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Results for “Prognostic model”

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PubMed1990

[Evaluation of 196 patients with chronic myeloid leukemia based on a standard prognosis model].

The clinical course of 196 patients with chronic myelocytic leukemia (CML) was studied. Prognostic factors were analyzed using a standard prognostic model. From a univariate analysis of patients with nonblastic Philadelphia chromosome-positive CML, splenomegaly, bone marrow fibrosis, percentage of blasts and promyelocytes in the peripheral blood and LDH activity were shown to be factors with a significant negative influence on survival. However, age and the platelet count did not influence survival. The standard prognostic model, generated with the 4 variables (1) percentage of blasts and promyelocytes, (2) spleen size, (3) platelet count and (4) age did not provide a useful representation of risk status in this heterogenous patient population. However, the addition of further variables (LDH, additional chromosomal aberrations, percentage of basophiles/eosinophiles and percentage of bone marrow blasts) to the standard model allowed a separation into 2 patient groups: one with low and the other with intermediate to high risk. Our data support the general validity of the prognostic model; however, the applicability of the model may be compromised in hematologic centers with a heterogenous CML population due to the selection of high-risk patients. In this situation additional risk factors may have to be added to the prognostic formula.

Biomarkers, Tumor↗

PubMed1992

Using Cox's proportional hazards model for prognostication in carcinoma of the upper aero-digestive tract.

One of the major short comings of the traditional TNM system is its limited potential for prognostication. With the development of multifactorial analysis techniques, such as Cox's proportional hazards model, it has become possible to simultaneously evaluate a large number of prognostic variables. Cox's model allows both the identification of prognostically relevant variables and the quantification of their prognostic influence. These characteristics make it a helpful tool for analysis as well as for prognostication. The goal of the present study was to develop a prognostic index for patients with carcinoma of the upper aero-digestive tract which makes use of all prognostically relevant variables. To accomplish this, the survival data of 800 patients with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx or larynx were analyzed. Sixty-one variables were screened for prognostic significance; of these only 19 variables (including age, tumor location, T, N and M stages, resection margins, capsular invasion of nodal metastases, and treatment modality) were found to significantly correlate with prognosis. With the help of Cox's equation, a prognostic index (PI) was computed for every combination of prognostic factors. To test the proposed model, the prognostic index was applied to 120 patients with carcinoma of the oral cavity or oropharynx. A comparison of predicted and observed survival showed good overall correlation, although actual survival tended to be better than predicted.

Adult↗

PubMed1996

Natural history and prognostic factors in 305 Swedish patients with primary sclerosing cholangitis.

BACKGROUND/AIMS--The course of primary sclerosing cholangitis (PSC) is highly variable and unpredictable. This study describes the natural history and outcome of PSC. These data were used to construct a prognostic model for patients with PSC. METHODS--A total of 305 Swedish patients with PSC were studied. The median follow up time was 63 (1-194) months and all patients could be traced for follow up. Some 79 patients died or had a liver transplant. The prognostic significance of clinical, biochemical, and histological findings at the time of diagnosis were evaluated using multivariate analysis. RESULTS--The estimated median survival from time of diagnosis to death or liver transplantation was 12 years. Cholangiocarcinoma was found in 24 (8%) of the patients and 134 (44%) of the patients were asymptomatic at the time of diagnosis. The estimated survival rate was significantly higher in the asymptomatic group (p < 0.001). However, 29 (22%) of the asymptomatic patients became symptomatic during the study period. It was found that age, serum bilirubin concentration, and histological stage at the time of diagnosis were independent predictors of a bad prognosis. These variables were used to construct a prognostic model. CONCLUSIONS--This prognostic model developed from a large homogeneous population of PSC patients should be of value for the timing of transplantation and patient counselling in PSC.

Adolescent↗

PubMed2020

Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods.

BACKGROUND: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms. METHOD: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs). RESULTS: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them. CONCLUSION: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers.

Adenocarcinoma of Lung↗

PubMed2001

Predicting patient's long-term clinical status after hip arthroplasty using hierarchical decision modelling and data mining.

