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PubMed1983

Prediction of adult height in children with chronic renal insufficiency.

The ability to accurately predict adult height (AH) has important implications for the management of children with chronic renal insufficiency (CRI). Serial predictions of AH were performed for 22 pediatric patients with CRI of variable duration and severity using the Tanner predictive method. The patients were followed until completion of growth. At the first predictions, the prediction error (PE) was within an acceptable range of +/- 2 cm in 59% of the patients. The mean size of the PE was 2.7 cm. It was larger in 7 patients with a bone age up to 11 years (4.9 cm) as compared to 15 patients with more advanced bone age (1.7 cm) and also larger in males (3.4 cm) than in females (2.4 cm). There was no correlation between size or direction of PE and (1) mode of treatment and (2) time elapsed between the first prediction and age at AH. A significant association was found between the direction of PE and the relative change in body height from the first prediction to adult age. Patients with decreasing SD scores were overpredicted and those with increasing SD scores were underpredicted at the initial assessment. Serial predictions gave large variations only in 2 girls who presented with unusual growth patterns. These results indicate that the method applied allows a reliable prognosis of future growth in most children with CRI.

Adolescent↗
PubMed1978

Prediction of gentamicin serum levels using a one-compartment open linear pharmacokinetic model.

The accuracy of predicting serum gentamicin levels based on a one-compartment open linear pharmacokinetic model was studied. Twenty-two patients accounted for 59 serum gentamicin levels which were measured by microbiologic assay and compared with predicted serum levels determined by pharmacokinetic calculation. Seventeen serum levels were collected at peak times, 15 at trough time and 27 at times between peak and trough. Forty-nine of the levels were obtained from patients with impaired renal function. Predicated gentamicin levels correlated well with measured serum levels (r = 0.85, p less than 0.001). Of the measured levels, 56% were within +/- 1 microgram/ml of the predicted levels. Of 49 levels collected from patients with impaired renal function, 59% were within +/- 1 microgram/ml of the predicted level. In 13 patients from whom multiple serum gentamicin levels were collected and predictions based on half-life or elimination rate obtained by fitting the first level, 83% of the measured levels were within +/- 1 microgram/ml of the predicted level. The one-compartment open linear pharmacokinetic calculations can be used to adequately predict serum gentamicin levels. In patients with changing or diminished renal function, pharmacokinetic predictions may not be accurate, and actual serum level determinations may be needed to monitor gentamicin therapy.

Adult↗
PubMedDate not supplied

Comparison of four methods of predicting serum gentamicin concentrations in adult patients with impaired renal function.

Four methods of predicting serum gentamicin concentrations (SGCs) in adult patients with stable impaired renal function were compared. Serum samples obtained from 17 patients receiving intravenous gentamicin therapy were assayed for gentamicin using a radioimmunoassay. Kinetic variables derived using four different methods were used to predict peak and trough concentrations. The Sawchuk-Zaske method uses individualized apparent volume of distribution (Vapp) and half-life (t1/2) derived from at least three SGCs following a given dose. The fitted method assumes Vapp equals 0.28 liters/kg lean body weight (LBW) and a t1/2 derived by a fitting technique using only one SGC. Both the Tozer and Hull methods assume Vapp equals 0.28 liters/kg LBW, and t1/2 is estimated from equations that take into account the patient's renal function (creatinine clearance). The predicted and measured SGCs for each method were compared using linear regression analysis. The prediction errors, defined as the predicted minus the measured SGC, were compared for the four methods. The correlation coefficients between the measured and predicted SGCs were significant at p less than 0.05 only for the Sawchuk-Zaske and fitted methods. For the Sawchuk-Zaske method, peak and trough r values were 0.73 and 0.91, respectively. For the fitted method, peak and trough r values were 0.53 and 0.71, respectively. No significant differences in the mean prediction errors were observed, except for the Sawchuk-Zaske method, which had significantly less mean prediction error than the Hull method. The Sawchuk-Zaske method was the most reliable and accurate pharmacokinetic technique. However, because it is more costly and less convenient than the other three methods, both the accuracy and cost should be considered when selecting a method for determining serum gentamicin concentrations for a particular patient.

