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PubMed1995

Abnormal uterine bleeding. Accuracy of vaginal probe ultrasound in predicting abnormal hysteroscopic findings.

OBJECTIVE: To determine the accuracy of vaginosonography in predicting the presence of abnormal intrauterine findings at hysteroscopy in women presenting with abnormal uterine bleeding. STUDY DESIGN: Two hundred thirty-eight women between the ages of 25 and 75 were evaluated for abnormal uterine bleeding by a combination of vaginal probe ultrasound, hysteroscopy and suction curettage. Ultrasound findings were classified as normal, abnormal or equivocal, and hysteroscopic findings as normal or abnormal. The ability of findings on ultrasound to predict a normal or abnormal hysteroscopic examination was evaluated. RESULTS: Ultrasound examination was normal, equivocal and abnormal in 57 (24%), 78 (33%) and 103 (43%) patients, respectively. If equivocal scans are excluded, ultrasound had a positive predictive value of 87%, negative predictive value of 89%, sensitivity of 94% and specificity of 89%. If equivocal scans are considered abnormal, the positive predictive value of an abnormal ultrasound examination was 75%, negative predictive value was 89%, sensitivity 96% and specificity 53%. Vaginosonography detected 99% of submucous myomas and 89% of endometrial polyps. CONCLUSION: Normal and abnormal vaginal probe ultrasound examination of the uterus is effective in predicting the presence or absence of endometrial pathology as determined by hysteroscopy and suction curettage. Vaginal probe ultrasound is an excellent screening examination for the presence of intrauterine pathology and can assist in planning further evaluation and treatment.

Adult↗

PubMed1993

Can bioelectrical impedance improve the prediction of body fat in patients with eating disorders?

Prediction equations for percentage body fat have been derived for a group of 93 female patients being treated for eating disorders, using fat measured by dual-energy X-ray absorptiometry (DXA) as the reference method. Separate bioelectrical impedance analysis (BIA) prediction equations were derived for patients with a body mass index above and below 16 kg/m2. This provided an improvement compared with the equations supplied by the manufacturer. The standard error of the estimate (SEE) for the entire group was 3.2% (r = 0.922). However, an alternative prediction equation based on anthropometry alone gave an SEE of 4.2% (r = 0.861). The ability of BIA and anthropometry alone to measure changes in percentage fat was assessed in a separate group of 24 females during treatment for eating disorders. The SEEs using the BIA and anthropometry prediction equations were 2.6% (r = 0.931) and 2.7% (r = 0.901) respectively. We conclude that in patients with eating disorders BIA does not provide any significant improvement over simple anthropometry prediction equations for the assessment of percentage fat or its changes. The apparently reasonable agreement between fat predicted by BIA and that measured by DXA is largely due to the inclusion of body habitus parameters in the BIA prediction equations.

Absorptiometry, Photon↗

PubMed1993

Comparison of Doppler with two-dimensional sonography and CA 125 for prediction of malignancy of pelvic masses.

OBJECTIVE: To assess the validity of Doppler flow sonography for the prediction of malignancy in adnexal masses and to compare the results with two-dimensional ultrasonographic examination and CA 125 levels. METHODS: In a cross-sectional study, 55 patients with adnexal masses were examined using Doppler sonography to measure the resistance index of tumor-associated blood flow profiles. In addition, abdominal and transvaginal ultrasound examinations were performed and preoperative CA 125 serum levels were assessed. RESULTS: Sixteen patients had malignant tumors and 39 had benign tumors. A resistance index cutoff of less than or equal to 0.8 showed the highest sensitivity (93.8%), with a specificity of 56.4%, a positive predictive value of 46.8%, and a negative predictive value of 95.7%. Compared with two-dimensional sonographic evaluation using either a scoring system or subjective assessment, and with CA 125 levels, the resistance index showed higher sensitivity and negative predictive value but lower specificity and positive predictive value. False-positive resistance index values were prevalent with endometriosis, leiomyomata, and mucinous cystadenoma. The combination of the resistance index with either the sonographic features or CA 125 increased the sensitivity and negative predictive value to 100%, with only a slight decrease in specificity and positive predictive value, but was not superior to the combination of two-dimensional sonography and CA 125. CONCLUSION: Doppler sonographic evaluation of resistance indexes in the vessels of adnexal masses increased the sensitivity of two-dimensional sonography and CA 125. However, 46% of positive Doppler results were false and 37.5% of the benign tumors had low resistance indexes, thus limiting the validity of this technique for screening programs.

