Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data

Rasmy, Laila; Nigo, Masayuki; Kannadath, Bijun S.; Xie, Ziqian; Mao, Bingyu; Patel, Kirti; Zhou, Yujia; Zhang, Wanheng; Ross, Angela; Xu, Hua; Zhi, Degui

doi:10.1016/s2589-7500(22)00049-8

Cited by 44 publications

(42 citation statements)

References 24 publications

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“…Delta) when compared with the previously observed strain in the pandemic [2] [3]. Near-capacity hospital and intensive care unit use were commonly reported during the peaks of pandemic waves [4]. Therefore, reliable, generalizable and sustainable methods for timely identification of high-risk L A T E X template Tri-light Warning System for Hospitalized COVID-19 Patients 3 patients are crucial for clinical decision making and efficient allocation of resources in the context of existing and emerging virus strains [5].…”

Section: Introductionmentioning

confidence: 81%

“…A study proposed a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19 from clinical and radiology report data (AUROC: 0.88) [33]. Another study developed a recurrent neural network-based model to predict the outcomes of patients with COVID-19 by using available electronic health records on admission to hospital (AUROC: 0.85-0.93) [4]. Also, our previous study used 3,522 PCR-confirmed COVID-19 inpatients from 39 hospitals and performed CT-based analysis combined with electronic health records and clinical laboratory results with prognostic estimation for the rapid risk stratification (AUROC 0.916-0.919) [16].…”

Section: Introductionmentioning

confidence: 99%

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A Tri-light Warning System for Hospitalized COVID-19 Patients: Credibility-based Risk Stratification under Data Shift

Liu

et al. 2022

Preprint

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Early warning of the novel coronavirus pneumonia (COVID-19) during the evolving pandemic waves is crucial for the timely treatment of patients and optimization of medical resource allocation. However, prior AI-based models often lack the reliability and performance validation under data distribution drifts, and are therefore problematic to be reliably utilized in real-world clinical practice. To address this challenge, we developed a tri-light warning system based on conformal prediction for rapidly stratification of COVID-19 inpatients. This system can automatically extract radiomic features from CT images and integrate clinical record information to output a prediction probability, as well as a credibility of each prediction. This system classifies patients in the general ward into red label (high risk) indicating a possible admission to ICU care, yellow label (uncertain risk) indicating closer monitoring, and green label (low risk) indicating a stable condition. The subsequent health policies can be further designed based on this system according to the specific needs of different hospitals. Extensive experiment from a multi-center cohort (n= 8,721) shows that our method is applicable to both the original strain and the variant strains of COVID-19. Given the rapid mutation rate of COVID-19, the proposed system demonstrates its potential to identify epidemiological risks early to improve patient stratification performance under data shift.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

A Tri-light Warning System for Hospitalized COVID-19 Patients: Credibility-based Risk Stratification under Data Shift

Liu

et al. 2022

Preprint

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“…Our previous work, however, finds that the deep learning models can be further advanced by adding more features or even unstructured data with minimal data processing. [27] Vancomycin does not have significant drug-drug interactions with other medications. Some medications, however, such as angiotensin (ACE) inhibitors, could affect patient creatinine levels, which is a key predictor of vancomycin eliminations in Bayesian models.…”

Section: Discussionmentioning

confidence: 99%

PK-RNN-V E: A Deep Learning Model Approach to Vancomycin Therapeutic Drug Monitoring Using Electronic Health Record Data

Nigo

Tran²,

Xie

et al. 2022

Preprint

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Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, however, although using carefully designed lab observations, were often developed in limited patient populations. The increasing availability of electronic health record (EHR) data offers an opportunity to develop TDM models for real-world patient populations. Here, we present a deep learning-based pharmacokinetic prediction model for vancomycin (PK-RNN-V E) using a large EHR dataset of 5,483 patients with 55,336 vancomycin administrations. PK-RNN-V E takes the patients real-time sparse and irregular observations and offers dynamic predictions. Our results show that RNN-PK-V E offers a root mean squared error (RMSE) of 5.39 and outperforms the traditional Bayesian model (VTDM model) with an RMSE of 6.29. We believe that PK-RNN-V E can provide a pharmacokinetic model for vancomycin and other antimicrobials that require TDM.

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“…Similarly the point-of-admission ISARIC 4C Deterioration score3 has been externally validated 58 60 65 66. Promising machine-learning alternatives have also been recently proposed,67 68 although these have not yet been independently validated in contrast to the 4C scores. Another approach has been to repurpose existing early warning scores, particularly the National Early Warning Score (NEWS) 2 score 69.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

et al. 2022

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ObjectivesTo develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation.DesignIn this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.SettingAll data used in this study were obtained from a single UK teaching hospital.ParticipantsWe developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.Primary and secondary outcome measuresThe primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.ResultsOur final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).ConclusionsOur model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.Trial registrationThe study is registered as ‘researchregistry5464’ on the Research Registry (www.researchregistry.com).

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Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data

Cited by 44 publications

References 24 publications

A Tri-light Warning System for Hospitalized COVID-19 Patients: Credibility-based Risk Stratification under Data Shift

A Tri-light Warning System for Hospitalized COVID-19 Patients: Credibility-based Risk Stratification under Data Shift

PK-RNN-V E: A Deep Learning Model Approach to Vancomycin Therapeutic Drug Monitoring Using Electronic Health Record Data

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

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