Construction of a prognostic model is presented for the long-term outcome after femoral neck fracture treatment with implantation of hip endoprosthesis. While the model is induced from the follow-up data, we show that the use of additional expert knowledge is absolutely crucial to obtain good predictive accuracy. A schema is proposed where domain knowledge is encoded as a hierarchical decision model of which only a part is induced from the data while the rest is specified by the expert. Although applied to hip endoprosthesis domain, the proposed schema is general and can be used for the construction of other prognostic models where both follow-up data and human expertise is available.

Aged↗

PubMed2005

Using tumor markers to predict the survival of patients with metastatic renal cell carcinoma.

PURPOSE: Approximately 30% of renal cell carcinomas (RCCs) present as metastatic disease. Molecular markers have the potential to characterize accurately the biological behavior of tumors and they may be useful for determining prognosis. MATERIALS AND METHODS: A custom tissue array was constructed using clear cell RCC from 150 patients with metastatic RCC who underwent nephrectomy prior to immunotherapy. The tissue array was stained for 8 molecular markers, namely Ki67, p53, gelsolin, carbonic anhydrase (CA)9, CA12, PTEN (phosphatase and tensin homologue deleted on chromosome 10), epithelial cell adhesion molecule and vimentin. Marker status and established clinical predictors of prognosis were considered when developing a prognostic model for disease specific survival. RESULTS: On univariate Cox regression analysis certain markers were statistically significant predictors of survival, namely CA9 (p <0.00001), p53 (p = 0.0072), gelsolin (p = 0.030), Ki67 (p = 0.036) and CA12 (p = 0.043). On multivariate Cox regression analysis that included all markers and clinical variables CA9 (p = 0.00002), PTEN (p <0.0001), vimentin (p = 0.0032), p53 (p = 0.028), T category (p = 0.0025) and performance status (p = 0.0013) were significant independent predictors of disease specific survival and they were used to construct a combined molecular and clinical prognostic model. The bias corrected concordance index (C-index) of this combined prognostic model was C = 0.68, which was significantly higher (p = 0.0033) than that of a multivariate clinical predictor model (C = 0.62) based on the UCLA Integrated Staging System (T category, histological grade and performance status). CONCLUSIONS: In patients with clear cell RCC a prognostic model for survival that includes molecular and clinical predictors is significantly more accurate than a standard clinical model using the combination of stage, histological grade and performance status.

Adult↗

PubMed2026

Large-scale multiethnic electronic health record resource for diabetes complications research: the North West London Diabetes Cohort (NWLDC) - cohort profile.

PURPOSE: The North West London Diabetes Cohort is established to provide systematic characterisation of a large diabetes population as a foundation for complications research and prognostic modelling. Many predictive modelling studies neglect the essential descriptive characterisation of underlying cohorts, focusing narrowly on model accuracy. This cohort profile addresses this gap by comprehensively describing the demographic composition, clinical characteristics and complication incidence patterns. The notably diverse, multiethnic population enables examination of ethnic disparities and supports future development of reliable prognostic models and evidence-based prevention strategies for diabetes complications. PARTICIPANTS: At baseline, 337 271 patients with diabetes were identified. It includes 279 067 patients with type 2 diabetes, 17 638 with type 1 diabetes, 33 590 with gestational diabetes and 6916 with unspecified diabetes. The earliest diabetes diagnosis dates to January 1932, with data updated to 27 May 2025. FINDINGS TO DATE: This cohort profile describes baseline characteristics of patients with comprehensive data collected on demographics (age, sex, Deprivation Index, ethnicity), clinical measures (glycated haemoglobin, body mass index, blood pressure, lipids and estimated glomerular filtration rate) and 14 major diabetes complications tracked longitudinally. Key findings for patients with type 2 diabetes reveal diabetic retinopathy as the most common complication (74.6 per 1000 person-years), followed by hypertension (51.0) and kidney disease (31.4). Cumulative incidence analyses using the Aalen-Johansen estimator, which accounts for mortality as a competing risk, demonstrated significant ethnic disparities, with black, Asian, mixed and other ethnic groups showing elevated risk compared with white patients. Time-varying Cox models identified strong clustering between cardiovascular and renal complications, confirming a cardiometabolic-renal syndrome. Mental health conditions (depression and anxiety) were prevalent throughout the disease timeline, occurring both before and after diabetes diagnosis. FUTURE PLANS: This cohort will be used as a platform for developing and validating prognostic models for diabetes complications, enabling risk stratification and targeted interventions. Future work will incorporate medication data to refine diabetes type classification, examine the effectiveness of antidiabetic medications in preventing different complications and address demographic differences in prognostic model performance and prediction accuracy. To better characterise lifestyle, further interrogation of electronic health record data will examine recording of advice given, including dietary advice, referral to weight management schemes and presence of alcohol consumption codes.

