Comparison Of Prognostic Models Research Articles

Validation and Comparison of Prognostic Models in Renal Carcinoma in a Tertiary Hospital.

Renal cell carcinoma (RCC) is the third most frequent urological neoplasia. Proper risk stratification is essential for adequate management. Various calculators are available. This project aims to evaluate the accuracy of the calculators applied to our patients. We performed a retrospective study of the nephrectomies due to RCC performed from January 2008 to December 2013. We applied the most widely used predictive models (University of California, Los Angeles Integrated Staging System (UISS), Stage, Size, Grade and Necrosis (SSIGN), Memorial Sloan Kettering Cancer Center (MSKCC) and International Metastatic RCC Database Consortium (IMDC)) to stratify patients in different risk groups. We evaluated progression-free survival (PFS) or death caused by RCC (cancer-specific survival (CSS)) or other causes (overall survival (OS)). We analysed 238 patients. The 5-year OS, CSS and PFS were 76%, 85% and 83%, whereas the 10-year OS, CSS and PFS were 47%, 75% and 77%, respectively. The 5-year survival analysis by risk groups according to the prognostic models showed that the PFS was 0% and 20.4% in high- and intermediate-risk metastatic RCC (mRCC). Moreover, the PFS was 90%, 95.2% and 98.9% in localised high-, intermediate- and low-risk RCC according to the UISS (area under the receiver operating characteristics curve (AUC): 0.93). The SSIGN model showed a CSS of 99% for the group with the lowest score and 5.3% for the group with the worst prognosis (AUC: 0.91). The OS of mRCC showed medians of 13.25 and 87 months according to MSKCC (AUC: 0.75) and 16, 23 and 85 months according to IMDC (AUC: 0.71) (high risk, intermediate and low). The validation of the predictive models carried out with our patients showed consistency in many of the results. Risk stratification should be implemented.

The effect of pH on stability and thermal performance of graphene oxide and copper oxide hybrid nanofluids for heat transfer applications: Application of novel machine learning technique

This paper investigates the effects of pH on stability and thermal properties of copper oxide (CuO), graphene oxide (GO), and their hybrid nanofluid (HNF) at different mixing ratios. Initially, sol-gel and Hummer’s method was employed for the synthesis of CuO and GOnanoparticles (NPs), and they are characterized with various techniques. The effects of two different surfactants were analyzed on nanofluid’s (NF’s) stability at different pH values. The properties like thermal conductivity (TC) and viscosity (VST) of NFs were measured at different volume concentration (0.1 vol% to 1.0 vol%) and temperature range of 30–60 °C, respectively. The TC and VST of GO/CuO (50:50) HNF are higher than that of GO/CuO (20:80). The figure of merit (FOM) is determined for the studied HNFs. The correlations were presented to calculate the TC as well as VST of HNFs. Two modern novel machine learning-based ensemble approaches were employed for predictive model development for TC and VST of considered HNFs. The comparison of prognostic models with Taylor’s diagram revealed that Bayesian optimized support vector machine (BoA-SVM) was superior to Bayesian optimized boosted regression trees (BoA-BRT) for both TC and VST models.

The Prediction of Survival after Surgical Management of Bone Metastases of the Extremities-A Comparison of Prognostic Models.

Individualized survival prognostic models for symptomatic patients with appendicular metastatic bone disease are key to guiding clinical decision-making for the orthopedic surgeon. Several prognostic models have been developed in recent years; however, most orthopedic surgeons have not incorporated these models into routine practice. This is possibly due to uncertainty concerning their accuracy and the lack of comparison publications and recommendations. Our aim was to conduct a review and quality assessment of these models. A computerized literature search in MEDLINE, EMBASE and PubMed up to February 2022 was done, using keywords: “Bone metastasis”, “survival”, “extremity” and “prognosis”. We evaluated each model’s performance, assessing the estimated discriminative power and calibration accuracy for the analyzed patients. We included 11 studies out of the 1779 citations initially retrieved. The 11 studies included seven different models for estimating survival. Among externally validated survival prediction scores, PATHFx 3.0, 2013-SPRING and potentially Optimodel were found to be the best models in terms of performance. Currently, it is still a challenge to recommend any of the models as the standard for predicting survival for these patients. However, some models show better performance status and other quality characteristics. We recommend future, large, multicenter, prospective studies to compare between PATHfx 3.0, SPRING 2013 and OptiModel using the same external validation dataset.

