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Results for “Prognostic model”

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PubMed2006

Validation of prognostic models among patients with advanced heart failure.

BACKGROUND: The ability to accurately predict heart failure outcomes is essential to guiding treatment decisions but several competing risk stratification models exist. METHODS AND RESULTS: We prospectively collected data on 280 patients with advanced heart failure recruited from 16 sites across the United States. Deaths and cardiac transplantations within the following 4 years were identified. Medline was searched to systematically identify widely cited heart failure severity classification models predicting long-term survival among patients with heart failure, and 4 were selected for validation. We used Kaplan-Meier survival curves, receiver-operating characteristic curves, and Cox proportional hazards modeling to identify the prognostic significance of each model's risk score and the individual contribution of the clinical components within each model. Average follow-up was 31.2 months; 148 deaths or transplantations occurred. Each model that we evaluated identified patients with significantly different prognoses. However, each was limited in overall predictive power, and many component patient characteristics did not have independent prognostic significance. Prognostic factors found to be most powerful within their models included: increasing age, ischemic cardiomyopathy, history of cardiomyopathy, ankle edema, decreased peak oxygen consumption, and absence of beta-blocker use. CONCLUSION: Although each of the models succeeded in risk-stratifying patients to some extent, all 4 models had shortcomings. There is a need for a contemporary model, derived from a patient population managed in accordance with current heart failure guidelines, applicable to all heart failure etiologies, relying on readily available clinical data.

Cardiac Output, Low↗

PubMed2006

Apoptosis of neuronal cells induced by serum of patients with acute brain injury: a new in vitro prognostic model.

OBJECTIVE: To investigate whether serum draining from the jugular bulb of patients with traumatic or haemorrhagic brain injury induced apoptosis of neuronal PC12 cells in vitro and whether the apoptotic rate correlated with patients' outcome at 6 months. DESIGN AND SETTING: Prospective clinical investigation in a 21-bed intensive care unit (ICU) in a university hospital. PATIENTS: Seventy patients who had suffered from acute brain injury requiring intensive care. INTERVENTIONS: Jugular bulb vein and systemic samples were obtained on admission to the ICU and after 48 h. PC12 cells were incubated in the presence of 10% of heat-inactivated patient's sera and apoptotic rate was determined by flow cytometry using annexin V and 7-aminoactinomycin D. RESULTS: Regional serum draining from the lesions induced higher early apoptosis of PC12 cells than systemic serum. Early apoptotic rate, Glasgow coma score, APACHE II score and the presence of pupil abnormalities were associated with mortality at 6 months in univariate statistical analyses. In logistic regression analysis only early apoptotic rate was an independent factor associated with mortality at 6 months (odds ratio: 1.502, 95% CI 1.2-1.9; p<0.001). The final model has a sensitivity of 82.4% and a specificity of 84.8% for predicting death within 6 months. CONCLUSIONS: We developed a simple and reproducible in vitro model for predicting outcome in patients with traumatic or haemorrhagic brain injury that survived in the early phase. Our in vitro model combined with clinical and radiological measurements might improve the value of prognostic models to predict acute brain injury patients' outcome.

Adolescent↗

PubMed1997

Predicting ten-year survival of patients with primary cutaneous melanoma: corroboration of a prognostic model.

BACKGROUND: Recently, the Pigmented Lesion Group at the University of Pennsylvania described a 4-variable model for predicting 10-year survival for patients with primary cutaneous melanoma. The variables are tumor thickness, anatomic site of the lesion, age, and gender. The objective of the current study was to test the validity of this model, employing the large data base of the New York University Melanoma Cooperative Group. METHODS: The predicted probabilities of 10-year survival for 780 patients with primary cutaneous melanoma were determined by multivariate logistic regression, using the 4 variables. RESULTS: The overall 10-year survival rate of the current study group was 78.4%. Of the four variables, tumor thickness, anatomic site of the lesion, and age were found to be independent predictors of survival. Although survival was better for women, gender was not a statistically significant factor in predicting 10-year survival when entered into the multivariate logistic regression model. In the current study, the probability of 10-year survival of patients with melanomas < 0.76 mm ranged from 93-99%, depending on the age and primary site. Age and site had more impact on the prognosis of intermediate and thick melanomas than on thin melanomas. Thus, for melanomas 0.76-1.69 mm, 1.70-3.60 mm, and thicker than 3.60 mm, the probabilities of survival ranged from 70-94%, 39-82%, and 23-68%, respectively. CONCLUSIONS: The wider ranges in survival rates for thicker melanomas, depending on the other variables, emphasize the importance of including variables in addition to tumor thickness in a prognostic model. Using a large data base from a medical center, the current study supports the prognostic multivariate model of the Pigmented Lesions Group of the University of Pennsylvania; however, the authors of the current study did not find gender to be statistically significant in this multivariate model.

