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PubMed1998

Predicting the pathology results of radical prostatectomy from preoperative information: a validation study.

BACKGROUND: There are now over 13 published models for predicting the outcomes of radical prostatectomy using preoperative information. Because their ability to predict the pathology of the prostatectomy is key in deciding who benefits the most from this surgery, it is important to know how well these models work for new data. METHODS: The patients in this study were 100 men diagnosed with prostate carcinoma in the prostate specific antigen (PSA)-based screening program at Washington University Medical Center. To test the models, the authors used preoperative information and the published algorithms to predict postoperative pathology outcomes. Statistical methods included plots of predicted probability against observed probability, boxplots of predicted probability against observed outcomes, logistic regression, and linear regression. RESULTS: Although none of the published models predicted the outcomes of radical prostatectomy perfectly, those that predicted tumor volume performed best, and in general those that were multivariate also performed best. Nevertheless, the ability of any of these models to discriminate binary outcomes was not very great. CONCLUSIONS: The results of this study suggest that preoperative variables based on serum PSA and the results of needle biopsies can be used in multivariate models to predict tumor volume, but these models need to be improved. Predicting locally advanced tumor stage is likely to be more difficult and may require information beyond what needle biopsies can provide.

Aged↗
PubMed2026

Pedigree-assisted genotype imputation enables cost-effective genomic prediction in Penaeus vannamei.

Genomic selection in Penaeus vannamei has long been constrained by the high cost of dense genotyping. To address this limitation, we evaluated genotype imputation from a low-density 1 K panel to a medium-density 55 K panel of the "Yellow Sea Array No. 1" and examined its impact on genomic prediction for harvest body weight in P. vannamei. A four-generation pedigree including 30 great-grandparents, 39 grandparents, 100 parents, and 608 offspring was genotyped using the 55 K panel. A two-step experimental design was implemented to (i) assess the performance of different imputation algorithms under reference population scenarios with varying proportions of siblings, and (ii) compare six alternative reference population structures incorporating parents, ancestors, and siblings. Genotype imputation using the pedigree-based method FImpute v3.0 consistently achieved higher accuracy than the population-based method Beagle v5.5. Using this pedigree-assisted approach, imputation accuracy increased from 0.73 when only parental genotypes were used to 0.84 with the inclusion of 10% siblings, and subsequently plateaued at 0.87-0.90 when sibling representation reached 20%. Across the six reference population structures, imputation accuracy was primarily driven by the availability of parental genotypes, ranging from 0.50 to 0.56 in the absence of parents to 0.88-0.89 when both parents and ancestral generations were included. Accuracy remained high when both parents were available (0.84-0.87 with siblings; 0.73 without siblings) but declined substantially when only one parent was genotyped (0.65-0.68). Imputation accuracy was positively associated with both minor allele frequency (MAF) and linkage disequilibrium (max r2LD), with LD exerting the stronger influence. Heritability estimates derived from imputed 55 K genotypes were highly consistent with those obtained from the original 55 K data (0.39 ± 0.14 vs. 0.41 ± 0.14), indicating that genotype imputation did not compromise variance component estimation. In predictive ability analyses, pedigree-based BLUP (PBLUP) achieved higher predictive ability than genomic BLUP (GBLUP) based on the 1 K panel, with predictive abilities of 0.42-0.44 for PBLUP compared with 0.34-0.35 for GBLUP. Using imputed genotypes for genomic prediction further improved predictive ability relative to the true 1 K panel, yielding values ranging from 0.35 to 0.47. Notably, when parental genotypes were included in the reference population, GBLUP based on imputed genotypes surpassed the predictive ability of PBLUP and approached that achieved with the original 55 K genotypes (0.45-0.47). Collectively, these results provide the first empirical evidence that low- to medium-density genotype imputation, combined with pedigree information, can effectively support genomic prediction in P. vannamei. This study establishes a cost-efficient and scalable framework for implementing genomic selection in P. vannamei and provides a practical reference for the application of genomic selection in other aquaculture species with constrained breeding budgets.

