Simple Prognostic Model Research Articles

Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group

The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.

A1110 - Validation of a simplified prognostic model for survival in patients with extrinsic malignant ureteral obstruction treated with tandem ureteral stents

A1110 - Validation of a simplified prognostic model for survival in patients with extrinsic malignant ureteral obstruction treated with tandem ureteral stents

Development of a prognostic model for 1-year survival after fragile hip fracture in Chinese

BackgroundNo prognostic model for the survival of fragile hip fracture has been developed for Asians. The goal of this study was to develop a simple and practical prognostic model to predict survival within 1 year after fragile hip fracture in Asians.MethodsA single-center retrospective cohort study was designed. Under a multivariable Cox proportional hazards regression model, we used the preoperative characteristics of patients to predict survival within 1 year after hip fracture. We built a full model and then used the least absolute shrinkage and selection operator (LASSO) method to further shrink the model coefficients and achieved variable screening. Finally, we obtained a LASSO model. The model performance was evaluated with Nagelkerke’s R2 and the concordance (c) statistic. We assessed the internal validity with a bootstrapping procedure of 1 000 repetitions.ResultsA total of 735 eligible patients were admitted to our department for hip fracture from January 2015 to December 2020, but 11 (1.5%) patients were lost to follow-up. Among the remaining patients, 68 (9.3%) died within 1 year after hip fracture. We identified 12 candidate predictors from the preoperative characteristics of the patients. The last model contained nine predictors: surgery, age, albumin, sex, serum creatinine, malignancy, hypertension, ability to live independently, and cardiovascular and cerebrovascular diseases. Among them, surgery, age, and albumin are effective predictors of survival. The discrimination c statistic of the model is 0.814 (95% confidence interval 0.762–0.865); the corrected value through internal validation is 0.795.ConclusionsThis prognostic model can accurately predict a 1-year survival rate for patients with fragile hip fractures. This information can help clinicians develop a reasonable and personalized treatment plan.

Proposal and Validation of a Novel Scoring System for Hepatocellular Carcinomas Beyond Curability Borders.

Optimal scoring system for clinical prognostic factors in patients with unresectable hepatocellular carcinoma (HCC) is currently uncertain. We aimed to develop and externally validate an easy to use tool, particularly for this population, and named it the “unresectable hepatocellular carcinoma prognostic index” (UHPI). We evaluated the data of patients with treatment‐naive unresectable HCC who were diagnosed in the training center from 2010 to 2019 (n = 209). A simple prognostic model was developed by assigning points for each covariate in proportion to the beta coefficients in the Cox multivariable model. Predictive performance and distinction ability of the UHPI were further evaluated in an independent European validation cohort (n = 147) and compared with 11 other available models. A simple scoring system was derived, assigning 0.5/1/2 scores for six independent covariates including, the Child‐Pugh score, Eastern Cooperative Oncology Group performance status, maximum tumor size, vascular invasion or extrahepatic metastasis, lymph node involvement, and alpha‐fetoprotein. The UHPI score, ranging from 0 to 6, showed superior performance in prognosis prediction and outperformed 11 other staging or prognostic models, giving the highest homogeneity (c‐index, 6‐month and 1‐year area under the receiver operator characteristic curves), lowest Akaike information criterion, and –2 log‐likelihood ratio values. The UHPI score allocated well the risk of patients with unresectable HCC for mortality within the first year, using two cut‐off values (low‐risk, <0.5; intermediate‐risk, 0.5‐2; high‐risk, >2). Conclusion: The UHPI score can predict prognosis better than other systems in subjects with unresectable HCC and can be used in clinical practice or trials to estimate the 6‐month and 1‐year survival probabilities for this group.

