Valid Prognostic Model Research Articles

Myelofibrosis prognostic models validated in an underserved minority population: A retrospective analysis.

e18529 Background: Contemporary prognostic models for myelofibrosis (MF) risk stratify patients using a heterogeneous composition of clinical and genetic factors. Little is known regarding how these scores perform in underserved populations. Our study assesses how well current models stratify risk at an inner-city tertiary care center which carries unique challenges including increased diversity, later disease presentation, and less access to healthcare resources. Methods: We retrospectively reviewed 88 MF patients referred to our center in the Bronx, NY between 2000 and 2023. Clinical and genetic data were used to calculate prognostic scoring for DIPSS, DIPSS plus, MIPSS70, MIPSSV2, and GIPSS. Overall survival (OS) was calculated from the time of diagnosis and non-deceased patients were censored at last follow-up. Patients with missing clinical or genetic data were excluded from individual analysis. Models were assessed via univariate and multivariate cox proportional hazards analysis followed by concordance analysis. Results: Mean age of the cohort was 66 years (range 29-88) with median survival of 7.7 years. Ethnicities included non-hispanic white (NHW) [30 (34.1%)], NH black [23 (26.1%)], NH asian [6 (6.8%)], and hispanics of all races [22 (25%)] (unknown n=7). DIPSS (n= 88, HR 1.77, CI 1.22 - 2.41, p-value = 0.0001), DIPSS plus (n=83, HR 1.54, 1.15-2.07, p = 0.0033), MIPSS70V2 (n=68, HR 2.06, CI 1.23 - 3.61, p = 0.0088), and GIPSS (n=69, HR 2.73, CI 1.24 - 3.78, p = 0.0063) were significantly associated with OS based on univariate analyses. Both DIPSS and GIPSS were significant even upon adjustment for each other (p=.009 and p=.043 respectively). MIPSS70 (n=73, HR 1.87, CI .93 - 3.88, p = 0.0842) did not meet significance. In terms of predictive accuracy based on C-index scores, DIPSS and GIPSS combined analysis scored highest (0.773), followed by GIPSS (.7226), DIPSS (0.719), MIPSSV2 (0.717), DIPSS Plus (0.6495), then MIPSS70 (0.6414). Conclusions: Our study shows that GIPSS, DIPSS, DIPSS plus, MIPSSV2 all function as valid prognostic models in the inner-city setting and that integrating both clinical and genetic models may have higher predictive value than either individual model alone. Additionally, GIPSS predicted superior survival outcomes to what was observed in our cohort for higher risk categories, prompting the need for further investigation. [Table: see text]

Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review.

The purpose of this systematic review was to assess risk of bias in existing prognostic models of hospital-induced delirium for medical-surgical units. APA PsycInfo, CINAHL, MEDLINE, and Web of Science Core Collection were searched on July 8, 2022, to identify original studies which developed and validated prognostic models of hospital-induced delirium for adult patients who were hospitalized in medical-surgical units. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was used for data extraction. The Prediction Model Risk of Bias Assessment Tool was used to assess risk of bias. Risk of bias was assessed across four domains: participants, predictors, outcome, and analysis. Thirteen studies were included in the qualitative synthesis, including ten model development and validation studies and three model validation only studies. The methods in all of the studies were rated to be at high overall risk of bias. The methods of statistical analysis were the greatest source of bias. External validity of models in the included studies was tested at low levels of transportability. Our findings highlight the ongoing scientific challenge of developing a valid prognostic model of hospital-induced delirium for medical-surgical units to tailor preventive interventions to patients who are at high risk of this iatrogenic condition. With limited knowledge about generalizable prognosis of hospital-induced delirium in medical-surgical units, existing prognostic models should be used with caution when creating clinical practice policies. Future research protocols must include robust study designs which take into account the perspectives of clinicians to identify and validate risk factors of hospital-induced delirium for accurate and generalizable prognosis in medical-surgical units.

Independent Validation of a Unifying Prognostic Model for Intracranial and Extracranial Metastasis-directed Radiotherapy.

The aim of this study was to analyze the validity of a unifying prognostic model, originally developed by Kowalchuk et al., because relevant variations in clinical practice and observed survival may impact on the performance of predictive tools. Retrospectively, data from a single institution were analyzed. The study included 253 patients managed with radiotherapy for spine and/or brain metastases. The Kowalchuk et al. score [3 parameters including performance status, number of organ systems involved with disease outside of the organ system of the treatment target, and treatment of intracranial target(s)] was assigned and the resulting prognostic strata compared. The decision tool developed by Kowalchuk et al. performed well, e.g., in terms of 2-year survival. Complementary information could be obtained by analyses of blood test results such as hemoglobin, albumin, and C-reactive protein. The new unifying score emerged as a valid prognostic model in our external validation database.

