Dataset Information

A multistate model of survival prediction and event monitoring in prefibrotic myelofibrosis.

ABSTRACT: Among 382 patients with WHO-defined prefibrotic myelofibrosis (pre-PMF) followed for a median of 6.9 years, fibrotic or leukemic transformation or death accounts for 15, 7, and 27% of cases, respectively. A multistate model was applied to analyze survival data taking into account intermediate states that are part of the clinical course of pre-PMF, including overt PMF and acute myeloid leukemia (AML). Within this multistate framework, multivariable models disclosed older age (>65 years) and leukocytosis (>15?×?109/L) as predictors of death and leukemic transformation. The risk factors for fibrotic progression included anemia and grade 1 bone marrow fibrosis. The outcome was further affected by high molecular risk (HMR) but not driver mutations. Direct transition to overt PMF, AML, or death occurred in 15.2, 4.7, and 17.3% of patients, respectively. The risk of AML was the highest in the first 5 years (7%), but leveled off thereafter. Conversely, the probability of death from overt PMF or AML increased more rapidly over time, especially when compared to death in the pre-PMF state without disease progression. The probability of being alive with pre-PMF status decreased to 70 and 30% at 10 and 20 years, respectively. This study highlights the aspects of the clinical course and estimates of disease progression in pre-PMF.

SUBMITTER: Carobbio A

PROVIDER: S-EPMC7566465 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A multistate model of survival prediction and event monitoring in prefibrotic myelofibrosis.

Carobbio Alessandra A Guglielmelli Paola P Rumi Elisa E Cavalloni Chiara C De Stefano Valerio V Betti Silvia S Rambaldi Alessandro A Finazzi Maria Chiara MC Thiele Juergen J Vannucchi Alessandro M AM Tefferi Ayalew A Barbui Tiziano T

Blood cancer journal 20201014 10

Among 382 patients with WHO-defined prefibrotic myelofibrosis (pre-PMF) followed for a median of 6.9 years, fibrotic or leukemic transformation or death accounts for 15, 7, and 27% of cases, respectively. A multistate model was applied to analyze survival data taking into account intermediate states that are part of the clinical course of pre-PMF, including overt PMF and acute myeloid leukemia (AML). Within this multistate framework, multivariable models disclosed older age (>65 years) and leuko ...[more]

PMID: 33056979

Similar Datasets

Project description:Colorectal cancer (CRC) has high incidence and is associated with high case fatality. In France, the 5-year survival, pooled across all cancer stages at diagnosis, ranges from 57% in men to 60% in women. About one third of patients diagnosed with CRC will develop a metachronous recurrence during the following years. It is of paramount importance to accurately identify factors associated with the increased risk of progression and death, in order to develop effective follow-up and treatment strategies. However, to accurately assess the role of patients’ specific characteristics in the progression of cancer several methodological challenges need to be overcome. One difficulty, common to prognostic studies of cancer, concerns the need to separate the effects of prognostic factors on different clinical endpoints, such as disease recurrence vs recurrence-free death. Another difficulty, encountered in prognostic studies, is that the cause of death is not available or not accurately coded. Yet, some patients are likely to die of causes not related to the disease of primary interest, especially in cancers with longer survival and in those that affect older subjects. Until recently, the existing statistical methodology was not able to simultaneously, deal with both difficulties, i.e. to account for (i) possibly different effects of prognostic factors on death vs recurrence, and (ii) unknown causes of death. However, this challenge has been addressed by the recent development of the Markov relative survival model (MRS) , which extends the Markov multi-state model to incorporate relative survival modelling. Simulations demonstrate that MRS is able to accurately estimate different effects of prognostic factors on the risk of each of several events, including separate effects on disease-specific vs other causes of death. To date, the MRS had not been applied in clinical or epidemiological studies. The aim of this study was to assess the potential advantages of the new multi-state relative survival model (MRS), proposed by Huszti et al. (2012), in a prognostic cancer study. To this end, we compared the MRS results with those obtained with two more conventional analyses, based on Cox’s proportional hazards model, and the multi-state Markov model proposed by Alioum and Commenges (2001). The three models were applied to explore the impact of prognostic factors on cancer-specific mortality and recurrence, in a large population-based French registry of colorectal cancer, with up to 25 years of follow-up.

Project description:IntroductionMany research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time.MethodologiesWe simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017.ResultsThe experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval.ConclusionsThe proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.

Dataset Information

A multistate model of survival prediction and event monitoring in prefibrotic myelofibrosis.

Publications

A multistate model of survival prediction and event monitoring in prefibrotic myelofibrosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets