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PubMed1993

[Comparative evaluation of prognostic models of the development of vibration disorders caused by local vibration].

The article compares the present domestic and foreign patterns of vibration disorders probability forecasting, uncovers the main causes of their difference and deals with the quest of a pattern, which is the most adequate to available empirical data. Extensive epidemiologic data on vibration morbidity helped to create a new forecasting pattern. The established dependence is expressed by an equation, a table and a plot, which enable to evaluate vibration disease risk under exposure to vibration of different levels and duration and to elucidate the safe length of service. The level of vibration was proved to reduce the latent period of vibration disease in accordance with range of exposure, probability intervals and length of service, prophylactic measures and insurance therefore should be differentiated for everyone exposed to vibration.

Humans↗
PubMed1997

Prognostic models in melanoma.

Predicting which patients with primary melanoma are at risk of developing metastastic disease is important for making rational therapeutic decisions. Tumor thickness alone is the most commonly used predictor of survival, but other clinical and pathologic variables also play an important role. We have developed two multivariate logistic regression models to predict survival in patients who have primary melanoma. The first of these models assigns patients to two groups based on radial or vertical growth phase. The probability of survival for those patients with vertical growth phase tumors was further determined based on a model using six variables (mitotic rate, tumor infiltrating lymphocytes, tumor thickness, anatomic site of the primary tumor, sex, and histologic regression) that have the greatest strength as independent predictors of survival. This model is 89% accurate for predicting survival in patients with vertical growth phase tumors. A second model has been developed that uses readily available clinical parameters to predict survival. Four variables (tumor thickness, anatomic site, age, and sex) entered into the model as powerful independent predictors. Clinical algorithms for assessing patient risk are provided.

Age Factors↗
PubMed2005

Predicting short-term disease progression among HIV-infected patients in Asia and the Pacific region: preliminary results from the TREAT Asia HIV Observational Database (TAHOD).

OBJECTIVES: HIV disease progression has been well documented in Western populations. This study aimed to estimate the short-term risk of AIDS and death from the TREAT Asia HIV Observational Database (TAHOD), a prospective, multicentre cohort study in Asia and the Pacific region. METHODS: Prospective data were analysed to estimate short-term disease progression. Endpoints were defined as the time from study entry to diagnosis with AIDS or death. Antiretroviral treatment was fitted as a time-dependent variable. Predictors of disease progression were assessed using Cox proportional hazards models, and prognostic models were developed using Weibull models. RESULTS: A total of 1260 patients with prospective follow-up data contributed 477 person-years of follow-up, during which 18 patients died and 34 were diagnosed with AIDS, a combined rate of 10.1 per 100 person-years. Compared with patients receiving antiretroviral treatment, patients not on treatment had a higher rate of disease progression (17.6 vs. 8.1 per 100 person-years, respectively). Baseline CD4 count was the strongest predictor of disease progression. Prognostic models, using either a baseline CD4 count as the sole marker or markers including baseline haemoglobin, AIDS-related symptoms and previous or current antiretroviral treatment, were successful at identifying patients at high risk of short-term disease progression. CONCLUSIONS: Similar to the situation in Western countries, baseline CD4 count was the strongest predictor of short-term disease progression. Prognostic models based on readily available clinical data and haemoglobin level should be useful in estimating short-term clinical risk in HIV-infected patients in Asia and the Pacific region.

Acquired Immunodeficiency Syndrome↗
PubMed2005

Mortality assessment in intensive care units via adverse events using artificial neural networks.

OBJECTIVE: This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial neural networks (ANNs). This approach is compared with two logistic regression (LR) models: the prognostic model used in most of the European ICUs, based on the simplified acute physiology score (SAPS II), and a LR that uses the same input variables of the ANN model. MATERIALS AND METHODS: A large dataset was considered, encompassing forty two ICUs of nine European countries. The recorded features of each patient include the final outcome, the case mix (e.g. age) and the intermediate outcomes, defined as the daily averages of the out of range values of four biometrics (e.g. heart rate). The SAPS II score requires 17 static variables (e.g. serum sodium), which are collected within the first day of the patient's admission. A nonlinear least squares method was used to calibrate the LR models while the ANNs are made up of multilayer perceptrons trained by the RPROP algorithm. A total of 13,164 adult patients were randomly divided into training (66%) and test (33%) sets. The two methods were evaluated in terms of receiver operator characteristic (ROC) curves. RESULTS: The event based models predicted the outcome more accurately than the currently used SAPS II model (P<0.05), with ROC areas within the ranges 83.9-87.1% (ANN) and 82.6-85.2% (LR) versus 80% (LR SAPS II). When using the same inputs, the ANNs outperform the LR (improvement of 1.3-2%). CONCLUSION: Better prognostic models can be achieved by adopting low cost and real-time intermediate outcomes rather than static data.