A Simplified Novel Prognostic Score to Predict Early Mortality and Disease Progression in Patients with Aggressive Adult T-Cell Leukemia/Lymphoma

▪Background: Adult T-cell leukemia/lymphoma (ATLL) is an aggressive mature peripheral T-cell neoplasm caused by HTLV-1 infection. Despite aggressive therapy, ATLL carries a very poor prognosis. Current prognostic models (i.e., the International Prognostic Index [IPI] and the Prognostic Index for T-cell lymphoma [PIT]) have shown to not completely predict survival in ATLL (Phillips, Cancer, 2010). Moreover, these models have not been validated in Latin American ATLL patients. Therefore, we aim to validate current prognostic systems (IPI, PIT) and to develop a novel model to predict early mortality in patients with ATLL.Methods: We conducted a retrospective cohort study by analyzing 12 demographic, clinical, and laboratory variables of 169 Latin American ATLL patients with acute and lymphomatous subtypes recruited by the “Grupo de Estudio Latinoamericano de Linfoproliferativos” (GELL). All patients received cancer-directed treatment. Conditional inference trees were used to construct a new prognostic model, the ATLL-GELL, by including serum lactate dehydrogenase (LDH) and the Eastern Cooperative Oncology Group (ECOG) performance status. Our primary outcome was overall survival (OS) at 15 months, while our secondary outcome was progression-free survival (PFS). The model identified a low-risk (LDH ≤2 times upper limit value [ULV] + ECOG score 0-1), and a high-risk (LDH >2 times ULV + ECOG score ≥2; or LDH >2 times ULV) group (Figure 1). The model was validated in an independent cohort of 95 North American ATLL patients with acute and lymphomatous subtypes from Miami, FL. We used the Kaplan-Meier method and the log-rank test to estimate the survival probabilities for the score. Cox regression was used to evaluate the risk of mortality and disease progression for each score. We used the C-index to determine the discriminatory power of each score.Results: Table 1 summarizes the demographic and clinical characteristics. The mean age was 57 (± 15.1) years with a male predominance (53%). Lymphomatous ATLL was the most common subtype (66%) in the training cohort, while acute ATLL (59%) in the testing cohort. Most patients had an Ann Arbor stage III-IV (88%) and received multiagent chemotherapy (76%) as first-line treatment (Table 1). With a median follow-up of 21 months, the 15-month OS and PFS rates of the training cohort were 29% and 27%, respectively. Patients with a high-risk ATLL-GELL score had a worse OS (16% vs. 61%, p<0.0001) and PFS (14% vs. 60%, p<0.0001) (Figure 2). Table 2 shows that the ATLL-GELL model had comparable prognostic discrimination than the IPI and PIT scores, according to the C-index. Similarly, the Cox regression analysis identified a better risk stratification of the new model for OS and PFS in the training and testing cohorts. In contrast, poor risk stratification for mortality and disease progression was found with the IPI and PIT scores in the training cohort. Table 2 shows that the risk for mortality and disease progression was higher in the high-intermediate (HR 7.91 and 8.07, respectively) than the high-risk (HR 7.12, and 7.85, respectively) group according to the IPI score. Moreover, the PIT score could not properly stratify patients' OS and PFS risk (Table 2). Figures 3 and 4 illustrate narrow and crossing survival curves between risk subgroups in both scores for OS and PFS estimates, respectively, suggesting poor visual discrimination of the IPI and PIT scores.Conclusion: ATLL carries a dismal prognosis, therefore we hypothesized that current prognostic systems might not yield an appropriate risk stratification since almost universally aggressive ATLL patients are at high risk for rapid disease progression and death. After considering different clinical-epidemiological and laboratory variables, our simplified prognostic model was able to predict early mortality and disease progression in patients with aggressive ATLL. Poor performance status, thus unfit for aggressive therapy, along with a high disease burden seems to reliably predict mortality in these patients. The ATLL-GELL model also showed better risk stratification and a more clear distinction of low- versus high-risk groups for aggressive ATLL than traditional models. We are currently validating our findings in a prospective cohort of Latin American ATLL patients and to improve clinical decision-making in those deemed at high risk for early mortality. [Display omitted] DisclosuresCastillo: Abbvie: Consultancy, Research Funding; BeiGene: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Janssen: Consultancy; Roche: Consultancy; TG Therapeutics: Research Funding.

PERFORM: Pulmonary embolism risk score for mortality in computed tomographic pulmonary angiography-confirmed patients.

