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PubMed2006

Utility of near-infrared reflectance spectroscopy to predict nutrient composition and in vitro digestibility of total mixed rations.

Total mixed ration (TMR) samples (n = 110) were analyzed for dry matter (DM), crude protein (CP), soluble CP, neutral detergent fiber (NDF), NDF CP, starch, ash, fat, total ethanol-soluble carbohydrate, and nonfiber carbohydrate (NFC). Rapidly and slowly degraded and undegraded in situ CP fractions and in vitro DM, organic matter, and NDF digestibility were determined on each TMR. The TMR were scanned using near-infrared reflectance spectroscopy (NIRS); spectra were retained with NIRS calibration and cross-validation statistics were determined using partial least squares regression methods. The CP, NDF, starch, in vitro DM, and in vitro indigestible NDF contents of TMR were predicted by NIRS with good degrees (R2 >0.85) of accuracy with proportionally low standard errors of prediction. Moderate utility of NIRS to predict the NFC (R2 = 0.83) and fat content (R2 = 0.81) of TMR was observed. Rapidly, slowly, and undegraded in situ CP fractions in TMR were not well predicted by NIRS. Similarly, soluble CP, NDF CP, total ethanol-soluble carbohydrate, and in vitro NDF digestibility (% of NDF) were not well predicted by NIRS. Ratios of nutrient range to reference laboratory method error were calculated and found to be positively related (R2 = 0.84) to NIRS predictability of a given TMR nutrient, suggesting some laboratory procedures were not precise enough to yield suitable NIRS predictions. Data suggest that NIRS has utility to predict basic nutrients such as CP, NDF, starch, NFC, and fat in TMR. However, difficulty was observed using NIRS in predicting key biological nutrients in TMR such as in situ CP fractions and in vitro NDF digestibility. Difficulty of NIRS in predicting these nutrients is related to the level of reference method error in relationship to the range of nutrient values in TMR, but other sources of prediction error may exist.

Animal Feed↗
PubMed1999

Comparison of clinical staging algorithms and 111indium-capromab pendetide immunoscintigraphy in the prediction of lymph node involvement in high risk prostate carcinoma patients.

BACKGROUND: The pretherapy prediction of occult lymph node involvement and the avoidance of otherwise futile and potentially morbid definitive local therapy is paramount in men with newly diagnosed prostate carcinoma. To identify patients with prostate carcinoma who likely have lymph node involvement and would benefit from staging lymphadenectomy prior to definitive local therapy, the authors compared the ability of several predictive staging algorithms and a radiolabeled monoclonal antibody scan to predict lymphatic metastases prior to treatment. METHODS: Between August 1991 and June 1994, 198 men with clinical T2 or T3 classified (TNM) prostate carcinoma (bone scan negative) who were at high risk of lymph node involvement underwent a 111In-capromab pendetide scan prior to staging lymphadenectomy. Several predictive models based on preoperative prostate specific antigen level, biopsy Gleason score, and clinical stage were selected to predict those men having a > or =20% probability of lymph node involvement. The ability to predict pathologic stage using several clinical algorithms and the monoclonal antibody scan was compared with pathologic examination of the lymph nodes. RESULTS: Overall, 39% of the pelvic lymph node specimens were positive for metastatic disease by pathologic analysis. Published algorithms predicting lymph node metastases had a positive predictive value (PPV) ranging from 40.5% to 46.6% and an area under the receiver operating characteristic curve (AUC) ranging from 0.52 to 0.61. The monoclonal antibody scan had a PPV of 66.7% and an AUC of 0.71. The differences between the PPV and the AUC for the individual clinical algorithms when compared with immunoscintigraphy were statistically significant. Combining the radiolabeled monoclonal antibody scan with clinical predictive models, a PPV of up to 72.1% could be obtained. CONCLUSIONS: These data suggest that the PPVs for the clinical predictive algorithms are similar and that the PPV of the radiolabeled monoclonal antibody scan alone or in combination with the algorithms has additional value in predicting lymph node involvement in prostate carcinoma patients at high risk of regional disease spread. These algorithms and the 111In-capromab pendetide scan may be used for the appropriate selection of candidates for definitive local therapy in men with clinically localized prostate carcinoma and significant risk of lymph node involvement.