Decision Trees↗
PubMed2004

The incidence and prediction of automatically detected intraoperative cardiovascular events in noncardiac surgery.

UNLABELLED: The objective of this study was to evaluate prognostic models for quality assurance purposes in predicting automatically detected intraoperative cardiovascular events (CVE) in 58458 patients undergoing noncardiac surgery. To this end, we assessed the performance of two established models for risk assessment in anesthesia, the Revised Cardiac Risk Index (RCRI) and the ASA physical status classification. We then developed two new models. CVEs were detected from the database of an electronic anesthesia record-keeping system. Logistic regression was used to build a complex and a simple predictive model. Performance of the prognostic models was assessed using analysis of discrimination and calibration. In 5249 patients (17.8%) of the evaluation (n = 29437) and 5031 patients (17.3%) of the validation cohorts (n = 29021), a minimum of one CVE was detected. CVEs were associated with significantly more frequent hospital mortality (2.1% versus 1.0%; P < 0.01). The new models demonstrated good discriminative power, with an area under the receiver operating characteristic curve (AUC) of 0.709 and 0.707 respectively. Discrimination of the ASA classification (AUC 0.647) and the RCRI (AUC 0.620) were less. Neither the two new models nor ASA classification nor the RCRI showed acceptable calibration. ASA classification and the RCRI alone both proved unsuitable for the prediction of intraoperative CVEs. IMPLICATIONS: The objective of this study was to evaluate prognostic models for quality assurance purposes to predict the occurrence of automatically detected intraoperative cardiovascular events in 58,458 patients undergoing noncardiac surgery. Two newly developed models showed good discrimination but, because of reduced calibration, their clinical use is limited. The ASA physical status classification and the Revised Cardiac Risk Index are unsuitable for the prediction of intraoperative cardiovascular events.

Aged↗
PubMed2006

Low serum albumin levels and liver metastasis are powerful prognostic markers for survival in patients with carcinomas of unknown primary site.

BACKGROUND: The authors investigated how lymphopenia and low serum albumin levels correlate with the prognosis of patients with carcinoma of unknown primary (CUP). METHODS: Univariate and multivariate prognostic factor analyses were conducted in a population of 317 consecutive patients with CUP who were evaluated at the Cross Cancer Institute of Edmonton, Alberta, Canada, from 1998 to 2004. RESULTS: The results from multivariate analysis showed that patients who had a performance status >/=2 (using the World Health Organization scale), a high overall comorbidity score (on the Adult Comorbidity Evaluation 27), liver metastasis, elevated serum lactate dehydrogenase (LDH) levels, lymphopenia (defined as an absolute lymphocyte count >/=0.7 x 10(9)/L), and low serum albumin levels had a worse prognosis. Based on the observation that the presence of liver metastasis and low serum albumin levels were the most powerful adverse prognostic factors, a classification scheme was delineated that took those 2 variables into account. A group of good-risk patients (no liver metastasis and normal serum albumin levels) and a group of poor-risk patients (liver metastasis and/or low serum albumin levels) were identified with median survivals of 371 days and 103 days, respectively (P < .0001). This classification was validated further in an independent data set of 124 patients who were evaluated at 2 French cancer centers: Among those patients, the median survival was 378 days in the good-risk group and 90 days in the poor-risk group (P < .0001). The new prognostic model substantially outperformed the previous standard prognostic model, which was based on performance status and serum LDH levels. CONCLUSIONS: Lymphopenia and low serum albumin levels were identified as 2 new independent markers of prognosis in patients with CUP. Although the authors confirmed the validity of the previous prognostic model, they developed and validated a more powerful, simple model based on the 2 most powerful adverse prognostic factors: liver metastasis and low serum albumin levels. These findings were confirmed in an independent cohort of patients with CUP, and consideration of the authors' improved prognostic model for survival of patients with CUP is warranted.