By integrating single-cell RNA-seq and bulk RNA-seq in sphingolipid metabolism, CACYBP was identified as a potential therapeutic target in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a heterogeneous disease with a dismal prognosis for advanced tumors. Immune-associated cells in the microenvironment substantially impact LUAD formation and progression, which has gained increased attention in recent decades. Sphingolipids have a profound impact on tumor formation and immune infiltration. However, few researchers have focused on the utilization of sphingolipid variables in the prediction of LUAD prognosis. The goal of this work was to identify the major sphingolipid-related genes (SRGs) in LUAD and develop a valid prognostic model based on SRGs. The most significant genes for sphingolipid metabolism (SM) were identified using the AUCell and WGCNA algorithms in conjunction with single-cell and bulk RNA-seq. LASSO and COX regression analysis was used to develop risk models, and patients were divided into high-and low-risk categories. External nine provided cohorts evaluated the correctness of the models. Differences in immune infiltration, mutation landscape, pathway enrichment, immune checkpoint expression, and immunotherapy were also further investigated in distinct subgroups. Finally, cell function assay was used to verify the role of CACYBP in LUAD cells. A total of 334 genes were selected as being most linked with SM activity for further investigation, and a risk model consisting of 11 genes was established using lasso and cox regression. According to the median risk value, patients were split into high- and low-risk groups, and the high-risk group had a worse prognosis. The low-risk group had more immune cell infiltration and higher expression of immune checkpoints, which illustrated that the low-risk group was more likely to benefit from immunotherapy. It was verified that CACYBP could increase the ability of LUAD cells to proliferate, invade, and migrate. The eleven-gene signature identified in this research may help physicians create individualized care plans for LUAD patients. CACYBP may be a new therapeutic target for patients with advanced LUAD.

Independent Validation of a Risk Stratification Model Predicting Survival in Elderly Patients Irradiated for Bone Metastases.

Many patients with bone metastases receive palliative radiotherapy. However, treatment personalization tools are needed, due to heterogeneous survival. The aim of this study was to analyze the validity of the prognostic survival model, originally developed by Rades et al., because international variations in clinical practice and survival outcomes may impact on the performance of predictive tools. Data from a single institution were retrospectively analyzed. The study included 305 patients managed with palliative radiotherapy for bone metastases. The Rades et al. score was assigned and the resulting 3 prognostic strata were compared. The median overall survival for the 3 strata was 48, 248, and 1065 days, respectively (p<0.001). However, the original break-down (17 points versus 18-25 points versus >25 points) was not in accordance with the overlapping survival curves in some of the subgroups, leading us to propose slight adjustments. The modified model also performed satisfactorily in older patients (age ≥80 years; median survival 26, 192 and 489 days, respectively, p<0.001). The original Rades et al. survival score was a valid prognostic model in our Norwegian validation database. However, inclusion of patients with 18 points into the poor prognosis group is suggested as a modification to enhance the score's performance.

Characterization of a lipid droplet and endoplasmic reticulum stress related gene risk signature to evaluate the clinical and biological value in hepatocellular carcinoma

IntroductionLipid metabolism and endoplasmic reticulum (ER) stress play an important role in the progression and metastasis of hepatocellular carcinoma (HCC). We aimed to establish lipid droplet (LD)-associated and ER stress-related gene risk signature as prognostic indicators.Materials and methodsLiterature searches for LD-associated proteins was screened and validated in The Cancer Genome Atlas (TCGA) and International Cancer Genome Collaboratory (ICGC) databases. A total of 371 samples were enrolled from the TCGA RNA-seq dataset (training cohort) and 240 samples from IGGC RNA-seq dataset (validation cohort). A 10-gene risk signature was established by the last absolute shrinkage and selection operator (LASSO) regression analysis. The prognostic value of the risk signature was evaluated by Cox regression, Kaplan–Meier and ROC Curve analyses. Biological features associated with LD and ER stress-related factors were explored by functional analysis and in vitro experiment.ResultsBased on the medical literatures, 124 lipid droplet-associated proteins were retrieved, and three genes failed to establish a valid prognostic model. ER stress was considered as an important component by functional analysis. A 10-gene risk signature compared the clinicopathology characteristics, immunosuppressive events and a nomogram in HCC patients.ConclusionLD-associated and ER stress-related gene risk signatures highlighted poor prognosis for clinicopathological features, positively correlate with macrophages and T cell immunoglobulin and mucin-3 (TIM-3) expression in the tumor microenvironment, and might act as independent prognostic factors.