Adnexal Diseases↗

PubMed1996

Prediction of long-term ventilatory support in trauma patients.

Mechanical ventilatory support requiring tracheal intubation may be necessary for variable lengths of time in injured patients. Criteria useful in predicting the need for prolonged tracheal intubation has not been clearly established in the trauma population. Early identification of patients requiring prolonged tracheal intubation and mechanical ventilatory support could lead to earlier tracheostomy and subsequent reductions in complications associated with prolonged endotracheal intubation. This study evaluated the ability of clinical measures of injury severity, mental status, oxygenation, and ventilation to predict the need for prolonged mechanical ventilatory support (> or = 14 days) early in the postinjury course of the adult trauma patient requiring endotracheal intubation and mechanical ventilatory support within the first 24 hours of injury. All adult trauma patients admitted to our Level I trauma center over a 4-year period between January 1990-December 1993 were evaluated. A total of 212 patients met study criteria that included intubation within the first 24 hours of injury, ventilatory support requirement > or = 72 hours, and a survival time of at least 14 days postinjury. Data pertaining to measures of injury severity (RTS, AIS, ISS), mental status (GCS), oxygenation [(A-a)O2], and ventilation (VE,EDC) were recorded for postinjury Day 1 and Day 5. There were 157 males and 55 females (age range of 16-91 years, mean 39.5 years). Mechanism of injury was blunt in 198 and penetrating in 14 patients. One hundred patients required prolonged mechanical ventilatory support. Data were analyzed by stepwise logistic regression analysis. Age and GCS values on Day 1 predicted the need for long term mechanical ventilatory support in a select group of patients, age 20 and GCS of 3 (P < 0.05). At Day 5, age, GCS, and (A-a)O2 gradient were predictive of the need for prolonged mechanical ventilatory support (P < 0.05). On Day 5, GCS of 3 predicted the need for long-term mechanic al ventilatory support regardless of age or (A-a)O2 gradient. GCS < or = 5 and (A-a)O2 > or = 150 predicted prolonged mechanical ventilatory support in young patients (age 20). At ages of 40 to 60, GCS < or = 7 and (A-a)O2 > or = 150 indicated the need for long term mechanical ventilatory support. In older patients (age > or = 80), GCS < or = 7 and (A-a)O2 gradient > or = 100 were predictive of long-term mechanical ventilatory support. Appropriate use of these clinical indicators may assist in early identification of patients requiring prolonged mechanical ventilatory support, and subsequent conversion from endotracheal intubation to tracheostomy with anticipated reduction in complications.

Adolescent↗

PubMed1996

An algorithm for predicting nonorgan confined prostate cancer using the results obtained from sextant core biopsies with prostate specific antigen level.

PURPOSE: We determined the enhanced ability to predict nonorgan confined prostate cancer using several histopathological and quantitative nuclear imaging parameters combined with serum prostate specific antigen (PSA). MATERIALS AND METHODS: Several independent pathological and quantitative image analysis variables obtained from sextant biopsy specimens, as well as preoperative PSA were used. The study population included 210 patients with pathologically staged disease (192 with PSA). All variables were examined by univariate and multivariate logistic regression analyses to assess ability to predict disease organ confinement status. RESULTS: Univariate logistic regression analysis demonstrated that, in decreasing order, quantitative nuclear grade, preoperative PSA, total percent tumor involvement, number of positive sextant cores, preoperative Gleason score and involvement of more than 5% of a base and/or apex biopsy were significant (p < or = 0.006) for prediction of disease organ confinement status. Backward stepwise logistic regression was applied to these univariately significant variables, including deoxyribonucleic acid ploidy, to calculate a multivariate model for prediction of disease organ confinement status. This algorithm had a sensitivity of 85.7%, specificity 71.3%, positive predictive value 72.9%, negative predictive value 84.7% and area under the receiver operating characteristic curve 85.9%. CONCLUSIONS: Information from pathological study of sextant prostate biopsies, preoperative PSA blood test and a new image analysis variable termed quantitative nuclear grade can be combined to create a multivariate algorithm that can predict more accurately nonorgan confined prostate cancer compared to previously reported methods.