Adult↗
PubMed1999

Modelling the effects of standard prognostic factors in node-positive breast cancer. German Breast Cancer Study Group (GBSG).

Prognostic models that predict the clinical course of a breast cancer patient are important in oncology. We propose an approach to constructing such models based on fractional polynomials in which useful transformations of the continuous factors are determined. The idea may be applied with all types of regression model, including Cox regression, the method of choice for survival-time data. We analyse a prospective study of node-positive breast cancer. Seven standard prognostic factors--age, menopausal status, tumour size, tumour grade, number of positive lymph nodes, progesterone and oestrogen receptor concentrations--were investigated in 686 patients, of whom 299 had an event for recurrence-free survival and 171 died. We determine a final model with transformations of prognostic factors and compare it with the more traditional approaches using categorized variables or assuming a straight line relationship. We conclude that analysis using fractional polynomials can extract important prognostic information which the traditional approaches may miss.

Breast Neoplasms↗
PubMed2000

Validation, calibration, revision and combination of prognostic survival models.

The problem of assessing the validity and value of prognostic survival models presented in the literature for a particular population for which some data has been collected is discussed. Methods are sketched to perform validation through 'calibration', that is by embedding the literature model in a larger calibration model. This general approach is exemplified for x-year survival probabilities, Cox regression and general non-proportional hazards models. Some comments are made on basic structural changes to the model, described as 'revision'. Finally, general methods are discussed to combine models from different sources. The methods are illustrated with a model for non-Hodgkin's lymphoma validated on a Dutch data set.

Calibration↗
PubMed2006

An international multicenter study evaluating the impact of an alternative biochemical failure definition on the judgment of prostate cancer risk.

PURPOSE: To evaluate the impact of an alternative biochemical failure (bF) definition on the performance of existing plus de novo prognostic models. METHODS AND MATERIALS: The outcomes data of 1,458 Australian and 703 Canadian men treated with external-beam radiation monotherapy between 1993 and 1997 were analyzed using a lowest prostate-specific antigen (PSA) level to date plus 2 ng/mL (L + 2) bF definition. Two existing prognostic models were scrutinized using discrimination (Somers Dxy [SDxy]) and calibration indices. Alternative prognostic models were also created using recursive partitioning analysis (RPA) and multivariate nomogram methods for comparison. RESULTS: Discrimination of bF was improved using the L + 2 definition compared with the American Society for Therapeutic Radiology and Oncology (ASTRO) definition using both the three-level risk model (SDxy 0.30 and 0.22, respectively) or the nomogram (SDxy 0.35 and 0.27, respectively). Both existing prognostic models showed only modest calibration accuracy. Using RPA, five distinct risk groups were identified based primarily on Gleason score (GS) and all subsequent divisions based on PSA. All GS 7-10 tumors were intermediate or high risk. This model and the developed nomogram showed improved discrimination over the existing models as well as accurate calibration against the Canadian data, apart from the 30-50% failure region. CONCLUSIONS: The L + 2 definition of bF provides improved capacity for discrimination of failure risk. New prognostic models based on this endpoint have overall statistical performance superior to those based on the ASTRO consensus definition but continue to have unreliable discrimination in the intermediate-risk region.

Adult↗
PubMed1999

Prognostic methods in medicine.

Prognosis--the prediction of the course and outcome of disease processes--plays an important role in patient management tasks like diagnosis and treatment planning. As a result, prognostic models form an integral part of a number of systems supporting these tasks. Furthermore, prognostic models constitute instruments to evaluate the quality of health care and the consequences of health care policies by comparing predictions according to care norms with actual results. Approaches to developing prognostic models vary from using traditional probabilistic techniques, originating from the field of statistics, to more qualitative and model-based techniques, originating from the field of artificial intelligence (AI). In this paper, various approaches to constructing prognostic models, with emphasis on methods from the field of AI, are described and compared.

Artificial Intelligence↗
PubMed2006

Prognostic scoring model based on multi-drug resistance status and cytogenetics in adult patients with acute myeloid leukemia.