Head-to-head comparison of prognostic models of spontaneous intracerebral hemorrhage: tools for personalized care and clinical trial in ICH

ABSTRACT To systematically compare 27 ICH models with regard to mortality and functional outcome at 1-month, 3-month and 1-year after ICH. The validation cohort was derived from the Beijing Registration of Intracerebral Hemorrhage. Poor functional outcome was defined as modified Rankin Scale score (mRS) ≥3 at 1-month, 3-month and 1-year after ICH, respectively. The area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration. A total number of 1575 patients were included. The mean age was 57.2 ± 14.3 and 67.2% were male. The median NIHSS score on admission was 11 (IQR: 3–21). For predicting mortality at 3-month after ICH, AUROC of 27 ICH models ranged from 0.604 to 0.856. In pairwise comparison, the ICH-FOS (0.856, 95%CI = 0.835–0.878, P < 0.001) showed statistically better discrimination than other models for mortality at 3-month after ICH (all P < 0.05). For predicting poor functional outcome (mRS≥3) at 3-month after ICH, AUROC of 27 ICH models ranged from 0.602 to 0.880. In pairwise comparison with other prediction models, the ICH-FOS was superior in predicting poor functional outcome at 3-month after ICH (all P < 0.001). The ICH-FOS showed the largest Cox and Snell R-square. Similar results were verified for mortality and poor functional outcome at 1-month and 1-year after ICH. Several risk models are externally validated to be effective for risk stratification and outcome prediction after ICH, especially the ICH-FOS, which would be useful tools for personalized care and clinical trial in ICH.

Comparison of prognostic models in advanced hepatocellular carcinoma patients undergoing Sorafenib: A multicenter study.

BackgroundSorafenib is the gold standard therapy for the advanced hepatocellular carcinoma (HCC). No scoring/staging is universally accepted to predict the survival of these patients. AimsTo evaluate the accuracy of the available prognostic models for HCC to predict the survival of advanced HCC patients treated with Sorafenib included in the Italian Liver Cancer (ITA.LI.CA.) multicenter cohort. MethodsThe performance of several prognostic scores was assessed through a Cox regression-model evaluating the C-index and the Akaike Information Criterion (AIC). ResultsData of 1129 patients were analyzed. The mean age of patients was 61.6 years, and 80.8% were male. During a median follow-up period of 13 months, 789 patients died. The median period of Sorafenib administration was 4 months. All the prognostic scores were able to predict the overall survival (p<0.001) at univariate analysis, except the Albumin-Bilirubin score. The Italian Liver Cancer score (CLIP) yielded the highest accuracy (C-index 0.604, AIC 9898), followed by the ITA.LI.CA. prognostic score (C-index 0.599, AIC 9915). ConclusionsThe CLIP score had the highest accuracy in predicting the overall survival of HCC patients treated with Sorafenib, although its performance remained poor. Further studies are needed to refine the current ability to predict the outcome of HCC patients undergoing Sorafenib.

THU478 - Comparison of prognostic models in predicting survival of patients with advanced hepatocellular carcinoma undergoing sorafenib treatment: a multicenter cohort study

THU478 - Comparison of prognostic models in predicting survival of patients with advanced hepatocellular carcinoma undergoing sorafenib treatment: a multicenter cohort study

Comparison of prognostic models in predicting survival of patients with advanced hepatocellular carcinoma undergoing sorafenib treatment: a multicenter cohort study

Background Currently Sorafenib treatment is the gold standard therapy for patients with advanced hepatocellular carcinoma (HCC). To date, no widely validated scores are available to predict the overall survival of these patients.

Prediction of survival after surgical management of femoral metastatic bone disease – A comparison of prognostic models

BackgroundOperative fixation for femoral metastatic bone disease is based on the principles of reducing pain and restoring function. Recent literature has proposed a number of prognostic models for appendicular metastatic bone disease. The aim of this study was to compare the accuracy of proposed soring systems in the setting of femoral metastatic bone disease in order to provide surgeons with information to determine the most appropriate scoring system in this setting. MethodsA retrospective cohort analysis of patients who underwent surgical management of femoral metastatic bone disease at a single institution were included. A pre-operative predicted survival for all 114 patients was retrospectively calculated utilising the revised Katagiri model, PathFx model, SSG score, Janssen nomogram, OPTModel and SPRING 13 nomogram. Univariate and multivariate Cox regression proportional hazard models were constructed to assess the role of prognostic variables in the patient group. Area under the receiver characteristics and Brier scores were calculated for each prognostic model from comparison of predicted survival and actual survival of patients to quantify the accuracy of each model. ResultsFor the femoral metastatic bone disease patients treated with surgical fixation, multivariate analysis demonstrated a number of pre-operative factors associated with survival in femoral metastatic bone disease, consistent with established literature. The OPTIModel demonstrated the highest accuracy at predicting 12-month (Area Under the Curve [AUC] = 0.79) and 24-month (AUC = 0.77) survival after surgical management. PathFx model was the most accurate at predicting 3-month survival (AUC = 0.70) and 6-month (AUC = 0.70) survival. The PathFx model was successfully externally validated in the femoral patient dataset for all time periods. ConclusionsAmong six prognostic models assessed in the setting of femoral metastatic bone disease, the present study observed the most accurate model for 3-month, 6-month, 12-month and 24-month survival. The results of this study may be utilised by the treating surgical team to determine the most accurate model for the required time period and therefore improve decision-making in the care of patients with femoral metastatic bone disease.