Aged↗
PubMed1999

New methods for accurate prediction of protein secondary structure.

A primary and a secondary neural network are applied to secondary structure and structural class prediction for a database of 681 non-homologous protein chains. A new method of decoding the outputs of the secondary structure prediction network is used to produce an estimate of the probability of finding each type of secondary structure at every position in the sequence. In addition to providing a reliable estimate of the accuracy of the predictions, this method gives a more accurate Q3 (74.6%) than the cutoff method which is commonly used. Use of these predictions in jury methods improves the Q3 to 74.8%, the best available at present. On a database of 126 proteins commonly used for comparison of prediction methods, the jury predictions are 76.6% accurate. An estimate of the overall Q3 for a given sequence is made by averaging the estimated accuracy of the prediction over all residues in the sequence. As an example, the analysis is applied to the target beta-cryptogein, which was a difficult target for ab initio predictions in the CASP2 study; it shows that the prediction made with the present method (62% of residues correct) is close to the expected accuracy (66%) for this protein. The larger database and use of a new network training protocol also improve structural class prediction accuracy to 86%, relative to 80% obtained previously. Secondary structure content is predicted with accuracy comparable to that obtained with spectroscopic methods, such as vibrational or electronic circular dichroism and Fourier transform infrared spectroscopy.

Algal Proteins↗
PubMed2003

Relationship of predicted postoperative product to postpneumonectomy cardiopulmonary complications.

BACKGROUND: This retrospective analytic study was to evaluate diffusing capacity of the lung for carbon monoxide (DLCO) and predicted postoperative product (PPP) as predictors of postpneumonectomy cardiopulmonary complications. METHODS: One-hundred fifty-one patients underwent pneumonectomy at Vancouver General Hospital from January 1992 to December 1997. The PPP was obtained by multiplying the predicted postoperative (ppo) FEV1 by the ppo DLCO, both expressed as % of predicted. The ppo FEV1 and DLCO were derived by calculating the proportional loss of functional lung from the resected lung. We also evaluated a new index, the measured product (MP), obtained by multiplying the measured preoperative FEV1 by DLCO, both expressed as % of predicted. RESULTS: Patients with complications had lower FEV1, lower DLCO, lower MP, lower ppo FEV1, lower ppo DLCO, and lower PPP than patients without complications. DLCO at 70% of predicted was the best predictor of postoperative complications, while PPP at 1400 was similar to MP at 5000 and ppo DLCO at 40% of predicted in predicting postoperative complications. The complication rate was 88% in patients with DLCO < 70% of predicted, compared with a complication rate of 19% in patients with DLCO > or = 70% of predicted (sensitivity = 68%, specificity = 93%), while the complication rate was 66% in patients with PPP < 1400, compared with a complication rate of 25% in patients with PPP > or = 1400 (sensitivity = 63%, specificity = 78%). CONCLUSIONS: DLCO < 70% of predicted is associated with increased risk of complications following pneumonectomy. PPP determined preoperatively allows a patient with a critically low value (< 40% of predicted) for one variable (either ppo FEV1 or ppo DLCO) to be accepted for surgery on the basis of a good value in the other. Patients with PPP > or = 1400 have a relatively low postpneumonectomy complication rate.

Female↗
PubMed2004

Prediction of cardiovascular risk in hemodialysis patients by data mining.