Clinical heterogenicity exists within an acute myeloid leukemia (AML) patient group with the same cytogenetic risk. Multi-drug resistance (MDR) is also regarded as one of the potential prognostic factors for AML. Accordingly, the prognostic scoring model can be generated based on both consideration of cytogenetic risk and the MDR status for AML. The CR rate, event-free (EFS) and overall survival (OS) were analysed according to cytogenetic risk, MDR status and clinical factors. Prognostic score was calculated by the sum of MDR status (0 for negative, 1 for positive) and dichotomized scoring for cytogenetic risk (0 for favorable/intermediate and 1 for unfavorable cytogenetics). MDR expression was noted in 36.6% of the patients and associated with a lower CR rate (p = 0.037). MDR, cytogenetics and the use of SCT were identified as independent prognostic factors for EFS and OS. The CR rate of the group scored with 0, 1 and 2 was 81.4, 66.7, and 44.4%, respectively (p = 0.050). The prognostic scoring model depicted a discriminating role in terms of EFS (p < 0.0001) and OS (p = 0.0001). The prognostic scoring model based on cytogenetic risk and MDR provided an improved method for evaluating the prognosis in AML and helped to stratify the risk of patients with the same cytogenetic risk.

Acute Disease↗
PubMed2005

Anemia in chronic heart failure patients: comparison between invasive and non-invasive prognostic markers.

BACKGROUND: The prognosis of chronic heart failure (CHF) remains poor despite advances in medical management. Several different variables determine prognosis. Recently anemia has emerged as an independent prognostic variable in the evaluation of CHF. It is therefore important to analyze the role of anemia in patients with mild to severe CHF already well characterized by hemodynamic, echo-Doppler, and cardiopulmonary exercise testing. OBJECTIVE: We performed this study to evaluate, in a large general cohort of CHF patients, the frequency of anemia and its correlation with their clinical profile. We assessed the prognostic value of anemia in relation to other known prognostic variables. METHODS: Two-dimensional echocardiography, right heart catheterization, cardiopulmonary tests and laboratory examinations were performed in a population of 980 consecutive patients with CHF (53 +/- 9.4 years, 85% male, LVEF 25 +/- 8%; 45% with NYHA class III-IV). A hemoglobin (Hb) concentration less than 12 g/dl was used to define anemic patients. The primary end point was cardiac death or urgent heart transplantation. RESULTS: Nineteen percent of patients were anemic. These patients had a lower body mass index (24 +/- 3 vs. 25 +/- 4 Kg/m2 p < 0.0004), a worse functional class (64% were in NYHA class III-IV vs 41% in the non-anemic group, p < 0.0001), poorer exercise capacity (12.4 vs. 14.8 ml/kg/min peak VO2, p < 0.0001) and increased right (7 +/- 5 vs. 5 +/- 4 mmHg, p < .0004) and left (21 +/- 9 vs. 19 +/- 10 p < 0.007) ventricular filling pressures. During a 3-year follow-up cardiac deaths occurred in 236 (24%) and 52 (5%) of patients received an urgent heart transplant. On univariate regression analysis anemia was significantly correlated with these "hard" cardiac events (39% of anemic patients vs 27% of non-anemic patients). By multivariate logistic regression analysis different prognostic models were identified using non-invasive, with or without peak VO2, or invasive parameters. The prognostic model including anemia (AUC(ROC): 0.720) showed similar accuracy in predicting cardiac events to other prognostic models with peak VO2 (AUC(ROC): 0.719) or invasive variables (AUC(ROC): 0.719). CONCLUSIONS: The present study demonstrates that anemia in CHF patients is associated with prognosis, worse NYHA functional class, exercise capacity and hemodynamic profiles. The relationship between anemia and mortality is independent of other simple non-invasive prognostic factors. Prognostic models with more complex or invasive independent predictors did not increase the accuracy to predict cardiac mortality or the need for urgent transplantation.

Anemia↗
PubMed2005

Predicting outcome after traumatic brain injury: development and validation of a prognostic score based on admission characteristics.