Development and Validation of a 6-Gene Hypoxia-Related Prognostic Signature For Cholangiocarcinoma.

Cholangiocarcinoma (CHOL) is highly malignant and has a poor prognosis. This study is committed to creating a new prognostic model based on hypoxia related genes. Here, we established a novel tumor hypoxia-related prognostic model consisting of 6 hypoxia-related genes by univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to predict CHOL prognosis and then the risk score for each patient was calculated. The results showed that the patients with high-risk scores had poor prognosis compared with those with low-risk scores, which was verified as an independent predictor by multivariate analysis. The hypoxia-related prognostic model was validated in both TCGA and GEO cohorts and exhibited excellent performance in predicting overall survival in CHOL. The PPI results suggested that hypoxia-related genes involved in the model may play a central role in regulating the hypoxic state. In addition, the presence of IDH1 mutations in the high-risk group was high, and GSEA results showed that some metabolic pathways were upregulated, but immune response processes were generally downregulated. These factors may be potential reasons for the high-risk group with worse prognosis. The analysis of different immune regulation-related processes in the high- and low-risk groups revealed that the expression of genes related to immune checkpoints would show differences between these two groups. We further verified the expression of the oncogene PPFIA4 in the model, and found that compared with normal samples, CHOL patients were generally highly expressed, and the patients with high-expression of PPFIA4 had a poor prognosis. In summary, the present study may provide a valid prognostic model for bile duct cancer to inform better clinical management of patients.

Hospital-induced Delirium among Medical Older Adults: Evaluating the Veracity of Prognostic Models

Several prognostic models have been developed and validated for delirium prediction among older adults. However, model development and validation studies need to be evaluated for risk of bias to establish the veracity of the prognostic models. This is a critical step before they can be implemented in clinical practice. Multiple systematic reviews have evaluated prognostic models of hospital-induced delirium. However, none of the existing systematic reviews evaluated the validity of models for non-surgical, medical hospitalized older adults. We conducted a scoping review to evaluate the validity of existing prognostic models of hospital-induced delirium in medical older adults. CINAHL, PsycINFO, PubMed, and Web of Science were searched for original studies. The database search yielded 4,312 records. Five studies were included in the qualitative synthesis. All the studies claimed to have developed valid prognostic models. However, the risk of bias assessment revealed that existing prognostic models of hospital-induced delirium in medical older adults are at a high risk of bias. Collectively, the statistical analysis was the greatest source of bias. Notably, while we have seen a proliferation of prognostic models for use in the surgical older adult population, efforts at developing prognostic models in the medical older adult population seem to have declined since the early 1990s. Newer methods of data collection, such as data mining of electronic health records, and statistical analysis, such as machine learning, have shown promise in accurate prediction of hospital-induced delirium while overcoming many challenges associated with manual data collection and traditional statistical analyses.

A novel immune-related gene signature predicting survival in sarcoma patients

Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified high-risk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma patients.

Assessment of acute heart failure prognosis: the promising role of prognostic models and biomarkers.

Numerous models and biomarkers have been proposed to estimate prognosis and improve decision-making in patients with acute heart failure (AHF). The present literature review provides a critical appraisal of externally validated prognostic models in AHF, combining clinical data and biomarkers. We perform a literature review of clinical studies, using the following terms: "acute heart failure," "acute decompensated heart failure," "prognostic models," "risk scores," "mortality," "death," "hospitalization," "admission," and "biomarkers." We searched MEDLINE and EMBASE databases from 1990 to 2020 for studies documenting prognostic models in AHF. External validation of each prognostic model to another AHF cohort, containing at least one biomarker, was prerequisites for study selection. Among 358 initially screened studies, 9 of them fulfilled all searching criteria. The majority of prognostic models were simplified, including a narrow number of variables (up to 10), with good performance regarding calibration and discrimination (c-statistics>0.65). Unfortunately, the derived and validated cohorts showed a wide variety in patients' characteristics (e.g., cause of AHF and therapy). Moreover, the prognostic models used various time-points and a plethora of combinations of variables determining different cut-off values. Although the application of valid prognostic models in AHF population is quite promising, a precise methodological approach should be set for the derivation and validation of prognostic models in AHF with unified characteristics to establish an effective performance in clinical practice.