Comparison of Prognostic Models in Acute Liver Failure: Decision is to be Dynamic.

ABSTRACTBackground and aimsAcute liver failure (ALF) is a rare disease entity with a high mortality. Management is dependent on accurate prognostication.Materials and methodsOne hundred consecutive patients presenting with ALF were prospectively evaluated. The King's college criteria (KCC), ALF early dynamic model (ALFED), sequential organ failure assessment (SOFA) score, and acute physiology and health evaluation II (APACHE II) scores were compared to predict mortality.ResultsThere were significant differences in means of all the scores between survivors and nonsurvivors. The SOFA 48 hours had the highest area under receiver operating characteristic curve (AUC) (0.857) closely followed by the ALFED score (0.844). The optimal cutoff for the SOFA score at 48 hours to predict subsequent survival outcome is ≥10 and for the ALFED score is ≥5. Sequential organ failure assessment 48 hours had a good sensitivity of 87%, and the ALFED score showed a good specificity of 84%. The decision curve analysis showed that between a threshold probability of 0.13 and 0.6, use of the SOFA score provided the maximum net benefit and at threshold probabilities of >0.6, the use of ALFED score provided the maximum clinical benefit.ConclusionDynamic scoring results in better prognostication in ALF. The SOFA 48 hours and ALFED score have good prognostication value in nonacetaminophen-induced liver failure.How to cite this articleSaluja V, Sharma A, Pasupuleti SSR, Mitra LG, Kumar G, Agarwal PM. Comparison of Prognostic Models in Acute Liver Failure: Decision is to be Dynamic. Indian J Crit Care Med 2019;23(12):574–581.

Comparison of prognostic models for selecting synchronous mRCC patients who would benefit from upfront cytoreductive nephrectomy followedtargeted therapy.

e16568Background: The IMDC model was developed as a prognostic model for mRCC patients on targeted therapy, and earlier report proposed that patients no more than 3 risk factors would be benefited ...

Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

BackgroundExternal validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD.MethodsWe relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores.ResultsDepending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile–3rd quartile = 0.655–0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUCADO – AUCBODE = 0.015 [95% confidence interval (CI) = −0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated – AUCBODE = 0.008 [95% CI = −0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency.ConclusionsOur analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research.

Comparison of prognostic models for hepatocellular carcinoma (HCC) in patients treated with the sorafenib: Results from a Canadian multi-center HCC database.

e15653 Background: Several staging systems and models (TNM, BCLC, Okuda, CLIP and ALBI) have been developed to estimate the prognosis of patients with HCC. Most of these were developed prior to the prevalent use of sorafenib. The purpose of this study was to compare the prognostic and discriminatory power of these models in predicting survival for HCC patients treated with sorafenib. Methods: Patients who received sorafenib for the treatment of HCC between January 1, 2008 and June 30, 2015 in the provinces of British Columbia and Alberta, as well as Princess Margaret Cancer Centre and Sunnybrook Odette Cancer Centre in Toronto, Ontario were included. Survival outcomes for each model were assessed with Kaplan-Meier (KM) curves and compared with the log-rank test. Time dependent area under the curve (t-AUC) was used to test the discriminatory power of each model (higher t-AUC = more discriminatory power). Akaike information criterion (AIC), a measure of goodness-of fit of models while penalizing overly complex models, was used to compare the models (lower AIC = better model). Results: A total of 681 patients were included in this analysis. Median age was 64 years (range 8-91). Majority were males (80%), had a Child-Pugh score A (86%), ECOG performance status 0 (30%) and 1 (60%). 37% of patients were of East Asian ethnicity. Most common etiology for liver disease was hepatitis B (33%) and C (29%). At start of sorafenib, most patients were BCLC stage C (92%) and TNM stage IV (61%). The median overall survival for the entire cohort was 9.2 months (95% CI 8-10.4). See table below for t-AUC and AIC results. Conclusions: According to ourlarge multi-center study, CLIP appears to be the most informative in predicting survival in HCC patients treated with sorafenib. Prospective studies are needed to determine its role in patient selection for clinical trials and in guiding treatment decisions. The TNM and BCLC staging systems were the least useful in predicting survival in this population. [Table: see text]