Aged↗

PubMed1997

Evaluation of a nomogram used to predict the pathologic stage of clinically localized prostate carcinoma.

BACKGROUND: A Nomogram based on pretreatment prostate specific antigen (PSA) level, tumor grade, and clinical stage has recently been developed and distributed to physicians. It was distributed to aid physicians in making treatment recommendations by predicting the probability of the final pathologic stage of clinically localized prostate carcinoma. The Nomogram was based on data for one patient population, and the validity of its application in general urologic practices had not yet been evaluated. METHODS: In the current study, the authors tested the performance of the Nomogram against data from their series of 697 men who underwent radical prostatectomy during the PSA era. Predictions made with the Nomogram were applied to the authors' data set, and the predictions were compared with actual outcomes of the authors' patients. A localized least-squares regression smoothing technique was used to determine whether the Nomogram was calibrated accurately for the authors' data and whether it discriminated across a full spectrum of patient characteristics. RESULTS: Many of the predicted probabilities of the Nomogram were accurate, but some were suboptimal when applied to the authors' data set. Although the Nomogram did discriminate quite well between organ-confined and nonconfined cancer, it had difficulty predicting high probabilities of seminal vesicle invasion and lymph node metastasis, which are the pathologic features with the most profound impact on prognosis. CONCLUSIONS: The Nomogram predicted organ-confined disease accurately. However, because not all of its predictions were completely calibrated when applied to the authors' data set, the authors conclude that the Nomogram may not be totally applicable to general urologic practice until further validation and possible modifications are performed.

Data Interpretation, Statistical↗

PubMed1998

[Role of postoperative estimate of maximum oxygen uptake in predicting cardiorespiratory insufficiency in the immediate postoperative period in thoracic surgery].

We sought to determine if predicted post-operative maximal oxygen uptake (VO2max/kg-PPO) was associated to the occurrence of respiratory or cardiac failure within the 60 days following lung surgery and to evaluate its validity as operability criterion. We studied 47 patients with chronic air-flow limitation (COPD) with FEV1 > 0.8 1 and without hypercapnia, that underwent lung surgery. Age was 56 (SD 11) years, FEV1 = 1.8 (SD 0.5) 1 (61% predicted (SD 13%) and FEV1/FVC = 55 (SD 7.5). Ten patients presented serious cardiac or respiratory complications (3 died). Significant correlation with complications was found for RV, TL-COsb-PPO, VO2max/kg, resection size and VO2max/kg-PPO. VO2max/kg-PPO correlation (-0.73) was significantly higher (p = 0.0016) than all the pulmonary function test (PFT) correlation and than VO2max/kg correlation (p = 0.049) as well. Cut-off points, positive and negative predictive values were respectively: 12.6 ml/min/kg, 0.75 y 0.90% for VO2max/kg-PPO; 17 ml/min/kg 0.83 and 0.87 for VO2max/kg and 148%, 0.67 and 0.82 for RV (the best of the pulmonary function tests). Multivariable models did not improve discriminant power. We conclude that, out of the studied variables, VO2max/kg-PPO showed higher correlation with the complications sought than PFT or VO2max/kg. As criterion to predict cardiac or respiratory failure, with the observed prevalence, its negative predictive values is good, but its positive predictive value is relatively low. None parameter was able to predict all the complications.

Adult↗

PubMed2026

Clinical Variable-Based Machine Learning for Predicting Early mCRPC Using Exclusively Clinical Variables: Development and Multicenter External Validation.

