PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Prognostic model”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12Linked to original sources
PubMed2005

Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia.

Sickle cell anemia (SCA) is a paradigmatic single gene disorder caused by homozygosity with respect to a unique mutation at the beta-globin locus. SCA is phenotypically complex, with different clinical courses ranging from early childhood mortality to a virtually unrecognized condition. Overt stroke is a severe complication affecting 6-8% of individuals with SCA. Modifier genes might interact to determine the susceptibility to stroke, but such genes have not yet been identified. Using Bayesian networks, we analyzed 108 SNPs in 39 candidate genes in 1,398 individuals with SCA. We found that 31 SNPs in 12 genes interact with fetal hemoglobin to modulate the risk of stroke. This network of interactions includes three genes in the TGF-beta pathway and SELP, which is associated with stroke in the general population. We validated this model in a different population by predicting the occurrence of stroke in 114 individuals with 98.2% accuracy.

Anemia, Sickle Cell↗
PubMed2001

Natural history and prognostic models in primary sclerosing cholangitis.

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by inflammation and fibrosis of the intra- and extra-hepatic bile ducts. Despite the recognition of immunological and genetic alterations cited as factors in its pathogenesis, the exact cause for PSC remains unknown. Observational cohort studies, however, have demonstrated that PSC is a progressive disease culminating in liver failure or death. Natural history assessment in PSC, however, has been complicated by variable rates of disease progression and the impact of clinical symptoms upon initial presentation. The development of mathematical models by multivariable regression techniques (most notably Cox proportional hazards regression) has allowed for an improved description of overall survival on an individual basis among patients with PSC. Additionally, these models have also been employed for determining the optimal selection and timing for liver transplantation when advanced disease is imminent.

Cholangitis, Sclerosing↗
PubMed2001

Prognostic model for early acute rejection after liver transplantation.

Hepatic graft rejection is a common complication after liver transplantation (LT), with a maximum incidence within the first weeks. The identification of high-risk patients for early acute rejection (EAR) might be useful for clinicians. A series of 133 liver graft recipients treated with calcineurin inhibitors was retrospectively assessed to identify predisposing factors for EAR and develop a mathematical model to predict the individual risk of each patient. The incidence of EAR (< or =45 days after LT) was 35.3%. Multivariate analysis showed that recipient age, underlying liver disease, and Child's class before LT were independently associated with the development of EAR. Combining these 3 variables, the following risk score for the development of EAR was obtained: EAR score [F(x)] = 2.44 + (1.14 x hepatitis C virus cirrhosis) + (2.78 x immunologic cirrhosis) + (2.51 x metabolic cirrhosis)--(0.08 x recipient age in years) + (1.65 x Child's class A) [corrected]. Risk for rejection = e(F(x))/1 + e(F(x)). The combination of age, cause of liver disease, and Child's class may allow us to predict the risk for EAR.

Calcineurin Inhibitors↗
PubMed2001

A prognostic model for assessment of the outcome of endodontic treatment: Effect of biologic and diagnostic variables.

OBJECTIVE: Many biological variables, endodontic treatment factors, and restorative considerations have been suggested in the literature to affect the outcome of endodontic treatment. However, few attempts have been made recently to study these variables further. The purpose of this study was to identify the biologic and endodontic treatment-associated variables that are most predictive of treatment outcome for conventional endodontic therapy and to determine the magnitude of risk these variables pose on the outcome. STUDY DESIGN: The population of this historical prospective cohort study comprised a total of 200 teeth with 441 root canals. Diagnostic and treatment information was abstracted from the original patient records. An endodontic follow-up examination was conducted 4 +/- 0.5 years after obturation. Each tooth/root was analyzed according to 3 indices of periradicular status at 2 time points. The main outcome measure was the presence of apical periodontitis. The criteria used for evaluation of the outcome were modified from Strindberg. Data were subjected to univariate and multivariate analysis. Logistic regression models were fit by using various clinical measures to determine which combination of biologic and treatment-associated factors best predicted treatment outcome. RESULTS: The preoperative pulp diagnosis, the periapical diagnosis, the preoperative periapical radiolucency size, and the sex of the patients were revealed, by means of univariate analysis, to exert a significant influence on endodontic treatment outcome (P <.05). In the logistic regression model, the strongest effect on postoperative healing was the presence and magnitude of preoperative apical periodontitis. In the presence of this variable, no other factor contributed value to the prediction. The correct prediction of this model was 74.7% (P <.05). CONCLUSION: The major biologic factors influencing the outcome of endodontic treatment appear to be the extent of microbiological insult to the pulp and periapical tissue, as reflected by the periapical diagnosis and the magnitude of periapical pathosis.

Acute Disease↗
PubMed2003

A prognostic model for predicting waiting-list mortality for a total national cohort of adult heart-transplant candidates.