Aged↗
PubMed1995

TOPITS: threading one-dimensional predictions into three-dimensional structures.

Homology modelling, currently, is the only theoretical tool which can successfully predict protein 3D structure. As 3D structure is conserved in sequence families, homology modelling allows to predict 3D structure for 20% of SWISSPROT. 20% of the proteins in PDB are remote homologues to another PDB protein. Threading techniques attempt to predict such remote homologues based on sequence information. Here, a new threading method is presented. First, for a list of PDB proteins, 3D structure was projected onto 1D strings of secondary structure and relative solvent accessibility. Then, secondary structure and accessibility were predicted by neural network systems (PHD). Finally, the predicted and observed 1D strings were aligned by dynamic programming. The resulting alignment was used to detect remote 3D homologues. Four results stand out. Firstly, even for an optimal prediction (assignment based on known structure), only about half the hits that ranked above a given threshold were correctly identified as remote homologues; only about 25% of the first hits were correct. Secondly, real predictions (PHD) were not much worse: about 20% of the first hits were correct. Thirdly, a simple filtering procedure improved prediction performance to about 30% correct first hits. The correct hit ranked among the first three for more than 23 out of 46 cases. Fourthly, the combination of the 1D threading and sequence alignments markedly improved the performance of the threading method TOPITS for some selected cases.

Amino Acid Sequence↗
PubMed1994

[Predictive value of ergospirometry in preoperative assessment of risk factors before lung resections].

Surgical resection for lung cancer provides the only real chance for cure. However, there is a high risk of postoperative complications including death for patients with pulmonary dysfunction. Therefore preoperative identification of patients at risk is necessary. Apart from history and physical examination three tests are currently used: 1. resting lung function (RFL), 2. invasive measurement of pulmonary vascular resistance (PVR) and 3. exercise testing with measurement of oxygen consumption (VO2). Main studies in the literature report the probability of abnormal tests for prediction of pulmonary complications (positive predictive value) and the probability of normal tests for prediction of uneventful outcome (negative predictive value) as follows: [table: see text] In conclusion, the "ideal" test predictive for morbidity and mortality after lung resection has not been found. The positive predictive values of RLF and PVR are disappointing, while the negative predictive values are acceptable. Measurement of VO2 is simple, noninvasive and might predict survivable morbidity, as suggested in the literature. Obviously, additional studies are necessary.

Exercise Test↗
PubMed1996

[Evaluation of the prognosis of patients with stage I non-small cell lung cancer with respect to predicted postoperative lung function].

One hundred and forty patients underwent absolute curative resction for stage I non-small lung cancer from 1982 to 1993 at our department. For these, prognosis and changes in quality of life (QOL) were evaluated retrospectively with respect to the predicted postoperative lung function. The average age of the patients was 62 years (range 31 to 84 years), and 103 males and 37 females were included. Seventy-five of the patients had adenocarcinoma, 61 squamous cell carcinoma, and 4 large cell carcinoma. These 140 patients were classified into two groups, H and N, according to the predicted postoperative %FEV1.0 and %VC. Group H patients (n = 39) had a predicted %FEV1.0 and/or %VC of 55% or less for postoperative respiratory disease. Group N patients (n = 101) had a predicted %FEV1.0 and %VC of 56% or more for expected normal respiratory conditions postoperatively. Group N patients showed a 98% one-year survival rate, and 72% five-year survival rate and good QOL postoperatively. On the other hand, group H patients showed 86% and 45% one- and five-year survival rates respectively, the same as those predicted for patients with stage II non-small cell lung cancer. Furthermore, group H patients more than 70 years old showed 80% and 17% one- and five-year survival rates, the same as those predicted for patients with stage IIIA non-small cell lung cancer, and poor prognosis in comparison with that of the group N patients more than 70 years old. Then, QOL was investigated one year postoperatively. The group H patients showed deterioration of performance status in comparison with the group N patients. Since there was a high incidence of postoperative respiratory disease in the patients with a predicted %FEV1.0 and/or %VC of 55% or less, physicians should avoid extended lung resection, which might cause deterioration of the cardiopulmonary reserve volume, in patients in whom respiratory disease is predicted to occur, particularly for patients more than 70 years old. In conclusion, physicians should consider limiting surgical intervention to preserve lung volume and QOL of patients more than 70 years old with restricted cardiopulmonary function.

