Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study

Ryan, Logan; Lam, Carson; Mataraso, Samson; Allen, Angier; Green‐Saxena, Abigail; Pellegrini, Emily; Hoffman, Jana; Barton, Christopher; McCoy, Andrea J.; Das, Ritankar

doi:10.1016/j.amsu.2020.09.044

Cited by 63 publications

(63 citation statements)

References 51 publications

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“…Limitations related to the applicability of the developed prediction models were reported by several studies. The most prominent limitation reported was the use of a single data source (one hospital from one geographical area) for the algorithm’s training [4] , [10] , [11] , [15] , [17] , [30] , [56] , [58] , [59] , [62] , [63] , [64] , [69] , [44] , [76] , [83] , [84] , [91] , [92] , [78] , [103] , [107] , [106] , [108] , [109] , [112] , [114] , [115] , [116] , [118] . Generalizability of the trained models can be enhanced by adding multiple data sources in future studies.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

Adamidi

Mitsis

Nikita

2021

Computational and Structural Biotechnology Journal

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

Adamidi

Mitsis

Nikita

2021

Computational and Structural Biotechnology Journal

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“…To the best of our knowledge, this is the first AI research aimed at lung resection surgery. Previous studies have provided encouraging information for the development of AI techniques used for medicinal prediction, such as the prediction of postoperative outcomes, mortality rate for mechanically ventilated patients, and the possibility of extubation in intensive care units [ 28 , 38 , 49 ]. AI approaches were introduced for faster risk evaluation than traditional approaches and were more effective via digitalization.…”

Section: Discussionmentioning

confidence: 99%

“…The modeling results were measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic curve (ROC), and area under the receiver operating characteristic curve (AUC) [ 36 ]. We compared the performance of the seven AUCs between algorithms [ 28 , 32 , 37 , 38 , 39 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

Chang

Hung

Wang

et al. 2021

IJERPH

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Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients’ previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician–patient communication to achieve better patient comprehension.

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“…Model 4 used seven features (age, lymphocyte (proportion), CRP, LDH, creatine kinase, urea and calcium) and yielded an AUC of 0.90 on the validation set. 19 Ryan et al 16 compared machine learning to Sepsis Related Organ Failure Assessment (qSOFA), Modified Early Warning Score (MEWS), and CURB-65 severity scores to predict patients outcome in Medical Information Mart for Intensive Care (MIMIC) and COVID-19 data from a community hospital. They have built an XGBoost model to predict in-hospital mortality of COVID-19 patients at 12-, 24-, 48-, and 72-hours.…”

Section: Introductionmentioning

confidence: 99%

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning

Aljouie¹,

Almazroa²,

Bokhari³

et al. 2021

JMDH

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Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. Objective The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). Methods Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. Results In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. Conclusion The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients’ prioritization in the current COVID-19 pandemic crisis.

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Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study

Cited by 63 publications

References 51 publications

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning

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