated the actual postoperative FEV1 by nearly 500 mL. Of the patients undergoing lobectomy, eight also received postoperative radiation therapy. When analyzed separately, patients receiving combined therapy lost an average of 5.47% of FEV1 per segment resected. This contrasts with a 2.84% per segment reduction in FEV1 for patients who did not receive radiation therapy. CONCLUSIONS: This simple calculation of predicted postoperative FEV1 and FVC correlates well with the actual postoperative FEV1 and FVC in patients undergoing lobectomy. The predicted postoperative FEV1 consistently underestimated the actual postoperative FEV1 by approximately 250 mL. The postoperative FEV1 and FVC for patients undergoing pneumonectomy is not accurately predicted using this equation. The predicted postoperative FEV1 for patients undergoing pneumonectomy was underestimated by an average of 500 mL and by greater than 250 mL in 12 of our 13 patients. Thus, by adding 250 mL to the above calculation of predicted postoperative FEV1, we improve our ability to we identify a minimal postoperative FEV1 for patients undergoing pneumonectomy. Finally, combined modality treatment with surgery followed by radiation therapy may result in additive lung function loss.

Adult↗
PubMed2006

Role of spirometry and exhaled nitric oxide to predict exacerbations in treated asthmatics.

OBJECTIVE: To evaluate the complementary roles of exhaled nitric oxide (NO) and spirometry to predict asthma exacerbations requiring one or more tapering courses of systemic corticosteroids. METHODS: We prospectively studied 44 nonsmoking asthmatics (24 women) aged 51 +/- 21 years (mean +/- SD) who were clinically stable for 6 weeks and receiving 250 mug of fluticasone/50 mug of salmeterol or equivalent for 3 years. Total exhaled NO (FENO), small airway/alveolar NO (CANO), large airway NO flux (J'awNO), and spirometry were measured. RESULTS: Baseline FEV(1) was 2.1 +/- 0.7 L, 70 +/- 20% of predicted after 180 mug of albuterol. Twenty-two of 44 asthmatics had one or more exacerbations over 18 months, 16 of 22 asthmatics had two exacerbations, and 6 of 22 asthmatics were hospitalized, including 1 asthmatic with near-fatal asthma. When baseline FEV(1) was </= 76% predicted, exacerbations occurred in 20 of 31 asthmatics (65%). If baseline FEV(1) was > 76% of predicted, exacerbations occurred only in 2 of 13 asthmatics (15%) [p = 0.003, chi(2)]. Using a receiver operating characteristic (ROC) curve for first exacerbation, the area under the curve was 0.67 with cutoff FEV(1) of 76% of predicted (sensitivity, 0.91; specificity, 0.50; positive predictive value, 0.65; negative predictive value, 0.85; positive likelihood ratio [LR(+)], 1.8; negative likelihood ratio [LR(-)], 0.18). When baseline FENO was >/= 28 parts per billion (ppb), exacerbations occurred in 13 of 17 asthmatics (76%); if baseline FENO was < 28 ppb, exacerbations occurred in only 9 of 27 asthmatics (33%) [p = 0.005, chi(2)]. Using the ROC curve for first exacerbation, the area under the curve was 0.71 with FENO cutoff point of 28 ppb (sensitivity, 0.59; specificity, 0.82; positive predictive value, 0.77; negative predictive value, 0.87; LR(+), 3.3; LR(-), 0.5). Independent of baseline FEV(1), FENO >/= 28 ppb increased the relative risk (RR) for exacerbation by 3.4 (95% confidence interval [CI], 1.3 to 9.1; Mantel-Haenszel, p = 0.007). An abnormal increase in CANO increased RR by 3.0 (95% CI, 0.9 to 9.9; p = 0.04), and abnormal J'awNO increased RR by 2.4 (95% CI, 1.0 to 5.6; p = 0.04). Independent of baseline FENO, FEV(1) </= 76% predicted increased RR by 1.7 (95% CI, 1.0 to 2.7; p = 0.02). Combined baseline FENO >/= 28 ppb and FEV(1) </= 76% of predicted identified 13 stable asthmatics with 85% probability for future exacerbation, whereas 9 asthmatics with FENO < 28 ppb and FEV(1) > 76% of predicted had a 0% probability of exacerbation. CONCLUSION: Combining FENO and FEV(1) percentage of predicted can stratify risk for asthma exacerbation.