298P A study of comparison of prognostic models in peripheral T cell lymphoma in a tertiary care centre in South India

298P A study of comparison of prognostic models in peripheral T cell lymphoma in a tertiary care centre in South India

Outcomes after Resection of Intrahepatic Cholangiocarcinoma: External Validation and Comparison of Prognostic Models

Published prognostic models for overall survival after liver resection for intrahepatic cholangiocarcinoma require external validation before use in clinical practice. From January 1993 to May 2013, consecutive patients who underwent resection of intrahepatic cholangiocarcinoma were identified from a prospective database. The Wang nomogram was derived in an Asian cohort (n = 367) and included clinicopathologic variables and preoperative CEA and cancer antigen 19-9 levels. The Hyder nomogram was derived in an Eastern and Western multicenter cohort (n = 514) using clinicopathologic variables only. The AJCC Cancer Staging System (7th ed) and the preoperative Fudan risk score were also evaluated. Prognostic performance was assessed in terms of discrimination, calibration, and stratification. One hundred and eighty-eight patients were included, with a median follow-up of 41 months. Median overall survival was 48.7 months and estimated 3-year and 5-year overall survival rates were 59% and 45%, respectively. Overall survival prediction accuracy, according to concordance-index calculation, was 0.72 with the Wang nomogram, 0.66 with the Hyder nomogram, 0.63 with the AJCC system, and 0.55 using the Fudan score. Both nomograms provided effective patient stratification in distinct survival groups. Both the Wang and Hyder nomograms provided accurate patient prognosis estimation after liver resection for intrahepatic cholangiocarcinoma and can be useful for decision making about adjuvant therapy. The Wang nomogram appears to be more appropriate in patients undergoing formal portal lymphadenectomy and requires preoperative CEA and cancer antigen 19-9 levels for optimal performance.

Gastroenteric neuroendocrine neoplasms classification: Comparison of prognostic models

Gastroenteric neuroendocrine neoplasms (GE-NENs) display highly variable clinical behavior. In an attempt to assess a better prognostic description, in 2010, the World Health Organization (WHO) updated its previous classification, and the European Neuroendocrine Tumors Society (ENETS) proposed a new grading and TNM-based staging system. In the current study, the authors evaluated the prognostic significance of these models and compared their efficacy in describing patients' long-term survival to assess the best prognostic model currently available for clinicians. The study cohort was composed of 145 patients with extrapancreatic GE-NEN who were observed from 1986 to 2008 at a single center and were classified according to the WHO and ENETS classifications. Survival evaluations were performed using Kaplan-Meyer analyses on 131 patients. Only deaths from neoplasia were considered. A P value < .05 was considered significant. Prognostic efficacy was assessed by determining the Harrell concordance index (c-index). Both the 2010 WHO and the ENETS classification were able to efficiently divide patients into classes with different prognoses. According to the model comparison, the ENETS TNM-based staging system appeared to be the strongest. All combined models were effective prognostic predictors, but the model that included the 2010 WHO classification plus ENETS staging had a higher c-index. Both the 2010 WHO classification and the ENETS staging system are valid instruments for GE-NENs prognostic assessment, with TNM-based stage appearing to be the best available choice for clinicians, both alone and in association with other classifications.

A phase III randomized intergroup trial (S0016) comparing CHOP plus rituximab with CHOP plus iodine-131-tositumomab for front-line treatment of follicular lymphoma: Results of subset analyses and a comparison of prognostic models.

