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Results for “Prognostic model”

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PubMed2006

Review article: scoring systems for assessing prognosis in critically ill adult cirrhotics.

BACKGROUND: Cirrhotic patients admitted to intensive care units (ICU) still have poor outcomes. Some current ICU prognostic models [Acute Physiology and Chronic Health Evaluation (APACHE), Organ System Failure (OSF) and Sequential Organ Failure Assessment (SOFA)] were used to stratify cirrhotics into risk categories, but few cirrhotics were included in the original model development. Liver-specific scores [Child-Turcotte-Pugh (CTP) and model for end-stage liver disease (MELD)] could be useful in this setting. AIM: To evaluate whether ICU prognostic models perform better compared with liver-disease specific ones in cirrhotics admitted to ICU. METHODS: We performed a structured literature review identifying clinical studies focusing on prognosis and risk factors for mortality in adult cirrhotics admitted to ICU. RESULTS: We found 21 studies (five solely dealing with gastrointestinal bleeding) published during the last 20 years (54-420 patients in each). APACHE II and III, SOFA and OSF had better discrimination for correctly predicting death compared with the CTP score. The MELD score was evaluated only in one study and had good predictive accuracy [receiver operator characteristic (ROC) curve: 0.81). Organ dysfunction models (OSF, SOFA) were superior compared with APACHE II and III (ROC curve: range 0.83-0.94 vs. 0.66-0.88 respectively). Cardiovascular, liver and renal system dysfunction were more frequently independently associated with mortality. CONCLUSIONS: General-ICU models had better performance in cirrhotic populations compared with CTP score; OSF and SOFA had the best predictive ability. Further prospective and validation studies are needed.

Critical Care↗

PubMed2003

How to assess musculoskeletal conditions. Prognostics.

Both patients and clinicians confronted with rheumatic disease need prognostic information to direct their management strategies. Prognosis refers to the prediction of outcome of disease. Clinicians confronted with prognostic problems in individual patients usually have to rely on evidence from prognostic studies on prognostic factors or clinical prediction models. Prognostic factors are patient characteristics that can be used to predict the outcome of disease. Clinical prediction models are sets of prognostic factors developed by logistic regression modelling. These models estimate the probabilities of the various forms of outcome of a disease and they can be valuable prognostic tools in daily clinical practice. To judge whether an article on prognosis fulfils the necessary methodological standards and contains relevant information, clinicians can use a number of guidelines that are described in this chapter. To find out whether the results of a prognostic study are applicable to individual patients in daily practice, the clinician should obtain clarity on three aspects: (i) generalizability, (ii) the possibility of extrapolating the results to the particular clinical situation, and (iii) feasibility. Decision analysis values the information provided by prognostic testing insofar as it alters treatment strategies. The treatment threshold is the probability of a particular disease outcome for which the net risks of treatment and no treatment are equal. Treatment is indicated if the probability of the disease outcome is higher than the treatment threshold.

Confounding Factors, Epidemiologic↗

PubMed1998

The chromatin pattern of cell nuclei is of prognostic value for renal cell carcinomas.

Using a series of 105 renal cell carcinomas (RCCs) we investigated whether features quantitatively describing the appearance of Feulgen-stained nuclei and, more particularly, of their chromatin (on the basis of computer-assisted microscopy) can contribute any significant prognostic information. Thirty morphonuclear and 8 nuclear DNA content-related variables were thus generated. The actual prognostic values of this set of cytometric variables was compared (by means of discriminant statistical analysis) to conventional diagnostic and/or prognostic markers including histopathological grades, tumour invasion levels and the presence or absence of metastases. We obtained complete clinical follow-ups for 49 of the 105 RCC patients under study, making it possible to define a subset of patients with a bad prognosis (i.e., who died in the 12 months following nephrectomy) and a subset of patients with a good prognosis (i.e., who survived at least 24 months following nephrectomy). An original method of data analysis related to artificial intelligence (decision tree induction) enabled a strong prognostic model to be set up. In the case of 10 new patients, this model identified all the dead patients as having a bad survival status, with a total of 8 correct predictions. Another prognostic model similarly generated enabled the correct predictions to be confirmed.