Animals↗
PubMed2000

Mid-trimester beta-hCG levels incorporated in a multifactorial model for the prediction of severe pre-eclampsia.

Pre-eclampsia remains a major cause of perinatal morbidity and mortality worldwide. Proposed predicting tests for early detection of pregnant women destined to develop pre-eclampsia remain unsatisfactory. The aim of this study was to investigate the clinical utility of combining mid-trimester maternal serum beta-human chorionic gonadotrophin (MShCG) levels with selected clinical determining factors as a multifactorial predictive test for pre-eclampsia. Thirty-nine cases with mild pre-eclampsia and 56 with severe pre-eclampsia were recruited as the study groups. Normotensive women (957) were enrolled as controls. Potential determining risk factors for severe pre-eclampsia were selected using a multiple logistic regression to build various combined prediction models. A receiver-operator characteristic curve was employed to assess the performance of each prediction test for pre-eclampsia. The prediction efficacy of each test was examined by the area under the curve (AUC). Our data show that mid-trimester MShCG levels significantly correlated with severity of pre-eclampsia (Spearman rank correlation coefficient=0.195, p<0.001). Women with mild pre-eclampsia had a 2.61-times greater chance, while women with severe pre-eclampsia had a 6.13-times greater chance of having MShCG exceeding 2.0 multiples of the median than did women with a normal pregnancy. A combined prediction model composed of MShCG levels, body mass index (BMI), parity, and age as a predictive test for severe pre-eclampsia was superior to MShCG levels alone (AUC 0.765 versus 0.648). The integrated multifactorial model could identify women at risk early on for developing severe pre-eclampsia, with a sensitivity of 70% and a specificity of 71%. Thus, we demonstrate a potentially effective and convenient method by which women at risk for developing severe pre-eclampsia can be identified early, based on a multifactorial predictive model composed of midtrimester MShCG levels, BMI, parity, and age.

Adult↗
PubMed2003

The value of magnetic resonance cholangiopancreatography in predicting common bile duct stones in patients with gallstone disease.

BACKGROUND: The application of available predictive scoring systems for the detection of common bile duct (CBD) stones has not reduced the number of patients who undergo unnecessary endoscopic retrograde cholangiopancreatography. The aim of this study was to create a predictive model for CBD stones and to assess the value of magnetic resonance cholangiopancreatography (MRCP) in prediction. METHODS: In 1998, 366 patients with gallstone disease (118 males, 248 females; mean age 57 (range 8-84) years) underwent cholecystectomy. Statistical analysis was performed on patient data obtained at the time of first presentation. RESULTS: CBD stones were demonstrated in 43 (12 per cent) of 366 patients. The predictive model for common duct stones included ultrasonography showing CBD stones or bile duct dilatation, age greater than 60 years, fever, serum alkaline phosphatase level above 670 units/l and serum amylase level above 95 units/l. In patients with a predicted probability greater than 5 per cent, CBD stones were present in 11 per cent, compared with 1 per cent in patients with a probability of 5 per cent or less. MRCP had an observed sensitivity of 95 per cent, specificity of 100 per cent, positive predictive value of 100 per cent and negative predictive value of 98 per cent. CONCLUSION: In patients with a predicted probability for CBD stones of more than 5 per cent, MRCP is recommended in order to confirm the presence or absence of stones and as guidance in further management.

Adolescent↗
PubMed2005

Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy.