BACKGROUND AND OBJECTIVE: Metastatic hormone-sensitive prostate cancer (mHSPC) exhibits heterogeneous progression patterns, with early progression to metastatic castration-resistant prostate cancer (mCRPC) within 12 months indicating aggressive tumor biology and poor prognosis. Current risk stratification tools (CHAARTED, LATITUDE) offer limited individualized prediction. Machine learning approaches are increasingly applied to predict prostate cancer progression, but most models show modest performance (AUC 0.68-0.72), limited external validation, or require genomic variables unavailable in routine practice. This study aimed to develop and externally validate a novel RINH algorithm for predicting early mCRPC progression (≤ 12 months) using exclusively clinical variables, positioning it as a superior alternative to conventional ML classifiers. METHODS: This multicenter study enrolled 412 patients with de novo mHSPC from seven Spanish academic centers using mixed retrospective-prospective data collection. Twenty clinical variables were recorded, including demographics, PSA, ISUP grade, metastatic localization, CHAARTED/LATITUDE classifications, and treatment modalities. Following RINH-based outlier exclusion (55 patients), 357 patients (29 with early progression, 8.1%) were used to train six ML algorithms: RINH, Logistic Regression, Linear Discriminant, Support Vector Machine, Random Forest, and Subspace Discriminant. A two-tiered validation strategy integrated stratified fivefold cross-validation across all centers and formal external validation using center 1 (n = 121, 19 events) for training and centers 2-7 (n = 207, 10 events) for independent testing. Performance metrics included AUC, sensitivity, specificity, accuracy, and F1-score. KEY FINDINGS AND LIMITATIONS: Artificial intelligence and machine learning (ML) are transforming oncology, promising personalized risk stratification beyond traditional clinical criteria. In metastatic hormone-sensitive prostate cancer (mHSPC), early progression to castration resistance (mCRPC) within 12 months signals aggressive biology and poor prognosis, yet current tools (CHAARTED, LATITUDE) offer limited individualized prediction. Multiple ML models have been proposed with variable success: most achieve modest performance (AUC 0.68-0.72), lack robust external validation, or rely on genomic variables inaccessible in routine practice. We propose a novel approach using the Rivality Index Neighborhood (RINH) algorithm, demonstrating superior predictive capacity in an initial multicenter validation with exclusively clinical variables. This study provides rigorous multicenter external validation, advancing toward implementable precision oncology tools. CONCLUSIONS AND CLINICAL IMPLICATIONS: The RINH algorithm achieves superior predictive performance for early mCRPC progression using exclusively clinical variables, representing a significant advance toward implementable risk stratification. However, low reliability scores in external validation underscore that excellent performance metrics alone do not guarantee stability. Before clinical deployment, validation in substantially larger cohorts with higher progression events is essential. If validated, this model could enable personalized, risk-adapted therapeutic strategies, refining patient selection for treatment intensification or de-escalation.

Humans↗

PubMed2025

Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study.

OBJECTIVE: Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts and behaviors (STB) included a limited number of data modalities and/or focused on adult populations. This study aimed to prospectively predict first-onset STB across 4-year follow-ups in adolescents using an existing STB history classification model that was previously applied to baseline data and a new machine learning model with 195 biopsychosocial features. METHOD: Participants were 7,503 unrelated adolescents (54.5% female, ages 9-11 years at baseline) from the multisite, longitudinal Adolescent Brain Cognitive Development (ABCD) Study. An existing baseline STB history classification model was applied to predict longitudinal first-onset STB in adolescents compared with healthy controls and clinical controls (individuals with a mental health disorder but no STB). A new elastic net logistic regression model with 195 features was trained on data from 14 sites (n = 5,220), and the resulting top 15 features were validated at 7 independent sites (n = 2,283). RESULTS: The previously developed model to classify STB lifetime history also prospectively predicted first-onset STB in adolescents with an area under the curve (AUC) [95% CI] of 0.73 [0.70, 0.75], p < .001, compared with healthy controls and AUC [95% CI] of 0.63 [0.60, 0.66], p < .001, compared with clinical controls. The newly trained model with top 15 features performed similarly with AUC [95% CI] of 0.73 [0.71, 0.76], p < .001, and AUC [95% CI] of 0.64 [0.60, 0.66], p < .001, for the same comparison groups. The most consistent predictors across models included female sex, sleep disturbances, and maladaptive home and school environments. CONCLUSION: The models predicted first-onset STB in adolescents with moderate accuracy. This study also confirmed the roles of well-established psychological risk factors for STB and identified several novel neurocognitive and brain imaging risk factors. Future studies should validate these models in large-scale diverse samples before clinical translation. PLAIN LANGUAGE SUMMARY: This study followed over 7,500 adolescents for 4 years and tested 2 machine learning models using psychological, social, and brain data to identify those at risk of experiencing suicidal thoughts or behaviors. Both models predicted first-time suicidal thoughts or behaviors with moderate accuracy. Key risk factors that were identified included being female, experiencing sleep problems, and negative home and school environments. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. Diverse cell lines and/or genomic datasets were not available. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Adolescent↗

PubMed2005

Prospective validation of a prediction model for isolating inpatients with suspected pulmonary tuberculosis.