BackgroundCurrent prognostic scores for pulmonary embolism (PE) were partly based on patients without PE confirmation via computed tomographic pulmonary angiography (CTPA), involving subjective parameters and complicated scoring methods. Therefore, we sought to develop an objective, accurate, and simple prognostic model in CTPA-confirmed patients to predict the risk of 30-day mortality.MethodsWe retrospectively evaluated 509 patients with objectively confirmed PE by CTPA from 2010 to 2017 in the Minhang Hospital, which is affiliated to Fudan University. Patients were randomly divided into the training and validation cohorts. The primary end point was 30-day mortality. The secondary end points were the time to recovery in 30 days and mortality in 15 days. We compared the predictive performance of Pulmonary Embolism Severity Index (PESI), simplified PESI (sPESI), and the PE risk score we developed, called PERFORM.FindingsPERFORM (ranging from 0 to 12 score) is based on the patient's age, heart rate, and partial pressure of arterial oxygen. The area under the curve was 0.718 (95% confidence interval [CI], 0.627–0.809) for the training cohort and 0.906 (95% CI, 0.846–0.966) for the validation cohort. PERFORM was as good as PESI and sPESI in predicting mortality. Patients in the low-risk group (PERFORM score < 5) had a shorter time to recovery, whereas those in the high-risk group (PERFORM score ≥ 5) had a high mortality.InterpretationPERFORM in CTPA-confirmed patients is an objective, accurate, and simple tool to predict the risk of 30-day mortality.FundingResearch Project of Shanghai Municipal Commission of Health and Family Planning (201740127), Shanghai Medical Key Subject Construction Project (ZK2019B08).

Development and validation of prognostic model to predict mortality among cirrhotic patients with acute variceal bleeding: A retrospective study.

Background and AimAcute variceal bleeding (AVB) is a serious complication associated with high mortality. The aim of our study was to investigate mortality predictors and to develop a new simplified prognostic model among cirrhotic patients with AVB.MethodsA simplified prognostic model was developed using multiple logistic regression after identifying significant predictors of 6‐week mortality.ResultsA total of 713 consecutive patients with AVB were enrolled. The 6‐week overall mortality rate was 18%. Multivariate analysis showed that shock, model for end‐stage liver disease (MELD) score, high‐risk stigmata of esophageal varices on endoscopic finding, and Glasgow Blatchford score were independent predictors of mortality. A new logistic model using these variables was developed. This model (cutoff value ≥ 4) area under the receiver operating characteristics (AUROC) was 0.93 and significantly higher than that of MELD score alone (0.74). Two validation analyses showed that the AUROC of our model was consistently high. The 6‐week rebleeding rate was 25.3%. Multivariate analysis showed that MELD score, Glasgow Blatchford score, history of upper GI bleeding, shock, and alcohol use were independent predictors of rebleeding.ConclusionOur new simplified model accurately and consistently predicted 6‐week mortality among patients with AVB using objective variables measured at admission. Patients with higher MELD scores should be closely monitored due to the higher probability of 6‐week rebleeding.

Prognostic model for patients with advanced cancer using a combination of routine blood test values.

The purpose of this study was to develop a simple prognostic model based on objective indicators alone, i.e., routine blood test data, without using any subjective variables such as patient's symptoms and physician's prediction. The subjects of this retrospective study were patients at the palliative care unit of Tohoku University Hospital, Japan. Eligible patients were over 20years old and had advanced cancer (n = 225). The model for predicting survival was developed based on Cox proportional hazards regression models for univariable and multivariable analyses of 20 items selected from routine blood test data. All the analyses were performed according to the TRIPOD statement ( https://www.tripod-statement.org/ ). The univariable and multivariable regression analyses identified total bilirubin, creatinine, urea/creatinine ratio, aspartate aminotransferase, albumin, total leukocyte count, differential lymphocyte count, and platelet/lymphocyte ratio as significant risk factors for mortality. Based on the hazard ratios, the area under the curve for the new risk model was 0.87 for accuracy, 0.83 for sensitivity, and 0.74 for specificity. Diagnostic accuracy was higher than provided by the Palliative Prognostic Score and the Palliative Prognostic Index. The Kaplan-Meier analysis demonstrated a survival significance of classifying patients according to their score into low-, medium-, and high-mortality risk groups having median survival times of 67days, 34days, and 11days, respectively (p < 0.001). We developed a simple and accurate prognostic model for predicting the survival of patients with advanced cancer based on routine blood test values alone that may be useful for appropriate advanced care planning in a palliative care setting.

Risk stratification for the prognosis of patients with chemoresistant urothelial cancer treated with pembrolizumab.