Adult↗

PubMedDate not supplied

[Mathematical model for prognostication of work capacity recovery in patients with myocardial infarct].

The impact of clinical and instrumental findings on prognosis was evaluated in patients with myocardial infarction and a mathematical model was proposed to predict their recovered working ability. The developed predictive index for prediction of recovered working ability is of high informative value and accuracy. It may be practically used by a cardiologist to choose a protocol of medical and rehabilitative measures in myocardial infarction.

Data Interpretation, Statistical↗

PubMed1975

Classification and prognostic aspects of schizophrenia.

The relation between initial clinical symptomatology and long-term course of illness appears to be reliable. Prognostic models, as well as an experienced physician, can predict long-term risk of schizophrenic defects from a few items. The prognostic models also seem to be valid for international comparisons of prognosis. The improved prognosis of schizophrenia over the last 30 years is ascertained. The series of about 4,500 cases provides a sufficiently large number of patients for the analyses of well-defined subgroups. This has been done especially for schizophrenia, using the Leonhard classification.

Humans↗

PubMed1990

Immunophenotype, histopathology and clinical stage: their predictive value in the prognosis of non-Hodgkin's lymphomas.

The relationship between immunophenotype, histopathology and clinical stage in influencing prognosis was evaluated in 99 cases of non-Hodgkin's lymphoma (NHL). All cases were histopathologically classified according to the system of the international conference on working formulation (WF), immunologically analysed by flow cytometry with a panel of monoclonal antibodies and clinically staged by the Ann Arbor scheme. Eighty eight percent of T- and 87.7% of B-phenotype NHLs respectively received combination chemotherapies with or without radiotherapy. Early stages I and II showed higher response rates (86% for T- and B-NHL) compared to the advanced ones III and IV (58% for T-NHL and 65% for the B-NHLs), p less than 0.05. Higher overall survival rates were observed in low and intermediate grade NHLs, p less than 0.05. The early stages further showed relatively higher survival rates in T- than in B-phenotype of intermediate grade NHL. Low grade NHL had the highest survival rates in both early and advanced stages, whereas the survival rate was the lowest in high grade NHL irrespective of the clinical stages. Immunophenotype, pathological grade and clinical stage jointly displayed varied predictive values in the prognosis of NHL. Since the present prognostic models are based on histological and staging criteria only, the results suggest that, phenotype should be included in the classification systems or prognostic models for the NHLs, thus facilitating the establishment of effective lineage specific therapies.

Adolescent↗

PubMed1999

Post-therapy serum prostate-specific antigen level and survival in patients with androgen-independent prostate cancer.

BACKGROUND: With an hypothesis that post-chemotherapy changes in serum prostate-specific antigen (PSA) levels might serve as a surrogate marker for assessing prostate cancer outcome (i.e., survival), we studied the relationship between pretherapy and post-therapy prognostic factors and survival in patients with androgen-independent prostate cancer. METHODS: A prognostic model for survival based on pretherapy and post-therapy parameters was developed from the clinical data on 254 patients with androgen-independent prostate cancer treated with 11 different protocol therapies at Memorial Sloan-Kettering Cancer Center. The model was validated by use of an independent dataset of 541 patients enrolled in two randomized phase III trials. RESULTS: In multivariate analysis, a post-therapy decline in PSA levels of 50% achieved in 12 weeks was a statistically significant factor associated with survival (two-sided P = .0012). A similar outcome was obtained with the use of an 8-week time frame. Elevated pretherapy level of serum lactate dehydrogenase (two-sided P = .0001), lower pretherapy level of hemoglobin (P = .0001), and younger age (two-sided P = .0430) had a statistically significant negative impact on outcome. Median survival times were 23, 17, and 9 months for low-, intermediate-, and high-risk groups of patients defined by the prognostic model, respectively. CONCLUSION: This study confirms the prognostic value of a post-therapy decline in PSA of 50% or greater from baseline in relation to survival in patients with androgen-independent prostate cancer treated with a variety of therapies. Two consecutive determinations at 4-week intervals can be used as an end point for efficacy in phase II trials of therapies in this disease.