BACKGROUND: Current guidelines for the control of nosocomial transmission of tuberculosis (TB) recommend respiratory isolation for all patients with suspected TB. Application of these guidelines has resulted in many patients without TB being isolated on admission to the hospital, significantly increasing hospital costs. This study was conducted to prospectively validate a clinical decision rule to predict the need for respiratory isolation in inpatients with suspected TB. METHODS: A cohort of 516 individuals, who presented to 2 New York City hospitals between January 16, 2001, and September 29, 2002, and who were isolated on admission for clinically suspected TB, were enrolled in the study. Face-to-face interviews were conducted to determine the presence of clinical variables associated with TB in the prediction model, including TB risk factors, clinical symptoms, and findings from physical examination and chest radiography. RESULTS: Of the 516 patients, 19 were found to have TB (prevalence, 3.7%; 95% confidence interval [CI], 2.2%-5.7%). The prediction rule had a sensitivity of 95% (95% CI, 74%-100%) and a specificity of 35% (95% CI, 31%-40%). Using a prevalence of TB of 3.7%, the positive predictive value was 9.6% and the negative predictive value was 99.7%. CONCLUSIONS: Among inpatients with suspected active pulmonary TB who are isolated on admission to the hospital, a prediction rule based on clinical and chest radiographic findings accurately identified patients at low risk for TB. Approximately one third of the unnecessary episodes of respiratory isolation could have been avoided had the prediction rule been applied. Future studies should assess the feasibility of implementing the rule in clinical practice.

Adult↗

PubMed1994

Comparison of flow cytometry with the surgeon regarding ability to predict the ultimate success of surgery for proliferative vitreoretinopathy.

OBJECTIVE: To analyze vitreous specimens obtained from human eyes with proliferative vitreoretinopathy (PVR) by use of flow cytometery (FC) to compare the capacity of FC with that of the surgeon's in predicting the ultimate outcome after PVR surgery. METHODS: Thirty-one vitreous aspirates were obtained at vitrectomy or from postoperative fluid-gas exchanges for complicated retinal detachment due to PVR. Samples were analyzed by FC for the concentration of total and proliferating cells. These measurements were compared with the surgeon's preoperative prognosis (good, poor, or indeterminate) for the ultimate result. At 12 weeks, the eyes were evaluated for clinical outcome. An unsuccessful outcome was one in which the retina was attached with macular pucker or hypotony or was anatomically detached. Anatomic attachment of the retina anterior and posterior to the scleral buckle without these findings was considered a successful outcome. RESULTS: Flow cytometry was capable of quantifying the concentration of total and proliferating cells in eyes with PVR. Eyes destined for an ultimately successful outcome demonstrated fewer total and proliferating cells than did eyes destined for an unsuccessful outcome. No differences were observed between eyes destined for development of macular pucker and those destined for anatomic redetachment. The surgeon was able to accurately predict a good or a poor outcome in 64.5% of the cases, with a sensitivity of 92% and a specificity of 89%. Using the total number of recovered cells, FC was able to correctly predict a good or a bad outcome with a 77% accuracy, whereas the surgeon was able to do so with a 65% accuracy. Combining the surgeon's prediction with the result of FC increased the diagnostic power of the prediction to 87%. CONCLUSIONS: The diagnostic power of FC was similar to that of the surgeon's in predicting ultimate outcome, but the two techniques probably evaluate different features of the disease process. The combination of the two techniques was the most powerful approach to the prediction of ultimate outcome after PVR surgery.

Cell Count↗

PubMed2006

Radiographic classification of temporal bone fractures: clinical predictability using a new system.