OBJECTIVES: The objective of this work was to contribute to the development, validation and application of data mining methods for prediction in decision support systems in medicine. The particular focus was on the prediction of cardiovascular risk factors in hemodialysis patients, specifically the interventricular septum (IVS) thickness of the heart of individual patients as an important quantitative indicator to diagnose left ventricular hypertrophy. The work was based on data from 63 long-term hemodialysis patients of the KfH Dialysis Centre in Jena, Germany. METHODS: The approach applied is based on data mining methods and involves four major steps: data based clustering, cluster based rule extraction, rulebase construction and cluster and rule based prediction. The methods employed include crisp and fuzzy algorithms. At each step, logical and medical validation of results was carried out. Different sets of randomly selected patient data were used to train, test and optimize the clusterbases and rulebases for prediction. RESULTS: Using the best clusterbase/rulebase combination designed, the IVS thickness cluster ('small' or 'large') was predicted correctly for 30 of the 35 patients with known IVS values in the training data set; no patient was predicted incorrectly and 5 were parity predicted. For the test data set, 4 of the 6 patients with known IVS values were predicted correctly, no patient incorrectly and 2 parity. These results did not substantially differ from those obtained using the second best clusterbase/rulebase combination which was finally recommended for use based on further performance criteria. The prediction of the IVS thickness clusters of the 22 patients with unknown IVS values also yielded good results that were (and could only be) validated by a medical individual risk assessment of these patients. CONCLUSIONS: The approach applied proved successful for the cluster and rule based prediction of a quantitative variable, such as IVS thickness, for individual patients from other variables relevant to the problem. The results obtained demonstrate the high potential of the approach and the methods developed and validated to support decision-making in hemodialysis and other fields of medicine by individual risk prediction.

Algorithms↗
PubMed2004

Population pharmacokinetic model of valproate and prediction of valproate serum concentrations in children with epilepsy.

AIM: Using sparse data of valproate (VPA) serum concentrations to build a population pharmacokinetic (PPK) model of VPA in Chinese children with epilepsy and to predict serum concentrations for new patients using a Bayesian approach. METHODS: Two hundred epileptic children, whose VPA serum concentrations were collected, were divided randomly into two groups (A and B, n=100 each). The PPK parameter values of group A were calculated to establish a PPK Model by using the NPEM Program of USC*PACK software. Based on it, VPA serum concentrations of group B were predicted with the Bayesian Fitting Program of the USC*PACK software. To assess the accuracy and precision of prediction, a paired-comparisons t-test was run between predicted and observed concentrations, and then the mean prediction error (MPE), mean square prediction error (MSPE), root mean square prediction error (RMSPE), and coincidence rates for different percentages of prediction error were all calculated. RESULTS: Optimum PPK parameters were: Ka, 2.522+/-2.743 h(-1); Vs, 0.329+/-0.496 L/kg; and Kel, 0.0438+/-0.0384 h(-1). For group B, there was no significant difference between predicted and observed concentrations. MPE was -0.43 mg/L, MSPE was 115.40 (mg/L)2, and RMSPE was 5.47 mg/L. The coincidence rates for percentages of prediction error, which were less than 5 %, 10 %, 15 %, 20 %, 25 %, and 30 %, were 62 %, 74 %, 82 %, 85 %, 89 %, and 93 %, respectively. CONCLUSION: A PPK model of VPA in epileptic children was successfully established. Based on it, VPA serum concentrations can be predicted accurately with a Bayesian approach.

Adolescent↗
PubMed2005

Applicability of commonly used Caucasian prediction equations for spirometry interpretation in India.

