Bridging the gap between the randomised clinical trial world and the real world by combination of population-based registry and electronic health record data: A case study in haemato-oncology

Kibbelaar, Robby E.; Oortgiesen, Berdien; Wal-Oost, A. M. van der; Boslooper, Karin; Coebergh, Jan Willem; Veeger, Nic J. G. M.; Joosten, Peter; Storm, Huib; Roon, E.N. Van; Hoogendoorn, Mels

doi:10.1016/j.ejca.2017.09.007

Cited by 34 publications

(32 citation statements)

References 33 publications

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“…Second, emerging evidence demonstrates that the RCT condition does not correspond with the clinical real one. 31 For example, over 90% of patients receiving gemcitabine monotherapy in 3 RCTs were with ECOG PS 0-1 or KPS ≥ 80. However, in the clinical real condition, this kind of patients are often recommended to receive combined therapy such as fluorouracil-leucovorin-irinotecan-oxaliplatin (FOLFIRINOX) 32 or gemcitabine plus S-1.…”

Section: Discussionmentioning

confidence: 99%

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real‐world study

Hang

Zhu

et al. 2018

Cancer Medicine

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It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C‐index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19‐9 (CA19‐9) log‐value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C‐index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C‐index: .699) and testing cohort (n = 133, C‐index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3‐risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy.

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Section: Discussionmentioning

confidence: 99%

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real‐world study

Hang

Zhu

et al. 2018

Cancer Medicine

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“…Clinical trials and randomised control trials are the backbone of evidence based medicine. However, their generalisability to the real-world population can be limited by the inclusion and exclusion criteria of the individual trials 29 . In this study, IPD from 4 clinical trials was used for model development and validation.…”

Section: Discussionmentioning

confidence: 99%

Prognostic model of survival outcomes in non-small cell lung cancer patients initiated on afatinib: pooled analysis of clinical trial data

Hopkins

Shahnam

Zhang

et al. 2019

Cancer Biology & Medicine

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Objective Several predictors of survival have been identified in EGFR-positive non-small cell lung cancer (NSCLC) patients treated with first generation EGFR inhibitors. Prognostic models of survival outcomes with afatinib have not been evaluated. Methods A prognostic tool for overall survival (OS)/ progression free survival (PFS) based on pre-treatment clinicopathological factors was developed for EGFR-positive advanced NSCLC patients treated with first-line afatinib using penalised regression of individual-participant data from LUX-Lung 3 and 6 ( n = 468). Favourable, intermediate and poor risk groups were identified and externally validated using LUX-Lung 1 ( n = 390) and LUX-Lung 2 ( n = 129) trials that initiated afatinib following previous chemotherapy or EGFR inhibitor treatment. Results Discriminative performance was good in the development and validation cohorts. For patients treated with first-line afatinib, the median OS for the favourable, intermediate and poor risk groups were > 47.7, 29.3 and 16.4 months, respectively, and the median PFS were 17.3, 13.2 and 8.3 months, respectively. The improvement in median OS with afatinib use compared to chemotherapy was > 12.4 months for the favourable risk group, whereas no OS benefit was apparent for the poor risk group. The improvement in median PFS with afatinib use compared to chemotherapy was 10.2 months for the favourable risk group and 3.2 months for the poor risk group. Conclusions A prognostic tool was developed and validated to identify favourable, intermediate and poor risk groups for OS/PFS in EGFR-positive advanced NSCLC patients treated with afatinib. The prognostic groups can inform the likely absolute OS/PFS benefit expected from afatinib compared to chemotherapy in first-line treatment.

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“…In addition, data can be used to carry out retrospective cohort studies or population based cohort studies. Kibbelaar et al proposed a method to combine data from population-based registries with detailed EHR to conduct an observational study and reported on a case study in an hemato-oncology randomized registry trial [51].…”

Section: Emr Use In Clinical Trialsmentioning

confidence: 99%

Electronic Medical Records and Machine Learning in Approaches to Drug Development

Shinozaki¹

2020

Artificial Intelligence in Oncology Drug Discovery and Development

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Electronic medical records (EMRs) were primarily introduced as a digital health tool in hospitals to improve patient care, but over the past decade, research works have implemented EMR data in clinical trials and omics studies to increase translational potential in drug development. EMRs could help discover phenotypegenotype associations, enhance clinical trial protocols, automate adverse drug event detection and prevention, and accelerate precision medicine research. Although feasible, data mining in EMRs still faces challenges. Existing machine learning tools may help overcome these bottlenecks in EMR mining to unlock new approaches in drug development. This chapter will explore the role of EMRs in drug development while evaluating the viability and bottlenecks of their uses in data mining. This will include discussions on EMR usage in drug development while highlighting successful outcomes in oncology and exploring ML tools to complement and enhance EMR as a widely accepted drug-research source, a section on current clinical applications of EMRs, and a conclusion to summarize and imagine what a future drug research pipeline from EMR to patient treatment may look like.

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Bridging the gap between the randomised clinical trial world and the real world by combination of population-based registry and electronic health record data: A case study in haemato-oncology

Cited by 34 publications

References 33 publications

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real‐world study

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real‐world study

Prognostic model of survival outcomes in non-small cell lung cancer patients initiated on afatinib: pooled analysis of clinical trial data

Electronic Medical Records and Machine Learning in Approaches to Drug Development

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