Humans↗

PubMed2006

Designing clinical trials for kidney cancer based on newly developed prognostic and predictive tools.

Several advances have been made in our understanding of the molecular aberrations in renal cell carcinoma that have led to the identification of novel prognostic and predictive biomarkers. At the same time, several novel agents targeting these molecular aberrations have shown promising efficacy in the therapy of advanced renal cancer. As these agents enter more advanced-stage clinical trials, efforts must be made to integrate exploration of these novel prognostic and predictive markers into clinical trial design. These elements then can be combined with more traditional prognostic features to form more robust prognostic models. Finally, predictive markers and prognostic models should be validated prospectively, either as part of clinical trials designed specifically for that purpose or as part of large phase-3 trials. Once validated, these features can be used to best inform prognosis and guide therapy based on individual patient characteristics.

Biomarkers, Tumor↗

PubMed2002

Predicting functional outcome and survival after acute ischemic stroke.

OBJECTIVE: Disability and mortality represent the most relevant clinical outcome after acute ischemic stroke. However, validated and comprehensive prognostic models for recovery have not been developed. An accurate model including all previously suggested independent outcome predictors could improve the design and analysis of clinical trials. We therefore developed prognostic models for functional dependence and death after 100 days in a large cohort of stroke patients. METHODS: From the German Stroke Database, 1754 prospectively collected records of patients with acute ischemic stroke were used for the development of prognostic models. Intubated patients and patients with low functional status before stroke were excluded. Functional independence was defined as a Barthel Index >/=95 after 100 days. Prognostic factors assessable within 72 hours after admission were identified by a systematic literature review. The final models of binary logistic regression analyses were internally validated and calibrated. RESULTS: The resulting cross-validated and calibrated models correctly classified more than 80 % of the patients and yielded the following prognostic factors for functional independence: Age, right and left arm paresis at admission, NIH-Stroke Scale at admission, Rankin Scale 48-72 hours later, gender, prior stroke, diabetes, fever, lenticulostriate infarction, neurological complications. The following variables were identified as prognostic factors for death: Age, NIH-Stroke Scale at admission, and fever. CONCLUSIONS: Our work gives an important insight into prognostic factors after acute ischemic stroke and presents predictive models with high prognostic accuracy. Together with a prospective validation study, currently underway, we hence hope to improve the prediction of functional outcome after ischemic stroke.

Activities of Daily Living↗

PubMed2002

Time-dependent Cox regression model is superior in prediction of prognosis in primary sclerosing cholangitis.

