PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Survival prediction”

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 19 recordsLinked to original sources

Comparisons of survival predictions using survival risk ratios based on International Classification of Diseases, Ninth Revision and Abbreviated Injury Scale trauma diagnosis codes.

BACKGROUND: We conducted a comparison of methods for predicting survival using survival risk ratios (SRRs), including new comparisons based on International Classification of Diseases, Ninth Revision (ICD-9) versus Abbreviated Injury Scale (AIS) six-digit codes. METHODS: From the Pennsylvania trauma center's registry, all direct trauma admissions were collected through June 22, 1999. Patients with no comorbid medical diagnoses and both ICD-9 and AIS injury codes were used for comparisons based on a single set of data. SRRs for ICD-9 and then for AIS diagnostic codes were each calculated two ways: from the survival rate of patients with each diagnosis and when each diagnosis was an isolated diagnosis. Probabilities of survival for the cohort were calculated using each set of SRRs by the multiplicative ICISS method and, where appropriate, the minimum SRR method. These prediction sets were then internally validated against actual survival by the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The 41,364 patients had 1,224 different ICD-9 injury diagnoses in 32,261 combinations and 1,263 corresponding AIS injury diagnoses in 31,755 combinations, ranging from 1 to 27 injuries per patient. All conventional ICD-9-based combinations of SRRs and methods had better Hosmer-Lemeshow goodness-of-fit statistic fits than their AIS-based counterparts. The minimum SRR method produced better calibration than the multiplicative methods, presumably because it did not magnify inaccuracies in the SRRs that might occur with multiplication. CONCLUSION: Predictions of survival based on anatomic injury alone can be performed using ICD-9 codes, with no advantage from extra coding of AIS diagnoses. Predictions based on the single worst SRR were closer to actual outcomes than those based on multiplying SRRs.

Abbreviated Injury Scale↗

EuroQol and survival prediction in terminal cancer patients: a multicenter prospective study in hospice-palliative care units.

GOALS OF WORK: Although the EuroQol (EQ-5D) is widely used for economic evaluation, it remains unclear whether it can be combined with medical data to predict survival in patients with terminal cancer. PATIENTS AND METHODS: We carried out this prospective study on 142 terminal cancer patients in four hospice-palliative care units. Association was sought between survival time and a range of variables such as cancer site, performance, previous treatment, age, sex, pain, and EuroQol. The EQ-5D was transformed into the corresponding EQ-5D utility. For univariate analysis, we estimated differences in survival with the Gehan generalized Wilcoxon test. For those variables that were significant, we performed multivariate analysis using the Cox proportional hazard model. MAIN RESULTS: Univariate analysis showed that sex, age, performance, previous use of chemotherapy, and the EQ-5D utility provided statistically significant prognostic survival information. The median survival time was 13.0 days for the group with an EQ-5D utility score lower than -0.5 and 21.0 days for the group with an EQ-5D utility score above -0.5. In multivariate analysis with the Cox proportional hazard model, an EQ-5D utility score < or = 0.5 (RR 1.57, 95% confidence interval 1.06-2.33) was an independent negative predictor of survival. CONCLUSIONS: The EQ-5D quality-of-life assessment tool might be useful for predicting survival time for terminal cancer patients.

Aged↗

Salivary caffeine clearance predicts survival in patients with liver cirrhosis.

OBJECTIVE: Quantification of liver function in patients with cirrhosis is difficult. Caffeine clearance (CCI) has been suggested as a more exact method than those commonly used. The aim of this work was to assess the usefulness of CCl in survival prediction for these patients. METHODS: Thirty-four patients with cirrhosis of the liver of various causes were included; 19 were class A or B in Child-Pugh's classification and 15 were class C. CCl was determined from saliva samples. The mean length of follow-up was 33.8 months. A bivariant survival analysis was carried out following the Kaplan-Meier method, together with a multivariant analysis using the Cox proportional hazards model. RESULTS: Twelve patients died during the follow-up period. CCl values < 0.24 ml/kg/min, age > 60 yr, and nonalcoholic cause of cirrhosis were factors predicting lower survival. CCl was the only independent predictive factor in the multivariant analysis. CONCLUSIONS: CCl enables us to predict survival in cirrhotic patients and, considering its harmlessness, simplicity, and cost, can be used as a routine procedure in the assessment of these patients.

Aged↗

Quality of life and survival prediction in terminal cancer patients: a multicenter study.