OBJECTIVE: To compare the traditional system of radiographic classification of temporal bone fractures (transverse vs longitudinal vs oblique) with a newer system (otic capsule violating vs otic capsule sparing) with respect to their ability to predict sequelae of temporal bone trauma. DESIGN: Retrospective chart and radiology review. SETTING: University trauma center and Department of Otolaryngology-Head and Neck Surgery. PATIENTS: Patients with temporal bone fractures. INTERVENTIONS: Clinic records and computed tomographic scans were reviewed to evaluate the clinical predictability of complications of temporal bone fractures. MAIN OUTCOME MEASURES: Complications of temporal bone fractures (ie, sensorineural hearing loss, conductive hearing loss, cerebrospinal fluid leakage, and facial nerve weakness) were recorded. Two classification schemes for temporal bone fractures were statistically analyzed and compared as to their ability to predict each complication. RESULTS: A total of 234 temporal bone fractures were identified; 30 cases met our strict criteria for inclusion. The traditional classification system of temporal bone fractures did not significantly predict temporal bone complications (P = .71). On the other hand, the otic capsule-based system did demonstrate statistically significant predictive ability (P < .001). Patients with otic capsule-violating fractures were 5 times more likely to have facial nerve injury, 25 times more likely to have sensorineural hearing loss, and 8 times more likely to have cerebrospinal fluid otorrhea than those with otic capsule-sparing fractures. CONCLUSIONS: The traditional radiographic classification system failed to demonstrate clinical predictability in our series. Furthermore, the newer system of classification (otic capsule sparing vs otic capsule violating) demonstrated statistically significant predictive ability for serious clinical outcomes associated with temporal bone fractures.

Accidental Falls↗

PubMed1988

Computer-derived equations for predicting survival postoperatively. Their usefulness and limitations.

We used multivariate analysis to determine whether survival following perforations of the gastrointestinal tract could be accurately predicted from preoperative data. Of 12 variables tested, four were found to have predictive value. These were age, pulmonary disease, preoperative shock, and the attending surgeon. When these four variables were employed in a logistic regression equation on 42 patients, it correctly predicted which 21 patients died before leaving the hospital. To produce an equation useful for other hospitals, we recalculated it without the attending surgeon variable. Again, the equation was used to predict survival. The correlation of predicted vs observed outcome remained high, and, using a 2 x 2 chi 2 test, the correlation was significant. We then cross validated the three-variable model on data from a second hospital. The model accurately predicted the new data equally well. We believe that predictive models can identify risk factors in a variety of patient populations and can determine who is likely to benefit from specific treatment modalities.

Age Factors↗

PubMed2006

Prediction of germline mutations and cancer risk in the Lynch syndrome.

CONTEXT: Identifying families at high risk for the Lynch syndrome (ie, hereditary nonpolyposis colorectal cancer) is critical for both genetic counseling and cancer prevention. Current clinical guidelines are effective but limited by applicability and cost. OBJECTIVE: To develop and validate a genetic counseling and risk prediction tool that estimates the probability of carrying a deleterious mutation in mismatch repair genes MLH1, MSH2, or MSH6 and the probability of developing colorectal or endometrial cancer. DESIGN, SETTING, AND PATIENTS: External validation of the MMRpro model was conducted on 279 individuals from 226 clinic-based families in the United States, Canada, and Australia (referred between 1993-2005) by comparing model predictions with results of highly sensitive germline mutation detection techniques. MMRpro models the autosomal dominant inheritance of mismatch repair mutations, with parameters based on meta-analyses of the penetrance and prevalence of mutations and of the predictive values of tumor characteristics. The model's prediction is tailored to each individual's detailed family history information on colorectal and endometrial cancer and to tumor characteristics including microsatellite instability. MAIN OUTCOME MEASURE: Ability of MMRpro to correctly predict mutation carrier status, as measured by operating characteristics, calibration, and overall accuracy. RESULTS: In the independent validation, MMRpro provided a concordance index of 0.83 (95% confidence interval, 0.78-0.88) and a ratio of observed to predicted cases of 0.94 (95% confidence interval, 0.84-1.05). This results in higher accuracy than existing alternatives and current clinical guidelines. CONCLUSIONS: MMRpro is a broadly applicable, accurate prediction model that can contribute to current screening and genetic counseling practices in a high-risk population. It is more sensitive and more specific than existing clinical guidelines for identifying individuals who may benefit from MMR germline testing. It is applicable to individuals for whom tumor samples are not available and to individuals in whom germline testing finds no mutation.

Adaptor Proteins, Signal Transducing↗

PubMed1997

Analysis of comparative modeling predictions for CASP2 targets 1, 3, 9, and 17.