Adult↗
PubMedDate not supplied

Is percentage of predicted maximal exercise oxygen consumption a better predictor of survival than peak exercise oxygen consumption for patients with severe heart failure?

BACKGROUND: Peak exercise oxygen consumption provides valuable short-term prognostic information in patients with heart failure. However, peak exercise oxygen consumption is determined not only by the cardiac output response to exercise but also by age, gender, and muscle mass. We investigated whether percentage of predicted maximal exercise oxygen consumption rather than an absolute value may be a better predictor of survival. METHODS: Peak exercise oxygen consumption was measured and percentage of predicted maximal exercise oxygen consumption was derived from two standard formulas (Wasserman and Astrand) in 272 ambulatory patients referred for transplant evaluation. The predictive ability of these variables was determined by comparison of Kaplan-Meier curves, univariable proportional-hazards models, and receiver operating characteristic curves. RESULTS: Neither method of determining percentage of predicted maximal exercise oxygen consumption significantly improved the prediction of survival over peak exercise oxygen consumption alone. Overall model discrimination, as assessed by area under the receiver operating characteristic curve, was not significantly improved with percentage of predicted maximal exercise oxygen consumption (Wasserman) rather than weight-normalized peak exercise oxygen consumption (0.71 +/- 0.04 versus 0.66 +/- 0.04; Z = 1.60, p = 0.11). All of the difference between percentage of predicted maximal exercise oxygen consumption-Wasserman and peak exercise oxygen consumption resulted from differences in women (areas under receiver operating characteristic curve = 0.68 +/- 0.09 and 0.74 +/- 0.09; p = 0.14); results for men were the same (both areas = 0.68 +/- 0.04). CONCLUSIONS: Normalization of peak exercise oxygen consumption for predicted values adds only minimal prognostic information. A peak exercise oxygen consumption < 14 ml/kg/min remains a reasonable guideline by which to time heart transplantation.

Adult↗
PubMed1996

Prediction of results from correspondence treatment for controlled drinking.

Identification of people who will benefit most from brief interventions is an important research challenge in the study of addictive disorders. The current study investigated predictors of response to correspondence interventions for alcohol abuse. We examined both subject retention and alcohol intake over a 12-month period. The primary focus was on the predictive utility of self-efficacy, stages of change and alcohol dependence. Self-efficacy performed relatively well in the study, predicting both retention and later consumption. When predicting 12-month consumption from pretest assessments or examining subject retention over the last 6 months, self-efficacy offered a significant contribution to multivariate analyses. However, in some other predictions a significant effect of self-efficacy was eliminated after the entry of other variables. Stages of change significantly predicted mid-way through treatment, but did not provide an independent prediction of overall retention or treatment response. Neither the degree of alcohol dependence nor level of alcohol problems figured in any of the predictions. Older subjects stayed longer in the study, and those with lower intake and higher pretest self-efficacy had the lowest consumption at 12 months. Results are compared with previous research on prediction of outcomes in addictive disorders.

Adult↗
PubMed1996

Racial variation in predicted and observed in-hospital death. A regional analysis.