BACKGROUND: Gene expression profiling of prostate carcinoma offers an alternative means to distinguish aggressive tumor biology and may improve the accuracy of outcome prediction for patients with prostate carcinoma treated by radical prostatectomy. METHODS: Gene expression differences between 37 recurrent and 42 nonrecurrent primary prostate tumor specimens were analyzed by oligonucleotide microarrays. Two logistic regression modeling approaches were used to predict prostate carcinoma recurrence after radical prostatectomy. One approach was based exclusively on gene expression differences between the two classes. The second approach integrated prognostic gene variables with a validated postoperative predictive model based on standard variables (nomogram). The predictive accuracy of these modeling approaches was evaluated by leave-one-out cross-validation (LOOCV) and compared with the nomogram. RESULTS: The modeling approach using gene variables alone accurately classified 59 (75%) tissue samples in LOOCV, a classification rate substantially higher than expected by chance. However, this predictive accuracy was inferior to the nomogram (concordance index, 0.75 vs. 0.84, P = 0.01). Models combining clinical and gene variables accurately classified 70 (89%) tissue samples and the predictive accuracy using this approach (concordance index, 0.89) was superior to the nomogram (P = 0.009) and models based on gene variables alone (P < 0.001). Importantly, the combined approach provided a marked improvement for patients whose nomogram-predicted likelihood of disease recurrence was in the indeterminate range (7-year disease progression-free probability, 30-70%; concordance index, 0.83 vs. 0.59, P = 0.01). CONCLUSIONS: Integration of gene expression signatures and clinical variables produced predictive models for prostate carcinoma recurrence that perform significantly better than those based on either clinical variables or gene expression information alone.

Aged↗
PubMed2006

Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia.

BACKGROUND: Response in adult acute lymphoblastic leukemia (ALL) can be achieved in a majority of patients. However, unlike pediatric ALL, recurrence is common in adult ALL, and the ability to predict at an early stage which patients are most likely to experience recurrence may help in devising new therapeutic approaches to prevent recurrence. METHODS: Peripheral blood plasma from 57 patients with confirmed ALL was obtained before induction therapy for proteomic analysis. Follow-up continued for a median period of 71 weeks. For each plasma sample, 4 fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces, and 12 surface-enhanced laser desorption/ionization (SELDI) spectra were generated. Peaks that correlated with recurrence were identified and decision trees were constructed and evaluated, using only 2 peaks per predictive tree. RESULTS: The best decision trees provided strong positive prediction of recurrence, with correct predictions 84% to 92% of the time, whereas negative prediction of patients who did not experience recurrence was less robust, with 62% to 74% accuracy. Prediction of recurrence was independent of cytogenetics, bone marrow blast count, lactate dehydrogenase, beta-2-microglobulin, or surface markers. Positive prediction of L3 morphological classification was achieved in 80% of test cases. CONCLUSIONS: Peripheral blood plasma is adequate to predict clinical behavior in ALL patients irrespective of the percentage of bone marrow blasts. Proteomic analysis of plasma offers a useful approach for profiling patients with ALL.

Adolescent↗
PubMed2004

Concise prediction models of anticancer efficacy of 8 drugs using expression data from 12 selected genes.

We developed concise, accurate prediction models of the in vitro activity for 8 anticancer drugs (5-FU, CDDP, MMC, DOX, CPT-11, SN-38, TXL and TXT), along with individual clinical responses to 5-FU using expression data of 12 genes. We first performed cDNA microarray analysis and MTT assay of 19 human cancer cell lines to sort out genes which were correlative in expression levels with cytotoxicities of the 8 drugs; we selected 13 genes with proven functional significance to drug sensitivity from a huge number of potent prediction marker genes. The correlation significance of each was confirmed using expression data quantified by real-time RT-PCR, and finally 12 genes (ABCB1, ABCG2, CYP2C8, CYP3A4, DPYD, GSTP1, MGMT, NQO1, POR, TOP2A, TUBB and TYMS) were selected as more reliable predictors of drug response. Using multiple regression analysis, we fixed 8 prediction formulae which embraced the variable expressions of the 12 genes and arranged them in order, to predict the efficacy of the drugs by referring to the value of Akaike's information criterion for each sample. These formulae appeared to accurately predict the in vitro efficacy of the drugs. For the first clinical application model, we fixed prediction formulae for individual clinical response to 5-FU in the same way using 41 clinical samples obtained from 30 gastric cancer patients and found to be of predictive value in terms of survival, time to treatment failure and tumor growth. None of the 12 selected genes alone could predict such clinical responses.