BACKGROUND & OBJECTIVE: The applicability of Caucasian prediction equations in interpreting spirometry data in Indian patients has not been studied. The present study was undertaken to see if Caucasian and north Indian prediction equations can be used interchangeably while interpreting routine spirometric data. METHODS: Forced vital capacity (FVC), forced expiratory volume in first second (FEV(1)), and FEV(1)/FVC ratio were recorded from 14733 consecutive spirometry procedures in adults. Predicted values and lower limits of normality were calculated using regression equations previously derived at this centre, and four commonly used Caucasian equations described by Knudson, Crapo, European Community for Coal and Steel (ECCS) and the Third National Health and Nutrition Examination Survey (NHANES III). For men, 90 per cent of predicted values were also derived. Kappa estimates were used to study agreement, and Bland Altman analysis was performed to quantify differences, between interpretations from Indian and Caucasian equations. Receiver operating characteristic (ROC) curves were constructed to assess utility of using a fixed percentage of Caucasian predicted values in categorizing FVC or FEV(1) as abnormal. RESULTS: The use of Caucasian prediction equations (and 90% of predicted values in men) resulted in poor agreement with Indian equation in most height and age categories among both men and women. Bland Altman analysis revealed a large bias and wide confidence limits between Caucasian and Indian equations, indicating that the two cannot be used interchangeably. ROC analysis failed to yield good results with use of any single fixed percentage of Caucasian predicted value while categorizing FVC or FEV(1). INTERPRETATION & CONCLUSION: Our results showed that the use of Caucasian prediction equations, or a fixed percentage of their predicted values, resulted in misinterpretation of spirometry data in a significant proportion of patients. There is a need to assess performance of more than one regression equation before choosing any single prediction equation.

Adolescent↗
PubMed1995

Accuracy of clinicians in predicting site and type of lesion as well as outcome in horses with colic.

OBJECTIVE: To assess the ability of clinicians to predict the site and type of lesion as well as outcome in horses with colic. DESIGN: Prospective case study. SAMPLE POPULATION: 139 horses admitted for evaluation of signs of colic. PROCEDURE: Six interns and residents examined horses with colic and predicted the segment of intestine that was affected, the type of lesion, and whether the horse would survive to discharge. Accuracy of prediction of site and type of lesion and survival prediction was compared between the first and second halves of the year, using chi 2 analysis and 95% confidence intervals on sensitivity and specificity. chi 2 Analysis was used to assess accuracy between predicted site and type of lesion and intraoperative or necropsy findings and to assess accuracy between predicted survival and actual outcome. RESULTS: Significant association existed between predicted segment of affected intestine or type of lesion and intraoperative findings (P < 0.05). There was a significant association between predicted survival and outcome (P < 0.001). Accuracy of survival prediction improved significantly (P = 0.002) during the year. CLINICAL IMPLICATIONS: Clinicians can accurately predict horses with colic that will survive surgery on the basis of clinical impressions. The ability to predict those horses that will survive improves with training.

Animals↗
PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗
PubMed1999

Validation in a community hospital setting of a clinical rule to predict preserved left ventricular ejection fraction in patients after myocardial infarction.

BACKGROUND: A previous study showed that patients with previous myocardial infarction (MI) who meet 4 simple clinical and/or electrocardiographic criteria have a left ventricular ejection fraction (LVEF) of 40% or greater, with a positive predictive value of 98%. The objective of this study was to validate this clinical rule in the community hospital setting. METHODS: Retrospective chart review in a 330-bed community hospital. Two hundred thirteen consecutive patients with MI were identified between June 1, 1993, and March 31, 1995. Left ventricular ejection fraction was predicted in a blinded fashion by means of the clinical rule before the actual LVEF test was reviewed. RESULTS: We identified 213 patients admitted with the primary discharge diagnosis of acute MI. All patients met standard clinical and enzymatic definitions for acute MI and had at least 1 measure of LVEF, such as echocardiography, ventricular angiography, or gated blood pool scan. The clinical rule predicted that 83 patients (39.0%) would have an LVEF of 40% or greater. Of these 83 patients, 71 had an ejection fraction of 40% or greater, for a positive predictive value of 86%. Of the 12 patients who were incorrectly predicted to have a preserved LVEF, 6 (50%) had an index non-Q-wave anterior MI (P<.001). Reanalyzing the patient population with a fifth variable (anterior non-Q-wave MI) added to the original 4 variables increased the positive predictive value to 91%. CONCLUSION: This simple clinical prediction rule has a positive predictive value of 86% when applied in the community hospital setting. Patients with anterior non-Q-wave MI may be 1 group in whom the rule is inaccurate, and expanding the clinical rule to 5 variables may increase the positive predictive value. When a technology-based assessment of left ventricular function is considered in patients after an MI, this prediction rule may allow for a more cost-effective patient selection, and as many as 40% of patients who have had acute MIs may require no testing at all.