BACKGROUND: Current trends in medical management of advanced heart failure and transplant medicine and the enactment of a national transplant law forced a change toward allocation driven by disease severity. OBJECTIVE: The aim of this study was to create a model for predicting waiting-list survival on the basis of simple clinical parameters. METHODS: The clinical profiles of all patients registered for heart transplantation in Germany in 1997 (n=889) were used as a derivation set, and the total German 1998 cohort (n=897) was used as a validation set. The model was validated by the c statistic and by comparison of risk stratified mortality rates. The validated model was fine tuned by the appropriate calibration procedures. The data were first classified into physiologic subscores: an urgency score, a left ventricular heart failure score, a right ventricular heart failure score, and a systemic heart failure score. A stepwise modeling procedure was undertaken using these subscores as factors as well as the recipient's age, ABO blood group, and body surface area. RESULTS: The urgency and the left ventricular subscore were found to be significantly associated with waiting-list mortality. A summary index termed German Transplant Society (GTS) score was then calculated on the basis of seven parameters contained in these two subscores. The GTS score was able to predict waiting-list mortality risks for the 1998 cohort: 1-year mortality before transplantation was 71%, 34%, 11% for the high, medium, and low risk groups, respectively. CONCLUSION: The use of this continuous disease severity index may improve the selection of cardiac transplant candidates.

Adult↗
PubMed2003

A preoperative clinical prognostic model for non-metastatic renal cell carcinoma.

OBJECTIVE: To develop a model to predict the outcome before surgery for non-metastatic renal cell carcinoma (RCC). PATIENTS AND METHODS: The records of 660 patients with non-metastatic RCC, operated at three European medical institutes, were reviewed. Univariate and multivariate analyses were used to assess the clinical and pathological variables affecting disease-free survival. RESULTS: The median (range) follow-up was 42 (2-180) months; the disease recurred in 110 patients (16%). The 2- and 5-year overall survival was 87% and 54%, respectively. Five variables were significant in the univariate analysis, i.e. clinical presentation, clinical and pathological size, tumour grade and stage (P < 0.05). The preoperative variables, e.g. clinical presentation and clinical tumour size, were retained from the multivariate model. A recurrence risk formula (RRF) was constructed from this model, as (1.28 x presentation (asymptomatic = 0; symptomatic = 1) + (0.13 x clinical size)). Using this equation, the 2- and 5-year disease-free survival was 96% and 93% for an RRF of < or = 1.2 and 83% and 68% for an RRF of > 1.2. CONCLUSION: A formula was developed which, independent of stage, can be used to predict the rate of treatment failure in patients who undergo nephrectomy for non-metastatic RCC. The RRF might be useful for more accurate sub-grouping of good-prognosis patients, and for counselling patients before surgery, their personalized follow-up or adjuvant treatment once available.

Adult↗
PubMed2002

External validation of a prognostic model for predicting survival of cirrhotic patients with refractory ascites.

OBJECTIVE: Cirrhotic patients with refractory ascites (RA) have a poor prognosis, although individual survival varies greatly. A model that could predict survival for patients with RA would be helpful in planning treatment. Moreover, in cases of potential liver transplantation, a model of these characteristics would provide the bases for establishing priorities of organ allocation and the selection of patients for a living donor graft. Recently, we developed a model to predict survival of patients with RA. The aim of this study was to establish its generalizability for predicting the survival of patients with RA. METHODS: The model was validated by assessing its performance in an external cohort of patients with RA included in a multicenter, randomized, controlled trial that compared large-volume paracentesis and peritoneovenous shunt. The values for actual and model-predicted survival of three risk groups of patients, established according to the model, were compared graphically and by means of the one-sample log-rank test. RESULTS: The model provided a very good fit to the survival data of the three risk groups in the validation cohort. We also found good agreement between the survival predicted from the model and the observed survival when patients treated with peritoneovenous shunt and with paracentesis were considered separately. CONCLUSION: Our survival model can be used to predict the survival of patients with RA and may be a useful tool in clinical decision making, especially in deciding priority for liver transplantation.

Actuarial Analysis↗
PubMed1994

The pathology of melanoma as a basis for prognostic models: the UCSF experience.

A patient survival model is proposed which allows visualization of a data base, and which includes only routine and commonly recorded attributes in most melanoma clinics. It is proposed that a network of such data be collected for meta-analysis (MELNET), which could make stratification within the individual subsets more significant by virtue of the large numbers. Such a network could then be fully tested in various melanoma clinics for clinical usefulness.

Databases, Factual↗
PubMed2003

Predicting fatal outcome in the early phase of severe acute pancreatitis by using novel prognostic models.