Antimetabolites, Antineoplastic↗
PubMed2006

A consensus procedure improving solvent accessibility prediction.

Prediction methods of structural features in 1D represent a useful tool for the understanding of folding, classification, and function of proteins, and, in particular, for 3D structure prediction. Among the structural aspects characterizing a protein, solvent accessibility has received great attention in recent years. The available methods proposed for predicting accessibility have never considered the combination of the results deriving from different methods to construct a consensus prediction able to provide more reliable results. A consensus approach that increases prediction accuracy using three high-performance methods is described. The results of our method for three different protein data sets show that up to 3.0% improvement in prediction accuracy of solvent accessibility may be obtained by a consensus approach. The improvement also extends to the correlation coefficient. Application of our consensus approach to the accessibility prediction using only three prediction methods gives results better than single methods combined for consensus formation. Currently, the scarce availability of predictors with similar parameters defining solvent accessibility hinders the testing of other methods in our consensus procedure.

Amino Acid Sequence↗
PubMed2005

Predicting final infarct size using acute and subacute multiparametric MRI measurements in patients with ischemic stroke.

PURPOSE: To identify early MRI characteristics of ischemic stroke that predict final infarct size three months poststroke. MATERIALS AND METHODS: Multiparametric MRI (multispin echo T2-weighted [T2W] imaging, T1-weighted [T1W] imaging, and diffusion-weighted imaging [DWI]) was performed acutely (<24 hours), subacutely (three to five days), and at three months. MRI was processed using maps of apparent diffusion coefficient (ADC), T2, and a self-organizing data analysis (ISODATA) technique. Analyses began with testing for individual MRI parameter effects, followed by multivariable modeling with assessment of predictive ability (R(2)) on final infarct size. RESULTS: A total of 45 patients were studied, 15 of whom were treated with tissue plasminogen activator (tPA) before acute MRI. The acute DWI and DWI-ISODATA mismatch lesion size, and the interactions of ADC, T2, and T2W imaging lesion with tPA remained in the final multivariable model (R(2) = 70%). A large acute DWI lesion or DWI < ISODATA lesion independently predicted increase in the final infract size, with predictive ability 68%. Predictive ability increased (R(2) = 83%) when subacute MRI parameters were included along with acute DWI, DWI-ISODATA mismatch, and acute T2W image lesion size by tPA treatment interaction. Subacute DWI > acute DWI lesion size predicted an increased final infarct size (P < 0.01). CONCLUSION: Acute-phase DWI and DWI-ISODATA mismatch strongly predict the final infarct size. An acute-to-subacute DWI lesion size change further increases the predictive ability of the model.

Acute Disease↗
PubMed1990

The distance to the stomach for feeding tube placement in children predicted from regression on height.

Nurses use several external measures referenced to the head and chest to gauge the insertion distance for orogastric and nasogastric (NG) tubes. Few of the measures have been tested. However, in previous studies height was the external measure most correlated with esophageal length both in children and adults. In this study, the ability of previously published regression equations on height to predict esophageal length for NG-tube insertion in 107 children was evaluated. The regression equations were examined for stability, predictive performance, and the likely positions of the tube. The data were heights and esophageal lengths obtained from esophageal manometry records and hospital charts. The predicted values for nasal insertions were biased and averaged 2.4 cm too long (R2prediction = .56, n = 30). Prediction errors greater than 5 cm in absolute value occurred in 25% of the nasally-referenced sample. The predicted values represented overestimates in 18 nasally referenced cases that were 11.5% longer on the average than the measured esophageal lengths. In contrast, the predicted values for the oral insertions were unbiased (R2prediction = .92, n = 77), and gave accurate predictions in the majority of cases. Eighty percent of the oral predictions were within +/- 1.5 cm of the measured esophageal length and represented percentage errors between 0 and 7% (M = 3%, n = 50).