Aged↗
PubMed2004

Improved prediction for N-termini of alpha-helices using empirical information.

The prediction of the secondary structure of proteins from their amino acid sequences remains a key component of many approaches to the protein folding problem. The most abundant form of regular secondary structure in proteins is the alpha-helix, in which specific residue preferences exist at the N-terminal locations. Propensities derived from these observed amino acid frequencies in the Protein Data Bank (PDB) database correlate well with experimental free energies measured for residues at different N-terminal positions in alanine-based peptides. We report a novel method to exploit this data to improve protein secondary structure prediction through identification of the correct N-terminal sequences in alpha-helices, based on existing popular methods for secondary structure prediction. With this algorithm, the number of correctly predicted alpha-helix start positions was improved from 30% to 38%, while the overall prediction accuracy (Q3) remained the same, using cross-validated testing. Although the algorithm was developed and tested on multiple sequence alignment-based secondary structure predictions, it was also able to improve the predictions of start locations by methods that use single sequences to make their predictions. Furthermore, the residue frequencies at N-terminal positions of the improved predictions better reflect those seen at the N-terminal positions of alpha-helices in proteins. This has implications for areas such as comparative modeling, where a more accurate prediction of the N-terminal regions of alpha-helices should benefit attempts to model adjacent loop regions. The algorithm is available as a Web tool, located at http://rocky.bms.umist.ac.uk/elephant.

Databases, Protein↗
PubMed2006

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes.

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.

Binding Sites↗
PubMed2004

Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome.

The positive and negative predictive values are standard measures used to quantify the predictive accuracy of binary biomarkers when the outcome being predicted is also binary. When the biomarkers are instead being used to predict a failure time outcome, there is no standard way of quantifying predictive accuracy. We propose a natural extension of the traditional predictive values to accommodate censored survival data. We discuss not only quantifying predictive accuracy using these extended predictive values, but also rigorously comparing the accuracy of two biomarkers in terms of their predictive values. Using a marginal regression framework, we describe how to estimate differences in predictive accuracy and how to test whether the observed difference is statistically significant.

Biomarkers↗
PubMed1996

A mechanistic algorithm for predicting blood:air partition coefficients of organic chemicals with the consideration of reversible binding in hemoglobin.

The objectives of the present study were (i) to develop a mechanistic algorithm for predicting blood:air partition coefficients (PCs) of volatile organic chemicals (VOCs), and (ii) to apply this algorithm to predict the rat blood:air PCs of several VOCs. The approach consisted initially of developing an algorithm to predict the blood:air PCs of VOCs solely based on the solubility phenomenon and then of extending the algorithm to include protein binding. The algorithm based on solubility phenomenon predicted blood:air PCs by dividing the estimated solubility of chemicals in blood by their saturable vapor concentrations at 37 degrees C. The rat blood:air PCs predicted using this algorithm were in close agreement with the experimental values for relatively hydrophilic VOCs such as ketones, alcohols, acetate esters, and diethyl ether (with an average ratio of 0.80 between predicted and experimental values), whereas there was a marked discrepancy in the case of relatively lipophilic VOCs such as alkanes, haloalkanes, and aromatic hydrocarbons (with an average ratio of 0.21 between predicted and experimental values). This discrepancy was hypothesized to be due to the occurrence of reversible binding of these substances in rat hemoglobin based on literature evidence of the existence of hydrophobic holes (or "xenon-binding" pockets). The association constants (Ka) for the presumed reversible hemoglobin binding of several alkanes, haloalkanes, and aromatic hydrocarbons were estimated from the difference between chemical concentration in rat erythrocytes predicted by the solubility-based algorithm and that deduced from the previously published experimental blood:air PCs for these chemicals (which presumably included contribution of hemoglobin binding in addition to "true" solubility). The Ka values estimated in this manner ranged from 504 to 4725 M-1 for the chemicals investigated in the present study. The a priori predictions of the percentage of several VOCs (diethyl ether, methyl isobutyl ketone, n-hexane, toluene, and chloroform) in rat erythrocytes obtained with the algorithm using these Ka estimates corresponded well with previously published experimental data. The mechanistic algorithm developed in the present study should be useful for predicting the "apparent" blood:air PCs of VOCs regardless of exposure concentrations, by accounting for the relative contributions of both the true chemical solubility and reversible hemoglobin binding.