Adolescent↗

PubMed2005

Extranodal natural killer T-cell lymphoma, nasal-type: a prognostic model from a retrospective multicenter study.

PURPOSE: Patients with natural killer T (NK/T) -cell lymphomas have poor survival outcome, and for this condition there is no optimal therapy. The purpose of this study was to design a prognostic model specifically for extranodal NK/T-cell lymphoma, which can identify high-risk patients who need more aggressive therapy. PATIENTS AND METHODS: This multicenter retrospective study was comprised of 262 patients who were diagnosed with NK/T-cell lymphoma. RESULTS: After a median follow-up duration of 51.2 months, 5-year overall survival rate in 262 patients was 49.5%. Prognostic factors for survival were "B" symptoms (P = .0003; relative risk, 2.202; 95% CI, 1.446 to 3.353), stage (P = .0006; relative risk, 2.366; 95% CI, 1.462 to 3.828), lactate dehydrogenase (LDH) level (P = .0005; relative risk, 2.278; 95% CI, 1.442 to 3.598), and regional lymph nodes (P = .0044; relative risk, 1.546; 95% CI, 1.009 to 2.367). Of 262 patients, 219 had complete information on four parameters. We identified four different risk groups: group 1, no adverse factor; group 2, one factor; group 3, two factors; and group 4, three or four factors. The new model showed a superior prognostic discrimination as compared with the International Prognostic Index (IPI). Notably, the distribution of patients was balanced when a new model was adopted (group 1, 27%; group 2, 31%; group 3, 20%; group 4, 22%), whereas 81% of patients were categorized as low or low-intermediate risks using IPI. CONCLUSION: The newly proposed model for extranodal NK/T-cell lymphoma demonstrated a more balanced distribution of patients into four groups with better prognostic discrimination as compared with the IPI.

Aged↗

PubMed1996

Hepatic phosphorus-31 magnetic resonance spectroscopy in primary biliary cirrhosis and its relation to prognostic models.

BACKGROUND: In vivo hepatic phosphorus-31 magnetic resonance spectroscopy (31P MRS) provides biochemical information about phosphorus metabolism. AIM: To assess 31P MRS as a prognostic marker in patients with primary biliary cirrhosis (PBC) in relation to the current clinical prognostic models. PATIENTS AND METHODS: Twenty three patients with PBC of varying functional severity and 16 matched healthy volunteers were studied using in vivo 31P MRS. Spectra were acquired using a 1.5 T spectroscopy system. Peak area ratios of phosphomonoesters (PME), inorganic phosphate (Pi), and phosphodiesters (PDE) and nucleotide triphosphate (NTP) were calculated. Pugh score, Christensen prognostic index, and R value according to the Mayo model were calculated from the clinical data. RESULTS: The PME/NTP, Pi/NTP, PME/PDE, and PME/Pi ratios and the PME signal height ratio (SHR) were significantly higher, while the PDE/NTP and PDE/SHR were significantly lower in PBC patients compared with healthy volunteers (p < 0.01). Significant correlations were seen between PME/Pi ratio and the prognostic index according to Christensen (r = 0.63, p < 0.001), R value according to the Mayo model (r = 0.45, p < 0.03), and with the Pugh score (r = 0.55, p < 0.007). CONCLUSIONS: This study shows that PME/Pi ratio obtained from 31P MRS correlates well with all three of the commonly used models of prognosis in patients with PBC. A longitudinal study with larger number of patients is required to confirm these findings and elucidate the biochemical changes underlying this phenomenon.

Adult↗

PubMed1995

Simple prognostic model to predict survival in patients with undifferentiated carcinoma of unknown primary site.