BACKGROUND: It remains unclear whether health-related quality of life (HRQoL) measurements from patients and staff can be combined with medical data to predict survival in patients with terminal cancer. METHODS: The correlations between survival and potential health-related quality-of-life (HRQoL) prognostic variables were explored in 2 independent cohorts of patients with terminal cancer (248 patients in Cohort 1 and 756 patients in Cohort 2) after adjusting for clinical and demographics variables using Cox regression models. RESULTS: At the onset of the terminal phase (Cohort 1), the hazards of dying increased by 28% in the presence of dyspnea and by 68% in the presence of nausea/emesis; however, the most important predictors of worse survival were the presence of liver metastases (hazard ratio [HR], 2.5; 95% confidence interval [95% CI], 1.8-3.8), lung tumor (HR, 2.4; 95% CI, 1.7-3.4), and tumor burden (HR, 2.0; 95% CI, 1.4-2.7). In contrast, for patients who were seen in later stages of their terminal disease (Cohort 2), dyspnea (HR, 1.5; 95% CI, 1.1-1.9) and the coexistence of weakness with a diagnosis of digestive tumors (HR, 5.2; 95% CI, 1.2-21.8), breast tumors (HR, 3.1; 95% CI, 1.6-6.2), and genitourinary tumors (HR, 3.5; 95% CI, 1.6-7.8) were more predictive of survival than the type of tumor primary. Emotional functioning along with anxiety, spiritual distress, and lack of insight were not associated consistently with survival in both cohorts. CONCLUSIONS: Health care professionals should focus on physical HRQoL indicators, such as nausea and emesis, dyspnea, and weakness, to gather prognostic clues in patients with terminal cancer. These symptoms may reflect consequences of cancer cachexia and the progress of patients toward this terminal syndrome. Psychosocial distress did not appear to be associated consistently with survival; however, future studies should clarify further the prognostic significance of "positive attitudes", such as hope and optimism, in patients with advanced cancer.

Adolescent↗

RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction.

Accurate survival prediction in cancer remains challenging due to the sparsity of somatic mutation profiles and the failure of existing models to capture higher-order gene-gene dependencies. Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein-protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 &#xb1; 0.044 (BRCA), 0.750 &#xb1; 0.039 (LUNG), 0.704 &#xb1; 0.041 (GBM), and 0.668 &#xb1; 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.

cancer survival prediction↗

Survival prediction in staged heart transplantation using Jarvik-7 artificial heart.

BACKGROUND: Because mechanical circulatory assist as a bridge to heart transplantation places a further strain on current donor shortage as well as on medical cost containment, safe and effective use of the device is essential. METHODS AND RESULTS: To predict survival before undertaking staged heart transplantation with the Jarvik-7 artificial heart, our 58 attempts were reviewed retrospectively. Scores of 1-4 were given for six preoperative factors based on results obtained by univariate and multivariate analyses between survivors and nonsurvivors of staged heart transplantation: transplant rejection (scored 4: S4) or postoperative heart failure (S3) as the indication, recipient height < 175 cm (S3), body surface area < 1.8 m2 (S3), hyperbilirubinemia > 24 microM/l (S2), weight < 60 kg (S2), and age > 40 years (S1). Of 14 survivors, 13 had a total score < 4 (sensitivity, 93%), with an average score of 1.6 in contrast to 5.5 for 44 nonsurvivors (p < 0.001). Among 26 patients scored < 4, 21 had heart transplantation, of whom 13 left the hospital. Of 32 patients scored > or = 4, only four could be discharged after transplantation (specificity, 70%). CONCLUSIONS: Multiple preoperative factors successfully predicted transplantability and survival in staged heart transplantation. The results underscore the importance of preoperative condition and patient selection to achieve successful and effective use of Jarvik-7 as a bridge to heart transplantation.

Actuarial Analysis↗

Comparison of staging systems to predict survival in hepatocellular carcinoma.

PURPOSE: Some new staging systems in hepatocellular carcinoma (HCC) have been described in the last years. The aim of this study was to compare the survival-predicting capacity of some variables and the prognostic classifications. METHODS: Demographic, clinical, analytical variables and tumour characteristics were collected in a study including 115 patients with HCC. Predictors of survival were identified using the Kaplan-Meier test and the Cox model. Comparison between different staging systems was carried out. RESULTS: The 1-, 2- and 3-year estimated survival was 65%, 45% and 30%, respectively. Child-Pugh score and alpha-fetoprotein level greater than 400 UI/l were independent predictors of survival in the Cox model. Although all systems correctly differentiated between patients regarding survival (Kaplan-Meier, log rank < 0.05 for all), the Barcelona Clinic Liver Cancer (BCLC) showed a better discriminatory ability than the other evaluated scores. In addition, the independent homogenizing ability and stratification value of BCLC was better than that of other systems. On the contrary, model for end-stage liver disease (MELD) showed the worst results. CONCLUSIONS: Child-Pugh score and alpha-fetoprotein levels were the only independent predictors of survival in patients with HCC. Child-Pugh score showed a better prediction value for survival when compared with MELD. BCLC is more accurate than the other prognostic models evaluated in this investigation.