Alcohols↗
PubMed1999

[Validation of a risk score for prediction of vomiting in the postoperative period].

BACKGROUND: A risk score to predict postoperative vomiting was presented in a recent issue of this journal. In the present study this score was evaluated at another hospital under different surgical and anaesthetic conditions. Furthermore, we examined whether the score, which was originally designed to predict the occurrence of postoperative vomiting (POV) only, is also useful for prediction of postoperative nausea and vomiting (PONV). METHODS: The risk score was applied to 226 patients undergoing inpatient orthopaedic surgery under standardised general anaesthesia (propofol, desflurane in N(2)O/O(2), fentanyl, vecuronium, postoperative opioid analgesia). For 24 hours postoperatively, the patients were followed up for the occurrence of nausea, retching, and vomiting. Perioperatively, risk factors for POV were recorded (gender, age, smoking habits, history of previous PONV or motion sickness, duration of anaesthesia). Using these risk factors the individual risk for suffering POV was calculated for each patient. With these data two ROC-curves (for prediction of POV and PONV respectively) were constructed and the area under the ROC-curve (AUC) as a means of the prediction probabilities of the score was calculated. RESULTS: The incidence of POV as predicted by the score (22,8%) fits well to the actual incidence of this event (19,5%). The score predicts the occurrence of POV significantly better than can be expected by a random estimation. In spite of different surgical and anaesthetic conditions, the accuracy of the prediction in the present dataset was not significantly different from that reported by the authors of the scores in their validation set. Furthermore, the prediction properties for POV (AUC: 0,73) were not different from the prediction of PONV (AUC: 0,72). CONCLUSION: The present risk score provides valid prognostic results even under modified surgical and anaesthetic conditions, and, thus, may obviously be applied to other institutions. Furthermore our results support the hypothesis, that individual risk factors rather than the type of surgery or anaesthetic management have a major impact on the occurrence of POV and PONV.

Adult↗
PubMed2006

Predictive value of upper gastrointestinal studies versus clinical signs for gastrointestinal leaks after laparoscopic gastric bypass.

BACKGROUND: The utility of routine upper gastrointestinal (UGI) studies after laparoscopic Roux-en-Y gastric bypass (LRYGB) is a matter of great debate. Because the morbidity and mortality rates associated with an unrecognized postoperative leak are high after LRYGB, diagnosis of a postoperative leak earlier would be of benefit. Clinical signs, however, may predict the diagnosis of a postoperative leak more often. This study explored the hypothesis that UGI studies are more predictive than clinical signs for the early diagnosis of a postoperative leak after LRYGB. METHODS: All patients who underwent LRYGB at the authors' institution were included in this study. Charts were reviewed to examine immediate clinical signs (heart rate, temperature, and white blood cell count within the first 24 h), UGI studies, and clinical course. Sensitivity, specificity, positive predictive value, negative predictive value, and efficiency of clinical signs and UGI studies were calculated. RESULTS: This study included 245 patients with a 3% rate of leak. The positive and negative predictive value of UGI studies were 67% and 99%, respectively. Only an elevated white blood count had a better predictive value (100% for negative predictive value). The efficiency of UGI studies (98%) was better than that of heart rate (83%), white blood count (8%), or temperature (95%). CONCLUSIONS: According to our data, UGI studies are the most predictive of an early leak diagnosis. Clinical signs alone may not be as useful in predicting leaks early after laparoscopic gastric bypasses. Routine early postoperative UGI studies are a reasonable approach to predicting leaks after LRYGB.