The use of immune checkpoint inhibitors to treat urothelial carcinoma (UC) is increasing rapidly without clear guidance for validated risk stratification. This multicenter retrospective study collected clinicopathological information on 463 patients, and 11 predefined variables were analyzed to develop a multivariate model predicting overall survival (OS). The model was validated using an independent dataset of 292 patients. Patient characteristics and outcomes were well balanced between the discovery and validation cohorts, which had median OS times of 10.2 and 12.5 mo, respectively. The final validated multivariate model was defined by risk scores based on the hazard ratios (HRs) of independent prognostic factors including performance status, site of metastasis, hemoglobin levels, and the neutrophil‐to‐lymphocyte ratio. The median OS times (95% confidence intervals [CIs]) for the low‐, intermediate‐, and high‐risk groups (discovery cohort) were not yet reached (NYR) (NYR–19.1), 6.8 mo (5.8‐8.9), and 2.3 mo (1.2‐2.6), respectively. The HRs (95% CI) for OS in the low‐ and intermediate‐risk groups vs the high‐risk group were 0.07 (0.04‐0.11) and 0.23 (0.15‐0.37), respectively. The objective response rates for in the low‐, intermediate‐, and high‐risk groups were 48.3%, 28.8%, and 10.5%, respectively. These differential outcomes were well reproduced in the validation cohort and in patients who received pembrolizumab after perioperative or first‐line chemotherapy (N = 584). In conclusion, the present study developed and validated a simple prognostic model predicting the oncological outcomes of pembrolizumab‐treated patients with chemoresistant UC. The model provides useful information for external validation, patient counseling, and clinical trial design.

The Burkitt Lymphoma International Prognostic Index (BL-IPI)

The Burkitt Lymphoma International Prognostic Index (BL-IPI)

Simple cardiovascular risk stratification using anthropometric measures instead of serum cholesterol. The MORGAM Prospective Cohort Project

Abstract Background Body composition predicts cardiovascular outcomes, but it is uncertain whether anthropometric measures can replace the more expensive serum total cholesterol for cardiovascular risk stratification in low resource settings. Purpose The purpose of the study was to compare the additive prognostic ability of serum total cholesterol with that of body mass index (BMI), waist/hip ratio (WHR), and estimated fat mass (EFM, calculated using a validated prediction equation), individually and combined. Methods We used data from the MORGAM (MONICA, Risk, Genetics, Archiving, and Monograph) Prospective Cohort Project, an international pooling of cardiovascular cohorts, to determine the relationship between anthropometric measures, serum cholesterol, and cardiovascular events, using multivariable Cox proportional-hazards regression analysis. We further investigated the ability of these measures to enhance prognostication beyond a simpler prediction model, consisting of age, sex, smoking status, systolic blood pressures, and country, using comparison of area under the receiver operating characteristics curve (AUCROC) derived from binary logistic regression models. The primary endpoint was major adverse cardiovascular events (MACE), defined as a composite of death from coronary heart disease, myocardial infarction, or stroke. Results The study population consisted of 52,188 apparently healthy subjects (56.3% men) aged 47±12 years ranging from 20 to 84, derived from 37 European cohorts, with baseline between 1982–2002 all followed for 10 years during which MACE occurred in 2465 (4.7%) subjects. All anthropometric measures (BMI: hazard ratio (HR) 1.04 [95% confidence interval (CI): 1.03–1.05] per kg/m2; WHR: HR 7.5 [4.0–14.0] per unit; EFM: HR 1.02 [1.01–1.02] per kg) as well as serum total cholesterol (HR 1.20 [1.16–1.24] per mmol/l) were significantly associated with MACE (P&amp;lt;0.001 for all), independently of age, sex, smoking status, systolic blood pressures, and country. The addition of serum cholesterol significantly improved the predictive ability of the simple model (AUCROC 0.818 vs. 0.814, P&amp;lt;0.001), as did the combination of WHR, BMI, and EFM (AUCROC 0.817 vs. 0.814, P=0.004). When assessed individually, BMI (AUCROC 0.816 vs. 0.814, P=0.004) and WHR (AUCROC 0.815 vs. 0.814, P=0.02) improved model performance, while EFM narrowly missed significance (AUCROC 0.815 vs. 0.814, P=0.06). There was no significant difference in the predictive ability of a model including serum cholesterol versus that including all three anthropometric measures (AUCROC 0.818 vs. 0.817, P=0.13). The figure shows the pertinent areas under the ROC curve in predicting MACE. Conclusion In this large population-based cohort study, the addition of a combination of anthropometric measures, i.e. BMI, WHR, and EFM, raised the predictive ability of a simple prognostic model comparable to that obtained by the addition of serum total cholesterol. Figure 1 Funding Acknowledgement Type of funding source: None