PURPOSE: We performed this study to identify prognostic factors in a subgroup of patients with carcinoma of unknown primary site treated with cisplatin combination chemotherapy. PATIENTS AND METHODS: Seventy-nine patients with poorly differentiated adenocarcinoma or undifferentiated carcinoma of unknown primary site were treated on two consecutive phase II chemotherapy protocols. The first protocol consisted of treatment with 3-week courses of cisplatin, etoposide, and bleomycin (BEP). In the second protocol, cisplatin was administered weekly combined with oral administration of etoposide (DDP/VP). To identify prognostic factors, univariate and multivariate analyses were conducted. RESULTS: In the univariate analysis, performance status, histology, liver or bone metastases, and serum levels of alkaline phosphatase and AST were significant variables to predict survival. In the multivariate analysis, performance status and alkaline phosphatase were the most important prognostic factors. CONCLUSION: Good-prognosis patients had a performance score of 0 (World Health Organization [WHO]) and an alkaline phosphatase serum level less than 1.25 times the upper limit of normal (N). These patients had a median survival duration greater than 4 years. Intermediate-prognosis patients were characterized by either a WHO performance status < or = 1 or an alkaline phosphatase level > or = 1.25 N. These patients had a median survival duration of 10 months and a 4-year survival rate of only 15%. The poor-prognosis group had both a WHO performance status > or = 1 and an alkaline phosphatase level > or = 1.25 N. These patients had a median survival duration of only 4 months and none survived beyond 14 months. Treatment strategies for these three groups are discussed. It is suggested that this prognostic model be validated in other patients series.

Adenocarcinoma↗

PubMed2026

Integrating single-cell transcriptomics to construct an oncogene-driven prognostic model and elucidate metabolic-immune crosstalk in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, its progression and treatment heterogeneity are mainly influenced by driver gene and tumor micro-environment (TME) interactions. Nevertheless, the mechanisms of this process at the single-cell level remain unclear. This study integrated TCGA and multi-center single-cell transcriptome data to identify a 575 genes HCC-specific core set, developing a single-cell "oncogene scoring" system to quantify individual carcinogenic activity. This score is significantly elevated in malignant and proliferative T cells and is closely associated with metabolic reprogramming, aberrant cell‒cell communication, and immunosuppressive phenotypes. Based on these characteristics, we constructed a machine learning-based Random Survival Forest (RSF) prognostic model validated in multiple independent cohorts, which classifies patients into distinct risk subtypes. The high-risk group exhibits genomic instability, increased tumor stemness, and immune evasion, while the low-risk group was more sensitive to drugs such as sorafenib. This study highlights the potential pathways by which high oncogenic activity is associated with HCC progression, suggesting a profound link with single-cell metabolic‒immune crosstalk. The constructed RSF model offers a promising computational framework for risk stratification and provides hypothesis-generating insights that may inform future personalized treatment strategies for HCC patients.

Hepatocellular carcinoma↗

PubMedDate not supplied

[A prognostic model of hepatitis A morbidity].

The growing interest to the study of the processes of the spread of hepatitis A (HA) in big cities of our country has stimulated the development of a new prognostic model at the Gamaleia Research Institute of Epidemiology and Microbiology. The model specifically takes into account a number of factors linked with the dynamics of the development of the disease in 6 stages and some regularities in the seasonal rises of HA morbidity. Quantitative relations in the mathematical model are determined by a system of nonlinear integral-differential equations with the first order partial derivatives and under the integral type boundary conditions, which increases the strictness of modeling of HA. The use of this new model has made it possible to carry out the prognostic-analytical study of HA morbidity among children in Perm and to evaluate a decrease in HA morbidity due to the hypothetical vaccination of children in spring months.

Adult↗

PubMed2002

A prognostic model for predicting the disappearance of left atrial thrombi among candidates for percutaneous transvenous mitral commissurotomy.

OBJECTIVES: We sought to develop a prognostic model to predict the disappearance of left atrial thrombi (LAT) among candidates for percutaneous transvenous mitral commissurotomy (PTMC). BACKGROUND: Complete LAT resolution can be achieved with oral anticoagulation, allowing a number of patients to safely undergo PTMC. METHODS: We randomly allocated 108 PTMC candidates with LAT into two subsets---one to derive the model and the other to validate it. The existence of LAT and its size were measured by transesophageal echocardiography. Patients were given oral anticoagulation and followed up for 6 to 34 months. There was a 62% disappearance rate of LAT. RESULTS: We developed the following model: P = 1/(1 + exponential [-8.1 + 1.8 NYHA + 0.7 area]), where NYHA = New York Heart Association functional class (from I to IV), and area = LAT area (in cm(2)). The model was well calibrated (goodness-of-fit test, p = 0.82) and well discriminated (area under the receiver-operating characteristics [ROC] curve = 0.92). Performance in the validating sample was equally good (area under the ROC curve = 0.94; goodness-of-fit test, p = 0.16). When a cut-off point of p > 0.7 was used to designate the LAT disappearance in the validating set, the model had a sensitivity, specificity and positive and negative predictive values of 93.3%, 79.2%, 84.9% and 90.5%, respectively. CONCLUSIONS: Combined clinical (NYHA functional class) and echocardiographic (LAT area) variables are predictive of the 34-month outcome of oral anticoagulation for LAT resolution among PTMC candidates. This simple and highly predictive model might be potentially useful for clinical assessment and proper management.