Adrenal Cortex Hormones↗
PubMed2006

Measuring and predicting maximal aerobic power in international-level intermittent sport athletes.

AIM: The purpose of this study was to measure actual VO2max during the multi-stage fitness test (MSFT) and to compare this with predicted values obtained using previously established, commonly used methods. We also wanted to determine a new and more accurate regression equation for the prediction of VO2max in intermittent sport athletes. METHODS: Twenty-six, elite, male, intermittent sport athletes performed the MSFT with oxygen uptake (VO2) and heart rate (HR) measured throughout. Paired t-tests were used to compare measured VO2max with predicted VO2max. Linear regression was used to determine the equation for the prediction of VO2max from the total number of shuttles completed. RESULTS: There were no differences between the two methods of predicting VO2max, however, both predicted values (53.6+/-3.9 and 51.3+/-4 mL x kg(-1) x min(-1)) were significantly lower (9.3% and 13.2%, respectively) than measured VO2max (59.1+/-6.6 mL x kg(-1) x min(-1), P < 0.001). Correlations between measured and predicted VO2max were similar for both prediction methods (r = 0.61, P = 0.013 and r = 0.68 and P = 0.004). We present a new prediction equation [Y (VO2max, mL x kg(-1) x min(-1)) = 0.38 x total number of shuttles completed +25.98] (where R = 0.69; R2 = 0.48; SEE = 4.9 mL x kg(-1) x min(-1); SEE% = 8.3) which provides a more valid method of predicting actual max in intermittent sport athletes. CONCLUSIONS: A new regression equation to predict VO2max in intermittent sport athletes has been established. Whilst some error in predicting VO2max still exists, the new equation will provide coaches and sport-scientists with a more suitable equation with which to predict VO2max in intermittent sport athletes.

Adolescent↗
PubMed1996

[Lung scintigraphy and ergospirometry in prediction of postoperative course in lung resection candidates with increased risk of postoperative complications].

Patients with impaired pulmonary function are at increased risk for the development of postoperative complications. We therefore analyzed 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. Twenty-five (mean age 63 y; 17 m) out of 84 consecutive lung resection candidates were considered at increased risk for postoperative complications due to impaired pulmonary function (FEV1 < 2L or diffusion for carbon monoxide (DLCO) < 50% predicted, or FEV1 and DLCO < or = 80% predicted combined with New York Heart Association dyspnea index > or = 2). Candidates underwent radionuclide perfusion scans and exercise testing to predict postoperative ( = ppo) values for FEV1, DLCO and maximal O2-uptake (VO2max). They all underwent thoracotomy for neoplastic lesions; 7 had pneumonectomies, 18 lobectomies. Six had postoperative complications (within 30 days), of whom three died. Three and 6 months postoperatively, pulmonary function tests and VO2max were repeated. 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 FEV1r = 0.78 and 0.81; for DLCOr = 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 DLCO was lower than the observed value (ml/min/mmHg: 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 DLCO (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 percent predicted) (p < 0.05) than patients without complication (group A). AVO2max-ppo < 10/ml/kg/min was associated with a 100% mortality. Although FEV1-ppo and DLco-ppo were lower in group B the difference did not reach significance. We conclude that radionuclide-based calculations of postoperative VO2max are predictive of perioperative 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.

Adult↗
PubMed1996

Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class.