Adolescent↗
PubMed2005

Predicting absolute contact numbers of native protein structure from amino acid sequence.

The contact number of an amino acid residue in a protein structure is defined by the number of C(beta) atoms around the C(beta) atom of the given residue, a quantity similar to, but different from, solvent accessible surface area. We present a method to predict the contact numbers of a protein from its amino acid sequence. The method is based on a simple linear regression scheme and predicts the absolute values of contact numbers. When single sequences are used for both parameter estimation and cross-validation, the present method predicts the contact numbers with a correlation coefficient of 0.555 on average. When multiple sequence alignments are used, the correlation increases to 0.627, which is a significant improvement over previous methods. In terms of discrete states prediction, the accuracies for 2-, 3-, and 10-state predictions are, respectively, 71.4%, 54.1%, and 18.9% with residue type-dependent unbiased thresholds, and 76.3%, 59.2%, and 21.8% with residue type-independent unbiased thresholds. The difference between accessible surface area and contact number from a prediction viewpoint and the application of contact number prediction to three-dimensional structure prediction are discussed.

Amino Acid Sequence↗
PubMed2003

Qualitative venous Doppler waveform analysis improves prediction of critical perinatal outcomes in premature growth-restricted fetuses.

BACKGROUND: Our aim was to test the hypothesis that qualitative ductus venosus and umbilical venous Doppler analysis improves prediction of critical perinatal outcomes in preterm growth-restricted fetuses with abnormal placental function. METHODS: Patients with suspected intrauterine growth restriction (IUGR) underwent uniform fetal assessment including umbilical artery (UA), ductus venosus (DV) and umbilical vein (UV) Doppler. Absent or reversed UA end-diastolic velocity (UA-AREDV), absence or reversal of atrial systolic blood flow velocity in the DV (DV-RAV) and pulsatile flow in the umbilical vein (P-UV) were examined for their efficacy to predict critical outcomes (stillbirth, neonatal death, perinatal death, acidemia and birth asphyxia) before 37 weeks' gestation. RESULTS: Seventeen (7.6%) stillbirths and 16 (7.1%) neonatal deaths were observed among 224 IUGR fetuses. Forty-one neonates were acidemic (19.8%) and seven (3.1%) had birth asphyxia. Logistic regression showed that UA-AREDV had the strongest association with perinatal mortality (R(2) = 0.49, P < 0.001), stillbirth (R(2) = 0.48, P < 0.001) and acidemia (R(2) = 0.22, P = 0.002) while neonatal death was most strongly related to DV-RAV and P-UV (R(2) = 0.33, P = 0.007). UA waveform analysis offered the highest sensitivity and negative predictive value and DV-RAV and P-UV had the best specificity and positive predictive values for outcome prediction. Overall, DV-RAV or P-UV offered the best prediction of acidemia and neonatal and perinatal death irrespective of the UA waveform. In fetuses with UA-AREDV, prediction of asphyxia and stillbirth was significantly enhanced by venous Doppler. CONCLUSION: Prediction of critical perinatal outcomes is improved when venous and umbilical artery qualitative waveform analysis is combined. The incorporation of venous Doppler into fetal surveillance is therefore strongly suggested for all preterm IUGR fetuses.

Asphyxia Neonatorum↗
PubMed2006

Bishop score and ultrasound assessment of the cervix for prediction of time to onset of labor and time to delivery in prolonged pregnancy.