Adult↗

Assessment of ventilatory variables in survival prediction of patients with chronic airflow obstruction: the importance of reversibility.

The relative usefulness of various indices of (ir) reversibility in predicting survival is reported for 129 patients with severe chronic airflow limitation (initial Forced Expiratory Volume in one second, FEV1, less than or equal to 1000 ml). The generally applied increase of FEV1 as a percentage of the initial FEV1 value (delta FEV1% in) and the increase of FEV1 as a percentage of the predicted FEV1 after an anticholinergic drug (delta FEV1%pred) are not related to survival. The increase of FEV1 as a percentage of the "maximal" attainable increase to the predicted level, as estimated by predicted minus initial FEV1 (delta FEV1%(predin], is, next to the severity of airflow obstruction after bronchodilation (FEV1pb% pred), significantly related to prognosis. After controlling for smoking, delta FEV1% (pred-in) was the best indicator of reversibility in prognosis prediction. Next to smoking and delta FEV1% (pred-in) also the irreversible part of airflow obstruction FEV1%FIV1 after bronchodilation appeared to influence survival independently in this particular patient population with severe airflow obstruction.

Cross-Sectional Studies↗

Calculating microbial survival parameters and predicting survival curves from non-isothermal inactivation data.

Irrespective of whether the isothermal semi-logarithmic survival curves of heat inactivated microbial cells or spores are linear or nonlinear, it is theoretically possible to numerically calculate their survival parameters from inactivation data obtained under non-isothermal conditions. A method to do the calculation, when the temperature history ('profile') is expressed algebraically, is demonstrated with simulated survival curves. It has been tested with the published survival data of Salmonella, whose nonlinear semi-logarithmic isothermal survival curves can be described by a power law model. The reported survival ratios of Salmonella, determined during non-isothermal heat treatments in a broth and in ground chicken breast, were used to estimate its isothermal survival parameters in the two media and their temperature dependence. These, in turn, were used to predict the cells' survival curves under different temperature 'profiles.' There was a good agreement between the predicted and the reported experimental survival curves in the broth case and reasonable agreement in the ground chicken breasts, where the database was considerably smaller The development of a mathematical method to calculate survival parameters from non-isothermal inactivation data will eliminate the need to determine these parameters under isothermal conditions, which can only be approximated and are technically difficult to perform. In many cases, the proposed method will also enable the determination of the survival parameters in the actual food or medium of interest, which may contain particles, or is too viscous to be heated and cooled effectively using the currently available experimental procedures. In principle, the described mathematical method can also be used to assess organisms' survival parameters in nonthermal inactivation processes, such as exposure to a dissipating chemical agent or the application of ultra high-pressure.

Animals↗

Predicting survival of lung transplantation candidates with idiopathic interstitial pneumonia: does PaO(2) predict survival?

OBJECTIVE: To find a parameter that would discriminate between the patients with idiopathic interstitial pneumonia who survived to undergo transplantation and those who died while waiting to undergo transplantation. METHODS: A retrospective review was performed of all lung transplant referrals for idiopathic interstitial pneumonia that were listed with United Network for Organ Sharing at the University of California San Diego from January 1990 to February 1999. Of the 331 patients who were listed, 48 met the eligibility criteria. Patient demographics, radiographic studies, pathology reports, and the results of resting and exercise cardiopulmonary function tests were recorded from each patient's chart. Patients were divided into the following two groups: those patients who survived until transplantation and those still waiting were classified as "alive"; and those patients who died before undergoing transplantation were classified as "deceased." RESULTS: Forty-three of 48 patients had a pathologic diagnosis. The cohort included 25 patients with usual interstitial pneumonitis, 3 patients with nonspecific interstitial pneumonitis, 1 patient with desquamative interstitial pneumonitis, and 14 patients with interstitial lung disease of unknown etiology. The only significant difference between the two groups was resting PaO(2) (p = 0.035). A stepwise multivariate analysis demonstrated that PaO(2) and FEV(1)/FVC ratio were significantly associated with survival (hazards ratio, 1.06; confidence interval, 0.99 to 1.13; p = 0.019). CONCLUSIONS: A survival analysis using PaO(2) and FEV(1)/FVC ratio values proved to be statistically significant, but a prospective trial is needed to determine the clinical relevance of these parameters for predicting survival in patients with idiopathic interstitial pneumonia.