Prognostic significance of eight immune-related genes on survival in patients with lung squamous cell carcinoma.

Aim: To build a valid prognostic model based on immune-related genes for lung squamous cell carcinoma (LUSC). Materials & methods: Differential expression of immune-related genesbetween LUSC and normal specimens from TCGA datasetand underlying molecular mechanisms were systematically analyzed. Constructing and validating the high-risk and low-risk groups for LUSC survival. Results: The immune-related gene-based prognosticindex (IRGPI) could predict the overall survival in patients with different clinicopathological characteristics. Functional enrichment analysis of differential expression of immune-related gene signature indicated distinctive molecular pathways between high-risk and low-risk groups. Conclusion: Analysis of IRGs in LUSC enable us to stratify patients into distinct risk groups, which may help to screen LUSC patients at risk and decision making on follow-up therapeutic intervention.

Development of a prognostic model of glioma based on immune‑related genes

Glioma is the most common type of primary brain cancer, and the prognosis of most patients with glioma, and particularly that of patients with glioblastoma, is poor. Tumor immunity serves an important role in the development of glioma. However, immunotherapy for glioma has not been completely successful, and thus, comprehensive examination of the immune-related genes (IRGs) of glioma is required. In the present study, differentially expressed genes (DEGs) and differentially expressed IRGs (DEIRGs) were identified using the edgeR package. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was used for functional enrichment analysis of DEIRGs. Survival-associated IRGs were selected via univariate Cox regression analysis. A The Cancer Genome Atlas prognostic model and GSE43378 validation model were established using lasso-penalized Cox regression analysis. Based on the median risk score value, patients were divided into high-risk and low-risk groups for clinical analysis. Receiver operating characteristic curve and nomogram analyses were used to assess the accuracy of the models. Reverse transcription-quantitative PCR was performed to measure the expression levels of relevant genes, such as cyclin-dependent kinase 4 (CDK4), interleukin 24 (IL24), NADPH oxidase 4 (NOX4), bone morphogenetic protein 2 (BMP2) and baculoviral IAP repeat containing 5 (BIRC5). A total of 3,238 DEGs, including 1,950 upregulated and 1,288 downregulated DEGs, and 97 DEIRGs, including 60 upregulated and 37 downregulated DEIRGs, were identified. ‘Neuroactive ligand-receptor interaction’ and ‘Cytokine-cytokine receptor interaction’ were the most significantly enriched pathways according to KEGG pathway analysis. A prognostic model and a validation prognostic model were created for glioma, including 15 survival-associated IRGs (FCER1G, NOX4, TRIM5, SOCS1, APOBEC3C, BIRC5, VIM, TNC, BMP2, CMTM3, IL24, JAG1, CALCRL, HNF4G and CDK4). Furthermore, multivariate Cox regression analysis results suggested that age, high WHO Grade by histopathology, wild type isocitrate dehydrogenase 1 and high risk score were independently associated with poor overall survival. The infiltration of B cells, CD8+ T cells, dendritic cells, macrophages and neutrophils was positively associated with the prognostic risk score. In the present study, several clinically significant survival-associated IRGs were identified, and a prognosis evaluation model of glioma was established.

Identification of immune-related genes as prognostic factors in bladder cancer

Bladder cancer is one of the most common cancers worldwide. The immune response and immune cell infiltration play crucial roles in tumour progression. Immunotherapy has delivered breakthrough achievements in the past decade in bladder cancer. Differentially expressed genes and immune-related genes (DEIRGs) were identified by using the edgeR package. Gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional enrichment analysis of DEIRGs. Survival-associated IRGs were identified by univariate Cox regression analysis. A prognostic model was established by univariate COX regression analysis, and verified by a validation prognostic model based on the GEO database. Patients were divided into high-risk and low-risk groups based on the median risk score value for immune cell infiltration and clinicopathological analyses. A regulatory network of survival-associated IRGs and potential transcription factors was constructed to investigate the potential regulatory mechanisms of survival-associated IRGs. Nomogram and ROC curve to verify the accuracy of the model. Quantitative real-time PCR was performed to validate the expression of relevant key genes in the prognostic model. A total of 259 differentially expressed IRGs were identified in the present study. KEGG pathway analysis of IRGs showed that the “cytokine-cytokine receptor interaction” pathway was the most significantly enriched pathway. Thirteen survival-associated IRGs were selected to establish a prognostic index for bladder cancer. In both TCGA prognostic model and GEO validation model, patients with high riskscore had worse prognosis compared to low riskscore group. A high infiltration level of macrophages was observed in high-risk patients. OGN, ELN, ANXA6, ILK and TGFB3 were identified as hub survival-associated IRGs in the network. EBF1, WWTR1, GATA6, MYH11, and MEF2C were involved in the transcriptional regulation of these survival-associated hub IRGs. The present study identified several survival-associated IRGs of clinical significance and established a prognostic index for bladder cancer outcome evaluation for the first time.