OBJECTIVES: To determine the ability of Bishop score and sonographic cervical length to predict time to spontaneous onset of labor and time to delivery in prolonged pregnancy. METHODS: Ninety-seven women underwent transvaginal ultrasound examination and palpation of the cervix at 291-296 days' gestation according to ultrasound fetometry at 12-20 weeks' gestation. Sonographic cervical length and Bishop score were recorded. Multivariate logistic regression analysis was used to determine which variables were independent predictors of the onset of labor/delivery < or = 24 h, < or = 48 h, and < or = 96 h. Receiver-operating characteristics (ROC) curves were drawn to assess diagnostic performance. RESULTS: In nulliparous women (n = 45), both Bishop score and sonographic cervical length predicted the onset of labor/delivery < or = 24 h and < or = 48 h (area under ROC curve for the onset of labor < or = 24 h 0.79 vs. 0.80, P = 0.94; for delivery < or = 24 h 0.81 vs. 0.85, P = 0.64; for the onset of labor < or = 48 h 0.73 vs. 0.74, P = 0.90; for delivery < or = 48 h 0.77 vs. 0.71, P = 0.50). Only Bishop score discriminated between nulliparous women who went into labor/delivered < or = 96 h or > 96 h. A logistic regression model including Bishop score and cervical length was superior to Bishop score alone in predicting delivery < or = 24 h (area under ROC curve 0.93 vs. 0.81, P = 0.03) and superior to Bishop score alone and cervical length alone in predicting the onset of labor < or = 24 h (area under ROC curve 0.90 vs. 0.79, P = 0.06; and 0.90 vs. 0.80, P = 0.06). In parous women (n = 52), Bishop score and sonographic cervical length predicted the onset of labor/delivery < or = 24 h (area under ROC curve for the onset of labor 0.75 vs. 0.69, P = 0.49; for delivery 0.74 vs. 0.70, P = 0.62), but only Bishop score discriminated between women who went into labor/delivered < or = 48 h and > 48 h. Three parous women had not gone into labor and six had not given birth at 96 h. In parous women logistic regression models including both Bishop score and cervical length did not substantially improve prediction of the time to onset of labor/delivery. CONCLUSIONS: In prolonged pregnancy Bishop score and sonographic cervical length have a similar ability to predict the time to the onset of labor and delivery. In nulliparous women the use of logistic regression models including Bishop score and cervical length is likely to offer better prediction of the onset of labor/delivery < or = 24 h than the use of the Bishop score alone.

Adolescent↗
PubMed2000

Predictive factors for residual disease in subsequent hysterectomy following conization for CIN III.

OBJECTIVE: The aim of this study was to determine predictive factors for post-cone residual disease in subsequent hysterectomy for CIN III. METHODS: From June 1994 to June 1999, 120 patients with CIN III who received hysterectomy within 6 months of conization regardless of marginal status were identified from 1450 conization cases. The demographic features and pathologic parameters were analyzed for the predictive rate of post-cone residual disease. RESULTS: Age >==50 years and parity >==5 were significant factors associated with residual disease. The incidence of residual disease was 56.5 and 29. 3% in patients >==50 and <50 years, respectively, and 61.8 and 36.0% in patients with parity >==5 and <5. Post-cone endocervical curettage (ECC) and multiple-quadrant disease were the only pathologic predictive factors identified. The incidence of residual disease was 64.6 and 29.2% in patients with positive ECC and negative ECC, respectively, and 48.4 and 25.9% in patient with multiple-quadrant disease and one- or two-quadrant disease. Other pathologic parameters, including endocervical margins, ectocervical margins, endocervical gland involvement, and depth of conization, were not predictive of residual disease. When ECC was combined individually with age, endocervical margins, or multiple-quadrant disease, there was no increase of positive predictive rate. CONCLUSIONS: (1) Age 50 years or more and parity >==5 were two demographic features that predicted post-cone residual disease. (2) ECC and multiple-quadrant disease were the only pathologic parameters that predicted post-cone residual disease. (3) With the appropriate application of the predictive factors, post-cone hysterectomy may be further decreased.

Adult↗
PubMed1995

The predictive value of four scoring systems in liver transplant recipients.