More precise prognostic models are needed for prediction of survival in patients with primary sclerosing cholangitis (PSC), particularly for the selection of candidates for liver transplantation. The aim of this study was to develop a time-dependent prognostic model for the calculation of updated short-term survival probability in PSC. Consecutive clinical and laboratory follow-up data from the time of diagnosis were collected from the files of 330 PSC patients from 5 European centers, followed for a median of 8.4 years since diagnosis. Time-fixed and time-dependent Cox regression analyses, as well as the additive regression model, were applied. The reliability of the models was tested by a cross-validation procedure. Bilirubin (on a logarithmic scale), albumin, and age at diagnosis of PSC were identified as independent prognostic factors in multivariate analysis of both the time-fixed and the time-dependent Cox regression models. The importance of bilirubin was more pronounced in the time-dependent model (hazard ratio [HR], 2.84) than in the time-fixed analysis (hazard ratio, 1.51). The additive regression model indicated that once the patients survive beyond the first 5 years, the impact on prognosis of albumin at diagnosis ceases. The time-dependent prognostic model was superior to the time-fixed variant in assigning low 1-year survival probabilities to patients that actually survived less than 1 year. In conclusion, a time-dependent Cox regression model has the potential to estimate a more precise short-term prognosis in PSC compared with the traditional time-fixed models.

Adolescent↗

PubMed2026

Lymphangiogenesis-related gene signature-based risk model for prognostic assessment of cervical cancer: immune-metabolic characterization and molecular subtype analysis.

BACKGROUND: Lymphangiogenesis promotes tumor dissemination and may shape the immune contexture of cervical cancer, yet lymphangiogenesis-related prognostic stratification and its immunometabolic implications remain insufficiently defined in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). METHODS: TCGA-CESC transcriptomes and clinical data were obtained from UCSC Xena and integrated with normal cervix tissues from the Genotype-Tissue Expression Project after batch correction. Prognostic LYMRGs were first identified from the differentially expressed set using univariable Cox proportional hazards analysis. Candidate genes were then reduced using an L1-regularized Cox model (Least Absolute Shrinkage and Selection Operator), and the remaining markers were entered into a multivariable Cox regression to obtain the final coefficients and compute an individualized risk score. The model's prognostic value was further assessed in an independent Gene Expression Omnibus dataset. In addition, expression patterns of the signature genes were leveraged for molecular subtyping of TCGA samples via non-negative matrix factorization (NMF). Immune infiltration and immunotherapy-associated characteristics were interrogated through a multi-algorithm strategy (single-sample gene set enrichment analysis, CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore . Additional analyses included pathway enrichment (GSEA/GO/KEGG), drug sensitivity prediction (pRRophetic/CellMiner), and ceRNA network analysis. RESULTS: A six-gene LYMRG signature robustly stratified survival. High-risk patients had significantly worse overall survival in The Cancer Genome Atlas with AUCs of 0.819/0.801/0.801 at 1/3/5 years, and in GSE52903 (P = 0.001) with AUCs of 0.733/0.719/0.725. NMF identified two subtypes with distinct prognosis (P = 0.01) and divergent immune landscapes. Risk groups and subtypes exhibited consistent differences in immune infiltration, checkpoint expression, TIDE/IPS patterns, and pathway enrichment. Predicted chemosensitivity differed by risk group, and the ceRNA network suggested candidate upstream lncRNA regulators of the signature. CONCLUSION: A lymphangiogenesis-related six-gene model enables clinically meaningful prognostic stratification of CESC and links lymphangiogenesis programs to distinct tumor immune phenotypes and therapeutic vulnerabilities.

cancer↗

PubMedDate not supplied

Validation of a prognostic index in the critically ill newborn.

OBJECTIVE: Elaborate and assess the degree of validity of a prognostic model for evaluating patients admitted to the Neonatal Intensive Care Unit (NICU). DESIGN: Cases and controls nested in a cohort. SETTING: NICU of two tertiary hospitals and another second level hospital. PATIENTS: The study was carried out in two phases. During the first phase (elaboration of a model), 336 newborns were studied; 112 belonged to the case group (dead patients) and 224 to the control group (live patients discharged). For the second phase (model validation), 300 patients were included that did not participate in the first phase, 100 cases and 200 controls. MEASUREMENTS: For each patient admitted to the study, clinical, paraclinical, perinatal and comorbidity factors were determined within the first 12 hours. Variables of statistical significance in the bivariate analysis were included in a logistic regression model with the objective of identifying a prognostic model. RESULTS: The variables that constituted the prognostic index were gestational age x birth weight, the paO2/FiO2 ratio x O2 saturation, arrest cardiac, major congenital malformations, septicemia and base excess. The model showed to have a sensitivity of 70% and a specificity of 91% during the elaboration cohort. In the validation cohort, sensitivity was 68% and specificity was 92%, a positive predictive value of 80%, negative predictive value of 85% and a correct classification rate was 84%. CONCLUSIONS: The Neonatal Mortality Prognostic Index (NMPI) developed in this study showed to be useful for the evaluation of hospital mortality for severely ill newborns admitted to NICU.