OBJECTIVE: To compare observed, predicted, and risk-adjusted hospital mortality rates in white and African-American patients and to determine whether, as prior studies suggest, African-American patients would have higher predicted risks of death and similar or higher risk-adjusted mortality. DESIGN: Retrospective cohort study. SETTING: Thirty hospitals in northeast Ohio. PATIENTS: A total of 88205 eligible patients consecutively discharged in the years 1991 through 1993 with the following 6 diagnoses: acute myocardial infarction, congestive heart failure, obstructive airways disease, gastrointestinal hemorrhage, pneumonia, and stroke. METHODS: We measured predicted risks of death at admission for each diagnosis using validated multivariable models based on standard clinical data abstracted from patients' medical records. We then adjusted in-hospital mortality rates in white and African-American patients for predicted risk of death and other covariates using logistic regression analysis. MAIN OUTCOME MEASURES: Predicted risk of death at admission and observed hospital mortality in white and African-American patients. RESULTS: Predicted risks of death were lower (P<.001) in African Americans for 4 of the 6 diagnoses. Adjusted odds of hospital death were lower (P<.01) in African Americans for 2 of the 6 diagnoses (congestive heart failure and obstructive airways disease) and similar for the other 4 diagnoses. For all diagnoses, in aggregate, the adjusted odds of hospital death were 13% lower in African-American compared with white patients (multivariable odds ratio, 0.87; 95% confidence interval, 0.80-0.94). Findings were similar if further adjustments were made for differences in length of stay, site of hospitalization, or discharge triage practices. CONCLUSION: Contrary to our a priori hypotheses, predicted risks of death and risk-adjusted mortality rates were generally lower in African-American patients. Our finding of lower predicted risk may reflect racial differences in hospital admission practices or in access to outpatient care. However, our findings suggest that, once hospitalized, African-American patients attained similar or better outcomes, as measured by an important measure--hospital mortality.

Adult↗
PubMed1997

[Predictive medicine and its ethics].

The concept of predictive medicine based on the detection of genetic markers for disease susceptibility stemmed from the finding that many diseases are associated with specific HLA alleles. This model suggested that similar associations probably existed with other genes located all along the human genome. The Human Specimen Study Center (HSSC) was created to assist in investigating this possibility and has contributed significantly to the knowledge contained in current genetic and physical human genome maps. Predictive medicine is intended not for patients but for healthy individuals, its goal being to determine whether their susceptibility to a specific disease is increased or not. Fetuses with evidence of disease are excluded from the province of predictive medicine, which can, however, determine whether a healthy fetus is at high risk for developing a disease in adolescence or adulthood. Predictive medicine is based on probabilities: it evaluates diseases susceptibility but cannot predict with 100% certainty that a specific disease will occur. Whereas many preventive interventions are directed at groups (e.g., immunization programs), predictive medicine is conducted on an individualized basis. For instance, glaucoma is a monogenic disease whose early detection can allow to prevent permanent loss of vision. The fruits of predictive medicine are expected to be greatest, however, in the polygenic multifactorial diseases that are prevalent in industrialized countries, such as diabetes mellitus, hypertension, myocardial infarction, hyperlipidemia, and arteriosclerosis. An ability to detect subjects who are susceptible to breast cancer would be extraordinarily useful, and may be a goal within reach since two breast cancer susceptibility genes have already been identified. Genes associated with increased susceptibility to colon cancer have also been reported. Predictive medicine raises a number of sensitive ethical issues. Individuals should be free to accept or decline disease susceptibility testing after having been fully informed. Confidentiality is vital. The results of susceptibility tests should not be made available to employers or insurance agencies. Susceptibility testing should be offered only if the disease requires a specific treatment or lifestyle modification. Unnecessary anxiety may be one of the main adverse effects of susceptibility testing. A large number of disease susceptibility or resistance genes will probably be identified in the near future, and this will inevitably have an impact on the way physicians approach their patients. Physicians in the XXIst century will spend an increasingly large proportion of their time counselling their patients on how to stay healthy. This trend can be expected to translate into a marked increase in life expectancy. Rather than seeking to add years to life, physicians will strive to add life to years.

Confidentiality↗
PubMed1997

Prediction of stentless aortic bioprosthesis size with transesophageal echocardiography and magnetic resonance imaging.