Evaluation of the United Kingdom-primary biliary cholangitis and global primary biliary cholangitis group prognostic models for primary biliary cholangitis patients treated with ursodeoxycholic acid in the U.S. population.

Background and AimThe United Kingdom‐primary biliary cholangitis (UK‐PBC) and global primary biliary cholangitis group (GLOBE) prognostic models have been recently developed to predict long‐term outcomes in primary biliary cholangitis (PBC). However, these predictive scores have not yet been well evaluated in the U.S. population.MethodsWe retrospectively reviewed newly diagnosed PBC patients at the Cleveland Clinic between November 1998 and February 2017. Adverse events were defined as liver transplantation, liver‐related mortality, and all‐cause mortality. Transplant‐free survival (TFS) was estimated using the Kaplan–Meier method. Predictive performances of all prognostic models were evaluated using the C‐statistic.ResultsWe identified 352 patients who used ursodeoxycholic acid therapy. Of them, 311 (88.4%) only had PBC, while 41 (11.6%) were diagnosed with PBC‐autoimmune hepatitis overlap. A total of 22 (6%), 47 (13%), and 55 (16%) patients had adverse events within 5, 10, and 15 years after diagnosis, respectively. In patients with PBC only, the C‐statistic in predicting 15‐year adverse events was 0.75 per GLOBE compared to 0.74 per UK‐PBC (P = 0.94), 0.73 per Rotterdam (P = 0.44), 0.66 per Barcelona (P = 0.004), 0.65 per Paris 1 (P = 0.005), 0.62 per Paris 2 (P < 0.0001), 0.60 per Toronto (P < 0.0001), and 0.60 per Mayo (P < 0.0001) scores. Median follow‐up was 9.2 years. Ten‐year TFS for patients who had optimal versus suboptimal treatment response was 92 versus 74% per Paris 1 (P < 0.0001), 95 versus 79% per Paris 2 (P = 0.0002), 93 versus 65% per Barcelona (P < 0.0001), and 96 versus 68% per Rotterdam (P < 0.0001) risk scores, respectively.ConclusionIn our cohort of PBC patients, the UK‐PBC and GLOBE scores were both accurate and reasonably valid prognostic models in the U.S. population.

Clinical Prediction Models for Patients With Nontraumatic Knee Pain in Primary Care: A Systematic Review and Internal Validation Study.

Study Design Systematic review and validation study. Background Many prognostic models of knee pain outcomes have been developed for use in primary care. Variability among published studies with regard to patient population, outcome measures, and relevant prognostic factors hampers the generalizability and implementation of these models. Objectives To summarize existing prognostic models in patients with knee pain in a primary care setting and to develop and internally validate new summary prognostic models. Methods After a sensitive search strategy, 2 reviewers independently selected prognostic models for patients with nontraumatic knee pain and assessed the methodological quality of the included studies. All predictors of the included studies were evaluated, summarized, and classified. The predictors assessed in multiple studies of sufficient quality are presented in this review. Using data from the Musculoskeletal System Study (BAS) cohort of patients with a new episode of knee pain, recruited consecutively by Dutch general medical practitioners (n = 372), we used predictors with a strong level of evidence to develop new prognostic models for each outcome measure and internally validated these models. Results Sixteen studies were eligible for inclusion. We considered 11 studies to be of sufficient quality. None of these studies validated their models. Five predictors with strong evidence were related to function and 6 to recovery, and were used to compose 2 prognostic models for patients with knee pain at 1 year. Running these new models in another data set showed explained variances (R2) of 0.36 (function) and 0.33 (recovery). The area under the curve of the recovery model was 0.79. After internal validation, the adjusted R2 values of the models were 0.30 (function) and 0.20 (recovery), and the area under the curve was 0.73. Conclusion We developed 2 valid prognostic models for function and recovery for patients with nontraumatic knee pain, based on predictors with strong evidence. A longer duration of complaints predicted poorer function but did not adequately predict chance of recovery. Level of Evidence Prognosis, levels 1a and 1b. J Orthop Sports Phys Ther 2017;47(8):518-529. Epub 16 Jun 2017. doi:10.2519/jospt.2017.7142.