Case-Control Studies↗

PubMed2001

Optimizing flow cytometric DNA ploidy and S-phase fraction as independent prognostic markers for node-negative breast cancer specimens.

Developing a reliable and quantitative assessment of the potential virulence of a malignancy has been a long-standing goal in clinical cytometry. DNA histogram analysis provides valuable information on the cycling activity of a tumor population through S-phase estimates; it also identifies nondiploid populations, a possible indicator of genetic instability and subsequent predisposition to metastasis. Because of conflicting studies in the literature, the clinical relevance of both of these potential prognostic markers has been questioned for the management of breast cancer patients. The purposes of this study are to present a set of 10 adjustments derived from a single large study that optimizes the prognostic strength of both DNA ploidy and S-phase and to test the validity of this approach on two other large multicenter studies. Ten adjustments to both DNA ploidy and S-phase were developed from a single node-negative breast cancer database from Baylor College (n = 961 cases). Seven of the adjustments were used to reclassify histograms into low-risk and high-risk ploidy patterns based on aneuploid fraction and DNA index optimum thresholds resulting in prognostic P values changing from little (P < 0.02) or no significance to P < 0.000005. Other databases from Sweden (n = 210 cases) and France (n = 220 cases) demonstrated similar improvement of DNA ploidy prognostic significance, P < 0.02 to P < 0.0009 and P < 0.12 to P < 0.002, respectively. Three other adjustments were applied to diploid and aneuploid S-phases. These adjustments eliminated a spurious correlation between DNA ploidy and S-phase and enabled them to combine independently into a powerful prognostic model capable of stratifying patients into low, intermediate, and high-risk groups (P < 0.000005). When the Baylor prognostic model was applied to the Sweden and French databases, similar significant patient stratifications were observed (P < 0.0003 and P < 0.00001, respectively). The successful transference of the Baylor prognostic model to other studies suggests that the proposed adjustments may play an important role in standardizing this test and provide valuable prognostic information to those involved in the management of breast cancer patients.

Breast Neoplasms↗

PubMed2026

A cuproptosis-related lncRNAs-based risk signature for predicting prognosis and immune status in glioma.

BACKGROUND: Glioma is one of the most prevalent primary malignant brain tumors, characterized by poor prognosis and limited treatment options. Recent studies have identified cuproptosis, a novel copper-dependent form of regulated cell death, as a critical mechanism involved in tumor progression. However, the role of cuproptosis-related long non-coding RNAs (lncRNAs) in glioma remains not fully clarified. This study aimed to develop and validate a prognostic model based on cuproptosis-associated lncRNAs to predict patient outcomes and guide individualizing therapeutic strategies. METHODS: Transcriptomic profiles and clinical data were obtained from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and the Chinese Glioma Genome Atlas (CGGA) databases. Cuproptosis -related prognostic lncRNAs were filtered via univariate and multivariate Cox and Least absolute shrinkage and selection operator (LASSO) regression analyses, which were selected to establish a prognostic model for glioma. Samples were divided into high- and low-risk groups, and the predictive performance of the prognostic model was evaluated based on receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival curves, and a nomogram. In addition, immune cell infiltration, tumor mutational burden (TMB), immunophenoscore (IPS), Tumor Immune Dysfunction and Exclusion (TIDE) and drug sensitivity were analyzed. Expression levels of selected lncRNAs and proteins were validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting. RESULTS: An 11-lncRNA signature associated with cuproptosis was established, and the risk score derived from this model was identified as an independent prognostic factor for glioma. The model exhibited excellent predictive ability, with area under the curve (AUC) values of 0.880, 0.913, and 0.866 for 1-, 3-, and 5-year survival, respectively. Higher TMB, immune checkpoint expression, and IPS were observed in the high-risk group and no significant difference was observed in TIDE between risk groups. Drug sensitivity analysis identified TPCA-1, KIN001-135, and ispinesib mesylate as potential therapeutic agents. Expression validation in glioma cells further supported the biological relevance of the selected lncRNAs. CONCLUSIONS: This cuproptosis-related lncRNA-based signature demonstrates strong prognostic value and may serve as a promising tool for glioma risk stratification and personalized treatment selection.