Adult↗

Survival prediction of terminally ill cancer patients by clinical symptoms: development of a simple indicator.

BACKGROUND: Although accurate prediction of survival is essential for palliative care, no clinical tools have been established. METHODS: Performance status and clinical symptoms were prospectively assessed on two independent series of terminally ill cancer patients (training set, n = 150; testing set, n = 95). On the training set, the cases were divided into two groups with or without a risk factor for shorter than 3 and 6 weeks survival, according to the way the classification achieved acceptable predictive value. The validity of this classification for survival prediction was examined on the test samples. RESULTS: The cases with performance status 10 or 20, dyspnea at rest or delirium were classified in the group with a predicted survival of shorter than 3 weeks. The cases with performance status 10 or 20, edema, dyspnea at rest or delirium were classified in the group with a predicted survival of shorter than 6 weeks. On the training set, this classification predicted 3 and 6 weeks survival with sensitivity 75 and 76% and specificity 84 and 78%, respectively. On the test populations, whether patients survived for 3 and 6 weeks or not was predicted with sensitivity 85 and 79% and specificity 84 and 72%, respectively. CONCLUSION: Whether or not patients live for 3 and 6 weeks can be acceptably predicted by this simple classification.

Aged↗

SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

bladder cancer↗

The Palliative Prognostic Index: a scoring system for survival prediction of terminally ill cancer patients.

Although accurate prediction of survival is essential for palliative care, few clinical methods of determining how long a patient is likely to live have been established. To develop a validated scoring system for survival prediction, a retrospective cohort study was performed with a training-testing procedure on two independent series of terminally ill cancer patients. Performance status (PS) and clinical symptoms were assessed prospectively. In the training set (355 assessments on 150 patients) the Palliative Prognostic Index (PPI) was defined by PS, oral intake, edema, dyspnea at rest, and delirium. In the testing sample (233 assessments on 95 patients) the predictive values of this scoring system were examined. In the testing set, patients were classified into three groups: group A (PPI< or =2.0), group B (2.0 4.0). Group B survived significantly longer than group C, and group A survived significantly longer than either of the others. Also, when a PPI of more than 6 was adopted as a cut-off point, 3 weeks' survival was predicted with a sensitivity of 80% and a specificity of 85%. When a PPI of more than 4 was used as a cutoff point, 6 weeks' survival was predicted with a sensitivity of 80% and a specificity of 77%. In conclusion, whether patients live longer than 3 or 6 weeks can be acceptably predicted by PPI.

Aged↗

A systematic review of physicians' survival predictions in terminally ill cancer patients.

OBJECTIVE: To systematically review the accuracy of physicians' clinical predictions of survival in terminally ill cancer patients. DATA SOURCES: Cochrane Library, Medline (1996-2000), Embase, Current Contents, and Cancerlit databases as well as hand searching. STUDY SELECTION: Studies were included if a physician's temporal clinical prediction of survival (CPS) and the actual survival (AS) for terminally ill cancer patients were available for statistical analysis. Study quality was assessed by using a critical appraisal tool produced by the local health authority. DATA SYNTHESIS: Raw data were pooled and analysed with regression and other multivariate techniques. RESULTS: 17 published studies were identified; 12 met the inclusion criteria, and 8 were evaluable, providing 1563 individual prediction-survival dyads. CPS was generally overoptimistic (median CPS 42 days, median AS 29 days); it was correct to within one week in 25% of cases and overestimated survival by at least four weeks in 27%. The longer the CPS the greater the variability in AS. Although agreement between CPS and AS was poor (weighted kappa 0.36), the two were highly significantly associated after log transformation (Spearman rank correlation 0.60, P < 0.001). Consideration of performance status, symptoms, and use of steroids improved the accuracy of the CPS, although the additional value was small. Heterogeneity of the studies' results precluded a comprehensive meta-analysis. CONCLUSIONS: Although clinicians consistently overestimate survival, their predictions are highly correlated with actual survival; the predictions have discriminatory ability even if they are miscalibrated. Clinicians caring for patients with terminal cancer need to be aware of their tendency to overestimate survival, as it may affect patients' prospects for achieving a good death. Accurate prognostication models incorporating clinical prediction of survival are needed.