Predictors of mortality in Parkinson's disease and the development of a prognostic model

BackgroundMost previous prognostic studies in Parkinson's disease have used unrepresentative cohorts with selection biases resulting in major heterogeneity in estimates of mortality. Prognosis is best studied by long-term follow-up of community-based incident cohorts. Prognostic models can predict a patient's risk and improve clinical trial design, but none has been published for use in Parkinson's disease. We aimed to describe mortality in this disorder, identify independent prognostic factors, and develop a valid prognostic model. MethodsAll incident cases of Parkinson's disease in Aberdeen, UK (Nov 1, 2002, to April 30, 2004, and April 1, 2006, to March 31, 2009) identified with multiple, community-based ascertainment strategies were followed prospectively with death notification from the National Health Service central register. Mortality rates and age-adjusted and sex-adjusted standardised mortality ratios (SMRs) were calculated with indirect standardisation to regional mortality data. Weibull regression was used to create a prognostic model with clinical predictors measured at diagnosis and validated internally. Findings198 incident cases of Parkinson's disease were identified. 91 of these 198 patients died after follow-up of up to 12 years (mean 6·1, SD 2·4). No losses to follow-up occurred. The mortality rate was 8·1 deaths per 100 person-years (95% CI 6·6–9·9) and was increased by 50% over those without Parkinson's disease (SMR 1·51, 95% CI 1·22–1·87). Independent baseline prognostic factors for increasing mortality were age (hazard ratio for 10 year increase 1·91, 1·43–2·54), male sex (1·84, 1·17–2·91), more bradykinesia (5 point increase 1·40, 1·14–1·72), more axial relative to limb features (1 unit increase in axial to limb ratio 1·81, 1·22–2·68), and comorbidity (1 unit increase in Charlson score 1·33, 1·14–1·56). A prognostic model incorporating these prognostic factors had good discrimination (Harrel's C-statistic=0·84) and calibration. An electronic predictive risk calculator was created. InterpretationThis estimate of mortality is greater than many previous estimates, probably because we used a more representative cohort with a higher proportion of frail and elderly people than in most previous studies. Identification of prognostic factors and their combination in a prognostic model will allow individualised risk prediction, and facilitate stratified medicine and improved clinical trial design. Further work will include external validation in an international cohort. FundingChief Scientist Office of the Scottish Government, Parkinson's UK.

A New Liver Allocation Score (LivAS) to Facilitate the Weighing of Urgency Against Utility in Liver Transplantation.

Introduction: A valid prognostic model with donor and recipient data for the prediction of 3-month mortality after transplantation would be useful to improve donor organ allocation by preoperative weighing of the predicted outcome after transplantation against the urgency of transplantation as predicted by the MELD-score. Methods: This is a multicentre analysis with data from two German transplant centres within the Eurotransplant community. Included were all consecutive liver transplants performed inadult recipients (minimum age 18 years) in Hannover from 01.09.2001-31.12.2010(n=770) and in Kiel from 01.01.2006-08.02.2013 (n=234). Excluded were all combined transplants and living-related transplants. The cohort from Hannover was randomised retrospectively into an internal training group for model development with multivariate binary logit link regression and an internal validation group. The cohort from Kiel was used as an external validation group. Results: Recipient score component: Recipient y = -1.60 + (0.023 x age at transplant in years) + (-0.129 x haemoglobin in g/dl) + (-0.001 x thrombocytes in tsd/μl) + (-0.003 x factor II in %) + (0.003 x creatinine in μmol/l) + (0.474 x artificial ventilation, if yes=1, if no=0) + (0.195 x preTX portal vein thrombosis, if yes=1, if no=0) + (0.894 * risk indication re-TX for primary non-function, if yes=1, if no=0). Donor score component: Donor y = -2.281 + (0.071 x donor liver with macrovesicular steatosis in %, if no biopsy = 0) + (0.544 x ICU stay >11.5 days) + (1.266 x donor sodium >160mmol/l). LivAS = 1,342 + (1,162 x Recipient y) + (0,616 x Donor y). The area under the receiver operating curve for the prediction of 3-month mortality after transplantation was >0.700 in the in the training cohort and the internal and external validation cohorts. The influence of the LivAs above versus below cut-off was significant in all three cohorts (p<0.001). The Goodness-of-Fit test results demonstrated good model fit for all cohorts. Conclusion: The proposed LivAS is a validated prognostic model that could improve organ allocation rules significantly.