Glioma↗

PubMed2002

Pretransplant model to predict posttransplant survival in liver transplant patients.

OBJECTIVE: To develop a prognostic model that determines patient survival outcomes after orthotopic liver transplantation (OLT) using readily available pretransplant variables. SUMMARY BACKGROUND DATA: The current liver organ allocation system strongly favors organ distribution to critically ill recipients who exhibit poor survival outcomes following OLT. A severely limited organ resource, increasing waiting list deaths, and rising numbers of critically ill recipients mandate an organ allocation system that balances disease severity with survival outcomes. Such goals can be realized only through the development of prognostic models that predict survival following OLT. METHODS: Variables that may affect patient survival following OLT were analyzed in hepatitis C (HCV) recipients at the authors' center, since HCV is the most common indication for OLT. The resulting patient survival model was examined and refined in HCV and non-HCV patients in the United Network for Organ Sharing (UNOS) database. Kaplan-Meier methods, univariate comparisons, and multivariate Cox proportional hazard regression were employed for analyses. RESULTS: Variables identified by multivariate analysis as independent predictors for patient survival following primary transplantation of adult HCV recipients in the last 10 years at the authors' center were entered into a prognostic survival model to predict patient survival. Accordingly, mortality was predicted by 0.0293 (recipient age) + 1.085 (log10 recipient creatinine) + 0.289 (donor female gender) + 0.675 urgent UNOS - 1.612 (log10 recipient creatinine times urgent UNOS). The above variables, in addition to donor age, total bilirubin, prothrombin time (PT), retransplantation, and warm and cold ischemia times, were applied to the UNOS database. Of the 46,942 patients transplanted over the last 10 years, 25,772 patients had complete data sets. An eight-factor model that accurately predicted survival was derived. Accordingly, the mortality index posttransplantation = 0.0084 donor age + 0.019 recipient age + 0.816 log creatinine + 0.0044 warm ischemia (in minutes) + 0.659 (if second transplant) + 0.10 log bilirubin + 0.0087 PT + 0.01 cold ischemia (in hours). Thus, this model is applicable to first or second liver transplants. Patient survival rates based on model-predicted risk scores for death and observed posttransplant survival rates were similar. Additionally, the model accurately predicted survival outcomes for HCV and non-HCV patients. CONCLUSIONS: Posttransplant patient survival can be accurately predicted based on eight straightforward factors. The balanced application of a model for liver transplant survival estimate, in addition to disease severity, as estimated by the model for end-stage liver disease, would markedly improve survival outcomes and maximize patients' benefits following OLT.

Adult↗

PubMed2002

The effects of two methods for customising the original SAPS II model for intensive care patients from South England.

Model customisation is used to adjust prognostic models by re-calibrating them to obtain more reliable mortality estimates. We used two methods for customising the Simplified Acute Physiology Score II model for 15,511 intensive care patients by altering the logit and the coefficients of the original equation. Both methods significantly improved model calibration, but customising the coefficients was slightly more effective. The Hosmer-Lemeshow chi(2)-value improved from 306.0 (p< 0.001) before, to 14.5 (p < 0.07) and 23.3 (p < 0.06) after customisation of the coefficients and the logit, respectively. Discrimination was not affected. The standardised mortality ratio for the entire population declined from 1.16 (95% confidence interval: 1.13-1.20, p < 0.001) to 0.99 (95% confidence interval: 0.96-1.02, p < 0.22) after customisation of the coefficients. The uniformity-of-fit for patients grouped by operative status and comorbidities also improved, but remained imperfect for patients stratified by location before intensive care unit admission. Amalgamation of large, regional databases could provide the basis for the re-calibration of standard prognostic models, which could then be used as a national reference system to allow more reliable comparisons of the efficacy and quality of care based on severity adjusted outcome measures.

