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Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma.

ABSTRACT: Multiple myeloma (MM) is a malignancy with varying survival outcomes and drivers of disease progression. Existing MM staging tools were developed using data from newly diagnosed patients. As patient characteristics and disease-related factors change between diagnosis and the initiation of second-line (2L) treatment, an unmet need exists for a tool that can evaluate risk of death at first relapse. We have developed a risk stratification algorithm (RSA) using data from patients with MM who were at 2L. Hazard ratios for independent predictors of overall survival (OS) were derived from a Cox models, and individual patient scores were calculated for total risk. K-adaptive partitioning for survival was used to stratify patients into groups based on their scores. Relative risk doubled with ascending risk group; median OSs for patients in group 1 (lowest risk)-4 (highest risk) were 61·6, 29·6, 14·2 and 5·9 months, respectively. Differences in OS between risk groups were significant. Similar stratification was observed when the RSA was applied to an external validation data set. In conclusion, we have developed a validated RSA that can quantify total risk, frailty risk and disease aggressiveness risk, and stratify patients with MM at 2L into groups with profoundly different survival expectations.

SUBMITTER: Hajek R

PROVIDER: S-EPMC6899684 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma.

Hájek Roman R Delforge Michel M Raab Marc S MS Schoen Paul P DeCosta Lucy L Spicka Ivan I Radocha Jakub J Pour Ludek L Gonzalez-McQuire Sebastian S Bouwmeester Walter W

British journal of haematology 20190806 4

Multiple myeloma (MM) is a malignancy with varying survival outcomes and drivers of disease progression. Existing MM staging tools were developed using data from newly diagnosed patients. As patient characteristics and disease-related factors change between diagnosis and the initiation of second-line (2L) treatment, an unmet need exists for a tool that can evaluate risk of death at first relapse. We have developed a risk stratification algorithm (RSA) using data from patients with MM who were at ...[more]

PMID: 31388996

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Project description:BackgroundAdjuvant therapy for patients with cervical cancer (CC) with intermediate-risk factors remains controversial. The objectives of the present study are to assess the prognoses of patients with early-stage CC with pathological intermediate-risk factors and to provide a reference for adjuvant therapy choice.Materials and methodsThis retrospective study included 481 patients with stage IB-IIA CC. Cox proportional hazards regression analysis, machine learning (ML) algorithms, Kaplan-Meier analysis, and the area under the receiver operating characteristic curve (AUC) were used to develop and validate prediction models for disease-free survival (DFS) and overall survival (OS).ResultsA total of 35 (7.3%) patients experienced recurrence, and 20 (4.2%) patients died. Two prediction models were built for DFS and OS using clinical information, including age, lymphovascular space invasion, stromal invasion, tumor size, and adjuvant treatment. Patients were divided into high-risk or low-risk groups according to the risk score cutoff value. The Kaplan-Meier analysis showed significant differences in DFS (p = .001) and OS (p = .011) between the two risk groups. In the traditional Sedlis criteria groups, there were no significant differences in DFS or OS (p > .05). In the ML-based validation, the best AUCs of DFS at 2 and 5 years were 0.69/0.69, and the best AUCs of OS at 2 and 5 years were 0.88/0.63.ConclusionTwo prognostic assessment models were successfully established, and risk grouping stratified the prognostic risk of patients with CC with pathological intermediate-risk factors. Evaluation of long-term survival will be needed to corroborate these findings.Implications for practiceThe Sedlis criteria are intermediate-risk factors used to guide postoperative adjuvant treatment in patients with cervical cancer. However, for patients meeting the Sedlis criteria, the choice of adjuvant therapy remains controversial. This study developed two prognostic models based on pathological intermediate-risk factors. According to the risk score obtained by the prediction model, patients can be further divided into groups with high or low risk of recurrence and death. The prognostic models developed in this study can be used in clinical practice to stratify prognostic risk and provide more individualized adjuvant therapy choices to patients with early-stage cervical cancer.

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Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma.

Publications

Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma.

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