Validation of the Revised International Prognostic Scoring System (R-IPSS) for Patients with Myelodysplastic Syndromes: Therapeutic Implications.

AbstractAbstract 2816▪▪This icon denotes a clinically relevant abstract Background:The International Prognostic Scoring System (IPSS) was recently revised under the auspices of MDS foundation as a collaborative international effort. The proposed R-IPSS is suggested to refine the prognostic value of the IPSS. Instead of the 4 original IPSS categories, 5 categories are proposed by R-IPSS. To validate this prognostic model and examine its utility for therapy decisions, we tested the new risk model in a large external single institution patient cohort. Methods:Data were collected retrospectively from the Moffitt Cancer Center (MCC) MDS database and chart review. The primary objective was to validate the new risk model. The R-IPSS score was calculated as reported. Patients were divided into 5 prognostic categories (very low, low, intermediate, high and very high risk). The Kaplan–Meier method was used to estimate median overall survival. Log rank test was used to compare Kaplan–Meier survival estimates between the groups. Results:The MCC MDS database captured 1157 patients. Complete data was available for 1029 patients to calculate the R-IPSS score. Median age was 68 years, and the most common WHO subtype was RCMD (29%). Two thirds of patients were low/int-1 IPSS risk, and 44% were int-2 or high risk MDAS. (Table-1). Among those, 729 patients (77%) were RBC transfusion dependent (TD), and 264 (26%) had serum ferritin >1000 ng/l. Six hundred eighteen patients (60%) received hypomethylating agent (HMA). The median duration of follow up was 68 months (mo). Median OS according to IPSS risk score was 90 mo (95%CI 75–105), 44 mo (95%CI 39–46), 18 mo (95%CI 15–21), and 14 mo (95%CI 11–17), for low, int-1, int-2, and high risk categories, respectively (p < 0.005). According to MD Anderson risk Score, the median OS was 108 mo (95%CI 91–126), 55 mo (95%CI 50–60), 25 mo (95%CI 22–28), and 14 mo (95%CI 12–16), for low, int-1, int-2, and high risk respectively (p < 0.005).Using the R-IPSS, 106 (10%), 311 (30%), 247 (24%), 201 (20%), and 164 (16%) were classified as very low, low, int, high, and very high risk. The median OS was 82 mo (95% CI 64–100), 57 mo (95% CI 46–68), 41 mo (95% CI 33–49), 24 mo (95% CI 20–28), and 14 mo (95% CI12–16) for each of the corresponding R-IPSS groups (p <0.005). Table-2 summarizes reclassification of each IPSS risk group by R-IPSS and expected OS accordingly.Among those patients who received HMA, the median OS from time of diagnosis was 76 mo, 55 mo, 42 mo, 25 mo, and 16 mo for very low, low, int, high, and very high risk respectively (p < 0.005). A survival benefit for HMA therapy was only statistically significant in patients with very high risk R-IPSS, with a corresponding median OS of 16 mo with HMA versus 7 mo with no HMA (p< 0.005). OS in patients with very high or high R-IPSS who underwent Allogeneic Stem cell transplant (ASCT) was improved compared to corresponding patients who received non-ASCT management. Patients who had very low, low, and int risk R-IPSS had no apparent OS benefit with ASCT. (Table-3). Conclusion:Our data validates the prognostic value of the proposed R-IPSS, but refines prognostic discrimination only for intermediate risk group of IPSS. Both the R-IPSS and IPSS were valid prognostic models for patients treated with HMA. The benefit of ASCT was restricted to patients with high and very high R-IPSS groups. The utility of the R-IPSS as a tool for therapeutic decisions should be further examined before wide adaptation.Table-1N=1029Age> 60 years798 (78%)GenderMale671 (65%)RaceWhite950 (92%)WHORA114 (11%)RARS93 (9%)RCMD303 (29%)Del5q32 (3%)RAEB-1218 (21%)RAEB-2171 (17%)MDS-U26 (3%)CMML23 (2%)MDS/MPN-U45 (4%)AML4 (<1%)IPSSLow179 (17%)Int-1513 (50%)Int-2254 (25%)High83 (8%)MD Anderson ScoreLow181 (18%)Int-1411 (40%)Int-2221 (22%)High213 (20%)Table-2IPSSR-IPSSMedian OS (mo)PLowVery low47 (26%)860.3n=179Low112 (63%)99Int20 (11%)70Int-1Very low59 (12%)800.003N=511Low192 (38%)49Int178 (35%)34High74 (14%)29Very High8 (1%)29Int-2Low7 (3%)370.005N=254Int46 (18%)30High112 (44%)18Very High89 (35%)14HighInt2 (2%)420.06N=83High14 (17%)14Very High67 (81%)14Table-3R-IPSSAllo SCTMedian OS (mo)PVery lowYes (n=4)690.8n=106No (n=102)86LowYes (n=14)420.8N=311No (n=297)57IntermediateYes(n=36)460.4n=246No (n=210)40HighYes (n=44)420.004n=200No (n=156)21Very HighYes (n=33)31<0.005n=164No (n=131)12 Disclosures:No relevant conflicts of interest to declare.