8001 Background: Advanced follicular lymphomas (FL) are considered incurable with chemotherapy and there is no consensus on the best treatment. Outcomes are variable, but can be partially predicted by defined prognostic factors. SWOG and CALGB compared the safety and efficacy of 2 immunochemotherapy regimens in a Phase III trial enrolling 554 patients between 3/1/2001 and 9/15/2008. Methods: Patients were eligible if they had bulky stage II, III or IV FL and had not received prior therapy. Patients randomized to CHOP-R received 6 cycles of CHOP every 21 days + 6 doses of rituximab. Patients randomized to CHOP-RIT received 6 cycles of CHOP, followed by consolidative radioimmunotherapy with tositumomab/iodine I-131 tositumomab. A Cox proportional hazards multi-variable regression analysis assessed the prognostic impact of age, stage, LDH, LN size and number, performance status, hemoglobin, β2 microglobulin, BM involvement, and B symptoms. The prognostic value of 3 multi-variable models were compared. Results: Outcomes were outstanding with either CHOP-R or CHOP-RIT (2 yr PFS: 76% vs 80% [ p =0.11]; 2 yr OS: 97% vs 93% [p =0.08], respectively). Subset analyses so far have not identified any subgroups clearly benefitting to a greater degree from CHOP-R or CHOP-RIT in terms of both PFS and OS. Cox multivariable regression analysis identified serum-β2M, LDH level, and FLIPI index as the strongest prognostic factors associated with worse PFS and OS. Conclusions: Both regimens produced outstanding PFS and OS, and no statistically significant differences between them were observed. FLIPI, FLIPI2, and LDH + β2M models were all strong predictors of patient outcomes. A combination of LDH + β2M was as good as the FLIPI index, and was simpler to apply. (Supported in part by NCI grants CA32102 and CA38926 from the NCI and GlaxoSmithKline.) [Table: see text]

Comparison of the sequential organ failure assessment score with the King's College Hospital criteria and the model for end-stage liver disease score for the prognosis of acetaminophen-induced acute liver failure

Acetaminophen-induced acute liver failure (ALF) is a complex multiorgan illness. An assessment of the prognosis is essential for the accurate identification of patients for whom survival without liver transplantation (LT) is unlikely. The aims of this study were the comparison of prognostic models [King's College Hospital (KCH), Model for End-Stage Liver Disease, Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation II (APACHE II)] and the identification of independent prognostic indicators of outcome. We evaluated consecutive patients with severe acetaminophen-induced ALF who were admitted to the intensive care unit. At admission, demographic, clinical, and laboratory parameters were recorded. The discriminative ability of each prognostic score at the baseline was evaluated with the area under the receiver operating characteristic curve (AUC). In addition, using a multiple logistic regression, we assessed independent factors associated with outcome. In all, 125 consecutive patients with acetaminophen-induced ALF were evaluated: 67 patients (54%) survived with conservative medical management (group 1), and 58 patients (46%) either died without LT (28%) or underwent LT (18%; group 2). Group 1 patients had significantly lower median APACHE II (10 versus 14) and SOFA scores (9 versus 12) than group 2 patients (P < 0.001). The independent indicators associated with death or LT were a longer prothrombin time (P = 0.007), the inspiratory oxygen concentration (P = 0.005), and the lactate level at 12 hours (P < 0.001). The KCH criteria had the highest specificity (83%) but the lowest sensitivity (47%), and the SOFA score had the best discriminative ability (AUC = 0.79). In conclusion, for patients with acetaminophen-induced ALF, the SOFA score performed better than the other prognostic scores, and this reflected the presence of multiorgan dysfunction. A further evaluation of SOFA with the KCH criteria is warranted.

A prognostic model in patients who receive chemotherapy for metastatic or recurrent gastric cancer: validation and comparison with previous models

To make up for the limitations of previous prognostic models, we developed and validated a model in patients with metastatic or recurrent gastric adenocarcinoma (AGC), and to compare with previous models. A total of 2,805 patients received chemotherapy for AGC in Asan Medical Center between January 2000 and December 2008 and were randomly split into training and validation sets of 1,870 and 935 patients, respectively. A prognostic model was developed from the training set. The median follow-up duration was 26.5months (range, 10.8-116.3), during which time 2,495 patients (88.9%) died. Eight factors associated with poor prognosis were identified by multivariate analysis: ECOG performance status ≥2 (2 points), no gastrectomy, peritoneal metastasis, bone metastasis (2 points), lung metastasis, alkaline phosphatase>120IU/l, albumin<3.3g/dL, and total bilirubin>1.2mg/dL. A prognostic model was developed by dividing patients into good (0-1 points), moderate (2-3), and poor (≥4) risk groups. The overall survival (OS) curves for three risk groups differed significantly for both the training and the validation sets (P<0.001 each). In the training set, the median OS for the three risk groups was 14.0, 9.4, and 5.1months, respectively. Application of three previous prognostic models to our validation set showed that the four models, including ours, had similar ability to predict survival outcomes (c-statistics, 0.5520-0.5836). Validation and comparison of prognostic models indicated that our model was as effective as the previous models to stratify the patients with AGC.