Aged↗

PubMed2005

Recurrent epithelial ovarian cancer: validation and improvement of an established prognostic index.

OBJECTIVES: To validate and improve an established prognostic index in patients with recurrent ovarian cancer. METHODS: A Canadian three-covariate prognostic index (tumour grade at diagnosis, initial performance status, and time to relapse/primary progression (TRP)) was validated in a well-defined cohort of comparable Danish patients. Potential parameters to be included in an improved prognostic index were revealed by univariate and multivariate analyses in the Danish validation group. RESULTS: The Canadian index validated in the Danish patient population (n=189) found a statistical significant difference in survival between the prognostic groups good and intermediate (P<0.0001), whereas there was no significant difference in survival between the prognostic groups intermediate and poor (P=0.51). In order to improve the accuracy of the index, the candidate parameters, treatment free interval (TFI), CA125 level and performance status, at time of relapse/primary progression, were added, whereas the parameters, tumour grade, and initial performance status, from the Canadian index were excluded. As the correlation coefficient between TRP and TFI was very high (r=0.91), TRP was substituted with TFI in the improved prognostic model. The final model was: 0.8 (performance status)+0.33 log (CA125)-1.31 log (TFI). The improved model was a good predictor of one-year survival (AUC 0.85; logistic regression; P<0.0001). The median survival (with 95% CI) of the four prognostic groups (A-D) was 50.6 (34.0-not available), 25.0 (22.1-33.6), 11.3 (8.5-12.9), and 5.2 (3.5-6.3) months, respectively. CONCLUSIONS: A novel prognostic model (the Copenhagen index) for patients with recurrent ovarian cancer is presented.

Cohort Studies↗

PubMed1997

Early discontinuation of treatment in children with uncomplicated epilepsy: a prospective study with a model for prediction of outcome.

PURPOSE: The main purpose of the present study was to identify predictor variables with significant influence on seizure outcome after discontinuation of treatment in children with uncomplicated epilepsy and to analyze whether these variables, included in a prognostic model could identify children in whom 1-year treatment would be sufficient. METHODS: Before initiation of treatment in children aged 2-16 years with uncomplicated epilepsy, the duration of treatment was randomized to 1 year (group I) or 3 years (group II). At the end of the allotted period, treatment was discontinued in 161 children who had been seizure fre during the previous 6 months. The mean follow-up period after treatment was 5.8 years. Twenty-three predictor variables were analyzed by survival methods regarding their influences on the outcome. RESULTS: At the latest follow-up check, 60 children (37%) had relapsed. The following predictor variables were selected by multiple regression analysis and constituted a model with a simple scoring system: age at seizure onset; seizure type; generalized, irregular spike-wave activity on EEG after 1 year of treatment; and persistent 3-Hz spike-wave activity after 6 months of treatment in children with absence epilepsy. In group I, the remission rate was 73% in children with high prognostic scores, 10% in children with low scores, and 40% in those with intermediate scores (log-rank test, p = 0.0001). CONCLUSIONS: After 1 year of treatment, our prognostic model identified children in whom treatment could be withdrawn at that time. Our model should be easily applicable in clinical practices and may be of clinical importance in determining the duration of treatment in children with uncomplicated epilepsy.

Adolescent↗

PubMed2000

Predicting patient outcome from acute renal failure comparing three general severity of illness scoring systems.