BACKGROUND AND AIMS OF THE STUDY: During stentless bioprosthetic aortic valve replacement, ischemic time may be decreased by the non-invasive prediction of bioprosthesis size, allowing earlier commencement of prosthesis preparation. In this study we examine whether the addition of transesophageal echocardiography (TEE) to transthoracic echocardiography (TTE) aids in the prediction of stentless bioprosthesis aortic valve size. We also report our preliminary experience with the use of magnetic resonance imaging (MRI) in bioprosthetic valve size prediction. METHODS: Eight patients in whom elective aortic valve replacement with a Toronto SPV valve was planned underwent preoperative TTE and MRI, and intraoperative TEE. RESULTS: In all cases the combination of TTE and TEE correctly predicted the size of Toronto SPV valve inserted. In three cases, TEE led to a revision of the TTE-based prediction. The need for sinotubuloplasty in two patients was correctly predicted by both TTE and TEE. MRI of the aortic annulus correctly predicted valve size in three of four cases, but could not reliably identify the sinotubular junction. CONCLUSIONS: In aortic valve replacement the accuracy of prediction of stentless bioprosthesis size is improved by the addition of TEE to TTE.

Aortic Valve↗
PubMed1998

A multivariate analysis of the survival of patients with aggressive lymphoma: variations in the predictive value of prognostic factors during the course of the disease. Groupe d'Etudes des Lymphomes de l'Adulte.

BACKGROUND: Aggressive non-Hodgkin's lymphoma is now often curable with chemotherapy. The International Prognostic Index (IPI) was recently developed to predict patient survival on the basis of pretreatment clinical features. However, classical multivariate regression models such as the IPI fail to detect time-dependent changes in the predictive value of covariates. In this study, an extension of the Cox proportional hazards model was used to determine whether the value of prognostic factors might decay over time. METHODS: A total of 1271 patients younger than 60 years, entered on the LNH-84 and LNH-87 studies of an ACVBP induction regimen (consisting of doxorubicin, cyclophosphamide, vindesine, bleomycin, methylprednisolone, and IT methotrexate), were analyzed in terms of overall survival and monthly risk of dying. By a standard method, prognostic factors were identified in a training sample and confirmed in a validation sample. The independently significant covariates were then included in a step-function regression model (3-step) that permitted determination of their value in predicting the short term and long term survival of the entire population. RESULTS: During the entire follow-up period (median, 5.5 years), lactate dehydrogenase level, tumor stage, performance status, and number of extranodal sites remained independently predictive of overall survival. However, these covariates had nonproportional hazard functions. The study of their time-dependent effect with the 3-step model confirmed that they were predictive of overall survival during the short term follow-up period of 3 months to 2 years. However, during the induction period of 0-3 months and the long term follow-up period of 2-10 years, there was only 1 independently predictive factor for each of these periods: performance status and tumor stage, respectively. CONCLUSIONS: The IPI factors are relevant to short term follow-up and permit the selection of routine or experimental therapeutic regimens. In contrast, only performance status is predictive of a patient's ability to tolerate induction chemotherapy, and only tumor stage is predictive of long term survival.

Antineoplastic Agents↗
PubMedDate not supplied

[Prognostic factors and predictive tables of non-localized prostate cancer that exclude therapy by radical prostatectomy].

OBJECTIVE: To know the predictive factors of the no localized prostatic cancer (NLPC) with the intention of improving the indications of the radical prostatectomy. METHODS: A longitudinal, observational, analytic and retrospective study is made with our first 216 radical prostatectomies. A multivariate analysis by logistic regression has been made. A predictive evacuation with the odds ratio of the risc factors, a ROC curve and predictive tables of the NLPC are obtained. RESULTS: Clinical stage, PSA and Gleason are predictive factors of the NLPC. The predictive evacuation with a cut point of probability p = 0.5 has a specificity of 81%, a sensibility of 70% and global diagnostic capacity of 75%. NLCP odds ratio are: Gleason 5,6,7/Gleason 2,3,4 = 2.6, Gleason 8,9,10/Gleason 5,6,7 = 3, Gleason 8,9,10/Gleason 2,3,4 = 7.6, T2/T1 = 2, T3/T2 = 5, T3/T1 = 10 and PSA = 1. After the study of the predictive tables it can be concluded that T3, Gleason > = 8 and PSA > = 30 have a very high NLPC probability. CONCLUSIONS: PSA, Gleason and clinical stage are NLPC predictive factors. Predictive tables to know the NLPC probability by these 3 factors are available.