Clinical and imaging predictors of recovery in patient with pulmonary emboli

Abstract Background Post-pulmonary emboli impairment (PPEI), comprised significant morbidity and mortality. A single center registry of patients with PE was established in order to estimate patients' risk, monitor clinical management and develop a simple model to assess the risk for PPEI for the entire spectrum of patients treated in general clinical practice. Methods For developing the mentioned model, apart from approved imaging parameters, potential novel imaging parameters were also evaluated. Recovery were assessed according to Pulmonary Embolism Thrombolysis (PEITHO) trial definition (Figure 1). Results 140 patients have been registered with at least 6-month imaging follow up. PPEI was confirmed in 75 (53.6%) patients with PE. The prognostic value of traditional and novel imaging parameters on 6-month complete recovery were summarized in Table. A multivariable logistic regression model was developed using 140 patients. Previous PE, HRfollowing five in-hospital-parameters accounted for simple prognostic model for incomplete recovery: history of PE, heart rate≥110, serum blood urea nitrogen level, RV/LV ratio and right atrium area, The overall prediction ability was excellent with 80.6 overall percentage for PPEI. Conclusions PPPEI rate was considerable in our study. Our proposed simple prediction model might have a role on identification of patients at risk for PPEI. Figure 1 Funding Acknowledgement Type of funding source: None

Models to predict disease-free survival for hepatocellular carcinoma patients with surgical resections.

Precise prognostic prediction for an individual hepatocellular carcinoma (HCC) patient before and after liver resection is important. We aimed to establish simple prognostic models to predict disease-free survival (DFS) for these patients. Six hundred and ninety-eight HCC patients with liver resections were reviewed. Preoperative (model 1) and postoperative (model 2) nomogram-based scoring systems were constructed by multivariate analyses, and DFS was estimated. Among 698 patients, 490 (70.2%) patients had tumor recurrence at a median follow-up of 84.4 months. Risk factors of tumor recurrence in model 1 included viral hepatitis, platelet count, albumin, indocyanine green retention rate, multiplicity of tumor, and radiologic total tumor volume (TTV). Prognostic variables identified in model 2 were viral hepatitis, platelet count, multiplicity of tumor, cirrhosis, microvascular invasion, and pathologic TTV. By nomogram in model 1, the patients were classified into three groups with 5-year DFS of 61.0%, 35.7%, and 21.1%, respectively (P < .0001). In model 2, the patients were divided into five groups with 5-year DFS of 58.0%, 43.7%, 24.0%, 15.4%, and 0.0%, respectively (P < .0001). Based on nomogram models, DFS for the patients who had liver resection for HCC can be predicted before liver resection and re-assessed after liver resection.

Derivation of a bedside score (MASH-P) to predict 6-month mortality in tuberculous meningitis

BackgroundTuberculous meningitis is commonly associated with a poor outcome. Simple bedside prognostic scores can help immensely in predicting the outcome. Materials and methodA total of 721 patients, from 5 of our previous studies, were included. With primary outcome measure as death, a prognostic model was derived using binary logistic regression. The model was assessed using discrimination and calibration, and internally validated using the bootstrap method. A bedside prognostic score was derived by rounding of the regression coefficients to the nearest integers. ResultsA total of 126 (17.48%) patients died. The final model found that higher age, stage III disease, baseline MBI ≤ 12, papilledema and hydrocephalus were significant predictors of death. The final model showed good discrimination as evident by an AUC = 83.1% (95% confidence interval 79.5%–86.7%, P < .001) and good calibration (Hosmer and Lemeshow test P = .579). The model remained valid after internal validation by boot strapping. A simple bedside score with the acronym MASH-P to denote variables baseline MBI (M), age (A), stage (S), hydrocephalus (H) and papilledema (P), was thus derived. The score can range from 0 to 10. Higher the score, higher is the probability of death; a score of 0 carries a predicted probability of just 1.7% while a score of 10 corresponds to a predicted probability of 65%. An electronic ready reckoner has also been developed to aid prognostication on the go. ConclusionMASH-P is a simple prognostic scoring model that can be used at bedside and aid in decision making as well as counselling.