The success rates reported for secondary structural class prediction with different methods are contradictory. On one side, the problem of recognizing the secondary structural class of a protein knowing only its amino acid composition appears completely solved by simply applying jury decision with an elliptically scaled distance function. Chou and coworkers repeatedly (see Crit. Rev. Biochem. Mol. Biol. 30:275-349, 1995) published prediction accuracies near 100%. On the other hand, traditional secondary structure prediction techniques achieve success rates of about 70% for the secondary structural state per residue and about 75% for structural class only with extensive input information (full sequence of the query protein, its amino acid composition and length, multiple alignments with homologous sequences). In this article, we resolve the paradox and consider (1) the question of the secondary structural class definition, (2) the role of the representativity of the test set of protein tertiary structure for the current state of the Protein Data Bank (PDB); and (3) we estimate the real impact of amino acid composition on secondary structural class. We formulate three objective criteria for a reasonable definition of secondary structural classes and show that only the criterion of Nakashima et al. (J. Biochem. 99:153-162, 1986) complies with all of them. Only this definition matches the distribution of secondary structural content in representative PDB subsets, whereas other criteria leave many proteins (up to 65% of all PDB entries) simply unassigned. We review critically specialized secondary-structural class prediction methods, especially those of Chou and coworkers, which claim almost 100% accuracy using only amino acid composition, and resolve the paradox that these prediction accuracies are better than those from secondary structure predictions from multiple alignments. We show (i) that these techniques rely on a preselection of test sets which removes irregular proteins and other proteins without any class assignment (about 35% of all PDB entries); and (ii) that even for preselected representative test sets, the success rate drops to 60% and lower for a 4-type classification (alpha, beta, alpha + beta, alpha/beta). The prediction accuracies fall to about 50% if the secondary structural class definition of Nakashima et al. is applied and only few irregular proteins are preselected and removed from automatically generated, representative subsets of the PDB. We have applied two new vector decomposition methods for secondary structural content prediction from amino acid composition alone, with and without consideration of amino acid compositional coupling in the learning set of tertiary structures respectively, to the problem of class prediction and achieve about 60% correct assignment among four classes (alpha, beta, mixed, irregular) as well as single sequence-based secondary structure prediction methods like GORIII and COMBI. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein and that consideration of compositional coupling does not improve the prediction success. The prediction program SSCP offering secondary structural class assignment for query compositions and sequences has been made available as a World Wide Web and E-mail service.

Amino Acids↗
PubMed2005

Combining prediction of secondary structure and solvent accessibility in proteins.

Owing to the use of evolutionary information and advanced machine learning protocols, secondary structures of amino acid residues in proteins can be predicted from the primary sequence with more than 75% per-residue accuracy for the 3-state (i.e., helix, beta-strand, and coil) classification problem. In this work we investigate whether further progress may be achieved by incorporating the relative solvent accessibility (RSA) of an amino acid residue as a fingerprint of the overall topology of the protein. Toward that goal, we developed a novel method for secondary structure prediction that uses predicted RSA in addition to attributes derived from evolutionary profiles. Our general approach follows the 2-stage protocol of Rost and Sander, with a number of Elman-type recurrent neural networks (NNs) combined into a consensus predictor. The RSA is predicted using our recently developed regression-based method that provides real-valued RSA, with the overall correlation coefficients between the actual and predicted RSA of about 0.66 in rigorous tests on independent control sets. Using the predicted RSA, we were able to improve the performance of our secondary structure prediction by up to 1.4% and achieved the overall per-residue accuracy between 77.0% and 78.4% for the 3-state classification problem on different control sets comprising, together, 603 proteins without homology to proteins included in the training. The effects of including solvent accessibility depend on the quality of RSA prediction. In the limit of perfect prediction (i.e., when using the actual RSA values derived from known protein structures), the accuracy of secondary structure prediction increases by up to 4%. We also observed that projecting real-valued RSA into 2 discrete classes with the commonly used threshold of 25% RSA decreases the classification accuracy for secondary structure prediction. While the level of improvement of secondary structure prediction may be different for prediction protocols that implicitly account for RSA in other ways, we conclude that an increase in the 3-state classification accuracy may be achieved when combining RSA with a state-of-the-art protocol utilizing evolutionary profiles. The new method is available through a Web server at http://sable.cchmc.org.

Amino Acid Sequence↗