Age and sex associate with outcome in older AML and high risk MDS patients treated with 10-day decitabine

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Real-world data of AML in Japan: results of JALSG clinical observational study-11 (JALSG-CS-11)

This report covers acute myeloid leukemia (AML) results from a multicenter, prospective observational study of AML, myelodysplastic syndromes, and chronic myelomonocytic leukemia in Japan. From August 2011 to January 2016, 3728 AML patients were registered. Among them, 42% were younger than 65, and the male-to-female ratio was 1.57:1. With a median follow-up time of 1807 days (95% confidence interval [CI]: 1732-1844 days), the estimated 5-year overall survival (OS) rate in AML patients (n = 3707) was 31.1% (95% CI: 29.5-32.8%). Trial-enrolled patients had a 1.7-fold higher OS rate than non-enrolled patients (5-year OS, 58.9% [95% CI: 54.5-63.1%] vs 35.5% [33.3-37.8%], p < 0.0001). Women had a higher OS rate than men (5-year OS, 34% [95% CI; 31.4-36.7%] vs 27.7% [25.7-29.7%], p < 0.0001). The OS rate was lower in patients aged 40 and older than those under 40, and even lower in those over 65 (5-year OS for ages < 40, 40-64, 65-74, ≥ 75: 74.5% [95% CI; 69.3-79.0%] vs 47.5% [44.4-50.6%] vs 19.3% [16.8-22.0%] vs 7.3% [5.5-9.4%], respectively). This is the first paper to present large-scale data on survival and clinical characteristics in Japanese AML patients.

A nomogram based on clinical features and molecular abnormalities for predicting the prognosis of patients with acute myeloid leukemia

Background

The high clinical and molecular heterogeneity of acute myeloid leukemia (AML) has led to an unsatisfactory clinical prognosis, thus we sought to incorporate both clinical features and molecular abnormalities to construct a new prognostic model.

Methods

A database search of the Gene Expression Omnibus (GEO) revealed 238 cases of adult AML. The independent risk factors were assessed using both univariate and multivariate Cox regression, as well as least absolute shrinkage and selection operator (LASSO) regression. The predictive accuracy, discriminatory power and clinical applicability of the nomogram were determined by the consistency index (C-index), calibration curves and decision curve analysis (DCA). In addition, a single-centre cohort of 135 cases was used for external validation.

Results

Multivariate Cox regression analysis showed that the independent influences on overall survival (OS) were age, type of disease, DNMT3A, IDH2 and TP53 mutations. The area under the curve (AUC) values for the training set were 0.755, 0.745 and 0.757 at 1, 2 and 3 years respectively; the AUC for the validation set were 0.648, 0.648 and 0.654 at 1, 2 and 3 years; and the AUC for the northwest China set were 0.692, 0.724 and 0.689 at 1, 2 and 3 years. The calibration and DCA indicated good consistency and clinical utility of the nomogram. Finally, younger (age <60 years) and elderly (age ≥60 years) patients were each divided into two risk groups with significantly different survival rates.

Conclusions

A nomogram consisting of five risk factors was developed for forecasting the prognosis of AML with guaranteed reliability.