Dataset Information

The 10-month mortality rate among older patients treated for digestive system cancer during the first wave of the COVID-19 pandemic: The CADIGCOVAGE multicentre cohort study.

ABSTRACT:

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has had a dramatic impact on cancer diagnosis and care pathways. Here, we assessed the mid-term impact of the COVID-19 pandemic on older adults with cancer before, during and after the lockdown period in 2020.

Materials and methods

We performed a retrospective, observational, multicentre cohort study of prospectively collected electronic health records. All adults aged 65 or over and having been newly treated for a digestive system cancer in our institution between January 2018 until August 2020 were enrolled.

Results

Data on 7,881 patients were analyzed. Although the overall 10-month mortality rate was similar in 2020 vs. 2018-2019, the mortality rate among for patients newly treated in the 2020 post-lockdown period was (after four months of follow-up) significantly higher. A subgroup analysis revealed higher mortality rates for (i) patients diagnosed in the emergency department during the pre-lockdown period, (ii) patients with small intestine cancer newly treated during the post-lockdown period, and (iii) patients having undergone surgery with curative intent during the post-lockdown period. However, when considering individuals newly treated during the lockdown period, we observed lower mortality rates for (i) patients aged 80 and over, (ii) patients with a biliary or pancreatic cancer, and (iii) patients diagnosed in the emergency department.

Discussion

There was no overall increase in mortality among patients newly treated in 2020 vs. 2018-2019. Longer follow-up is needed to assess the consequences of the pandemic. A subgroup analysis revealed significant intergroup differences in mortality.

SUBMITTER: Aparicio T

PROVIDER: S-EPMC9883010 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

The 10-month mortality rate among older patients treated for digestive system cancer during the first wave of the COVID-19 pandemic: The CADIGCOVAGE multicentre cohort study.

Aparicio Thomas T Layese Richard R Hemery François F Tournigand Christophe C Paillaud Elena E De Angelis Nicola N Quero Laurent L Ganne Nathalie N Prat Fredéric F Pachev Atanas A Galula Gilles G Benderra Marc-Antoine MA Canouï-Poitrine Florence F

Journal of geriatric oncology 20230127 2

<h4>Introduction</h4>The coronavirus disease 2019 (COVID-19) pandemic has had a dramatic impact on cancer diagnosis and care pathways. Here, we assessed the mid-term impact of the COVID-19 pandemic on older adults with cancer before, during and after the lockdown period in 2020.<h4>Materials and methods</h4>We performed a retrospective, observational, multicentre cohort study of prospectively collected electronic health records. All adults aged 65 or over and having been newly treated for a dige ...[more]

PMID: 36709553

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Project description:Clinical evidence suggests that some patients diagnosed with coronavirus disease 2019 (COVID-19) experience a variety of complications associated with significant morbidity, especially in severe cases during the initial spread of the pandemic. To support early interventions, we propose a machine learning system that predicts the risk of developing multiple complications. We processed data collected from 3,352 patient encounters admitted to 18 facilities between April 1 and April 30, 2020, in Abu Dhabi (AD), United Arab Emirates. Using data collected during the first 24 h of admission, we trained machine learning models to predict the risk of developing any of three complications after 24 h of admission. The complications include Secondary Bacterial Infection (SBI), Acute Kidney Injury (AKI), and Acute Respiratory Distress Syndrome (ARDS). The hospitals were grouped based on geographical proximity to assess the proposed system's learning generalizability, AD Middle region and AD Western & Eastern regions, A and B, respectively. The overall system includes a data filtering criterion, hyperparameter tuning, and model selection. In test set A, consisting of 587 patient encounters (mean age: 45.5), the system achieved a good area under the receiver operating curve (AUROC) for the prediction of SBI (0.902 AUROC), AKI (0.906 AUROC), and ARDS (0.854 AUROC). Similarly, in test set B, consisting of 225 patient encounters (mean age: 42.7), the system performed well for the prediction of SBI (0.859 AUROC), AKI (0.891 AUROC), and ARDS (0.827 AUROC). The performance results and feature importance analysis highlight the system's generalizability and interpretability. The findings illustrate how machine learning models can achieve a strong performance even when using a limited set of routine input variables. Since our proposed system is data-driven, we believe it can be easily repurposed for different outcomes considering the changes in COVID-19 variants over time.

Dataset Information

The 10-month mortality rate among older patients treated for digestive system cancer during the first wave of the COVID-19 pandemic: The CADIGCOVAGE multicentre cohort study.

Introduction

Materials and methods

Results

Discussion

Publications

The 10-month mortality rate among older patients treated for digestive system cancer during the first wave of the COVID-19 pandemic: The CADIGCOVAGE multicentre cohort study.

Similar Datasets

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