BACKGROUND: A major problem of studies on acute renal failure (ARF) arises from a lack of prognostic tools able to express the medical complexity of the syndrome adequately and to predict patient outcome accurately. Our study was thus aimed at evaluating the predictive ability of three general prognostic models [version II of the Acute Physiology and Chronic Health Evaluation (APACHE II), version II of the Simplified Acute Physiology Score (SAPS II), and version II of the Mortality Probability Model at 24 hours (MPM24 II)] in a prospective, single-center cohort of patients with ARF in an intermediate nephrology care unit. METHODS: Four hundred twenty-five patients consecutively admitted for ARF to the Nephrology and Internal Medicine Department over a five-year period were studied (272 males and 153 females, median age 71 years, interquartile range 61 to 78, median APACHE II score 23, interquartile range 18 to 28). Acute tubular necrosis (ATN) accounted for 68.7% (292 out of 425) of patients. Renal replacement therapies (hemodialysis or continuous hemofiltration) were used in 64% (272 out of 425) of ARF patients. RESULTS: Observed mortality was 39.1% (166 out of 425). The mean predicted mortality was 36.2% with APACHE II (P = 0.571 vs. observed mortality), 39.3% with SAPS II (P = 0.232), and 45.1% with MPM24 II (P < 0.0001). Lemeshow-Hosmer goodness-of-fit C and H statistics were 15.67 (P = 0.047) and 12.05 (P = 0.15) with APACHE II, 32.53 (P = 0.0001), 39.8 (P = 0.0001) with SAPS II, 21.86 (P = 0.005), and 20. 24 (P = 0.009) with MPM24 II, respectively. Areas under the receiver operating characteristic (ROC) curve were 0.75, 0.77, and 0.85, respectively. CONCLUSIONS: The APACHE II model was a slightly better calibrated predictor of group outcome in ARF patients, as compared with the SAPS II and MPM24 II outcome prediction models. The MPM24 II model showed the best discrimination capacity, in comparison with both APACHE II and SAPS II models, but it constantly and significantly overestimated mean predicted mortality in ARF patients. None of the models provided sufficient confidence for the prediction of outcome in individual patients. A high degree of caution must be exerted in the application of existing general prognostic models for outcome prediction in ARF patients.

APACHE↗

PubMed2026

Effect of Tertiary Lymphoid Structures on Immune Cell Infiltration in the Tumor Microenvironment and Prognosis in Lung Adenocarcinoma.

Tertiary lymphoid structures (TLSs) modulate immune responses in various solid tumors, but their comprehensive role in lung adenocarcinoma (LUAD) remains unclear. In this study, we analyzed RNA-seq data from 539 LUAD patients in The Cancer Genome Atlas (TCGA) and microarray data from 223 samples from the Gene Expression Omnibus (GEO, GSE13213, and GSE37745). TLS signatures were evaluated via unsupervised consensus clustering based on 12 chemokine transcriptome signatures. The relationships between TLS and clinical characteristics, tumor microenvironment (TME) cell infiltration, and prognosis were assessed using ESTIMATE and CIBERSORT. A prognostic model was established using LASSO regression and validated with external datasets. Additionally, H&E and IHC analyses were performed to explore associations between intratumoral TLS density, immune-related molecular expression, and patient prognosis in LUAD. Consensus clustering of the TCGA cohort revealed two distinct LUAD patient clusters according to TLS abundance. Cluster 1 exhibited greater immune cell infiltration, more favorable prognosis, and increased expression of immune checkpoint molecules. We developed a prognostic model comprising eight survival-associated genes that act as independent prognostic factors for patient survival. H&E/IHC analyses revealed that TLS density-regardless of pathological stage-was associated with better prognosis; higher intratumoral TLS density/proportion was also related to more favorable outcomes. IHC confirmed that survival-associated genes (CD5, HLA-DMB, and P2RY13) are independent prognostic indicators in LUAD. Our study demonstrated the close relationship between TLS signatures and an active immune microenvironment, highlighting their potential as independent prognostic indicators in LUAD.

Humans↗

PubMed1998

Prognostic factors for survival in patients with brain metastases from renal cell carcinoma.