Comparative modeling targets 1, 3, 9 and 17 were predicted by alignment of multiple sequences and structures, when available, followed by minimization using the program AMMP. The minimization used improved potentials, and distance restraints for regions of common structure. New prediction procedures were evaluated. Three tested solvent corrections did not significantly improve the predictions. Target 17 had 85.3% sequence identity with the parent and no insertions or deletions. The prediction had a root-mean-square deviation from target 17 of 0.56 A on C alpha atoms, and 0.59 A for the ligand atoms, which verified the accuracy of the minimization. Targets 1, 3, and 9 had 36.4%, 46.7%, and 33.3% identity with the parent sequences, and predictions resulted in root-mean-square deviations for 79-85% of C alpha atoms of 1.49, 1.11, and 1.24 A, respectively. Conformational differences between parent and target crystal structures were difficult to predict. The use of distance restraints and multiple structures improved the positioning of gaps in sequence alignment. Distance restraints did not overcome errors in sequence alignment or ambiguities due to conformational variation in proteins. Predictions for targets 3 and 9 successfully reduced large deviations between parent and target structures.

Animals↗

PubMed1997

A retrospective analysis of CASP2 threading predictions.

Analysis of CASP2 protein threading results shows that the success rate of structure predictions varies widely among prediction targets. We set "critical" thresholds in fold recognition specificity and threading model accuracy at the points where "incorrect" CASP2 predictions just outnumber "correct" predictions. Using these thresholds we find that correct predictions were made for all of those targets and for only those targets where more than 50% of target residues may be superimposed on previously known structures. Three-fourths of these correct predictions were furthermore made for targets with greater than 12% residue identity in structural alignment, where characteristic sequence motifs are also present. Based on these observations we suggest that the sustained performance of threading methods is best characterized by counting the numbers of correct predictions for targets of increasing "difficulty." We suggest that target difficulty may be assigned, once the true structure of the target is known, according to the fraction of residues superimposable onto previously known structures and the fraction of identical residues in those structural alignments.

Models, Molecular↗

PubMed1998

Prediction of the three-dimensional structure of proteins using the electrostatic screening model and hierarchic condensation.

We describe a method for predicting the three-dimensional (3-D) structure of proteins from their sequence alone. The method is based on the electrostatic screening model for the stability of the protein main-chain conformation. The free energy of a protein as a function of its conformation is obtained from the potentials of mean force analysis of high-resolution x-ray protein structures. The free energy function is simple and contains only 44 fitted coefficients. The minimization of the free energy is performed by the torsion space Monte Carlo procedure using the concept of hierarchic condensation. The Monte Carlo minimization procedure is applied to predict the secondary, super-secondary, and native 3-D structures of 12 proteins with 28-110 amino acids. The 3-D structures of the majority of local secondary and super-secondary structures are predicted accurately. This result suggests that control in forming the native-like local structure is distributed along the entire protein sequence. The native 3-D structure is predicted correctly for 3 of 12 proteins composed mainly from the alpha-helices. The method fails to predict the native 3-D structure of proteins with a predominantly beta secondary structure. We suggest that the hierarchic condensation is not an appropriate procedure for simulating the folding of proteins made up primarily from beta-strands. The method has been proved accurate in predicting the local secondary and super-secondary structures in the blind ab initio 3-D prediction experiment.

Algorithms↗

PubMed1999

Improved ab initio predictions with a simplified, flexible geometry model.

Structure predictions for nine targets from the CASP3 meeting are presented and compared with the experimental structure. These predictions are made using the simplified flexible geometry representation of protein structure and potentials which mimic the physical forces involved in protein folding with no help from multiple sequences. The major differences from the CASP2 potentials are identified, and the prediction successes and failures are related to the underlying potentials. Target T0065 was successfully folded from an extended chain to 3.8 A CA RMS of the native structure. The quality of the secondary structure component of all predictions was significantly improved, averaging a Q3 of 64%, which was spread evenly across the fold types, all alpha, alpha/beta mixed, and mainly beta. A number of the other predictions had a spatial arrangement of the secondary structure segments close to native, although the major problem with most predictions was the over-extension of secondary structure segments which often coalesced independent segments together. For target T0056 the ab initio prediction was the closest to the native structure of all methods including threading according to the Hubbard RMS coverage plots.

Amino Acid Sequence↗