The early prediction of outcome after traumatic brain injury (TBI) is important for several purposes, but no prognostic models have yet been developed with proven generalizability across different settings. The objective of this study was to develop and validate prognostic models that use information available at admission to estimate 6-month outcome after severe or moderate TBI. To this end, this study evaluated mortality and unfavorable outcome, that is, death, and vegetative or severe disability on the Glasgow Outcome Scale (GOS), at 6 months post-injury. Prospectively collected data on 2269 patients from two multi-center clinical trials were used to develop prognostic models for each outcome with logistic regression analysis. We included seven predictive characteristics-age, motor score, pupillary reactivity, hypoxia, hypotension, computed tomography classification, and traumatic subarachnoid hemorrhage. The models were validated internally with bootstrapping techniques. External validity was determined in prospectively collected data from two relatively unselected surveys in Europe (n = 796) and in North America (n = 746). We evaluated the discriminative ability, that is, the ability to distinguish patients with different outcomes, with the area under the receiver operating characteristic curve (AUC). Further, we determined calibration, that is, agreement between predicted and observed outcome, with the Hosmer-Lemeshow goodness-of-fit test. The models discriminated well in the development population (AUC 0.78-0.80). External validity was even better (AUC 0.83-0.89). Calibration was less satisfactory, with poor external validity in the North American survey (p < 0.001). Especially, observed risks were higher than predicted for poor prognosis patients. A score chart was derived from the regression models to facilitate clinical application. Relatively simple prognostic models using baseline characteristics can accurately predict 6-month outcome in patients with severe or moderate TBI. The high discriminative ability indicates the potential of this model for classifying patients according to prognostic risk.

Adolescent↗
PubMed2003

Prognostic factors for the vascular components of erectile dysfunction in patients on renal replacement therapy.

A total of 76 male patients on renal replacement therapy (RRT) were investigated. Erectile dysfunction (ED) was defined as insufficient erection during visual erotic stimulation (VES) or during sleep as measured with Rigiscan and Erectiometer. Data on medical history, physical examination, and laboratory variables were collected. Furthermore, penile pharmacological duplex ultrasonography (PPDU) was performed. Univariate and multivariate logistic regressions were used to determine prognostic values and to develop prognostic models. Independent prognostic factors for ED were the number of cardiovascular events, waist-hip ratio, body mass index, and acceleration time (AT) as measured with PPDU. Independent prognostic factors for an abnormal AT (>100 ms) were number of cardiovascular events, age category, and the presence of carotid bruits. Independent prognostic factors for insufficient veno-occlusion during PPDU were number of cardiovascular events and supine diastolic blood pressure. The vascular contribution to ED in patients on RRT is substantial. Data from medical history, limited physical examination, and PPDU contribute to the prediction of the vascular contribution to ED.

Adolescent↗
PubMed2005

Validation and extension of the Memorial Sloan-Kettering prognostic factors model for survival in patients with previously untreated metastatic renal cell carcinoma.

PURPOSE: To validate the Motzer et al prognostic factors model for survival in patients with previously untreated metastatic renal cell carcinoma (RCC) and to identify additional independent prognostic factors. PATIENTS AND METHODS: Data were collected on 353 previously untreated metastatic RCC patients enrolled onto clinical trials between 1987 and 2002. RESULTS: Four of the five prognostic factors identified by Motzer were independent predictors of survival. In addition, prior radiotherapy and presence of hepatic, lung, and retroperitoneal nodal metastases were found to be independent prognostic factors. Using the number of metastatic sites as surrogate for individual sites (none or one v two or three sites), Motzer's definitions of risk groups were expanded to accommodate these two additional prognostic factors. Using this expanded criteria, favorable risk is defined as zero or one poor prognostic factor, intermediate risk is two poor prognostic factors, and poor risk is more than two poor prognostic factors. According to Motzer's definitions, 19% of patients were favorable risk, 70% were intermediate risk, and 11% were poor risk; median overall survival times for these groups were 28.6, 14.6, and 4.5 months, respectively (P < .0001). Using the expanded criteria, 37% of patients were favorable risk, 35% were intermediate risk, and 28% were poor risk; median overall survival times of these groups were 26.0, 14.4, and 7.3 months, respectively (P < .0001). CONCLUSION: These data validate the model described by Motzer et al. Additional independent prognostic factors identified were prior radiotherapy and sites of metastasis. Incorporation of these additional prognostic factors into the Motzer et al model can help better define favorable risk, intermediate risk, and poor risk patients.

Adult↗