BACKGROUND: Patients presenting with brain metastases from renal cell carcinoma portend a poor prognosis, with a reported median survival of 4-6 months. Given their short life expectancy, these patients generally have been excluded from clinical trials that assess the efficacy of medical treatments. However, clinical impression suggests that some patients may achieve long term palliation. METHODS: The clinical features of 68 patients who were treated at the Institut Gustave Roussy for brain metastases from renal cell carcinoma were collected retrospectively. Using univariate and multivariate analyses, a prognostic model based on independent prognostic factors was established. An external data set of 57 patients was used to validate the model. RESULTS: The median survival was 7 months. On univariate analysis survival was related significantly to the following adverse prognostic factors: no initial nephrectomy, left side and temporal location of brain metastases, presence of fever or weight loss, erythrocyte sedimentation rate > 50 mm/h, and time from initial diagnosis to brain metastases < or = 18 months. Multivariate analyses identified the previous variable as well as the presence of other visceral metastases as independent prognostic factors. Forty-four patients (65%) with no or 1 adverse prognostic factor (average risk group) had a median survival of 8 months and a 26% 1-year survival rate. Twenty-four patients (35%) with 2 adverse prognostic factors (poor risk group) had a median survival of 3 months and a 1-year survival rate of 9%. This model proved to be discriminant in an external data set; the median survival of patients assigned to the average risk group was 11 months (46% 1-year survival rate) compared with 4 months (9% 1-year survival rate) for patients assigned to the poor risk group. CONCLUSIONS: Patients presenting with brain metastases from renal cell carcinoma and poor risk prognostic factors are highly unlikely to benefit from medical treatments except symptomatic procedures. Conversely, the enrollment of patients with average risk prognostic factors into clinical trials dealing with chemotherapy or immunotherapy may be considered.

Adolescent↗

PubMed2005

Risk factors predicting survival of liver transplantation.

Prognostic models were developed for analyzing graft survival in a single-center study consisting of all 388 adult liver transplantations performed during 20 years. Proportional hazard models and generalized linear models were used to assess which risk factors, related to donor and recipient characteristics as well as graft preservation and operation, had an effect on graft survival. The prognostic modeling evidenced favorable trends in graft survival time during the successive quinquennials 1982-1987, 1988-1992, and 1993-1997, in comparison to the referent time period 1998-2002. Significant predictors of graft survival time were donor's age, recipient-donor gender compatibility, recipient's blood group, intraoperative blood transfusion, size of the transplanted organ, and indication for transplantation. Conventional histocompatibility matching did not correlate with graft outcome.

Adult↗

PubMed1988

Lethal "thin" malignant melanoma. Identifying patients at risk.

Thin melanomas can metastasize and be lethal. The purpose of this review was to identify negative risk factors in patients with melanomas less than 0.76 mm thick. Six hundred and eighty-one (681) such patients are reviewed in this study. Of those referred without metastatic disease (583 patients), metastases developed in 4.8% after a mean followup of 3.6 years. Of those referred with metastatic disease (98 patients), mortality was 35% after a mean followup of 5.9 years. Male patients (p less than 0.04) and patients with axial primaries (p less than 0.05) were at an increased risk of metastasis. Severe histologic regression was present in 40% of the primary lesions that metastasized and in only 17% of similar lesions that did not (p less than 0.001). Increased age was associated with increased local skin metastases, but not with increased nodal or distant metastases. A prognostic model was designed, using two clinical risk factors (axial primary site and male sex) and two histologic risk factors (Clark's Level IV and severe histologic regression). The prognostic model identified a low-risk population--women with extremity primaries--with an actuarial risk of metastasis at 10 years that was less than 3%. Patients with either (1) both clinical risk factors or (2) one clinical risk factor and one histologic risk factor were identified as high-risk patients. Their actuarial risk of metastasis was 11% at 5 years and 22% at 10 years (p = 0.0084). Identifying high-risk and low-risk patients with thin melanomas may improve guidelines for the application of adjuvant therapies to this population.

Adult↗

PubMedDate not supplied

Prognostic index models in stage I and II endometrial carcinoma.

BACKGROUND: In most studies, authors recommend the use of independent variables in clinical decision-making, but no guidelines are given about how to use all the extracted information. MATERIALS AND METHODS: By combining two or three prognostically independent variables and using their relative prognostic impact (results from Cox analyses), a novel way of identifying high- and low-risk groups was developed. A total of 336 women with stage I-II endometrial carcinoma of medium or high risk were included. Twenty-one clinico- pathological variables, were initially studied in univariate analyses and significant variables were used for the construction of prognostic indices. Six prognostic indices were constructed, which then were used to calculate individual index values for each patient. RESULTS: The index values were highly prognostic and used for the identification of a limited high-risk group (10% of the patients) and a large low-risk group (90% of the patients). Indices 1-3 were constructed for pre-treatment situations and included age, degree of differentiation, nuclear grade and S-phase fraction in various combinations. They identified a high- and a low-risk group with a 5-year disease-specific survival of 31-37% and 74.79%, respectively. Indices 4 and 5 were constructed using variables available after treatment and index 6 for patients with no evidence of disease (NED) after treatment completion. The 5-year survival for the latter indices were 30-52% and 75-85%, respectively. CONCLUSION: The use of prognostic indices in different clinical situations was generally superior to the use of single variables for the identification of well-defined high- and low-risk groups.