Adult↗

PubMed2002

Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.

Modelling of censored survival data is almost always done by Cox proportional-hazards regression. However, use of parametric models for such data may have some advantages. For example, non-proportional hazards, a potential difficulty with Cox models, may sometimes be handled in a simple way, and visualization of the hazard function is much easier. Extensions of the Weibull and log-logistic models are proposed in which natural cubic splines are used to smooth the baseline log cumulative hazard and log cumulative odds of failure functions. Further extensions to allow non-proportional effects of some or all of the covariates are introduced. A hypothesis test of the appropriateness of the scale chosen for covariate effects (such as of treatment) is proposed. The new models are applied to two data sets in cancer. The results throw interesting light on the behaviour of both the hazard function and the hazard ratio over time. The tools described here may be a step towards providing greater insight into the natural history of the disease and into possible underlying causes of clinical events. We illustrate these aspects by using the two examples in cancer.

Antineoplastic Agents↗

PubMed2005

[Prediction of premature pension after stationary rehabilitation due to adipositas--a prognostic model based on routinely collected data of the State Insurance Institution of Baden-Württemberg].

Up to 60 % of the German population can be marked as obese. Due is to its frequency and its associated diseases like cardiovascular disorders and disorders of the musculoskeletal system adipositas is a severe burden on the German health care system. This burden is caused by costs of the disease and costs due to premature pensioning. In this study logistic regression modelling has been performed by means of routinely collected data of patients of the regional statutory pension insurance institute Landesversicherungsanstalt Baden-Württemberg (LVA-BW) rehabilitated due to adipositas (n = 599). The aim was to detect influential variables for the prognosis of premature pensioning (n = 135). The data of the patients were obtained from a research database of the "RehaNet" project which includes data of the standardized discharge report of the Federation of German Pension Insurance Institutes and quality assurance questionnaires of the LVA-BW. Three variables remain in the model after a step-down procedure for modelling by logistic regression. The selected variables are age (in years), the physician's statement about the patients limitations of movement after rehabilitation (yes/no) and about the patients ability to work in future (more/less than half-day). After internal validation of the model by bootstrap methods the model achieves a sensitivity of 73 %, a specificity of 87 %, a positive and a negative predictive value of 57 and 93 % respectively. The area under the curve (AUC) of the ROC analysis is 0.87, so the model achieves a good prognostic value. Thus, this model is a valuable test for the exclusion of possible premature pension while or after rehabilitation due to adipositas. It was found that the situation of "no premature pensioning" of patients rehabilitated due to adipositas can be predicted quite accurately with little information (three variables). This reveals a perspective for further research in the possibility of an early, risk-adapted and individualised intervention after stationary rehabilitation for adipositas to keep employment.

Databases, Factual↗

PubMed2026

Prognostic modeling of overall survival in metastatic pancreatic cancer: an inflammation-based tool validated in PANTHEIA-SEOM cohort.

PURPOSE: To develop and internally validate the PANTHEIA-SIRI prognostic model, which integrates log-transformed systemic inflammation response index (SIRI) with clinical predictors, to estimate overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (mPDAC) treated with first-line chemotherapy. METHODS: We used data from the multicenter PANTHEIA-SEOM registry. OS was defined from chemotherapy start. The model was fitted as a Weibull accelerated failure time model in the survival-analysis population with multiple imputation. Predictors were log-transformed baseline SIRI, modeled with restricted cubic splines, ECOG, tumor burden, chemotherapy regimen, and anorexia-cachexia syndrome. Internal validation used a separate, non-overlapping cohort from the same registry; the centers contributing to each cohort are listed in a supplementary annex. TRIPOD was followed. Discrimination was assessed with Harrell´s C-index and calibration with IPCW Brier scores and IPA. RESULTS: The derivation cohort comprised 672 patients with SIRI data (593 analyzed for survival) across 22 Spanish hospitals (2015-2025); 80.1% had died after a median OS of 9.9 months. The imputation-pooled derivation C-index was 0.654 (95% CI, 0.627-0.681); optimism-corrected, 0.629. Internal validation used 62 separate patients from the same registry; 96.8% had died after a median OS of 9.2 months. The validation C-index was 0.603 (95% CI, 0.518-0.687). Calibration was adequate at 6 and 12 months. CONCLUSIONS: The PANTHEIA-SIRI model provides individualized OS estimates in mPDAC with routine clinical predictors. Its open-access calculator ( https://pantheia-siri.shinyapps.io/calc/ ) may support prognostic communication, treatment-intensity selection, and supportive-care planning. Routine clinical implementation will require further validation in larger, fully independent cohorts.