Prognostic factors in patients treated with taxane-based chemotherapy for recurrent or metastatic endometrial cancer: Proposal for a new prognostic model

Objective. Taxane-based chemotherapy has been recently introduced as an effective therapeutic option in recurrent or metastatic endometrial carcinoma (RMEC). The aim of the study was to determine the prognostic factors in RMEC after taxane-based chemotherapy. Methods. One hundred ten patients who received paclitaxel-containing regimen for RMEC were retrospectively evaluated. Potential prognostic factors for overall survival were identified with the Kaplan–Meier method in univariate and the Cox regression model in multivariate analysis. Results. Performance status (PS) and relapse within the field of previous external radiation were independent prognostic factors for overall survival ( p = 0.007 and p = 0.026 respectively). Non-endometriod histology was associated with a shorter median survival compared to endometriod adenocarcinoma (14.46 vs. 17.57 months, p = 0.093), but histology was not an independent prognostic factor (HR = 1.43, 95% CI: 0.82–2.48, p = 0.21). Stratification according to PS and relapse within the irradiation field identified three risk groups with distinctly different prognosis (median survival 27.36, 16.71, and 11.33 months for the group of favorable, intermediate, and unfavorable prognosis respectively, p < 0.001). Within the favorable prognosis group, 34% of patients had a probability of 5-year survival. Conclusion. PS at diagnosis and relapse within the irradiated area may constitute a valid prognostic model in RMEC patients who receive taxane-based chemotherapy and are able to identify long-term survivors.

Evaluation of Model for End-Stage Liver Disease for Prediction of Mortality in Decompensated Chronic Hepatitis B

We aimed to study the predictive ability of model for end-stage liver disease (MELD) for short-term mortality in chronic hepatitis B. All patients admitted from 1996 to 2003 because of chronic hepatitis B and its related complications were identified by electronic search of the hospital database. MELD and Child-Turcotte-Pugh (CTP) scores on initial admissions were calculated. Cox proportional hazard model was used to determine the factors associated with mortality. The area under receiver operator characteristics curve (AUC) was used to determine the predictive abilities of the two models for 3-month and 1-yr mortalities. A total of 2,073 patients was admitted because of liver-related problems and 506 patients had chronic hepatitis B-related complications. Two hundred fifty-six (51%) patients died and 16 (3%) patients underwent liver transplantation. In multivariate analysis, MELD and CTP scores were independent predictors of 3-month and 1-yr mortality. Other independent predictors of mortality included older age, hepatocellular carcinoma (HCC), lamivudine treatment, and lower serum sodium. At both 3 months and 1 yr, the AUC of the MELD score (0.65 and 0.63, respectively) was significantly lower than that of the CTP score (0.75 and 0.77, respectively) (p < 0.0001). The differences remained significant when only liver cirrhosis patients without HCC at presentation were analyzed, but the AUC of the two scores became comparable when patients on lamivudine were excluded. The MELD score is a valid prognostic model in decompensated chronic hepatitis B. Lamivudine treatment may affect the performance of MELD score. Other variables including those in CTP score may improve its predictive ability.