Adult↗

PubMed2003

Prediction of clinical behaviour and treatment for cancers.

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Algorithms↗

PubMed2002

Proliferation marker KI-S5 discriminates between favorable and adverse prognosis in advanced stages of neuroblastoma with and without MYCN amplification.

BACKGROUND: The biologic behavior of neuroblastoma is notoriously variable, and even carefully elaborated prognostic models fail to predict the clinical course in a portion of cases. Because the proliferative activity is determined by the sum of all molecular imbalances that influence cell cycling, the authors investigated the potential prognostic relevance of the tumor growth fraction in neuroblastoma. METHODS: A retrospective analysis was conducted on a cohort of 161 neuroblastoma patients with a median follow-up period of 72.8 months. Tumors were classified according to Hughes typing and grading criteria. The proliferative index (PI) was assessed immunohistochemically on archival biopsy specimens using monoclonal antibody Ki-S5 (Ki-67), and the MYCN status was determined by means of Southern blot analysis. RESULTS: The PI, MYCN status, International Neuroblastoma Staging System (INSS) stage, International Neuroblastoma Pathology Classification grade, Hughes grade, and the patients' age at diagnosis were all found to be significant predictors of event free survival by univariate Kaplan-Meier analysis. However, the PI identified prognostically distinct subsets in higher tumor stages and Grade 2 and 3 neuroblastomas as well as tumors with unfavorable histology, and enabled risk stratification in tumors with and without MYCN amplification (P = 0.034 and 0.002, respectively). Multivariate Cox regression analysis selected INSS stage (relative risk [RR], 4.05; P < 0.0001) and the PI (RR, 2.49; P = 0.007) as the sole independent prognostic indicators, whereas MYCN entered the selection only after exclusion of the PI. CONCLUSIONS: It emerges that the PI as a single factor has greater predictive power than the MYCN status. Proliferation measurements therefore might significantly improve the accuracy of current prognostic models for neuroblastoma.

Antibodies, Monoclonal↗

PubMed2005

Modeling prognosis for patients with malignant astrocytic gliomas: quantifying the expression of multiple genetic markers and clinical variables.

The disparate lengths of survival among patients with malignant astrocytic gliomas (anaplastic astrocytomas [AAs] and glioblastoma multiforme [GBM]) cannot be adequately accounted for by clinical variables (patient age, histology, and recurrent status). Using real-time quantitative reverse transcription-polymerase chain reaction, we quantified the expression of four genes that were putative prognostic markers (CDK4, IGFBP2, MMP2, and RPS9) in a set of 43 AAs, 41 GBMs, and seven adjacent normal brain tissues. We previously explicated the expression and prognostic value of PAX6, PTEN, VEGF, and EGFR in these glioma tissues and established a comprehensive prognostic model (Zhou et al., 2003). This study attempts to improve that model by including four additional genetic markers, which exhibited a differential expression (P < 0.001) among tumor grades and between tumor and normal tissues. By including eight log-scaled gene expression variables, three clinical variables, and interaction terms among the eight genes, we established a prognostic model that accounted for two thirds of the variation (R2) in survival for this set of patients. To improve the R2 of the model without compromising its clinical utility, our data demonstrated that incorporating genes from different pathways markedly strengthens the model. Spearman rank correlation analysis of gene expression demonstrated a statistically significant positive correlation (P < 0.01) between the expression of IGFBP2-MMP2 and IGFBP2-VEGF in GBMs, but not in AAs. This finding suggests that the expression of IGFBP2 is associated with pathways activated specifically in GBMs that result in enhancing invasiveness and angiogenesis.

Astrocytoma↗