Cachexia↗

PubMed1999

A prognostic model that makes quantitative estimates of probability of relapse for breast cancer patients.

Tumor-node-metastasis (TNM) staging is the standard system for the estimation of prognosis of breast cancer patients. However, this system does not exploit information yielded by markers of the biological aggressiveness of breast cancer and is clearly unsatisfactory for optimal-treatment decision-making and for patient counseling. We have developed a prognostic model, based on a few routinely evaluated prognostic variables, that produces quantitative estimates for risk of relapse of individual breast cancer patients. We used data concerning 2441 of 2990 consecutive breast cancer patients to develop an artificial neural network (ANN) for the prediction of the probability of relapse over 5 years. The prognostic variables used were: patient age, tumor size, number of axillary metastases, estrogen and progesterone receptor levels, S-phase fraction, and tumor ploidy. Performances of the model were evaluated in terms of discrimination ability and quantitative precision. Predictions were validated on an independent series of 310 patients from an institution in another country. The ANN discriminated patients according to their risk of relapse better than the TNM classification (P = 0.0015). The quantitative precision of the model's estimates was accurate and was confirmed on the series from the second institution. The 5-year relapse risk yielded by the model varied greatly within the same TNM class, particularly for patients with four or more nodal metastases. The model discriminates prognosis better than the TNM classification and is able to identify patients with strikingly different risks of relapse within each TNM class.

Adult↗

PubMed2005

A prognostic model for HIV seroconversion among injection drug users as a tool for stratification in clinical trials.

OBJECTIVE: The main goal of this study was to construct a prognostic model for HIV seroconversion among injection drug users (IDUs) using easy-to-measure risk indicators. DESIGN: Cox proportional hazards regression modeling was used for risk stratification in a heterogeneous population of IDUs with regards to HIV risk-taking behaviors. METHODS: Subjects were recruited in a prospective cohort of IDUs followed between September 1992 and October 2001. A total of 1602 men, seronegative at enrollment with at least 1 follow-up visit, were included in the analyses. Only variables that consistently predict HIV seroconversion in several settings were considered. The final model was used to assign a risk score for each participant. RESULTS: Three risk indicators were included in the risk score to predict HIV seroconversion: unstable housing, average cocaine injections per day, and having shared a syringe with a known HIV-positive partner. Kaplan-Meier survival functions were generated and risk score values stratified in 3 groups. HIV incidence rates per 100 person-years were as follows: 0.91 (95% CI, 0.55-1.52) for the low-risk group, 3.10 (95% CI, 2.49-3.84) for the moderate-risk group, and 7.82 (95% CI, 6.30-9.73) for the high-risk group (log-rank P value < 0.0001). CONCLUSION: If validated in other settings, this risk score may improve the prediction of outcome and allow more accurate stratification in clinical trials.

Adolescent↗

PubMed2006

Validation of two prognostic models predicting outcome at two years after diagnosis in a new cohort of children with epilepsy: the Dutch Study of Epilepsy in Childhood.