BACKGROUND/AIMS: Survival in acute pancreatitis and particularly in severe acute and necrotizing pancreatitis is a combination of therapy-associated and patient-related factors. There are only few relevant methods for predicting fatal outcome in acute pancreatitis. Scores such as Ranson, Imrie, Blamey, and APACHE II are practical in assessing the severity of the disease, but are not sufficiently validated for predicting fatal outcome among patients with severe acute pancreatitis. The aim of this study was to construct a novel prediction model for predicting fatal outcome in the early phase of severe acute pancreatitis (SAP) and to compare this model with previously reported predictive systems. METHODS: Hospital records of 253 patients with SAP were retrospectively analyzed. 234 patients with adequate data were included to the test set to construct five logistic regression and three artificial neural network (ANN) models. Two models were tested in an independent prospective validation set of 60 consecutive patients with SAP and compared with previously reported predictive systems. RESULTS: The prediction model considered optimal was a logistic model with four variables: age, highest serum creatinine value within 60-72 h from primary admission, need for mechanical ventilation, and chronic health status. In the validation set, the predictive accuracy, determined by the area under the receiver operating characteristic curve value, was 0.862 for the chosen model, 0.847 for the ANN model using eight variables, 0.817 for APACHE II, 0.781 for multiple organ dysfunction score, 0.655 for Ranson, and 0.536 for Imrie scores. CONCLUSIONS: Ranson and Imrie scores are inaccurate indicators of the mortality in SAP. A novel predictive model based on four variables can reach at least the same predictive performance as the APACHE II system with 14 variables.

APACHE↗
PubMed2002

Predicting outcome after acute and subacute stroke: development and validation of new prognostic models.

BACKGROUND AND PURPOSE: Statistical models to predict the outcome of patients with acute and subacute stroke could have several uses, but no adequate models exist. We therefore developed and validated new models. METHODS: Regression models to predict survival to 30 days after stroke and survival in a nondisabled state at 6 months were produced with the use of established guidelines on 530 patients from a stroke incidence study. Three models were produced for each outcome with progressively more detailed sets of predictor variables collected within 30 days of stroke onset. The models were externally validated and compared on 2 independent cohorts of stroke patients (538 and 1330 patients) by calculating the area under receiver operating characteristic curves (AUC) and by plotting calibration graphs. RESULTS: Models that included only 6 simple variables (age, living alone, independence in activities of daily living before the stroke, the verbal component of the Glasgow Coma Scale, arm power, ability to walk) generally performed as well as more complex models in both validation cohorts (AUC 0.84 to 0.88). They had good calibration but were overoptimistic in patients with the highest predicted probabilities of being independent. There were no differences in AUCs between patients seen within 48 hours of stroke onset and those seen later; between ischemic and hemorrhagic strokes; and between those with and without a previous stroke. CONCLUSIONS: The simple models performed well enough to be used for epidemiological purposes such as stratification in trials or correction for case mix. However, clinicians should be cautious about using these models, especially in hyperacute stroke, to influence individual patient management until they have been further evaluated. Further research is required to test whether additional information from brain imaging improves predictive accuracy.

Acute Disease↗
PubMedDate not supplied

Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets.

Clinical decision making often requires estimates of the likelihood of a dichotomous outcome in individual patients. When empirical data are available, these estimates may well be obtained from a logistic regression model. Several strategies may be followed in the development of such a model. In this study, the authors compare alternative strategies in 23 small subsamples from a large data set of patients with an acute myocardial infarction, where they developed predictive models for 30-day mortality. Evaluations were performed in an independent part of the data set. Specifically, the authors studied the effect of coding of covariables and stepwise selection on discriminative ability of the resulting model, and the effect of statistical "shrinkage" techniques on calibration. As expected, dichotomization of continuous covariables implied a loss of information. Remarkably, stepwise selection resulted in less discriminating models compared to full models including all available covariables, even when more than half of these were randomly associated with the outcome. Using qualitative information on the sign of the effect of predictors slightly improved the predictive ability. Calibration improved when shrinkage was applied on the standard maximum likelihood estimates of the regression coefficients. In conclusion, a sensible strategy in small data sets is to apply shrinkage methods in full models that include well-coded predictors that are selected based on external information.

Aged↗
PubMedDate not supplied

Withholding or starting antibiotic treatment in patients with dementia and pneumonia: prediction of mortality with physicians' judgment of illness severity and with specific prognostic models.

BACKGROUND: To help decision makers plan treatment, the authors assessed clinical predictors of mortality from nursing home-acquired pneumonia in patients with dementia. METHODS: Pneumonia patients treated without (n = 165) or with antibiotics (n = 541) were enrolled in a prospective cohort study in 61 nursing homes. RESULTS: In both groups, clinical judgment of illness severity was a strong predictor for 1-week mortality. Despite large differences in frailty and mortality (83% in untreated patients and 15% in treated patients), separate multivariable logistic models included similar specific predictors. DISCUSSION: Despite profound differences between the 2 independent groups, predictors for short-term mortality were largely similar. We found that, when combined with physicians' clinical judgment, 3 readily assessed predictors (respiratory rate, fluid intake, and eating dependency) helped predict mortality. Our results, if confirmed in an independent population, can help make decision making about antibiotic treatment of pneumonia in patients with dementia more evidence-based.

Aged↗