Analysis of Variance↗

Survival predictions of amalgam restorations.

The purpose of this study was to evaluate survival predictions made for four different amalgam alloy restorations, using a mixture model involving the standard Weibull function. The amalgam alloys were placed by students and staff in patients attending a dental hospital, and 1680 restorations were examined over periods of up to 18 years. Based on maximum likelihood estimations of the parameters of the mixture model distribution, predictive survival distributions were generated and found to match closely the actuarial survival estimates established from the same data. The 13-year restoration survivals of one low-copper alloy could be predicted accurately from the 6-year survival results. However, another low-copper alloy and two high-copper alloys with much lower restoration failure rates required 18 years of data for accurate long-term survival predictions.

Actuarial Analysis↗

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↗

Staging by positron emission tomography predicts survival in patients with non-small cell lung cancer.

BACKGROUND: Positron emission tomography (PET) scanning is used increasingly to detect and stage lung cancer, but the test performance characteristics and relationship of PET to patient outcomes remain undefined. OBJECTIVE: To determine the test performance characteristics and relationship of PET scanning stage to patient outcomes relative to the 1997 International System for the Staging of Lung Cancer. DESIGN: Survival analysis using pathologic staging as the criterion standard for comparison of survival as predicted by staging by PET and CT. SETTING: University-based hospital. PATIENTS: All consecutive patients undergoing PET scanning for the evaluation of possible non-small cell lung cancer (NSCLC) during a 5-year period. MAIN OUTCOME MEASURES: Long-term survival of patients with NSCLC after staging by PET. RESULTS: One hundred fifty-two thoracic PET scans were obtained for the staging of possible NSCLC during a 5-year period. One hundred twenty-three patients (81%) demonstrated increased (18)F-fluorodeoxyglucose uptake. The overall sensitivity and specificity of PET for detecting malignancy were 95% and 67%, respectively, compared with 100% and 27% for chest CT. PET and CT had similar accuracy for staging the overall extent of disease (91% and 89%, respectively). PET stage correlated highly with survival using either nodal location or overall stage (p = 0.003, p = 0.002), as did pathologic staging (p = 0.0001, p = 0.0001). CT scan results did not accurately predict survival (p = 0.608, p = 0.338). CONCLUSION: PET scanning is a highly sensitive technologic advance in detecting and staging of thoracic malignancy and may more accurately predict the likelihood of long-term survival in patients with NSCLC than chest CT does.

Carcinoma, Non-Small-Cell Lung↗

A comparison of multivariable mathematical methods for predicting survival--III. Accuracy of predictions in generating and challenge sets.

This paper concludes a study of "performance variability" when four methods of multivariable analysis--multiple linear regression, discriminant function analysis, multiple logistic regression, and two arrangements of Cox's proportional hazards regression--were applied to the same stratified random samples of "generating sets" containing seven different statistical distributions of cogent biologic attributes in a composite staging system for a large cohort of patients with lung cancer. Each model developed from the generating sets was also applied for predictions in a previously sequestered "challenge set". Across the different generating sets, the multivariable methods showed good agreement with one another in the stepwise choice of first two powerful predictor variables, but not in the sequence of subsequent choices or in the standardized coefficients assigned to the same collection of "forced" variables. In concordance of predictions for individual patients in the generating sets, the overall proportions of disagreement for pairs of methods ranged from 0 to 28%, and kappa values ranged from 0.49 to 1.00. The accuracy of individual predictions showed relatively similar results when the different methods were applied to the same generating set. Across the generating sets, the different methods showed similar total results but substantial variations in predictions for alive and dead patients. When the models from the generating sets were applied for predictions in the challenge set, the results showed an analogous pattern: similar accuracy within models for overall and live/dead predictions, but substantial variations in live/dead predictions across models derived from different generating sources. The results showed that the multivariable methods often had good agreement with one another in predictions for groups but not for individual persons; and that no single method was superior to the others or to the composite staging system. We conclude that multivariable analytic methods may be most effective and consistent if used to find the few most powerful predictor variables, omitting the many other variables that may be "statistically significant" but less cogent. The powerful predictors may sometimes be best constructed, before the analysis begins, as composite variables containing appropriate unions or ordinal arrangements of elemental candidate variables.

Cohort Studies↗