Aged↗
PubMed1997

Analyzing predictive models following definitive radiotherapy for prostate carcinoma.

BACKGROUND: As we approach the 21st century, clinically useful predictive models for prostate carcinoma are urgently needed to stratify patients reliably for future treatment strategies. Recently, many investigators have developed models that employ prostate specific antigen (PSA)-based constructs or groupings in an attempt to predict outcome accurately following definitive radiotherapy. This investigation was conducted to determine which of these models provides the closest "fit" to independent clinical outcome data measuring biochemical freedom from failure (bNED control), thereby warranting further exploration. METHODS: Six models were analyzed in a definitive radiotherapy series of 421 patients with localized prostate carcinoma treated with a median dose of 74 Gray (Gy) between March 1988 and November 1994. A stepwise Cox proportional hazards multivariate analysis (MVA) was performed to predict for bNED control using the following covariates: PSA, Gleason's score, stage, dose, PSA density, and perineural invasion. Subsequent MVAs were performed for each model incorporating the new construct or prognostic groupings. The adequacy of the models was confirmed using plots of score residuals against time to bNED failure and comparisons were made used Akaike's Information Criteria (AIC) in which a smaller value corresponds to a statistically improved model based on explained variation and the number of predictors. Because PSA was distributed in a log-normal fashion in the current study population, the model-building process was duplicated using a logarithmic transformation analysis. Biochemical failure was defined as 2 consecutive elevations in the PSA > or = 1.5 ng/mL. The median follow-up time was 34 months (range, 2-87 months). RESULTS: Initially, the model developed by Pisansky et al. appeared the most predictive due to the parsimony in their risk estimate, which is the sole predictor of outcome, as well as its associated lowest AIC value. However, after the logarithmic transformation analysis, all the models appeared to be equally predictive of bNED outcome. CONCLUSIONS: A plethora of accurate models for predicting outcome following definitive radiotherapy for prostate carcinoma recently have been engineered, all of which are essentially equally predictive in this data base (via a logarithmic conversion process). This analysis should be corroborated in other large radiotherapy series.

Follow-Up Studies↗
PubMed1998

Predicting the outcome of radiotherapy for prostate carcinoma: a model-building strategy.

BACKGROUND: Clinical research of prostate carcinoma could be enhanced by models that allow early and reliable prediction of outcome. In this study, the authors describe a model-building strategy and compare different models. METHODS: The sample population was comprised of 158 patients treated definitively with radiotherapy. Univariate and multivariate logistic regression analyses were conducted to identify prognostic factors and select the best predictive model. Variables included age, race, method of diagnosis (needle biopsy vs. transurethral resection of the prostate), stage, grade, pretreatment prostate specific antigen (PSA), in-treatment PSA (PSA(tx)), posttreatment PSA (PSA(post)), and nadir PSA. The following indices were used to compare discriminatory power: log-likelihood function, Akaike information criterion, the generalized coefficient of determination, and the area under the receiver operating characteristic curve. RESULTS: At last follow-up, 49 patients (31%) had recurrence of carcinoma. By univariate analysis, the failure rate was significantly higher in patients with advanced stage, higher grade, higher pretherapy PSA, and nadir PSA > 1 ng/mL (P < 0.0001). Pretherapy PSA was associated significantly with stage, age, and nadir PSA (P = 0.001, P = 0.001, and P = 0.001, respectively). All PSA measurements were significantly interrelated. Nadir PSA was the most predictive variable. Significant gains (P = 0.01) in predictive power were derived from inclusion of PSA(tx), but not PSA (post). Age, race, stage, grade, and method of diagnosis contributed predictive power in addition to that derived from PSA levels (P = 0.01, log-likelihood test). The authors' model of choice predicts outcome with an overall correctness, sensitivity, specificity, and false-negative rate of 81.8%, 87.2%, 79.6%, and 12.8%, respectively. CONCLUSIONS: Applying the strategy described, a model was selected that allowed accurate prediction of failure shortly after the completion of therapy.