PURPOSE: To validate two prognostic models for childhood-onset epilepsy designed to predict a terminal remission of <6 months at 2 years after diagnosis in children referred to the hospital. METHODS: A hospital-based cohort of children with newly diagnosed epilepsy was recruited and followed up for 2 years to validate previously developed models. One model was based on variables collected at intake, and the other was based on intake variables plus variables collected during the first 6 months of follow-up. The accuracy of both models was estimated by measuring the area under the receiver-operant-characteristic curves (ROC area). RESULTS: The ROC area of the model developed with intake variables was 0.69 [95% confidence interval (CI), 0.64-0.74] for the original cohort and 0.62 (95% CI, 0.55-0.69) for the validation cohort. The best combination of sensitivity and specificity for the original cohort was 61.6% and 69.1%, whereas it was 60.0% and 61.4% for the validation cohort. For the model with intake and 6-month variables combined, the ROC area was 0.78 (95% CI, 0.73-0.82) for the original cohort and 0.71 (95% CI, 0.64-0.78) for the validation cohort. The sensitivity and specificity were 72.6% and 73.1%, respectively, for the original cohort and 67.4% and 60.2%, respectively, for the validation cohort. CONCLUSIONS: Although both models predict outcome better than chance, they are insufficiently accurate to be of practical value. Both models performed marginally less well with the validation cohort than with the original cohort, but in both instances, the model based on intake and 6-month variables was more accurate.

Adolescent↗

PubMed2005

Prognostic model for HIV-1 disease progression in patients starting antiretroviral therapy was validated using independent data.

SETTING AND OBJECTIVE: The Antiretroviral Therapy (ART) Cohort Collaboration published models predicting progression to AIDS or death (the complement of AIDS-free survival) and death (the complement of absolute survival). The objective is to validate the model on independent data from CASCADE. STUDY DESIGN: Discrimination was assessed using concordance statistics, and calibration was examined by comparing predicted survival curves with the corresponding Kaplan-Meier estimates. Accuracy was assessed by comparing predicted percentage probability of survival with the Kaplan-Meier estimate at yearly intervals after start of therapy. RESULTS: There was little loss of model discrimination when applying the model to CASCADE. Overall predicted calibration curves agreed with Kaplan-Meier survival curves. Predicted probabilities of AIDS or death at 3 years after starting HAART ranged from 4.3% in the low-risk group to 20.5% in high-risk patients, with corresponding Kaplan-Meier estimates ranging from 4.0% to 18.3%; for death predictions, the probabilities ranged from 1.2% to 7.3% and estimates from 1.1% to 8.6%. CONCLUSION: The predictions from the model agree with observed outcomes in CASCADE to within the 95% upper and lower Kaplan-Meier estimates. The prognostic model appears to be accurate in terms of discrimination and calibration, giving reliable and transportable predictions up to 3 years after the start of HAART.

Adult↗

PubMed2000

The added value that increasing levels of diagnostic information provide in prognostic models to estimate hospital mortality for adult intensive care patients.

OBJECTIVE: To investigate in a systematic, reproducible way the potential of adding increasing levels of diagnostic information to prognostic models for estimating hospital mortality. DESIGN: Prospective cohort study. SETTING: Thirty UK intensive care units (ICUs) participating in the ICNARC Case Mix Programme. PATIENTS: Eight thousand fifty-seven admissions to UK ICUs. MEASUREMENTS AND RESULTS: Logistic regression analysis incorporating APACHE II score, admission type and increasing levels of diagnostic information was used to develop models to estimate hospital mortality for intensive care patients. The 53 UK APACHE II diagnostic categories were substituted with data from a hierarchical, five-tiered (type of condition required surgery or not, body system, anatomical site, physiological/pathological process, condition) coding method, the ICNARC Coding Method. The inter-rater reliability using the ICNARC Coding Method to code reasons for admission was good (kappa = 0.70). All new models had good discrimination (AUC = 0.79-0.81) and similar or better calibration compared with the UK APACHE II model (Hosmer-Lemeshow goodness-of-fit H = 18.03 to H = 26.77 for new models versus H = 63.51 for UK APACHE II model). CONCLUSION: The UK APACHE II model can be simplified by extending the admission type and substituting the 53 UK APACHE II diagnostic categories with nine body systems, without losing discriminative power or calibration.

APACHE↗

PubMed1998

[Prognostic model of the space station contamination stage].

Forty two non-metallic materials, 8 human metabolites and a process liquid (ethylene glycol) were selected for development of a prognostic model of space station contamination by harmful trace admixtures (HTAs). Removal technologies made allowance for absorption by atmospheric condensate (AC) and filter adsorption. Calculations took in 18 HTAs representative of 8 classes of compounds. Simulation modeling allowed to determine HTA migration rates and percent ratio (1), calculate concentrations of contaminants in the atmosphere and atmospheric condensate (2), and to assess filter efficiency by comparison of loads on the filter and a refrigeration/drying set (3). Comparison of empirical and measured data permitted conclusions about adequacy of the model and its potentiality for predicting ramifications of nominal and contingency situations.

Environmental Pollution↗