OBJECTIVE: To compare 4 general severity classification scoring systems concerning prognosis of outcome in 123 liver transplant recipients. The compared scoring systems were: the mortality prediction model (admission model and 24 h model); the simplified acute physiology score; the acute physiology and chronic health evaluation (Apache II) and the acute organ systems failure score. DESIGN: Retrospective, consecutive sample. SETTING: Adult intensive care unit in a university hospital. PATIENTS: 123 adult liver allograft recipients after admission to the intensive care unit. MEASUREMENTS AND MAIN RESULTS: The scoring systems were calculated as described by the authors to classify the severity of illness after admission of the allograft recipients to the intensive care unit. The mean and median values of survivors and the group of patients, that died during hospital stay were compared. Receiver-operating characteristics were plotted for all scoring systems and the areas under the curves of receiver-operating characteristics were calculated. The predictive value of the 4 scoring systems was tested using a variety of sensitivity analyses. The mortality prediction model (24 h model) was found to have a high significance (p < 0.001) in predicting mortality and showed the greatest area under the curve (0.829). Simplified acute physiology score (p < 0.001) and acute physiology and chronic health evaluation (Apache II) (p < 0.01) had a high significance as well, but did not hit the level of prognosis of mortality prediction model, as shown in the area under the curves. Accordingly, sensitivity was highest in MPM-24 h (83%), followed by SAPS (72%) and Apache II (71%). MPM-24 h had a total misclassification rate of 22% (SAPS = 32%, Apache II = 33%). MPM-admission failed in predicting mortality (sensitivity = 52%). Organ systems failure score seemed not to be useful in liver transplant recipients. CONCLUSION: General disease classification systems, such as the mortality prediction model, simplified acute physiology score or acute physiology and chronic health evaluation are good mortality prediction models in patients after liver transplantation. We suggest that there is no need for improvement of a special scoring system.

Adolescent↗
PubMed2001

Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute cholecystitis: artificial neural networks improve the prediction of conversion.

Laparoscopic cholecystectomy is now also performed for acute cholecystitis. In the presence of inflammatory conditions, technical difficulties leading to conversion to open cholecystectomy may occur and overshadow the advantages of the laparoscopic approach. Factors associated with these undue events combined with techniques capable of learning from them may help in determining when to completely avoid the laparoscopic procedure. In this study we determined predictors of conversion in acute cholecystitis and tested their predictive ability by means of statistical multivariate analysis and artificial neural networks. Between January 1994 and February 1997, 225 patients underwent laparoscopic cholecystectomy for acute cholecystitis. Preoperative and operative data were prospectively collected on standardized forms. The first 180 laparoscopically approached cases entered the training set, which was learned by both the statistical and the artificial neural networks methods. Conversion was first studied in relation to a set of preoperative data. Prediction models were then fitted by both of these methods. The last 45 operated cases, which remained unknown to the learning systems, served for testing the fitted models. The forward stepwise logistic regression technique, the forward stepwise linear discriminant analysis, and the artificial neural networks method enabled positive prediction of conversion in 0%, 27%, and 100% of the cases, and a negative prediction in 80%, 85.5%, and 97% respectively, in the training set. A positive prediction of conversion in 0%, 25%, and 67% of the cases, and a negative prediction in 82%, 88%, and 94%, respectively, in the untrained, validation set of patients. An artificial neural networks based model provides a practical tool for the prediction of successful laparoscopic cholecystectomies and their conversion. The high degree of certainty of prediction in untrained cases reveals its potential, and justifies, under appropriate conditions, the complete avoidance of laparoscopy and turning directly to open cholecystectomy.

Acute Disease↗
PubMed2002

Magnetization transfer can predict clinical evolution in patients with multiple sclerosis.