Adolescent↗
PubMed2002

Comparison of POSSUM with P-POSSUM for prediction of mortality in infrarenal abdominal aortic aneurysm repair.

The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) is a simple and valid scoring system in predicting mortality and morbidity rates. The Portsmouth predictor equation (P-POSSUM) has been shown to be a more accurate predictor of death than the POSSUM in vascular patients. The length of hospital stay (LOS) equation has been suggested to be of value in predicting total length of stay. The aim of this study was to test the validity of the POSSUM, P-POSSUM, and LOS in predicting outcome of patients undergoing abdominal aortic aneurysm (AAA) repair. POSSUM scores in 118 patients who underwent AAA repair by a single consultant were recorded retrospectively. Observed rates of mortality, morbidity, and length of hospital stay were correlated with the rates predicted by POSSUM, P-POSSUM, and LOS equations in three groups: all cases, 93 elective repairs, and emergency AAA repairs. The POSSUM and the P-POSSUM performed similarly in terms of accuracy of prediction, with all predicted values being not significantly different from those observed. The POSSUM tended to overpredict mortality compared to the P-POSSUM. The POSSUM predicted morbidity well. The LOS equation failed to predict significantly observed total hospital stay. POSSUM and P-POSSUM outcome risk equations are thus valid in predicting mortality for all cases and emergency AAA repairs. The POSSUM morbidity equation predicts complications quantitatively.

Aged↗
PubMed2005

Evaluation of P-POSSUM in surgery for obstructing colorectal cancer and correlation of the predicted mortality with different surgical options.

PURPOSE: This study examined the accuracy of Portsmouth Physiologic and Operative Severity Score for enUmeration of Mortality and Morbidity system (P-POSSUM) in predicting the mortality of patients who underwent operations for obstructing colorectal cancer. It also is attempted to analyze the actual mortality and the predicted P-POSSUM mortality of different surgical options for obstructing left-sided cancer. METHODS: Data on patients who underwent surgery for obstructing colorectal cancer during 1998 to 2002 were collected. Mortality predicted by P-POSSUM was compared to the actual mortality with the method of linear analysis. The accuracy of using P-POSSUM to predict mortality in this group of patients was assessed by Hosmer and Lemeshow goodness of fit test and Receiver Operator Characteristic curve analysis. The predicted and actual mortality of patients who underwent different surgical options also were analyzed. RESULTS: A total of 160 patients were included in the study and 18 patients died postoperatively. The operative mortality was 11.3 percent. P-POSSUM predicted overall mortality of 15 percent. The observed and predicted mortality was found to have no significant lack of fit (chi-squared = 5.98; degree of freedom = 3; P = 0.11). The area under Receiver Operator Characteristic curve analysis was 0.75. For patients with left-sided tumors, P-POSSUM predicted mortality and actual mortality of patients who had resection without anastomosis were both significantly higher than patients with single-stage resection and primary anastomosis (P = 0.044 and 0.011, respectively). CONCLUSIONS: P-POSSUM system is valid for prediction of overall mortality in patients with operations for obstructing colorectal cancer. Estimation of P-POSSUM predicted mortality during operation and its ability to correlate with choice of procedure is an area that is worth further study in emergency colorectal surgery.

Adult↗