Adolescent↗

PubMed2006

Impact of immune parameters on long-term survival in metastatic renal cell carcinoma.

PURPOSE: The purpose of this study was to evaluate the impact of immunologic prognostic factors in combination with established clinical prognostic factors in patients with metastatic renal cell carcinoma (mRCC). PATIENTS AND METHODS: A total of 120 consecutive patients with mRCC received interleukin-2 (IL-2) -based immunotherapy. Baseline tumor biopsies were available from 85 of these patients. Potential prognostic factors were analyzed by univariate and multivariate analyses. RESULTS: Multivariate analysis (N = 120) identified high lactate dehydrogenase, lymph node metastases, low hemoglobin, low Karnofsky performance status, and bone metastases as independent poor prognostic clinical factors. The impact of these clinical factors has been demonstrated by others. Multivariate analysis (n = 85) also identified a high blood neutrophil count (> 6.0 x 10(9)/L; hazard ratio, 2.0; P = .015), the presence of intratumoral neutrophils (> 0 cells/mm2 tumor tissue; hazard ratio, 2.3; P = .001), and low intratumoral CD57+ natural killer cell count (< 50 cells/mm2 tumor tissue; hazard ratio, 2.1; P = .01) as independent poor prognostic immunologic factors. These three independent immunologic parameters had significant discriminatory power as supplemental risk factors in prognostic models based on the clinical risk factors, identifying subgroups within the favorable clinical group with estimated 5-year survival rates of 60%, 25%, and 0%, respectively. These findings were apparent in both our own prognostic model and in an extended Memorial Sloan-Kettering Cancer Center (New York, NY) prognostic model. CONCLUSION: This study points on five clinical and three supplemental immunologic independent prognostic factors of survival in patients with mRCC receiving IL-2.

Adult↗

PubMed1995

Testing models predicting severity of respiratory syncytial virus infection on the PICNIC RSV database. Pediatric Investigators Collaborative Network on Infections in Canada.

OBJECTIVES: To determine the sensitivity and specificity of published prognostic models to predict morbidity resulting from lower respiratory tract disease caused by respiratory syncytial virus in an independent pediatric population and to assess the accuracy of single risk factors in predicting adverse outcome. DESIGN: All articles obtained from a MEDLINE search that used the terms prognosis or sequelae and respiratory syncytial virus, and from the references of these articles, were reviewed. Studies were included if risk factors and outcomes were defined and if information was available in a database of prospectively enrolled patients with respiratory syncytial virus infections. A probability of adverse outcome was assigned to each patient in the cohort using prognostic models described in the articles. A test was considered positive if the probability of the adverse outcome was 5% or more. PATIENTS: Six hundred eighty-nine patients hospitalized with respiratory syncytial virus in seven tertiary care centers across Canada were prospectively enrolled in the Pediatric Investigators Collaborative Network on Infections in Canada database. MAIN OUTCOME MEASURES: The sensitivity and specificity of single predictors and of models in predicting severe disease were determined. RESULTS: The sensitivity of single predictors varied from 17% to 46%. A model that used age and oxygen saturation at admission in previously well infants had a sensitivity of 98% and a specificity of 47% when predicting intensive care unit admission. Another model that included age at hospitalization, gestational age, presence of an underlying condition, and respiratory syncytial virus subtype used to predict the outcome of a high severity index had a sensitivity of 77% and a specificity of 76%. When the above model was modified by exclusion of viral subgroup, sensitivity increased to 94%, but specificity decreased to 46%. CONCLUSION: Previously described prognostic models were generalizable to an independent study population.

Canada↗