Identifying a Simple Clinical Prognostic Model for Aggressive Adult T-Cell Leukemia/Lymphoma in Latin American Population and Its Validation: A Large International Study of the Latin America Working Group for Lymphomas (GELL)

Identifying a Simple Clinical Prognostic Model for Aggressive Adult T-Cell Leukemia/Lymphoma in Latin American Population and Its Validation: A Large International Study of the Latin America Working Group for Lymphomas (GELL)

Time to Second Treatment As a Proxy for Overall Survival in CLL/SLL: Identifying Risk Factors to Help Guide Treatment Selection

Time to Second Treatment As a Proxy for Overall Survival in CLL/SLL: Identifying Risk Factors to Help Guide Treatment Selection

865P - Treatment outcomes of 3rd treatment in a real-world metastatic castration resistant prostate cancer (mCRPC) population: Results from the Dutch CAPRI-registry

865P - Treatment outcomes of 3rd treatment in a real-world metastatic castration resistant prostate cancer (mCRPC) population: Results from the Dutch CAPRI-registry

Clinical prognostic model for older patients with advanced non-small cell lung cancer.

Older patients with non-small cell lung cancer (NSCLC) are often not prescribed standard therapy. It is important to know which older patients would be candidates for aggressive therapy based on their prognosis, and to develop a model that can help determine prognosis. Data on older patients (≥70 years) enrolled on 38 NCI cooperative group trials of advanced NSCLC from 1991 to 2011 were analyzed. Multivariable Cox PH model was built with a stepwise selection. We derived a prognostic score using the estimated Cox PH regression coefficient. We then calculated the area under receiver operating characteristic (ROC) curve of survival in the testing set. The final analysis included 1467 patients, who were randomly divided into a training (n = 963) and a testing set (n = 504). The prognostic risk score was calculated as: 3 (if male) + 3 (if PS = 1) + 8 (if PS = 2) + 11 (if initial stage = IV) + 4 (if weight loss). Patients were classified into two prognostic groups: good (0-8) and poor (≥9). The median survival in the two groups in the testing set were 13.15 (95% CI, 10.82-15.91) and 8.52 months (95% CI, 7.5-9.63), respectively. The model had area under the 1-year and 2-year ROCs (0.6 and 0.65, respectively) that were higher than existing models. Male gender, poor performance status, distant metastases and recent weight loss predict for poor overall survival (OS) in older patients with advanced NSCLC. This study proposes a simple prognostic model for older adults with advanced NSCLC.

A Simplified Risk Stratification Method for Women With Stage I Endometrial Carcinoma.

Available risk stratification methods for women with endometrial carcinoma are controversially defined. We sought to develop a simplified and an individualized prognostic index for cancer recurrence in women with International Federation of Gynecology and Obstetrics (FIGO) stage I endometrial carcinoma, solely of endometrioid histology. We identified 976 women who underwent a hysterectomy and did not receive any adjuvant therapy. Cox proportional hazards model was used to identify independent predictors of recurrence. Prognostic groups were created based on the number of independent predictors of recurrence (0, 1, or 2 or 3 risk factors). These groups were then validated using a separate cohort of 611 women treated at another academic institution. The model's performance for predicting cancer recurrence was measured by the concordance probability estimate along with a 95% confidence interval. Median follow-up was 65 months. The final recurrence model included 3 risk groups based on 3 independent predictors of recurrence (tumor grade 2 or 3, the presence of lymphovascular space invasion and stage IB). Five-year recurrence rates were 4%, 16%, and 44% for groups 0, 1, and 2 or 3, respectively. The performance of the model was very good with a concordance probability estimate of 0.72 and 0.80 for the development and validation cohorts, respectively. On the basis of 3 well-known prognostic factors, we have developed and externally validated a simplified prognostic model that accurately predicts cancer recurrence in women with stage I endometrial carcinoma. This simplified predictive tool may be helpful in estimating individualized risk of recurrence and guide counseling with regard to adjuvant treatment.

Prognosis of Patients with Waldenström Macroglobulinemia: A Simplified Model

Prognosis of Patients with Waldenström Macroglobulinemia: A Simplified Model