Aged↗
PubMed2001

Improved prediction of metastasis in tongue carcinomas, combining vascular and nuclear tumor parameters.

BACKGROUND: Predicting the presence of metastasis, based on tumor or tumor-related characteristics is of utmost importance. The authors studied the significance of tumor DNA features and tumor-related angiogenesis to predict the occurrence of metastasis in squamous cell carcinomas (SCCs) of the tongue. METHODS: Paraplast blocks from resection specimens of 20 metastasized and 20 nonmetastasized SCCs of the tongue with a minimum follow-up of 24 months were used. Tissue sections were stained with anti-CD34 monoclonal antibodies for vessel visualization, and according to Feulgen to stain DNA. Using image analysis, data from both stainings were computed for each of the 40 carcinomas. A logistic regression model to predict the presence of metastasis, based on vascular and nuclear morphology features, was developed. RESULTS: The intratumor variation of chromatin condensation and the percentage vessels smaller than 5 microm in diameter were selected for the model. The model correctly predicted metastasis in 90% of patients and excluded metastasis correctly in 75% of nonmetastasized tumors. Taking into account the prevalence of metastasis in SCC of the tongue of between 30% and 60%, this means a predictive value for a negative outcome of between 95% and 83%. CONCLUSIONS: The proposed model shows an improvement of predictive values compared with previous models with single parameters. Therefore, a multiparameter model appears to predict the multiparameter process of metastasis better.

Carcinoma, Squamous Cell↗
PubMed2000

Comparison of different approaches to incidence prediction based on simple interpolation techniques.

The paper compares three different methods for performing disease incidence prediction based on simple interpolation techniques. The first method assumes that the age-period specific numbers of observed cases follow a Poisson distribution and the other two methods assume a normal distribution for the incidence rates. The main emphasis of the paper is on assessing the reliability of the three methods. For this purpose, ex post predictions produced by each method are checked for different cancer sites using data from the Cancer Control Region of Turku in Finland. In addition, the behaviour of the estimators of predicted expected values and prediction intervals, crucial for investigation of the reliability of prediction, are assessed using a simulation study. The prediction method making use of the Poisson assumption appeared to be the most reliable of the three approaches. The simulation study found that the estimator of the length of the prediction interval produced by this method has the smallest coverage error and is the most precise.

Adult↗
PubMed2005

Prediction of recurrence in advanced gastric cancer patients after curative resection by gene expression profiling.

The prognosis of patients with advanced gastric cancer remains unfavorable. Even after curative resection, 40% of patients with advanced gastric cancer die of recurrence. Conventional clinicopathlogic findings are sometimes inadequate for predicting recurrence in individuals. Hence, we tried to construct a new diagnostic system, which predicts recurrence in patients with advanced gastric cancer after curative resection based on molecular analysis. Gastric cancer progression is a function of multiple genetic events that may affect the expression of large number of genes. We performed gene expression profiling with 2,304 genes in 60 advanced gastric cancer patients who underwent curative resection using a PCR array technique, a high-throughput quantitative RT-PCR technique. The diagnostic system, which was constructed from the learning set comprised of 40 patients with the most informative 29 genes, classified each case into a good-signature or poor-signature group. Then, we confirmed the predictive performance in an additional test set comprised of 20 patients, and the prediction accuracy for recurrence was 75%. Kaplan-Meier analysis revealed significant difference between the good-signature and the poor-signature group (p = 0.0125). Especially in patients with smaller tumor (< or = 5 cm), less developed LN metastasis (N(0,1)), or earlier stage (stages I and II), the prediction accuracy was high (88.9%, 84.6%, or 81.8%, respectively). Our diagnostic system based on systematic analysis of gene expression profiling can predict the recurrence at clinically meaningful level. By combining our system with conventional clinicopathologic factors, we can improve the prediction of recurrence in patients with advanced gastric cancer who underwent curative surgery.

Aged↗