The clinical course of multiple sclerosis (MS) is highly variable ranging from benign to aggressive, and is difficult to predict. Since magnetization transfer (MT) imaging can detect focal abnormalities in normal-appearing white matter (NAWM) before the appearance of lesions on conventional MRI, we hypothesized that changes in MT might be able to predict the clinical evolution of MS. We assessed MR data from MS patients who were subsequently followed clinically for 5 years. We computed the mean MT ratio (MTr) in gray matter, in lesions identified on T2-weighted MRI, and in NAWM, as well as in a thick central brain slice for each patient. Patients were divided into stable and worsening groups according to their change in Expanded Disability Status Scale (EDSS) scores over 5 years. We calculated the sensitivity, specificity, predictive value, and odds ratio of the baseline MTr measures in order to assess their prognostic utility. We found significant differences in baseline MTr values in NAWM (p = 0.005) and brain slice (p = 0.03) between clinically stable and worsening MS patients. When these MTr values were compared with changes in EDSS over 5 years, a strong correlation was found between the EDSS changes and MTr values in both NAWM (SRCC = -0.76, p < 0.001) and in the brain slice (SRCC = 0.59, p = 0.01). Baseline NAWM MTr correctly predicted clinical evolution in 15/18 patients (1 false positive and 2 false negatives), yielding a positive predictive value of 77.78 %, a negative predictive value of 88.89 %, and an odds ratio of 28. The relationship between 5-year changes in EDSS and MTr values in T2 weighted MRI lesions was weaker (SRCC = -0.43, p = 0.07). Our data support the notion that the quantification of MTr in the NAWM can predict the clinical evolution of MS. Lower MTr values predict poorer long-term clinical outcome. Abnormalities of MTr values in the NAWM are more relevant to the development of future patient disability than those in the T2-weighted MRI lesions.

Adult↗
PubMed2006

The validity of predicting maximal oxygen uptake from perceptually regulated graded exercise tests of different durations.

The purpose of this study was to assess the validity of predicting maximal oxygen uptake [V(.-)((O)(2)(max))] from sub-maximal V(.-)((O)(2)) values elicited during perceptually regulated exercise tests of 2- and 4-min duration. Nineteen physically active men and women (age range 19-23 years) volunteered to participate in two graded exercise tests to volitional exhaustion to measure V(.-)((O)(2)(max)) [V(.-)((O)(2)(max))(GXT)], at the beginning and end of a 2-week period, and four incremental, perceptually regulated tests to predict [V(.-)((O)(2)(max))] in the intervening period. Effort production tests comprised 2 x 2-min and 2 x 4-min bouts on a cycle ergometer, perceptually regulated at intensities of 9, 11, 13, 15 and 17 on the Borg 6-20 rating of perceived (RPE) scale, in that order. Individual linear relationships between RPE and V(.-)((O)(2) for RPE ranges of 9-17, 11-17 and 9-15 were extrapolated to RPE 20 to predict [V(.-)((O)(2)(max))]. The prediction of [V(.-)((O)(2)(max))] was not moderated by gender. Although, [V(.-)((O)(2)(max))] estimated from RPE 9-17 of trial 1 of the 2-min protocol was significantly lower (P < 0.05) than [V(.-)((O)(2)(max))(GXT)], and V(.-)((O)(2)(max)) predicted from the 4-min trials, the V(.-)((O)(2)(max)) predicted from trial 2 of the 2-min protocol was a more accurate prediction of [V(.-)((O)(2)(max))(GXT)], across all trials. The intraclass correlation coefficient (R) was also higher between [V(.-)((O)(2)(max))(GXT)], and [V(.-)((O)(2)(max))] predicted from trial 2 of the 2-min protocol compared to both trials in the 4-min protocol (R = 0.95, 0.88 and 0.79, respectively). Similar results were observed for RPE ranges 9-15 and 11-17. Results suggest that a sub-maximal, perceptually guided, graded exercise protocol, particularly of a 2-min duration, provides acceptable estimates of maximal aerobic power, which are not moderated by gender.

Adult↗